HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

PubMed

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

The use of baseline measurements and geophysical models for the estimation of crustal deformations and the terrestrial reference system. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Bock, Y.

1982-01-01

Four possible estimators are investigated for the monitoring of crustal deformations from a combination of repeated baseline length measurements and adopted geophysical models, particularly an absolute motion plate model. The first estimator is an extension of the familiar free adjustment. The next two are Bayesian type estimators, one weak and one strong. Finally, a weighted constraint estimator is presented. The properties of these four estimators are outlined and their physical interpretations discussed. A series of simulations are performed to test the four estimators and to determine whether or not to incorporate a plate model for the monitoring of deformations. The application of these estimations to the maintenance of a new conventional terrestrial reference system is discussed.

POLICY VARIATION, LABOR SUPPLY ELASTICITIES, AND A STRUCTURAL MODEL OF RETIREMENT

PubMed Central

MANOLI, DAY; MULLEN, KATHLEEN J.; WAGNER, MATHIS

2015-01-01

This paper exploits a combination of policy variation from multiple pension reforms in Austria and administrative data from the Austrian Social Security Database. Using the policy changes for identification, we estimate social security wealth and accrual elasticities in individuals’ retirement decisions. Next, we use these elasticities to estimate a dynamic programming model of retirement decisions. Finally, we use the estimated model to examine the labor supply and welfare consequences of potential social security reforms. PMID:26472916

Evaluation of quality of precipitation products: A case study using WRF and IMERG data over the central United States

NASA Astrophysics Data System (ADS)

Zhang, J.; Lin, L. F.; Bras, R. L.

2017-12-01

Hydrological applications rely on the availability and quality of precipitation products, specially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products are complementary—model-based products exhibiting high quality during winters while satellite-based products seem to be better during summers. To explore that behavior, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°×0.1° every hour) precipitation products from Weather Research and Forecasting (WRF) simulations and from version-4 Integrated Multi-satellite Retrievals for GPM (IMERG) early and final runs. The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States in 2016. The results show that the WRF and IMERG final-run estimates are nearly unbiased while the IMERG early-run estimates positively biased. The results also show that the WRF estimates exhibit high correlations with the reference data when the temperature falls below 280°K and the IMERG estimates (i.e., both early and final runs) do so when the temperature exceeds 280°K. Moreover, the temperature threshold of 280°K, which distinguishes the quality of the WRF and the IMERG products, does not vary significantly with either season or location. This study not only adds insight into current precipitation research on the quality of precipitation products but also suggests a simple way for choosing either a model- or satellite-based product or a hybrid model/satellite product for applications.

Estimating site occupancy rates for aquatic plants using spatial sub-sampling designs when detection probabilities are less than one

USGS Publications Warehouse

Nielson, Ryan M.; Gray, Brian R.; McDonald, Lyman L.; Heglund, Patricia J.

2011-01-01

Estimation of site occupancy rates when detection probabilities are <1 is well established in wildlife science. Data from multiple visits to a sample of sites are used to estimate detection probabilities and the proportion of sites occupied by focal species. In this article we describe how site occupancy methods can be applied to estimate occupancy rates of plants and other sessile organisms. We illustrate this approach and the pitfalls of ignoring incomplete detection using spatial data for 2 aquatic vascular plants collected under the Upper Mississippi River's Long Term Resource Monitoring Program (LTRMP). Site occupancy models considered include: a naïve model that ignores incomplete detection, a simple site occupancy model assuming a constant occupancy rate and a constant probability of detection across sites, several models that allow site occupancy rates and probabilities of detection to vary with habitat characteristics, and mixture models that allow for unexplained variation in detection probabilities. We used information theoretic methods to rank competing models and bootstrapping to evaluate the goodness-of-fit of the final models. Results of our analysis confirm that ignoring incomplete detection can result in biased estimates of occupancy rates. Estimates of site occupancy rates for 2 aquatic plant species were 19–36% higher compared to naive estimates that ignored probabilities of detection <1. Simulations indicate that final models have little bias when 50 or more sites are sampled, and little gains in precision could be expected for sample sizes >300. We recommend applying site occupancy methods for monitoring presence of aquatic species.

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.

An inverse finance problem for estimation of the volatility

NASA Astrophysics Data System (ADS)

Neisy, A.; Salmani, K.

2013-01-01

Black-Scholes model, as a base model for pricing in derivatives markets has some deficiencies, such as ignoring market jumps, and considering market volatility as a constant factor. In this article, we introduce a pricing model for European-Options under jump-diffusion underlying asset. Then, using some appropriate numerical methods we try to solve this model with integral term, and terms including derivative. Finally, considering volatility as an unknown parameter, we try to estimate it by using our proposed model. For the purpose of estimating volatility, in this article, we utilize inverse problem, in which inverse problem model is first defined, and then volatility is estimated using minimization function with Tikhonov regularization.

Survival modeling for the estimation of transition probabilities in model-based economic evaluations in the absence of individual patient data: a tutorial.

PubMed

Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J

2014-02-01

Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model (Markov) that needs the parameterization of transition probabilities, and only has summary KM plots available.

Physiological motion modeling for organ-mounted robots.

PubMed

Wood, Nathan A; Schwartzman, David; Zenati, Marco A; Riviere, Cameron N

2017-12-01

Organ-mounted robots passively compensate heartbeat and respiratory motion. In model-guided procedures, this motion can be a significant source of information that can be used to aid in localization or to add dynamic information to static preoperative maps. Models for estimating periodic motion are proposed for both position and orientation. These models are then tested on animal data and optimal orders are identified. Finally, methods for online identification are demonstrated. Models using exponential coordinates and Euler-angle parameterizations are as accurate as models using quaternion representations, yet require a quarter fewer parameters. Models which incorporate more than four cardiac or three respiration harmonics are no more accurate. Finally, online methods estimate model parameters as accurately as offline methods within three respiration cycles. These methods provide a complete framework for accurately modelling the periodic deformation of points anywhere on the surface of the heart in a closed chest. Copyright © 2017 John Wiley & Sons, Ltd.

A new approach on seismic mortality estimations based on average population density

NASA Astrophysics Data System (ADS)

Zhu, Xiaoxin; Sun, Baiqing; Jin, Zhanyong

2016-12-01

This study examines a new methodology to predict the final seismic mortality from earthquakes in China. Most studies established the association between mortality estimation and seismic intensity without considering the population density. In China, however, the data are not always available, especially when it comes to the very urgent relief situation in the disaster. And the population density varies greatly from region to region. This motivates the development of empirical models that use historical death data to provide the path to analyze the death tolls for earthquakes. The present paper employs the average population density to predict the final death tolls in earthquakes using a case-based reasoning model from realistic perspective. To validate the forecasting results, historical data from 18 large-scale earthquakes occurred in China are used to estimate the seismic morality of each case. And a typical earthquake case occurred in the northwest of Sichuan Province is employed to demonstrate the estimation of final death toll. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20 %, and opens the door for conducting final death forecasts with a qualitative and quantitative approach. Limitations and future research are also analyzed and discussed in the conclusion.

Estimating the decline in excess risk of cerebrovascular disease following quitting smoking--a systematic review based on the negative exponential model.

PubMed

Lee, Peter N; Fry, John S; Thornton, Alison J

2014-02-01

We attempted to quantify the decline in stroke risk following quitting using the negative exponential model, with methodology previously employed for IHD. We identified 22 blocks of RRs (from 13 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We tried to estimate the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block. The method failed to converge or produced very variable estimates of H in nine blocks with a current smoker RR <1.40. Rejecting these, and combining blocks by amount smoked in one study where problems arose in model-fitting, the final analyses used 11 blocks. Goodness-of-fit was adequate for each block, the combined estimate of H being 4.78(95%CI 2.17-10.50) years. However, considerable heterogeneity existed, unexplained by any factor studied, with the random-effects estimate 3.08(1.32-7.16). Sensitivity analyses allowing for reverse causation or differing assumed times for the final quitting period gave similar results. The estimates of H are similar for stroke and IHD, and the individual estimates similarly heterogeneous. Fitting the model is harder for stroke, due to its weaker association with smoking. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Impact of density information on Rayleigh surface wave inversion results

NASA Astrophysics Data System (ADS)

Ivanov, Julian; Tsoflias, Georgios; Miller, Richard D.; Peterie, Shelby; Morton, Sarah; Xia, Jianghai

2016-12-01

We assessed the impact of density on the estimation of inverted shear-wave velocity (Vs) using the multi-channel analysis of surface waves (MASW) method. We considered the forward modeling theory, evaluated model sensitivity, and tested the effect of density information on the inversion of seismic data acquired in the Arctic. Theoretical review, numerical modeling and inversion of modeled and real data indicated that the density ratios between layers, not the actual density values, impact the determination of surface-wave phase velocities. Application on real data compared surface-wave inversion results using: a) constant density, the most common approach in practice, b) indirect density estimates derived from refraction compressional-wave velocity observations, and c) from direct density measurements in a borehole. The use of indirect density estimates reduced the final shear-wave velocity (Vs) results typically by 6-7% and the use of densities from a borehole reduced the final Vs estimates by 10-11% compared to those from assumed constant density. In addition to the improved absolute Vs accuracy, the resulting overall Vs changes were unevenly distributed laterally when viewed on a 2-D section leading to an overall Vs model structure that was more representative of the subsurface environment. It was observed that the use of constant density instead of increasing density with depth not only can lead to Vs overestimation but it can also create inaccurate model structures, such as a low-velocity layer. Thus, optimal Vs estimations can be best achieved using field estimates of subsurface density ratios.

Tracking control of WMRs on loose soil based on mixed H2/H∞ control with longitudinal slip ratio estimation

NASA Astrophysics Data System (ADS)

Gao, Haibo; Chen, Chao; Ding, Liang; Li, Weihua; Yu, Haitao; Xia, Kerui; Liu, Zhen

2017-11-01

Wheeled mobile robots (WMRs) often suffer from the longitudinal slipping when moving on the loose soil of the surface of the moon during exploration. Longitudinal slip is the main cause of WMRs' delay in trajectory tracking. In this paper, a nonlinear extended state observer (NESO) is introduced to estimate the longitudinal velocity in order to estimate the slip ratio and the derivative of the loss of velocity which are used in modelled disturbance compensation. Owing to the uncertainty and disturbance caused by estimation errors, a multi-objective controller using the mixed H2/H∞ method is employed to ensure the robust stability and performance of the WMR system. The final inputs of the trajectory tracking consist of the feedforward compensation, compensation for the modelled disturbances and designed multi-objective control inputs. Finally, the simulation results demonstrate the effectiveness of the controller, which exhibits a satisfactory tracking performance.

Quantifying cancer cell receptors with paired-agent fluorescent imaging: a novel method to account for tissue optical property effects

NASA Astrophysics Data System (ADS)

Sadeghipour, Negar; Davis, Scott C.; Tichauer, Kenneth M.

2018-02-01

Dynamic fluorescence imaging approaches can be used to estimate the concentration of cell surface receptors in vivo. Kinetic models are used to generate the final estimation by taking the targeted imaging agent concentration as a function of time. However, tissue absorption and scattering properties cause the final readout signal to be on a different scale than the real fluorescent agent concentration. In paired-agent imaging approaches, simultaneous injection of a suitable control imaging agent with a targeted one can account for non-specific uptake and retention of the targeted agent. Additionally, the signal from the control agent can be a normalizing factor to correct for tissue optical property differences. In this study, the kinetic model used for paired-agent imaging analysis (i.e., simplified reference tissue model) is modified and tested in simulation and experimental data in a way that accounts for the scaling correction within the kinetic model fit to the data to ultimately extract an estimate of the targeted biomarker concentration.

Estimating abundance

USGS Publications Warehouse

Sutherland, Chris; Royle, Andy

2016-01-01

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms (Anguis fragilis).

Estimating abundance: Chapter 27

USGS Publications Warehouse

Royle, J. Andrew

2016-01-01

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms (Anguis fragilis).

Estimating Teacher and School Effectiveness in Pittsburgh: Value-Added Modeling and Results. Final Report

ERIC Educational Resources Information Center

Lipscomb, Stephen; Gill, Brian; Booker, Kevin; Johnson, Matthew

2010-01-01

At the request of Pittsburgh Public Schools (PPS) and the Pittsburgh Federation of Teachers (PFT), Mathematica is developing value-added models (VAMs) that aim to estimate the contributions of individual teachers, teams of teachers, and schools to the achievement growth of their students. The analyses described in this report are intended as an…

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262

Theoretical Model for Volume Fraction of UC, 235U Enrichment, and Effective Density of Final U 10Mo Alloy

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

Devaraj, Arun; Prabhakaran, Ramprashad; Joshi, Vineet V.

2016-04-12

The purpose of this document is to provide a theoretical framework for (1) estimating uranium carbide (UC) volume fraction in a final alloy of uranium with 10 weight percent molybdenum (U-10Mo) as a function of final alloy carbon concentration, and (2) estimating effective 235U enrichment in the U-10Mo matrix after accounting for loss of 235U in forming UC. This report will also serve as a theoretical baseline for effective density of as-cast low-enriched U-10Mo alloy. Therefore, this report will serve as the baseline for quality control of final alloy carbon content

Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects.

PubMed

Dai, Tianjiao; Shete, Sanjay

2016-08-30

In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To reduce the side effects and costs associated with first-stage interventions in a SMART design, we proposed a novel time-varying SMART design in which individuals are re-randomized to the second-stage interventions as soon as a pre-fixed intermediate response is observed. With this strategy, the duration of the first-stage intervention will vary. We developed a time-varying mixed effects model and a joint model that allows for modeling the outcomes of interest (intermediate and final) and the random durations of the first-stage interventions simultaneously. The joint model borrows strength from the survival sub-model in which the duration of the first-stage intervention (i.e., time to response to the first-stage intervention) is modeled. We performed a simulation study to evaluate the statistical properties of these models. Our simulation results showed that the two modeling approaches were both able to provide good estimations of the means of the final outcomes of all the embedded interventions in a SMART. However, the joint modeling approach was more accurate for estimating the coefficients of first-stage interventions and time of the intervention. We conclude that the joint modeling approach provides more accurate parameter estimates and a higher estimated coverage probability than the single time-varying mixed effects model, and we recommend the joint model for analyzing data generated from time-varying SMART designs. In addition, we showed that the proposed time-varying SMART design is cost-efficient and equally effective in selecting the optimal embedded adaptive intervention as the standard SMART design.

A new approach for estimating the Jupiter and Saturn gravity fields using Juno and Cassini measurements, trajectory estimation analysis, and a dynamical wind model optimization

NASA Astrophysics Data System (ADS)

Galanti, Eli; Durante, Daniele; Iess, Luciano; Kaspi, Yohai

2017-04-01

The ongoing Juno spacecraft measurements are improving our knowledge of Jupiter's gravity field. Similarly, the Cassini Grand Finale will improve the gravity estimate of Saturn. The analysis of the Juno and Cassini Doppler data will provide a very accurate reconstruction of spacial gravity variations, but these measurements will be very accurate only over a limited latitudinal range. In order to deduce the full gravity fields of Jupiter and Saturn, additional information needs to be incorporated into the analysis, especially with regards to the planets' wind structures. In this work we propose a new iterative approach for the estimation of Jupiter and Saturn gravity fields, using simulated measurements, a trajectory estimation model, and an adjoint based inverse thermal wind model. Beginning with an artificial gravitational field, the trajectory estimation model is used to obtain the gravitational moments. The solution from the trajectory model is then used as an initial guess for the thermal wind model, and together with an optimization method, the likely penetration depth of the winds is computed, and its uncertainty is evaluated. As a final step, the gravity harmonics solution from the thermal wind model is given back to the trajectory model, along with an estimate of their uncertainties, to be used as a priori for a new calculation of the gravity field. We test this method both for zonal harmonics only and with a full gravity field including tesseral harmonics. The results show that by using this method some of the gravitational moments are fitted better to the `observed' ones, mainly due to the added information from the dynamical model which includes the wind structure and its depth. Thus, it is suggested that the method presented here has the potential of improving the accuracy of the expected gravity moments estimated from the Juno and Cassini radio science experiments.

Maximum Likelihood Shift Estimation Using High Resolution Polarimetric SAR Clutter Model

NASA Astrophysics Data System (ADS)

Harant, Olivier; Bombrun, Lionel; Vasile, Gabriel; Ferro-Famil, Laurent; Gay, Michel

2011-03-01

This paper deals with a Maximum Likelihood (ML) shift estimation method in the context of High Resolution (HR) Polarimetric SAR (PolSAR) clutter. Texture modeling is exposed and the generalized ML texture tracking method is extended to the merging of various sensors. Some results on displacement estimation on the Argentiere glacier in the Mont Blanc massif using dual-pol TerraSAR-X (TSX) and quad-pol RADARSAT-2 (RS2) sensors are finally discussed.

Evaluation of scour potential of cohesive soils : final report, August 2009.

DOT National Transportation Integrated Search

2009-08-01

Prediction of scour at bridge river crossings is an evolving process. Hydraulic models to estimate water velocity and, therefore, the shear stresses that erode soil are reasonably well developed. The weak link remains methods for estimating soil erod...

Innovative methods for calculation of freeway travel time using limited data : final report.

DOT National Transportation Integrated Search

2008-01-01

Description: Travel time estimations created by processing of simulated freeway loop detector data using proposed method have been compared with travel times reported from VISSIM model. An improved methodology was proposed to estimate freeway corrido...

Occupational Choice: A Conditional Logit Model with Special Reference to Wage Subsidies and Occupational Choice. Final Report.

ERIC Educational Resources Information Center

Boskin, Michael J.

A model of occupational choice based on the theory of human capital is developed and estimated by conditional logit analysis. The empirical results estimated the probability of individuals with certain characteristics (such as race, sex, age, and education) entering each of 11 occupational groups. The results indicate that individuals tend to…

Multistage degradation modeling for BLDC motor based on Wiener process

NASA Astrophysics Data System (ADS)

Yuan, Qingyang; Li, Xiaogang; Gao, Yuankai

2018-05-01

Brushless DC motors are widely used, and their working temperatures, regarding as degradation processes, are nonlinear and multistage. It is necessary to establish a nonlinear degradation model. In this research, our study was based on accelerated degradation data of motors, which are their working temperatures. A multistage Wiener model was established by using the transition function to modify linear model. The normal weighted average filter (Gauss filter) was used to improve the results of estimation for the model parameters. Then, to maximize likelihood function for parameter estimation, we used numerical optimization method- the simplex method for cycle calculation. Finally, the modeling results show that the degradation mechanism changes during the degradation of the motor with high speed. The effectiveness and rationality of model are verified by comparison of the life distribution with widely used nonlinear Wiener model, as well as a comparison of QQ plots for residual. Finally, predictions for motor life are gained by life distributions in different times calculated by multistage model.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.

PubMed

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan; Fisman, David N

2017-01-01

Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015-2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. The 2015-2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R 0 approximately 1.4 for all fits). Lower R 0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.

The Use of Multilevel Modeling to Estimate Which Measures Are Most Influential in Determining an Institution's Placement in Carnegie's New Doctoral/Research University Classification Schema

ERIC Educational Resources Information Center

Micceri, Theodore

2007-01-01

This research sought to determine whether any measure(s) used in the Carnegie Foundation's classification of Doctoral/Research Universities contribute to a greater degree than other measures to final rank placement. Multilevel Modeling (MLM) was applied to all eight of the Carnegie Foundation's predictor measures using final rank…

Multi-chain Markov chain Monte Carlo methods for computationally expensive models

NASA Astrophysics Data System (ADS)

Huang, M.; Ray, J.; Ren, H.; Hou, Z.; Bao, J.

2017-12-01

Markov chain Monte Carlo (MCMC) methods are used to infer model parameters from observational data. The parameters are inferred as probability densities, thus capturing estimation error due to sparsity of the data, and the shortcomings of the model. Multiple communicating chains executing the MCMC method have the potential to explore the parameter space better, and conceivably accelerate the convergence to the final distribution. We present results from tests conducted with the multi-chain method to show how the acceleration occurs i.e., for loose convergence tolerances, the multiple chains do not make much of a difference. The ensemble of chains also seems to have the ability to accelerate the convergence of a few chains that might start from suboptimal starting points. Finally, we show the performance of the chains in the estimation of O(10) parameters using computationally expensive forward models such as the Community Land Model, where the sampling burden is distributed over multiple chains.

Estimation of daily protein intake based on spot urine urea nitrogen concentration in chronic kidney disease patients.

PubMed

Kanno, Hiroko; Kanda, Eiichiro; Sato, Asako; Sakamoto, Kaori; Kanno, Yoshihiko

2016-04-01

Determination of daily protein intake in the management of chronic kidney disease (CKD) requires precision. Inaccuracies in recording dietary intake occur, and estimation from total urea excretion presents hurdles owing to the difficulty of collecting whole urine for 24 h. Spot urine has been used for measuring daily sodium intake and urinary protein excretion. In this cross-sectional study, we investigated whether urea nitrogen (UN) concentration in spot urine can be used to predict daily protein intake instead of the 24-h urine collection in 193 Japanese CKD patients (Stages G1-G5). After patient randomization into 2 datasets for the development and validation of models, bootstrapping was used to develop protein intake estimation models. The parameters for the candidate multivariate regression models were male gender, age, body mass index (BMI), diabetes mellitus, dyslipidemia, proteinuria, estimated glomerular filtration rate, serum albumin level, spot urinary UN and creatinine level, and spot urinary UN/creatinine levels. The final model contained BMI and spot urinary UN level. The final model was selected because of the higher correlation between the predicted and measured protein intakes r = 0.558 (95 % confidence interval 0.400, 0.683), and the smaller distribution of the difference between the measured and predicted protein intakes than those of the other models. The results suggest that UN concentration in spot urine may be used to estimate daily protein intake and that a prediction formula would be useful for nutritional control in CKD patients.

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

PubMed

Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

2018-05-24

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

A statistical method to estimate low-energy hadronic cross sections

NASA Astrophysics Data System (ADS)

Balassa, Gábor; Kovács, Péter; Wolf, György

2018-02-01

In this article we propose a model based on the Statistical Bootstrap approach to estimate the cross sections of different hadronic reactions up to a few GeV in c.m.s. energy. The method is based on the idea, when two particles collide a so-called fireball is formed, which after a short time period decays statistically into a specific final state. To calculate the probabilities we use a phase space description extended with quark combinatorial factors and the possibility of more than one fireball formation. In a few simple cases the probability of a specific final state can be calculated analytically, where we show that the model is able to reproduce the ratios of the considered cross sections. We also show that the model is able to describe proton-antiproton annihilation at rest. In the latter case we used a numerical method to calculate the more complicated final state probabilities. Additionally, we examined the formation of strange and charmed mesons as well, where we used existing data to fit the relevant model parameters.

Human casualties in earthquakes: Modelling and mitigation

USGS Publications Warehouse

Spence, R.J.S.; So, E.K.M.

2011-01-01

Earthquake risk modelling is needed for the planning of post-event emergency operations, for the development of insurance schemes, for the planning of mitigation measures in the existing building stock, and for the development of appropriate building regulations; in all of these applications estimates of casualty numbers are essential. But there are many questions about casualty estimation which are still poorly understood. These questions relate to the causes and nature of the injuries and deaths, and the extent to which they can be quantified. This paper looks at the evidence on these questions from recent studies. It then reviews casualty estimation models available, and finally compares the performance of some casualty models in making rapid post-event casualty estimates in recent earthquakes.

Estimating Cumulative Traffic Loads, Final Report for Phase 1

DOT National Transportation Integrated Search

2000-07-01

The knowledge of traffic loads is a prerequisite for the pavement analysis process, especially for the development of load-related distress prediction models. Furthermore, the emerging mechanistically based pavement performance models and pavement de...

Integrating resource selection information with spatial capture--recapture

USGS Publications Warehouse

Royle, J. Andrew; Chandler, Richard B.; Sun, Catherine C.; Fuller, Angela K.

2013-01-01

4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Regional Development Impacts Multi-Regional - Multi-Industry Model (MRMI) Users Manual,

DTIC Science & Technology

1982-09-01

indicators, described in Chapter 2, are estimated as well. Finally, MRMI is flexible, as it can incorporate alternative macroeconomic , national inter...national and regional economic contexts and data sources for estimating macroeconomic and direct impacts data. Considerations for ensuring consistency...Chapter 4 is devoted to model execution and the interpretation of its output. As MRMI forecasts are based upon macroeconomic , national inter-industry

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Expectation Maximization Algorithm for Box-Cox Transformation Cure Rate Model and Assessment of Model Misspecification Under Weibull Lifetimes.

PubMed

Pal, Suvra; Balakrishnan, Narayanaswamy

2018-05-01

In this paper, we develop likelihood inference based on the expectation maximization algorithm for the Box-Cox transformation cure rate model assuming the lifetimes to follow a Weibull distribution. A simulation study is carried out to demonstrate the performance of the proposed estimation method. Through Monte Carlo simulations, we also study the effect of model misspecification on the estimate of cure rate. Finally, we analyze a well-known data on melanoma with the model and the inferential method developed here.

Simplification of a light-based model for estimating final internode length in greenhouse cucumber canopies.

PubMed

Kahlen, Katrin; Stützel, Hartmut

2011-10-01

Light quantity and quality affect internode lengths in cucumber (Cucumis sativus), whereby leaf area and the optical properties of the leaves mainly control light quality within a cucumber plant community. This modelling study aimed at providing a simple, non-destructive method to predict final internode lengths (FILs) using light quantity and leaf area data. Several simplifications of a light quantity and quality sensitive model for estimating FILs in cucumber have been tested. The direct simplifications substitute the term for the red : far-red (R : FR) ratios, by a term for (a) the leaf area index (LAI, m(2) m(-2)) or (b) partial LAI, the cumulative leaf area per m(2) ground, where leaf area per m(2) ground is accumulated from the top of each plant until a number, n, of leaves per plant is reached. The indirect simplifications estimate the input R : FR ratio based on partial leaf area and plant density. In all models, simulated FILs were in line with the measured FILs over various canopy architectures and light conditions, but the prediction quality varied. The indirect simplification based on leaf area of ten leaves revealed the best fit with measured data. Its prediction quality was even higher than of the original model. This study showed that for vertically trained cucumber plants, leaf area data can substitute local light quality data for estimating FIL data. In unstressed canopies, leaf area over the upper ten ranks seems to represent the feedback of the growing architecture on internode elongation with respect to light quality. This highlights the role of this domain of leaves as the primary source for the specific R : FR signal controlling the final length of an internode and could therefore guide future research on up-scaling local processes to the crop level.

Robust image modeling techniques with an image restoration application

NASA Astrophysics Data System (ADS)

Kashyap, Rangasami L.; Eom, Kie-Bum

1988-08-01

A robust parameter-estimation algorithm for a nonsymmetric half-plane (NSHP) autoregressive model, where the driving noise is a mixture of a Gaussian and an outlier process, is presented. The convergence of the estimation algorithm is proved. An algorithm to estimate parameters and original image intensity simultaneously from the impulse-noise-corrupted image, where the model governing the image is not available, is also presented. The robustness of the parameter estimates is demonstrated by simulation. Finally, an algorithm to restore realistic images is presented. The entire image generally does not obey a simple image model, but a small portion (e.g., 8 x 8) of the image is assumed to obey an NSHP model. The original image is divided into windows and the robust estimation algorithm is applied for each window. The restoration algorithm is tested by comparing it to traditional methods on several different images.

Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-05-29

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final globalmore » uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.« less

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

Cost and schedule estimation study report

NASA Technical Reports Server (NTRS)

Condon, Steve; Regardie, Myrna; Stark, Mike; Waligora, Sharon

1993-01-01

This report describes the analysis performed and the findings of a study of the software development cost and schedule estimation models used by the Flight Dynamics Division (FDD), Goddard Space Flight Center. The study analyzes typical FDD projects, focusing primarily on those developed since 1982. The study reconfirms the standard SEL effort estimation model that is based on size adjusted for reuse; however, guidelines for the productivity and growth parameters in the baseline effort model have been updated. The study also produced a schedule prediction model based on empirical data that varies depending on application type. Models for the distribution of effort and schedule by life-cycle phase are also presented. Finally, this report explains how to use these models to plan SEL projects.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model

PubMed Central

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan

2017-01-01

Abstract Background Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. Methods We used the previously described “incidence decay with exponential adjustment” (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015–2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. Results The 2015–2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R0 approximately 1.4 for all fits). Lower R0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. Conclusions A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance. PMID:29497629

Modeling human perception and estimation of kinematic responses during aircraft landing

NASA Technical Reports Server (NTRS)

Schmidt, David K.; Silk, Anthony B.

1988-01-01

The thrust of this research is to determine estimation accuracy of aircraft responses based on observed cues. By developing the geometric relationships between the outside visual scene and the kinematics during landing, visual and kinesthetic cues available to the pilot were modeled. Both fovial and peripheral vision was examined. The objective was to first determine estimation accuracy in a variety of flight conditions, and second to ascertain which parameters are most important and lead to the best achievable accuracy in estimating the actual vehicle response. It was found that altitude estimation was very sensitive to the FOV. For this model the motion cue of perceived vertical acceleration was shown to be less important than the visual cues. The inclusion of runway geometry in the visual scene increased estimation accuracy in most cases. Finally, it was shown that for this model if the pilot has an incorrect internal model of the system kinematics the choice of observations thought to be 'optimal' may in fact be suboptimal.

Abuse behavior of high-power, lithium-ion cells

NASA Astrophysics Data System (ADS)

Spotnitz, R.; Franklin, J.

Published accounts of abuse testing of lithium-ion cells and components are summarized, including modeling work. From this summary, a set of exothermic reactions is selected with corresponding estimates of heats of reaction. Using this set of reactions, along with estimated kinetic parameters and designs for high-rate batteries, models for the abuse behavior (oven, short-circuit, overcharge, nail, crush) are developed. Finally, the models are used to determine that fluorinated binder plays a relatively unimportant role in thermal runaway.

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

Liu, Y.; Liu, Z.; Zhang, S.

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Simple Electrolyzer Model Development for High-Temperature Electrolysis System Analysis Using Solid Oxide Electrolysis Cell

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

JaeHwa Koh; DuckJoo Yoon; Chang H. Oh

2010-07-01

An electrolyzer model for the analysis of a hydrogen-production system using a solid oxide electrolysis cell (SOEC) has been developed, and the effects for principal parameters have been estimated by sensitivity studies based on the developed model. The main parameters considered are current density, area specific resistance, temperature, pressure, and molar fraction and flow rates in the inlet and outlet. Finally, a simple model for a high-temperature hydrogen-production system using the solid oxide electrolysis cell integrated with very high temperature reactors is estimated.

A method for automatic feature points extraction of human vertebrae three-dimensional model

NASA Astrophysics Data System (ADS)

Wu, Zhen; Wu, Junsheng

2017-05-01

A method for automatic extraction of the feature points of the human vertebrae three-dimensional model is presented. Firstly, the statistical model of vertebrae feature points is established based on the results of manual vertebrae feature points extraction. Then anatomical axial analysis of the vertebrae model is performed according to the physiological and morphological characteristics of the vertebrae. Using the axial information obtained from the analysis, a projection relationship between the statistical model and the vertebrae model to be extracted is established. According to the projection relationship, the statistical model is matched with the vertebrae model to get the estimated position of the feature point. Finally, by analyzing the curvature in the spherical neighborhood with the estimated position of feature points, the final position of the feature points is obtained. According to the benchmark result on multiple test models, the mean relative errors of feature point positions are less than 5.98%. At more than half of the positions, the error rate is less than 3% and the minimum mean relative error is 0.19%, which verifies the effectiveness of the method.

Multiple-reflection model of human skin and estimation of pigment concentrations

NASA Astrophysics Data System (ADS)

Ohtsuki, Rie; Tominaga, Shoji; Tanno, Osamu

2012-07-01

We describe a new method for estimating the concentrations of pigments in the human skin using surface spectral reflectance. We derive an equation that expresses the surface spectral reflectance of the human skin. First, we propose an optical model of the human skin that accounts for the stratum corneum. We also consider the difference between the scattering coefficient of the epidermis and that of the dermis. We then derive an equation by applying the Kubelka-Munk theory to an optical model of the human skin. Unlike a model developed in a recent study, the present equation considers pigments as well as multiple reflections and the thicknesses of the skin layers as factors that affect the color of the human skin. In two experiments, we estimate the pigment concentrations using the measured surface spectral reflectances. Finally, we confirm the feasibility of the concentrations estimated by the proposed method by evaluating the estimated pigment concentrations in the skin.

Hydrologic Record Extension of Water-Level Data in the Everglades Depth Estimation Network (EDEN) Using Artificial Neural Network Models, 2000-2006

USGS Publications Warehouse

Conrads, Paul; Roehl, Edwin A.

2007-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystem Science provides support for EDEN and the goal of providing quality assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the water-surface models, 25 real-time water-level gaging stations were added to the network of 253 established water-level gaging stations. To incorporate the data from the newly added stations to the 7-year EDEN database in the greater Everglades, the short-term water-level records (generally less than 1 year) needed to be simulated back in time (hindcasted) to be concurrent with data from the established gaging stations in the database. A three-step modeling approach using artificial neural network models was used to estimate the water levels at the new stations. The artificial neural network models used static variables that represent the gaging station location and percent vegetation in addition to dynamic variables that represent water-level data from the established EDEN gaging stations. The final step of the modeling approach was to simulate the computed error of the initial estimate to increase the accuracy of the final water-level estimate. The three-step modeling approach for estimating water levels at the new EDEN gaging stations produced satisfactory results. The coefficients of determination (R2) for 21 of the 25 estimates were greater than 0.95, and all of the estimates (25 of 25) were greater than 0.82. The model estimates showed good agreement with the measured data. For some new EDEN stations with limited measured data, the record extension (hindcasts) included periods beyond the range of the data used to train the artificial neural network models. The comparison of the hindcasts with long-term water-level data proximal to the new EDEN gaging stations indicated that the water-level estimates were reasonable. The percent model error (root mean square error divided by the range of the measured data) was less than 6 percent, and for the majority of stations (20 of 25), the percent model error was less than 1 percent.

An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

PubMed

Remontet, L; Bossard, N; Belot, A; Estève, J

2007-05-10

Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression. Copyright 2006 John Wiley & Sons, Ltd.

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

NASA Astrophysics Data System (ADS)

Li, Shan; Zhang, Shaoqing; Liu, Zhengyu; Lu, Lv; Zhu, Jiang; Zhang, Xuefeng; Wu, Xinrong; Zhao, Ming; Vecchi, Gabriel A.; Zhang, Rong-Hua; Lin, Xiaopei

2018-04-01

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

Spatio-temporal change in forest cover and carbon storage considering actual and potential forest cover in South Korea.

PubMed

Nam, Kijun; Lee, Woo-Kyun; Kim, Moonil; Kwak, Doo-Ahn; Byun, Woo-Hyuk; Yu, Hangnan; Kwak, Hanbin; Kwon, Taesung; Sung, Joohan; Chung, Dong-Jun; Lee, Seung-Ho

2015-07-01

This study analyzes change in carbon storage by applying forest growth models and final cutting age to actual and potential forest cover for six major tree species in South Korea. Using National Forest Inventory data, the growth models were developed to estimate mean diameter at breast height, tree height, and number of trees for Pinus densiflora, Pinus koraiensis, Pinus rigida, Larix kaempferi, Castanea crenata and Quercus spp. stands. We assumed that actual forest cover in a forest type map will change into potential forest covers according to the Hydrological and Thermal Analogy Groups model. When actual forest cover reaches the final cutting age, forest volume and carbon storage are estimated by changed forest cover and its growth model. Forest volume between 2010 and 2110 would increase from 126.73 to 157.33 m(3) hm(-2). Our results also show that forest cover, volume, and carbon storage could abruptly change by 2060. This is attributed to the fact that most forests are presumed to reach final cutting age. To avoid such dramatic change, a regeneration and yield control scheme should be prepared and implemented in a way that ensures balance in forest practice and yield.

The College Mathematics Experience and Changes in Majors: A Structural Model Analysis.

ERIC Educational Resources Information Center

Whiteley, Meredith A.; Fenske, Robert H.

1990-01-01

Testing of a structural equation model with college mathematics experience as the focal variable in 745 students' final decisions concerning major or dropping out over 4 years of college yielded separate model estimates for 3 fields: scientific/technical, quantitative business, and business management majors. (Author/MSE)

Confirmation of model-based dose selection for a Japanese phase III study of rivaroxaban in non-valvular atrial fibrillation patients.

PubMed

Kaneko, Masato; Tanigawa, Takahiko; Hashizume, Kensei; Kajikawa, Mariko; Tajiri, Masahiro; Mueck, Wolfgang

2013-01-01

This study was designed to confirm the appropriateness of the dose setting for a Japanese phase III study of rivaroxaban in patients with non-valvular atrial fibrillation (NVAF), which had been based on model simulation employing phase II study data. The previously developed mixed-effects pharmacokinetic/pharmacodynamic (PK-PD) model, which consisted of an oral one-compartment model parameterized in terms of clearance, volume and a first-order absorption rate, was rebuilt and optimized using the data for 597 subjects from the Japanese phase III study, J-ROCKET AF. A mixed-effects modeling technique in NONMEM was used to quantify both unexplained inter-individual variability and inter-occasion variability, which are random effect parameters. The final PK and PK-PD models were evaluated to identify influential covariates. The empirical Bayes estimates of AUC and C(max) from the final PK model were consistent with the simulated results from the Japanese phase II study. There was no clear relationship between individual estimated exposures and safety-related events, and the estimated exposure levels were consistent with the global phase III data. Therefore, it was concluded that the dose selected for the phase III study with Japanese NVAF patients by means of model simulation employing phase II study data had been appropriate from the PK-PD perspective.

An optimal pole-matching observer design for estimating tyre-road friction force

NASA Astrophysics Data System (ADS)

Faraji, Mohammad; Johari Majd, Vahid; Saghafi, Behrooz; Sojoodi, Mahdi

2010-10-01

In this paper, considering the dynamical model of tyre-road contacts, we design a nonlinear observer for the on-line estimation of tyre-road friction force using the average lumped LuGre model without any simplification. The design is the extension of a previously offered observer to allow a muchmore realistic estimation by considering the effect of the rolling resistance and a term related to the relative velocity in the observer. Our aim is not to introduce a new friction model, but to present a more accurate nonlinear observer for the assumed model. We derive linear matrix equality conditions to obtain an observer gain with minimum pole mismatch for the desired observer error dynamic system. We prove the convergence of the observer for the non-simplified model. Finally, we compare the performance of the proposed observer with that of the previously mentioned nonlinear observer, which shows significant improvement in the accuracy of estimation.

A Model for Remote Depth Estimation of Buried Radioactive Wastes Using CdZnTe Detector.

PubMed

Ukaegbu, Ikechukwu Kevin; Gamage, Kelum A A

2018-05-18

This paper presents the results of an attenuation model for remote depth estimation of buried radioactive wastes using a Cadmium Zinc Telluride (CZT) detector. Previous research using an organic liquid scintillator detector system showed that the model is able to estimate the depth of a 329-kBq Cs-137 radioactive source buried up to 12 cm in sand with an average count rate of 100 cps. The results presented in this paper showed that the use of the CZT detector extended the maximum detectable depth of the same radioactive source to 18 cm in sand with a significantly lower average count rate of 14 cps. Furthermore, the model also successfully estimated the depth of a 9-kBq Co-60 source buried up to 3 cm in sand. This confirms that this remote depth estimation method can be used with other radionuclides and wastes with very low activity. Finally, the paper proposes a performance parameter for evaluating radiation detection systems that implement this remote depth estimation method.

Fitting a Structured Juvenile-Adult Model for Green Tree Frogs to Population Estimates from Capture-Mark-Recapture Field Data

USGS Publications Warehouse

Ackleh, A.S.; Carter, J.; Deng, K.; Huang, Q.; Pal, N.; Yang, X.

2012-01-01

We derive point and interval estimates for an urban population of green tree frogs (Hyla cinerea) from capture-mark-recapture field data obtained during the years 2006-2009. We present an infinite-dimensional least-squares approach which compares a mathematical population model to the statistical population estimates obtained from the field data. The model is composed of nonlinear first-order hyperbolic equations describing the dynamics of the amphibian population where individuals are divided into juveniles (tadpoles) and adults (frogs). To solve the least-squares problem, an explicit finite difference approximation is developed. Convergence results for the computed parameters are presented. Parameter estimates for the vital rates of juveniles and adults are obtained, and standard deviations for these estimates are computed. Numerical results for the model sensitivity with respect to these parameters are given. Finally, the above-mentioned parameter estimates are used to illustrate the long-time behavior of the population under investigation. ?? 2011 Society for Mathematical Biology.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

A bias-corrected estimator in multiple imputation for missing data.

PubMed

Tomita, Hiroaki; Fujisawa, Hironori; Henmi, Masayuki

2018-05-29

Multiple imputation (MI) is one of the most popular methods to deal with missing data, and its use has been rapidly increasing in medical studies. Although MI is rather appealing in practice since it is possible to use ordinary statistical methods for a complete data set once the missing values are fully imputed, the method of imputation is still problematic. If the missing values are imputed from some parametric model, the validity of imputation is not necessarily ensured, and the final estimate for a parameter of interest can be biased unless the parametric model is correctly specified. Nonparametric methods have been also proposed for MI, but it is not so straightforward as to produce imputation values from nonparametrically estimated distributions. In this paper, we propose a new method for MI to obtain a consistent (or asymptotically unbiased) final estimate even if the imputation model is misspecified. The key idea is to use an imputation model from which the imputation values are easily produced and to make a proper correction in the likelihood function after the imputation by using the density ratio between the imputation model and the true conditional density function for the missing variable as a weight. Although the conditional density must be nonparametrically estimated, it is not used for the imputation. The performance of our method is evaluated by both theory and simulation studies. A real data analysis is also conducted to illustrate our method by using the Duke Cardiac Catheterization Coronary Artery Disease Diagnostic Dataset. Copyright © 2018 John Wiley & Sons, Ltd.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

PubMed

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

Preliminary Assessment of the Flow of Used Electronics, In ...

EPA Pesticide Factsheets

Electronic waste (e-waste) is the largest growing municipal waste stream in the United States. The improper disposal of e-waste has environmental, economic, and social impacts, thus there is a need for sustainable stewardship of electronics. EPA/ORD has been working to improve our understanding of the quantity and flow of electronic devices from initial purchase to final disposition. Understanding the pathways of used electronics from the consumer to their final disposition would provide insight to decision makers about their impacts and support efforts to encourage improvements in policy, technology, and beneficial use. This report is the first stage of study of EPA/ORD's efforts to understand the flows of used electronics and e-waste by reviewing the regulatory programs for the selected states and identifying the key lessons learned and best practices that have emerged since their inception. Additionally, a proof-of-concept e-waste flow model has been developed to provide estimates of the quantity of e-waste generated annually at the national level, as well as for selected states. This report documents a preliminary assessment of available data and development of the model that can be used as a starting point to estimate domestic flows of used electronics from generation, to collection and reuse, to final disposition. The electronics waste flow model can estimate the amount of electronic products entering the EOL management phase based on unit sales dat

Variance-Stable R-Estimators.

DTIC Science & Technology

1984-05-01

By means of the concept of change-of variance function we investigate the stability properties of the asymptotic variance of R-estimators. This allows us to construct the optimal V-robust R-estimator that minimizes the asymptotic variance at the model, under the side condition of a bounded change-of variance function. Finally, we discuss the connection between this function and an influence function for two-sample rank tests introduced by Eplett (1980). (Author)

Are Online Exams an Invitation to Cheat?

ERIC Educational Resources Information Center

Harmon, Oskar R.; Lambrinos, James; Kennedy, Peter, Ed.

2008-01-01

In this study, the authors use data from two online courses in principles of economics to estimate a model that predicts exam scores from independent variables of student characteristics. In one course, the final exam was proctored, and in the other course, the final exam was not proctored. In both courses, the first three exams were unproctored.…

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV-infected infants and toddlers

PubMed Central

Zhao, Wei; Cella, Massimo; Della Pasqua, Oscar; Burger, David; Jacqz-Aigrain, Evelyne

2012-01-01

AIMS To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration–time curve (AUC) targeted dosage and individualize therapy. METHODS The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation–estimation method. RESULTS The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 l h−1 (RSE 6.3%), apparent central volume of distribution 4.94 l (RSE 28.7%), apparent peripheral volume of distribution 8.12 l (RSE14.2%), apparent intercompartment clearance 1.25 l h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t. CONCLUSIONS The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC0–t was developed from the final model and can be used routinely to optimize individual dosing. PMID:21988586

Prophylactic ranitidine treatment in critically ill children – a population pharmacokinetic study

PubMed Central

Hawwa, Ahmed F; Westwood, Paul M; Collier, Paul S; Millership, Jeffrey S; Yakkundi, Shirish; Thurley, Gillian; Shields, Mike D; Nunn, Anthony J; Halliday, Henry L; McElnay, James C

2013-01-01

Aims To characterize the population pharmacokinetics of ranitidine in critically ill children and to determine the influence of various clinical and demographic factors on its disposition. Methods Data were collected prospectively from 78 paediatric patients (n = 248 plasma samples) who received oral or intravenous ranitidine for prophylaxis against stress ulcers, gastrointestinal bleeding or the treatment of gastro-oesophageal reflux. Plasma samples were analysed using high-performance liquid chromatography, and the data were subjected to population pharmacokinetic analysis using nonlinear mixed-effects modelling. Results A one-compartment model best described the plasma concentration profile, with an exponential structure for interindividual errors and a proportional structure for intra-individual error. After backward stepwise elimination, the final model showed a significant decrease in objective function value (−12.618; P < 0.001) compared with the weight-corrected base model. Final parameter estimates for the population were 32.1 l h−1 for total clearance and 285 l for volume of distribution, both allometrically modelled for a 70 kg adult. Final estimates for absorption rate constant and bioavailability were 1.31 h−1 and 27.5%, respectively. No significant relationship was found between age and weight-corrected ranitidine pharmacokinetic parameters in the final model, with the covariate for cardiac failure or surgery being shown to reduce clearance significantly by a factor of 0.46. Conclusions Currently, ranitidine dose recommendations are based on children's weights. However, our findings suggest that a dosing scheme that takes into consideration both weight and cardiac failure/surgery would be more appropriate in order to avoid administration of higher or more frequent doses than necessary. PMID:23016949

Econometrics of exhaustible resource supply: a theory and an application. Final report

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

Epple, D.; Hansen, L.P.

1981-12-01

An econometric model of US oil and natural gas discoveries is developed in this study. The econometric model is explicitly derived as the solution to the problem of maximizing the expected discounted after tax present value of revenues net of exploration, development, and production costs. The model contains equations representing producers' formation of price expectations and separate equations giving producers' optimal exploration decisions contingent on expected prices. A procedure is developed for imposing resource base constraints (e.g., ultimate recovery estimates based on geological analysis) when estimating the econometric model. The model is estimated using aggregate post-war data for the Unitedmore » States. Production from a given addition to proved reserves is assumed to follow a negative exponential path, and additions of proved reserves from a given discovery are assumed to follow a negative exponential path. Annual discoveries of oil and natural gas are estimated as latent variables. These latent variables are the endogenous variables in the econometric model of oil and natural gas discoveries. The model is estimated without resource base constraints. The model is also estimated imposing the mean oil and natural gas ultimate recovery estimates of the US Geological Survey. Simulations through the year 2020 are reported for various future price regimes.« less

An Approach to Estimate the Flow Through an Irregular Fracture

NASA Astrophysics Data System (ADS)

Liu, Q. Q.; Fan, H. G.

2011-09-01

A new model to estimate the flow in a fracture has been developed in this paper. This model used two sinusoidal-varying walls with different phases to replace the flat planes in the cubic law model. The steady laminar flow between non-symmetric sinusoidal surfaces was numerically solved. The relationships between the effective hydraulic apertures and the phase retardation for different amplitudes and wavelengths are investigated respectively. Finally, a formula of the effective hydraulic aperture of the fracture was carried out based on the numerical results.

Slice sampling technique in Bayesian extreme of gold price modelling

NASA Astrophysics Data System (ADS)

Rostami, Mohammad; Adam, Mohd Bakri; Ibrahim, Noor Akma; Yahya, Mohamed Hisham

2013-09-01

In this paper, a simulation study of Bayesian extreme values by using Markov Chain Monte Carlo via slice sampling algorithm is implemented. We compared the accuracy of slice sampling with other methods for a Gumbel model. This study revealed that slice sampling algorithm offers more accurate and closer estimates with less RMSE than other methods . Finally we successfully employed this procedure to estimate the parameters of Malaysia extreme gold price from 2000 to 2011.

PVWatts Version 1 Technical Reference

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

Dobos, A. P.

2013-10-01

The NREL PVWatts(TM) calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes several hidden assumptions about performance parameters. This technical reference details the individual sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimation.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles.

PubMed

Nam, Kanghyun

2015-11-11

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle's cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

A Model-Based Approach to Infer Shifts in Regional Fire Regimes Over Time Using Sediment Charcoal Records

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; Hooten, M.; Higuera, P. E.; Marlon, J. R.; McLachlan, J. S.; Kelly, R.

2016-12-01

Sediment charcoal records are used in paleoecological analyses to identify individual local fire events and to estimate fire frequency and regional biomass burned at centennial to millenial time scales. Methods to identify local fire events based on sediment charcoal records have been well developed over the past 30 years, however, an integrated statistical framework for fire identification is still lacking. We build upon existing paleoecological methods to develop a hierarchical Bayesian point process model for local fire identification and estimation of fire return intervals. The model is unique in that it combines sediment charcoal records from multiple lakes across a region in a spatially-explicit fashion leading to estimation of a joint, regional fire return interval in addition to lake-specific local fire frequencies. Further, the model estimates a joint regional charcoal deposition rate free from the effects of local fires that can be used as a measure of regional biomass burned over time. Finally, the hierarchical Bayesian approach allows for tractable error propagation such that estimates of fire return intervals reflect the full range of uncertainty in sediment charcoal records. Specific sources of uncertainty addressed include sediment age models, the separation of local versus regional charcoal sources, and generation of a composite charcoal record The model is applied to sediment charcoal records from a dense network of lakes in the Yukon Flats region of Alaska. The multivariate joint modeling approach results in improved estimates of regional charcoal deposition with reduced uncertainty in the identification of individual fire events and local fire return intervals compared to individual lake approaches. Modeled individual-lake fire return intervals range from 100 to 500 years with a regional interval of roughly 200 years. Regional charcoal deposition to the network of lakes is correlated up to 50 kilometers. Finally, the joint regional charcoal deposition rate exhibits changes over time coincident with major climatic and vegetation shifts over the past 10,000 years. Ongoing work will use the regional charcoal deposition rate to estimate changes in biomass burned as a function of climate variability and regional vegetation pattern.

Estimation of frequency based flood peaks for an ungauged watershed using field calibration : final report.

DOT National Transportation Integrated Search

1997-06-01

The present study has been conducted to evaluate eight flood prediction models for an ungauged small watershed. These models are either frequently used by or were developed by Louisiana Department of Transportation and Development (LADOTD). The eight...

Improving the S-Shape Solar Radiation Estimation Method for Supporting Crop Models

PubMed Central

Fodor, Nándor

2012-01-01

In line with the critical comments formulated in relation to the S-shape global solar radiation estimation method, the original formula was improved via a 5-step procedure. The improved method was compared to four-reference methods on a large North-American database. According to the investigated error indicators, the final 7-parameter S-shape method has the same or even better estimation efficiency than the original formula. The improved formula is able to provide radiation estimates with a particularly low error pattern index (PIdoy) which is especially important concerning the usability of the estimated radiation values in crop models. Using site-specific calibration, the radiation estimates of the improved S-shape method caused an average of 2.72 ± 1.02 (α = 0.05) relative error in the calculated biomass. Using only readily available site specific metadata the radiation estimates caused less than 5% relative error in the crop model calculations when they were used for locations in the middle, plain territories of the USA. PMID:22645451

Planning Inmarsat's second generation of spacecraft

NASA Astrophysics Data System (ADS)

Williams, W. P.

1982-09-01

The next generation of studies of the Inmarsat service are outlined, such as traffic forecasting studies, communications capacity estimates, space segment design, cost estimates, and financial analysis. Traffic forecasting will require future demand estimates, and a computer model has been developed which estimates demand over the Atlantic, Pacific, and Indian ocean regions. Communications estimates are based on traffic estimates, as a model converts traffic demand into a required capacity figure for a given area. The Erlang formula is used, requiring additional data such as peak hour ratios and distribution estimates. Basic space segment technical requirements are outlined (communications payload, transponder arrangements, etc), and further design studies involve such areas as space segment configuration, launcher and spacecraft studies, transmission planning, and earth segment configurations. Cost estimates of proposed design parameters will be performed, but options must be reduced to make construction feasible. Finally, a financial analysis will be carried out in order to calculate financial returns.

Maximum likelihood estimation for semiparametric transformation models with interval-censored data

PubMed Central

Mao, Lu; Lin, D. Y.

2016-01-01

Abstract Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through simulation studies and application to an HIV/AIDS study conducted in Thailand. PMID:27279656

Multicollinearity in hierarchical linear models.

PubMed

Yu, Han; Jiang, Shanhe; Land, Kenneth C

2015-09-01

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

78 FR 73726 - Endangered Fish and Wildlife; Final Rule To Remove the Sunset Provision of the Final Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-09

... expanded speed/risk models, one study estimated that the 2008 vessel speed rule reduced the risk of lethal... are completely absent there or that the risk of strikes does not exist. One recent study concluded... the world's most critically endangered large whale species and one of the world's most endangered...

Development of a methodology for electronic waste estimation: A material flow analysis-based SYE-Waste Model.

PubMed

Yedla, Sudhakar

2016-01-01

Improved living standards and the share of services sector to the economy in Asia, and the use of electronic equipment is on the rise and results in increased electronic waste generation. A peculiarity of electronic waste is that it has a 'significant' value even after its life time, and to add complication, even after its extended life in its 'dump' stage. Thus, in Indian situations, after its life time is over, the e-material changes hands more than once and finally ends up either in the hands of informal recyclers or in the store rooms of urban dwellings. This character makes it extremely difficult to estimate electronic waste generation. The present study attempts to develop a functional model based on a material flow analysis approach by considering all possible end uses of the material, its transformed goods finally arriving at disposal. It considers various degrees of uses derived of the e-goods regarding their primary use (life time), secondary use (first degree extension of life), third-hand use (second degree extension of life), donation, retention at the respective places (without discarding), fraction shifted to scrap vendor, and the components reaching the final dump site from various end points of use. This 'generic functional model' named SYE-Waste Model, developed based on a material flow analysis approach, can be used to derive 'obsolescence factors' for various degrees of usage of e-goods and also to make a comprehensive estimation of electronic waste in any city/country. © The Author(s) 2015.

GDP and efficiency of Russian economy

NASA Astrophysics Data System (ADS)

Borodachev, Sergey M.

2018-01-01

The goal is to study GDP (gross domestic product) as an unobservable characteristic of the Russian national economy state on the basis of more reliable observed data on gross output (systems output) and final consumption (systems control). To do this, the dynamic Leontief model is presented in a system-like form and its parameters and GDP dynamics are estimated by the Kalman filter (KF). We consider that all previous year's investments affect the growth of the gross output by the next year. The weights of these investments in the sum are equal to unity and decrease in geometric progression. The estimation of the model parameters was carried out by the maximum likelihood method. The original data on the gross output and final consumption in the period from 1995 to 2015 years where taken from the Rosstat website, where maximally aggregated economy of Russia is reflected in the system of national accounts. The growth of direct costs and capital expenditures at gross output increase has been discovered, which indicates the extensive character of the development of the economy. Investments are being absorbed 2 - 4 years; any change of them causes a surge of commissioned fixed assets fluctuation with a period of 2 years. Then these parameter values were used in the KF to estimate the states of the system. The emerging tendency of the transition of GDP growth to its fall means that the rate of growth of final consumption is higher than the rate of GDP growth. In general, the behavior of the curve of Rosstat GDP obviously follows the declared investments, whereas in the present calculation it is closer to the behavior of final consumption. Estimated GDP and investments that really increased it were significantly less after the crisis of 2008-2009 years than officially published data.

In search of best fitted composite model to the ALAE data set with transformed Gamma and inversed transformed Gamma families

NASA Astrophysics Data System (ADS)

Maghsoudi, Mastoureh; Bakar, Shaiful Anuar Abu

2017-05-01

In this paper, a recent novel approach is applied to estimate the threshold parameter of a composite model. Several composite models from Transformed Gamma and Inverse Transformed Gamma families are constructed based on this approach and their parameters are estimated by the maximum likelihood method. These composite models are fitted to allocated loss adjustment expenses (ALAE). In comparison to all composite models studied, the composite Weibull-Inverse Transformed Gamma model is proved to be a competitor candidate as it best fit the loss data. The final part considers the backtesting method to verify the validation of VaR and CTE risk measures.

Fuel Burn Estimation Using Real Track Data

NASA Technical Reports Server (NTRS)

Chatterji, Gano B.

2011-01-01

A procedure for estimating fuel burned based on actual flight track data, and drag and fuel-flow models is described. The procedure consists of estimating aircraft and wind states, lift, drag and thrust. Fuel-flow for jet aircraft is determined in terms of thrust, true airspeed and altitude as prescribed by the Base of Aircraft Data fuel-flow model. This paper provides a theoretical foundation for computing fuel-flow with most of the information derived from actual flight data. The procedure does not require an explicit model of thrust and calibrated airspeed/Mach profile which are typically needed for trajectory synthesis. To validate the fuel computation method, flight test data provided by the Federal Aviation Administration were processed. Results from this method show that fuel consumed can be estimated within 1% of the actual fuel consumed in the flight test. Next, fuel consumption was estimated with simplified lift and thrust models. Results show negligible difference with respect to the full model without simplifications. An iterative takeoff weight estimation procedure is described for estimating fuel consumption, when takeoff weight is unavailable, and for establishing fuel consumption uncertainty bounds. Finally, the suitability of using radar-based position information for fuel estimation is examined. It is shown that fuel usage could be estimated within 5.4% of the actual value using positions reported in the Airline Situation Display to Industry data with simplified models and iterative takeoff weight computation.

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles

PubMed Central

Nam, Kanghyun

2015-01-01

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data. PMID:26569246

Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation.

PubMed

Martinez-Berti, Enrique; Sánchez-Salmerón, Antonio-José; Ricolfe-Viala, Carlos

2017-08-19

The goal of this research work is to improve the accuracy of human pose estimation using the Deformation Part Model (DPM) without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to DPM, which was formerly defined based only on red-green-blue (RGB) channels, in order to obtain a four-dimensional DPM (4D-DPM). In addition, computational complexity can be controlled by reducing the number of joints by taking it into account in a reduced 4D-DPM. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematics models. In this context, the main goal of this paper is to analyze the effect on pose estimation timing cost when using dual quaternions to solve the inverse kinematics.

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1995-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Hidden Markov models for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J. (Inventor)

1993-01-01

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

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

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044

System for valuing changes to environmental and historic amenities : final report for the academy for community & transportation innovation.

DOT National Transportation Integrated Search

2004-10-01

This report provides a model for estimating the impact of highway projects on the environment and cultural amenities. The model was developed by the University of Kentucky Center for Business and Economic Research and was supported by funding from th...

A Model for Remote Depth Estimation of Buried Radioactive Wastes Using CdZnTe Detector

PubMed Central

2018-01-01

This paper presents the results of an attenuation model for remote depth estimation of buried radioactive wastes using a Cadmium Zinc Telluride (CZT) detector. Previous research using an organic liquid scintillator detector system showed that the model is able to estimate the depth of a 329-kBq Cs-137 radioactive source buried up to 12 cm in sand with an average count rate of 100 cps. The results presented in this paper showed that the use of the CZT detector extended the maximum detectable depth of the same radioactive source to 18 cm in sand with a significantly lower average count rate of 14 cps. Furthermore, the model also successfully estimated the depth of a 9-kBq Co-60 source buried up to 3 cm in sand. This confirms that this remote depth estimation method can be used with other radionuclides and wastes with very low activity. Finally, the paper proposes a performance parameter for evaluating radiation detection systems that implement this remote depth estimation method. PMID:29783644

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prophylactic ranitidine treatment in critically ill children--a population pharmacokinetic study.

PubMed

Hawwa, Ahmed F; Westwood, Paul M; Collier, Paul S; Millership, Jeffrey S; Yakkundi, Shirish; Thurley, Gillian; Shields, Mike D; Nunn, Anthony J; Halliday, Henry L; McElnay, James C

2013-05-01

To characterize the population pharmacokinetics of ranitidine in critically ill children and to determine the influence of various clinical and demographic factors on its disposition. Data were collected prospectively from 78 paediatric patients (n = 248 plasma samples) who received oral or intravenous ranitidine for prophylaxis against stress ulcers, gastrointestinal bleeding or the treatment of gastro-oesophageal reflux. Plasma samples were analysed using high-performance liquid chromatography, and the data were subjected to population pharmacokinetic analysis using nonlinear mixed-effects modelling. A one-compartment model best described the plasma concentration profile, with an exponential structure for interindividual errors and a proportional structure for intra-individual error. After backward stepwise elimination, the final model showed a significant decrease in objective function value (-12.618; P < 0.001) compared with the weight-corrected base model. Final parameter estimates for the population were 32.1 l h(-1) for total clearance and 285 l for volume of distribution, both allometrically modelled for a 70 kg adult. Final estimates for absorption rate constant and bioavailability were 1.31 h(-1) and 27.5%, respectively. No significant relationship was found between age and weight-corrected ranitidine pharmacokinetic parameters in the final model, with the covariate for cardiac failure or surgery being shown to reduce clearance significantly by a factor of 0.46. Currently, ranitidine dose recommendations are based on children's weights. However, our findings suggest that a dosing scheme that takes into consideration both weight and cardiac failure/surgery would be more appropriate in order to avoid administration of higher or more frequent doses than necessary. © 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Some analytical models to estimate maternal age at birth using age-specific fertility rates.

PubMed

Pandey, A; Suchindran, C M

1995-01-01

"A class of analytical models to study the distribution of maternal age at different births from the data on age-specific fertility rates has been presented. Deriving the distributions and means of maternal age at birth of any specific order, final parity and at next-to-last birth, we have extended the approach to estimate parity progression ratios and the ultimate parity distribution of women in the population.... We illustrate computations of various components of the model expressions with the current fertility experiences of the United States for 1970." excerpt

Calculating system reliability with SRFYDO

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

Morzinski, Jerome; Anderson - Cook, Christine M; Klamann, Richard M

2010-01-01

SRFYDO is a process for estimating reliability of complex systems. Using information from all applicable sources, including full-system (flight) data, component test data, and expert (engineering) judgment, SRFYDO produces reliability estimates and predictions. It is appropriate for series systems with possibly several versions of the system which share some common components. It models reliability as a function of age and up to 2 other lifecycle (usage) covariates. Initial output from its Exploratory Data Analysis mode consists of plots and numerical summaries so that the user can check data entry and model assumptions, and help determine a final form for themore » system model. The System Reliability mode runs a complete reliability calculation using Bayesian methodology. This mode produces results that estimate reliability at the component, sub-system, and system level. The results include estimates of uncertainty, and can predict reliability at some not-too-distant time in the future. This paper presents an overview of the underlying statistical model for the analysis, discusses model assumptions, and demonstrates usage of SRFYDO.« less

Estimation and control of droplet size and frequency in projected spray mode of a gas metal arc welding (GMAW) process.

PubMed

Anzehaee, Mohammad Mousavi; Haeri, Mohammad

2011-07-01

New estimators are designed based on the modified force balance model to estimate the detaching droplet size, detached droplet size, and mean value of droplet detachment frequency in a gas metal arc welding process. The proper droplet size for the process to be in the projected spray transfer mode is determined based on the modified force balance model and the designed estimators. Finally, the droplet size and the melting rate are controlled using two proportional-integral (PI) controllers to achieve high weld quality by retaining the transfer mode and generating appropriate signals as inputs of the weld geometry control loop. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Estimating restricted mean treatment effects with stacked survival models

PubMed Central

Wey, Andrew; Vock, David M.; Connett, John; Rudser, Kyle

2016-01-01

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution, and then marginalizing over the covariate distribution. Since the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean-squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large and small-volume centers. PMID:26934835

Estimating Housing Vacancy Rate in Qingdao City with Npp-Viirs Nighttime Light and Geographical National Conditions Monitoring Data

NASA Astrophysics Data System (ADS)

Niu, X.

2018-04-01

Accompanying China's rapid urbanization in recent decades, especially in the new millennium, the housing problem has become one of the most important issues. The estimation and analysis of housing vacancy rate (HVR) can assist decision-making in solving this puzzle. It is particularly significant to government departments. This paper proposed a practical model for estimating the HVR in Qingdao city using NPP-VIIRS nighttime light composed data, Geographic National Conditions Monitoring data (GNCMD) and resident population distribution data. The main steps are: Firstly, pre-process the data, and finally forming a series of data sets with 500*500 grid as the basic unit; Secondly, select 400 grids of different types within the city as sample grids for SVM training, and establish a reasonable HVR model; Thirdly, using the model to estimate HVR in Qingdao and employing spatial statistical analysis methods to reveal the spatial differentiation pattern of HVR in this city; Finally test the accuracy of the model with two different methods. The results conclude that HVR in the southeastern coastal area of Qingdao city is relatively low and the low-low clusters distributed in patches. Simultaneously, in other regions it shows the tendency of the low value accumulation in the downtown area and the increasing trend towards the outer suburbs. Meanwhile the suburban and scenery regions by the side of the sea and mountains are likely to be the most vacant part of the city.

A Dynamic Model of Human and Livestock Tuberculosis Spread and Control in Urumqi, Xinjiang, China

PubMed Central

Liu, Shan; Li, Aiqiao; Feng, Xiaomei; Zhang, Xueliang

2016-01-01

We establish a dynamical model for tuberculosis of humans and cows. For the model, we firstly give the basic reproduction number R 0. Furthermore, we discuss the dynamical behaviors of the model. By epidemiological investigation of tuberculosis among humans and livestock from 2007 to 2014 in Urumqi, Xinjiang, China, we estimate the parameters of the model and study the transmission trend of the disease in Urumqi, Xinjiang, China. The reproduction number in Urumqi for the model is estimated to be 0.1811 (95% confidence interval: 0.123–0.281). Finally, we perform some sensitivity analysis of several model parameters and give some useful comments on controlling the transmission of tuberculosis. PMID:27525034

Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI

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

Chen, Kho Chia; Kane, Ibrahim Lawal; Rahman, Haliza Abd

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parametermore » estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.« less

Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI

NASA Astrophysics Data System (ADS)

Chen, Kho Chia; Bahar, Arifah; Kane, Ibrahim Lawal; Ting, Chee-Ming; Rahman, Haliza Abd

2015-02-01

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.

On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

NASA Astrophysics Data System (ADS)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

2017-10-01

With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models.

PubMed

Molléro, Roch; Pennec, Xavier; Delingette, Hervé; Garny, Alan; Ayache, Nicholas; Sermesant, Maxime

2018-02-01

Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.

Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

USGS Publications Warehouse

Baldwin, Austin K.; Graczyk, David J.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher

2012-01-01

The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R2 values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R2 values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R2 values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.

Allometric Equations for Aboveground and Belowground Biomass Estimations in an Evergreen Forest in Vietnam.

PubMed

Nam, Vu Thanh; van Kuijk, Marijke; Anten, Niels P R

2016-01-01

Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam.

Allometric Equations for Aboveground and Belowground Biomass Estimations in an Evergreen Forest in Vietnam

PubMed Central

Nam, Vu Thanh; van Kuijk, Marijke; Anten, Niels P. R.

2016-01-01

Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam. PMID:27309718

Lightning energetics: Estimates of energy dissipation in channels, channel radii, and channel-heating risetimes

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

Borovsky, J.E.

1998-05-01

In this report, several lightning-channel parameters are calculated with the aid of an electrodynamic model of lightning. The electrodynamic model describes dart leaders and return strokes as electromagnetic waves that are guided along conducting lightning channels. According to the model, electrostatic energy is delivered to the channel by a leader, where it is stored around the outside of the channel; subsequently, the return stroke dissipates this locally stored energy. In this report this lightning-energy-flow scenario is developed further. Then the energy dissipated per unit length in lightning channels is calculated, where this quantity is now related to the linear chargemore » density on the channel, not to the cloud-to-ground electrostatic potential difference. Energy conservation is then used to calculate the radii of lightning channels: their initial radii at the onset of return strokes and their final radii after the channels have pressure expanded. Finally, the risetimes for channel heating during return strokes are calculated by defining an energy-storage radius around the channel and by estimating the radial velocity of energy flow toward the channel during a return stroke. In three appendices, values for the linear charge densities on lightning channels are calculated, estimates of the total length of branch channels are obtained, and values for the cloud-to-ground electrostatic potential difference are estimated. {copyright} 1998 American Geophysical Union« less

Comparison of Development Test and Evaluation and Overall Program Estimate at Completion

DTIC Science & Technology

2011-03-01

of the overall model and parameter. In addition to 36 the Shapiro-Wilkes test , and Cook’s Distance overlay plot we used the Breusch - Pagan test to...Transformed Model Finally, we evaluated our log transformed model using the Breusch - Pagan test . The results return a value of .51, thus confirming our...COMPARISON OF DEVELOPMENT TEST AND EVALUATION AND OVERALL

Atmospheric Spray Freeze-Drying: Numerical Modeling and Comparison With Experimental Measurements.

PubMed

Borges Sebastião, Israel; Robinson, Thomas D; Alexeenko, Alina

2017-01-01

Atmospheric spray freeze-drying (ASFD) represents a novel approach to dry thermosensitive solutions via sublimation. Tests conducted with a second-generation ASFD equipment, developed for pharmaceutical applications, have focused initially on producing a light, fine, high-grade powder consistently and reliably. To better understand the heat and mass transfer physics and drying dynamics taking place within the ASFD chamber, 3 analytical models describing the key processes are developed and validated. First, by coupling the dynamics and heat transfer of single droplets sprayed into the chamber, the velocity, temperature, and phase change evolutions of these droplets are estimated for actual operational conditions. This model reveals that, under typical operational conditions, the sprayed droplets require less than 100 ms to freeze. Second, because understanding the heat transfer throughout the entire freeze-drying process is so important, a theoretical model is proposed to predict the time evolution of the chamber gas temperature. Finally, a drying model, calibrated with hygrometer measurements, is used to estimate the total time required to achieve a predefined final moisture content. Results from these models are compared with experimental data. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Kansas environmental and resource study: A Great Plains model. Extraction of agricultural statistics from ERTS-1 data of Kansas. [wheat inventory and agriculture land use

NASA Technical Reports Server (NTRS)

Morain, S. A. (Principal Investigator); Williams, D. L.

1974-01-01

The author has identified the following significant results. Wheat area, yield, and production statistics as derived from satellite image analysis, combined with a weather model, are presented for a ten county area in southwest Kansas. The data (representing the 1972-73 crop year) are compared for accuracy against both the USDA August estimate and its final (official) tabulation. The area estimates from imagery for both dryland and irrigated winter wheat were within 5% of the official figures for the same area, and predated them by almost one year. Yield on dryland wheat was estimated by the Thompson weather model to within 0.1% of the observed yield. A combined irrigated and dryland wheat production estimate for the ten county area was completed in July, 1973 and was within 1% of the production reported by USDA in February, 1974.

Soil hydraulic properties estimate based on numerical analysis of disc infiltrometer three-dimensional infiltration curve

NASA Astrophysics Data System (ADS)

Latorre, Borja; Peña-Sancho, Carolina; Angulo-Jaramillo, Rafaël; Moret-Fernández, David

2015-04-01

Measurement of soil hydraulic properties is of paramount importance in fields such as agronomy, hydrology or soil science. Fundamented on the analysis of the Haverkamp et al. (1994) model, the aim of this paper is to explain a technique to estimate the soil hydraulic properties (sorptivity, S, and hydraulic conductivity, K) from the full-time cumulative infiltration curves. The method (NSH) was validated by means of 12 synthetic infiltration curves generated with HYDRUS-3D from known soil hydraulic properties. The K values used to simulate the synthetic curves were compared to those estimated with the proposed method. A procedure to identify and remove the effect of the contact sand layer on the cumulative infiltration curve was also developed. A sensitivity analysis was performed using the water level measurement as uncertainty source. Finally, the procedure was evaluated using different infiltration times and data noise. Since a good correlation between the K used in HYDRUS-3D to model the infiltration curves and those estimated by the NSH method was obtained, (R2 =0.98), it can be concluded that this technique is robust enough to estimate the soil hydraulic conductivity from complete infiltration curves. The numerical procedure to detect and remove the influence of the contact sand layer on the K and S estimates seemed to be robust and efficient. An effect of the curve infiltration noise on the K estimate was observed, which uncertainty increased with increasing noise. Finally, the results showed that infiltration time was an important factor to estimate K. Lower values of K or smaller uncertainty needed longer infiltration times.

Probabilistic estimation of residential air exchange rates for ...

EPA Pesticide Factsheets

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure. Published in the Journal of

Vector autoregressive models: A Gini approach

NASA Astrophysics Data System (ADS)

Mussard, Stéphane; Ndiaye, Oumar Hamady

2018-02-01

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger's causality tests are properly derived based on U-statistics.

aerosol radiative effects and forcing: spatial and temporal distributions

NASA Astrophysics Data System (ADS)

Kinne, Stefan

2014-05-01

A monthly climatology for aerosol optical properties based on a synthesis from global modeling and observational data has been applied to illustrate spatial distributions and global averages of aerosol radiative impacts. With the help of a pre-industrial reference for aerosol optical properties from global modeling, also the aerosol direct forcing (ca -0.35W/m2 globally and annual averaged) and their spatial and seasonal distributions and contributions by individual aerosol components are estimated. Finally, CCN and IN concentrations associated with this climatology are applied to estimate aerosol indirect effects and forcing.

PVWatts Version 5 Manual

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

Dobos, A. P.

2014-09-01

The NREL PVWatts calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes includes several built-in parameters that are hidden from the user. This technical reference describes the sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimate. This reference is applicable to the significantly revised version of PVWatts released by NREL in 2014.

[Estimation of forest canopy chlorophyll content based on PROSPECT and SAIL models].

PubMed

Yang, Xi-guang; Fan, Wen-yi; Yu, Ying

2010-11-01

The forest canopy chlorophyll content directly reflects the health and stress of forest. The accurate estimation of the forest canopy chlorophyll content is a significant foundation for researching forest ecosystem cycle models. In the present paper, the inversion of the forest canopy chlorophyll content was based on PROSPECT and SAIL models from the physical mechanism angle. First, leaf spectrum and canopy spectrum were simulated by PROSPECT and SAIL models respectively. And leaf chlorophyll content look-up-table was established for leaf chlorophyll content retrieval. Then leaf chlorophyll content was converted into canopy chlorophyll content by Leaf Area Index (LAD). Finally, canopy chlorophyll content was estimated from Hyperion image. The results indicated that the main effect bands of chlorophyll content were 400-900 nm, the simulation of leaf and canopy spectrum by PROSPECT and SAIL models fit better with the measured spectrum with 7.06% and 16.49% relative error respectively, the RMSE of LAI inversion was 0. 542 6 and the forest canopy chlorophyll content was estimated better by PROSPECT and SAIL models with precision = 77.02%.

Benefits Estimation Model for Automated Vehicle Operations: Phase 2 Final Report

DOT National Transportation Integrated Search

2018-01-01

Automated vehicles have the potential to bring about transformative safety, mobility, energy, and environmental benefits to the surface transportation system. They are also being introduced into a complex transportation system, where second-order imp...

Analysis of design attributes and crashes on the Oregon highway system : final report.

DOT National Transportation Integrated Search

2001-08-01

This report has investigated the statistical relationship between crash activity and roadway design attributes on the Oregon state : highway system. Crash models were estimated from highway segments distinguished by functional classification (freeway...

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2017-07-01

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

Linear functional minimization for inverse modeling

DOE PAGES

Barajas-Solano, David A.; Wohlberg, Brendt Egon; Vesselinov, Velimir Valentinov; ...

2015-06-01

In this paper, we present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulicmore » head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Finally, addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.« less

Neural Net Gains Estimation Based on an Equivalent Model

PubMed Central

Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

2016-01-01

A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146

Neural Net Gains Estimation Based on an Equivalent Model.

PubMed

Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory

2016-01-01

A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.

Maximum Likelihood Estimation with Emphasis on Aircraft Flight Data

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.

1985-01-01

Accurate modeling of flexible space structures is an important field that is currently under investigation. Parameter estimation, using methods such as maximum likelihood, is one of the ways that the model can be improved. The maximum likelihood estimator has been used to extract stability and control derivatives from flight data for many years. Most of the literature on aircraft estimation concentrates on new developments and applications, assuming familiarity with basic estimation concepts. Some of these basic concepts are presented. The maximum likelihood estimator and the aircraft equations of motion that the estimator uses are briefly discussed. The basic concepts of minimization and estimation are examined for a simple computed aircraft example. The cost functions that are to be minimized during estimation are defined and discussed. Graphic representations of the cost functions are given to help illustrate the minimization process. Finally, the basic concepts are generalized, and estimation from flight data is discussed. Specific examples of estimation of structural dynamics are included. Some of the major conclusions for the computed example are also developed for the analysis of flight data.

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Zheng, Yuejiu; Ouyang, Minggao; Han, Xuebing; Lu, Languang; Li, Jianqiu

2018-02-01

Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

An economic model for evaluating high-speed aircraft designs

NASA Technical Reports Server (NTRS)

Vandervelden, Alexander J. M.

1989-01-01

A Class 1 method for determining whether further development of a new aircraft design is desirable from all viewpoints is presented. For the manufacturer the model gives an estimate of the total cost of research and development from the preliminary design to the first production aircraft. Using Wright's law of production, one can derive the average cost per aircraft produced for a given break-even number. The model will also provide the airline with a good estimate of the direct and indirect operating costs. From the viewpoint of the passenger, the model proposes a tradeoff between ticket price and cruise speed. Finally all of these viewpoints are combined in a Comparative Aircraft Seat-kilometer Economic Index.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.

PubMed

Peressutti, Devis; Penney, Graeme P; Housden, R James; Kolbitsch, Christoph; Gomez, Alberto; Rijkhorst, Erik-Jan; Barratt, Dean C; Rhode, Kawal S; King, Andrew P

2013-05-01

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method. Copyright © 2013 Elsevier B.V. All rights reserved.

Final STS-35 Columbia descent BET products and results for LaRC OEX investigations

NASA Technical Reports Server (NTRS)

Oakes, Kevin F.; Findlay, John T.; Jasinski, Rachel A.; Wood, James S.

1991-01-01

Final STS-35 'Columbia' descent Best Estimate Trajectory (BET) products have been developed for Langley Research Center (LaRC) Orbiter Experiments (OEX) investigations. Included are the reconstructed inertial trajectory profile; the Extended BET, which combines the inertial data and, in this instance, the National Weather Service atmospheric information obtained via Johnson Space Center; and the Aerodynamic BET. The inertial BET utilized Inertial Measurement Unit 1 (IMU1) dynamic measurements for deterministic propagation during the ENTREE estimation process. The final estimate was based on the considerable ground based C-band tracking coverage available as well as Tracking Data and Relay Satellite System (TDRSS) Doppler data, a unique use of the latter for endo-atmospheric flight determinations. The actual estimate required simultaneous solutions for the spacecraft position and velocity, spacecraft attitude, and six IMU parameters - three gyro biases and three accelerometer scale factor correction terms. The anchor epoch for this analysis was 19,200 Greenwich Mean Time (GMT) seconds which corresponds to an initial Shuttle altitude of approximately 513 kft. The atmospheric data incorporated were evaluated based on Shuttle derived considerations as well as comparisons with other models. The AEROBET was developed based on the Extended BET, the measured spacecraft configuration information, final mass properties, and the final Orbiter preoperation databook. The latter was updated based on aerodynamic consensus incrementals derived by the latest published FAD. The rectified predictions were compared versus the flight computed values and the resultant differences were correlated versus ensemble results for twenty-two previous STS entry flights.

Flood Scenario Simulation and Disaster Estimation of Ba-Ma Creek Watershed in Nantou County, Taiwan

NASA Astrophysics Data System (ADS)

Peng, S. H.; Hsu, Y. K.

2018-04-01

The present study proposed several scenario simulations of flood disaster according to the historical flood event and planning requirement in Ba-Ma Creek Watershed located in Nantou County, Taiwan. The simulations were made using the FLO-2D model, a numerical model which can compute the velocity and depth of flood on a two-dimensional terrain. Meanwhile, the calculated data were utilized to estimate the possible damage incurred by the flood disaster. The results thus obtained can serve as references for disaster prevention. Moreover, the simulated results could be employed for flood disaster estimation using the method suggested by the Water Resources Agency of Taiwan. Finally, the conclusions and perspectives are presented.

Scale effects on the evapotranspiration estimation over a water-controlled Mediterranean ecosystem and its influence on hydrological modelling

NASA Astrophysics Data System (ADS)

Carpintero, Elisabet; González-Dugo, María P.; José Polo, María; Hain, Christopher; Nieto, Héctor; Gao, Feng; Andreu, Ana; Kustas, William; Anderson, Martha

2017-04-01

The integration of currently available satellite data into surface energy balance models can provide estimates of evapotranspiration (ET) with spatial and temporal resolutions determined by sensor characteristics. The use of data fusion techniques may increase the temporal resolution of these estimates using multiple satellites, providing a more frequent ET monitoring for hydrological purposes. The objective of this work is to analyze the effects of pixel resolution on the estimation of evapotranspiration using different remote sensing platforms, and to provide continuous monitoring of ET over a water-controlled ecosystem, the Holm oak savanna woodland known as dehesa. It is an agroforestry system with a complex canopy structure characterized by widely-spaced oak trees combined with crops, pasture and shrubs. The study was carried out during two years, 2013 and 2014, combining ET estimates at different spatial and temporal resolutions and applying data fusion techniques for a frequent monitoring of water use at fine spatial resolution. A global and daily ET product at 5 km resolution, developed with the ALEXI model using MODIS day-night temperature difference (Anderson et al., 2015a) was used as a starting point. The associated flux disaggregation scheme, DisALEXI (Norman et al., 2003), was later applied to constrain higher resolution ET from both MODIS and Landsat 7/8 images. The Climate Forecast System Reanalysis (CFSR) provided the meteorological data. Finally, a data fusion technique, the STARFM model (Gao et al., 2006), was applied to fuse MODIS and Landsat ET maps in order to obtain daily ET at 30 m resolution. These estimates were validated and analyzed at two different scales: at local scale over a dehesa experimental site and at watershed scale with a predominant Mediterranean oak savanna landscape, both located in Southern Spain. Local ET estimates from the modeling system were validated with measurements provided by an eddy covariance tower installed in the dehesa (38 ° 12 'N, 4 ° 17' W, 736 m a.s.l.). The results supported the ability of ALEXI/DisALEXI model to accurately estimate turbulent and radiative fluxes over this complex landscape, both at 1 Km and at 30 m spatial resolution. The application of the STARFM model gave significant improvement in capturing the spatio-temporal heterogeneity of ET over the different seasons, compared with traditional interpolation methods using MODIS and Landsat ET data. At basin scale, the physically-based distributed hydrological model WiMMed has been applied to evaluate ET estimates. This model focuses on the spatial interpolation of the meteorological variables and the physical modelling of the daily water balance at the cell and watershed scale, using daily streamflow rates measured at the watershed outlet for final comparison.

World Ocean Circulation Experiment (WOCE) Young Investigator Workshops

NASA Technical Reports Server (NTRS)

Austin, Meg

2004-01-01

The World Ocean Circulation Experiment (WOCE) Young Investigator Workshops goals and objectives are: a) to familiarize Young Investigators with WOCE models, datasets and estimation procedures; b) to offer intensive hands-on exposure to these models ard methods; c) to build collaborations among junior scientists and more senior WOCE investigators; and finally, d) to generate ideas and projects leading to fundable WOCE synthesis projects. To achieve these goals and objectives, the Workshop will offer a mixture of tutorial lectures on numerical models and estimation procedures, advanced seminars on current WOCE synthesis activities and related projects, and the opportunity to conduct small projects which put into practice the techniques advanced in the lectures.

Accounting for Age Uncertainty in Growth Modeling, the Case Study of Yellowfin Tuna (Thunnus albacares) of the Indian Ocean

PubMed Central

Dortel, Emmanuelle; Massiot-Granier, Félix; Rivot, Etienne; Million, Julien; Hallier, Jean-Pierre; Morize, Eric; Munaron, Jean-Marie; Bousquet, Nicolas; Chassot, Emmanuel

2013-01-01

Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation. PMID:23637773

Neural Network Modeling for Gallium Arsenide IC Fabrication Process and Device Characteristics.

NASA Astrophysics Data System (ADS)

Creech, Gregory Lee, I.

This dissertation presents research focused on the utilization of neurocomputing technology to achieve enhanced yield and effective yield prediction in integrated circuit (IC) manufacturing. Artificial neural networks are employed to model complex relationships between material and device characteristics at critical stages of the semiconductor fabrication process. Whole wafer testing was performed on the starting substrate material and during wafer processing at four critical steps: Ohmic or Post-Contact, Post-Recess, Post-Gate and Final, i.e., at completion of fabrication. Measurements taken and subsequently used in modeling include, among others, doping concentrations, layer thicknesses, planar geometries, layer-to-layer alignments, resistivities, device voltages, and currents. The neural network architecture used in this research is the multilayer perceptron neural network (MLPNN). The MLPNN is trained in the supervised mode using the generalized delta learning rule. It has one hidden layer and uses continuous perceptrons. The research focuses on a number of different aspects. First is the development of inter-process stage models. Intermediate process stage models are created in a progressive fashion. Measurements of material and process/device characteristics taken at a specific processing stage and any previous stages are used as input to the model of the next processing stage characteristics. As the wafer moves through the fabrication process, measurements taken at all previous processing stages are used as input to each subsequent process stage model. Secondly, the development of neural network models for the estimation of IC parametric yield is demonstrated. Measurements of material and/or device characteristics taken at earlier fabrication stages are used to develop models of the final DC parameters. These characteristics are computed with the developed models and compared to acceptance windows to estimate the parametric yield. A sensitivity analysis is performed on the models developed during this yield estimation effort. This is accomplished by analyzing the total disturbance of network outputs due to perturbed inputs. When an input characteristic bears no, or little, statistical or deterministic relationship to the output characteristics, it can be removed as an input. Finally, neural network models are developed in the inverse direction. Characteristics measured after the final processing step are used as the input to model critical in-process characteristics. The modeled characteristics are used for whole wafer mapping and its statistical characterization. It is shown that this characterization can be accomplished with minimal in-process testing. The concepts and methodologies used in the development of the neural network models are presented. The modeling results are provided and compared to the actual measured values of each characteristic. An in-depth discussion of these results and ideas for future research are presented.

Investigations of potential bias in the estimation of lambda using Pradel's (1996) model for capture-recapture data

USGS Publications Warehouse

Hines, James E.; Nichols, James D.

2002-01-01

Pradel's (1996) temporal symmetry model permitting direct estimation and modelling of population growth rate, u i , provides a potentially useful tool for the study of population dynamics using marked animals. Because of its recent publication date, the approach has not seen much use, and there have been virtually no investigations directed at robustness of the resulting estimators. Here we consider several potential sources of bias, all motivated by specific uses of this estimation approach. We consider sampling situations in which the study area expands with time and present an analytic expression for the bias in u i We next consider trap response in capture probabilities and heterogeneous capture probabilities and compute large-sample and simulation-based approximations of resulting bias in u i . These approximations indicate that trap response is an especially important assumption violation that can produce substantial bias. Finally, we consider losses on capture and emphasize the importance of selecting the estimator for u i that is appropriate to the question being addressed. For studies based on only sighting and resighting data, Pradel's (1996) u i ' is the appropriate estimator.

Minimum mean squared error (MSE) adjustment and the optimal Tykhonov-Phillips regularization parameter via reproducing best invariant quadratic uniformly unbiased estimates (repro-BIQUUE)

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard

2008-02-01

In a linear Gauss-Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed. In this paper, the α-weighted S-homBLE (Best homogeneously Linear Estimate) is derived via formulas originally used for variance component estimation on the basis of the repro-BIQUUE (reproducing Best Invariant Quadratic Uniformly Unbiased Estimate) principle in a model with stochastic prior information. In the present model, however, such prior information is not included, which allows the comparison of the stochastic approach (α-weighted S-homBLE) with the well-established algebraic approach of Tykhonov-Phillips regularization, also known as R-HAPS (Hybrid APproximation Solution), whenever the inverse of the “substitute matrix” S exists and is chosen as the R matrix that defines the relative impact of the regularizing term on the final result.

Reference frame access under the effects of great earthquakes: a least squares collocation approach for non-secular post-seismic evolution

NASA Astrophysics Data System (ADS)

Gómez, D. D.; Piñón, D. A.; Smalley, R.; Bevis, M.; Cimbaro, S. R.; Lenzano, L. E.; Barón, J.

2016-03-01

The 2010, (Mw 8.8) Maule, Chile, earthquake produced large co-seismic displacements and non-secular, post-seismic deformation, within latitudes 28°S-40°S extending from the Pacific to the Atlantic oceans. Although these effects are easily resolvable by fitting geodetic extended trajectory models (ETM) to continuous GPS (CGPS) time series, the co- and post-seismic deformation cannot be determined at locations without CGPS (e.g., on passive geodetic benchmarks). To estimate the trajectories of passive geodetic benchmarks, we used CGPS time series to fit an ETM that includes the secular South American plate motion and plate boundary deformation, the co-seismic discontinuity, and the non-secular, logarithmic post-seismic transient produced by the earthquake in the Posiciones Geodésicas Argentinas 2007 (POSGAR07) reference frame (RF). We then used least squares collocation (LSC) to model both the background secular inter-seismic and the non-secular post-seismic components of the ETM at the locations without CGPS. We tested the LSC modeled trajectories using campaign and CGPS data that was not used to generate the model and found standard deviations (95 % confidence level) for position estimates for the north and east components of 3.8 and 5.5 mm, respectively, indicating that the model predicts the post-seismic deformation field very well. Finally, we added the co-seismic displacement field, estimated using an elastic finite element model. The final, trajectory model allows accessing the POSGAR07 RF using post-Maule earthquake coordinates within 5 cm for ˜ 91 % of the passive test benchmarks.

An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

NASA Astrophysics Data System (ADS)

Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge

2017-01-01

A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.

MODELING FOR SLUDGE ESTIMATION AND REDUCTION. (R824732)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Statistical Bayesian method for reliability evaluation based on ADT data

NASA Astrophysics Data System (ADS)

Lu, Dawei; Wang, Lizhi; Sun, Yusheng; Wang, Xiaohong

2018-05-01

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products’ reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen; Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values; Third, the lifetime and reliability values are estimated on the basis of the estimation parameters; Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.

Uncertainty in modeled upper ocean heat content change

NASA Astrophysics Data System (ADS)

Tokmakian, Robin; Challenor, Peter

2014-02-01

This paper examines the uncertainty in the change in the heat content in the ocean component of a general circulation model. We describe the design and implementation of our statistical methodology. Using an ensemble of model runs and an emulator, we produce an estimate of the full probability distribution function (PDF) for the change in upper ocean heat in an Atmosphere/Ocean General Circulation Model, the Community Climate System Model v. 3, across a multi-dimensional input space. We show how the emulator of the GCM's heat content change and hence, the PDF, can be validated and how implausible outcomes from the emulator can be identified when compared to observational estimates of the metric. In addition, the paper describes how the emulator outcomes and related uncertainty information might inform estimates of the same metric from a multi-model Coupled Model Intercomparison Project phase 3 ensemble. We illustrate how to (1) construct an ensemble based on experiment design methods, (2) construct and evaluate an emulator for a particular metric of a complex model, (3) validate the emulator using observational estimates and explore the input space with respect to implausible outcomes and (4) contribute to the understanding of uncertainties within a multi-model ensemble. Finally, we estimate the most likely value for heat content change and its uncertainty for the model, with respect to both observations and the uncertainty in the value for the input parameters.

Uncertainty estimations for moment tensor inversions: the issue of the 2012 May 20 Emilia earthquake

NASA Astrophysics Data System (ADS)

Scognamiglio, Laura; Magnoni, Federica; Tinti, Elisa; Casarotti, Emanuele

2016-08-01

Seismic moment tensor is one of the most important source parameters defining the earthquake dimension and style of the activated fault. Geoscientists ordinarily use moment tensor catalogues, however, few attempts have been done to assess possible impacts of moment magnitude uncertainties upon their analysis. The 2012 May 20 Emilia main shock is a representative event since it is defined in literature with a moment magnitude value (Mw) spanning between 5.63 and 6.12. A variability of ˜0.5 units in magnitude leads to a controversial knowledge of the real size of the event and reveals how the solutions could be poorly constrained. In this work, we investigate the stability of the moment tensor solution for this earthquake, studying the effect of five different 1-D velocity models, the number and the distribution of the stations used in the inversion procedure. We also introduce a 3-D velocity model to account for structural heterogeneity. We finally estimate the uncertainties associated to the computed focal planes and the obtained Mw. We conclude that our reliable source solutions provide a moment magnitude that ranges from 5.87, 1-D model, to 5.96, 3-D model, reducing the variability of the literature to ˜0.1. We endorse that the estimate of seismic moment from moment tensor solutions, as well as the estimate of the other kinematic source parameters, requires coming out with disclosed assumptions and explicit processing workflows. Finally and, probably more important, when moment tensor solution is used for secondary analyses it has to be combined with the same main boundary conditions (e.g. wave-velocity propagation model) to avoid conflicting results.

Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania.

PubMed

Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik

2015-12-01

Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

Gap-junction coupling and ATP-sensitive potassium channels in human β -cell clusters: Effects on emergent dynamics

NASA Astrophysics Data System (ADS)

Loppini, A.; Pedersen, M. G.; Braun, M.; Filippi, S.

2017-09-01

The importance of gap-junction coupling between β cells in pancreatic islets is well established in mouse. Such ultrastructural connections synchronize cellular activity, confine biological heterogeneity, and enhance insulin pulsatility. Dysfunction of coupling has been associated with diabetes and altered β -cell function. However, the role of gap junctions between human β cells is still largely unexplored. By using patch-clamp recordings of β cells from human donors, we previously estimated electrical properties of these channels by mathematical modeling of pairs of human β cells. In this work we revise our estimate by modeling triplet configurations and larger heterogeneous clusters. We find that a coupling conductance in the range 0.005 -0.020 nS/pF can reproduce experiments in almost all the simulated arrangements. We finally explore the consequence of gap-junction coupling of this magnitude between β cells with mutant variants of the ATP-sensitive potassium channels involved in some metabolic disorders and diabetic conditions, translating studies performed on rodents to the human case. Our results are finally discussed from the perspective of therapeutic strategies. In summary, modeling of more realistic clusters with more than two β cells slightly lowers our previous estimate of gap-junction conductance and gives rise to patterns that more closely resemble experimental traces.

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data

PubMed Central

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2012-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions. PMID:23645976

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.

PubMed

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2013-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.

Synthesis of capillary pressure curves from post-stack seismic data with the use of intelligent estimators: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

NASA Astrophysics Data System (ADS)

Golsanami, Naser; Kadkhodaie-Ilkhchi, Ali; Erfani, Amir

2015-01-01

Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy. The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed.

Sea ice - atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

NASA Technical Reports Server (NTRS)

Steffen, Konrad; Schweiger, A.; Maslanik, J.; Key, J.; Haefliger, M.; Weaver, R.

1991-01-01

In the past six months, work has continued on energy flux sensitivity studies, ice surface temperature retrievals, corrections to Advanced Very High Resolution Radiometer (AVHRR) thermal infrared data, modelling of cloud fraction retrievals, and radiation climatologies. We tentatively conclude that the SSM/I may not provide accurate enough estimates of ice concentration and type to improve our shorter term energy flux estimates. SSM/I derived parameters may still be applicable in longer term climatological flux characterizations. We hold promise for a system coupling observation to a ice deformation model. Such a model may provide information on ice distribution which can be used in energy flux calculations. Considerable variation was found in modelled energy flux estimates when bulk transfer coefficients are modulated by lead fetch. It is still unclear what the optimum formulation is and this will be the subject of further work. Data sets for ice surface temperature retrievals were assembled and preliminary data analysis was started. Finally, construction of a conceptual framework for further modelling of the Arctic radiation flux climatology was started.

Rapid Non-Gaussian Uncertainty Quantification of Seismic Velocity Models and Images

NASA Astrophysics Data System (ADS)

Ely, G.; Malcolm, A. E.; Poliannikov, O. V.

2017-12-01

Conventional seismic imaging typically provides a single estimate of the subsurface without any error bounds. Noise in the observed raw traces as well as the uncertainty of the velocity model directly impact the uncertainty of the final seismic image and its resulting interpretation. We present a Bayesian inference framework to quantify uncertainty in both the velocity model and seismic images, given noise statistics of the observed data.To estimate velocity model uncertainty, we combine the field expansion method, a fast frequency domain wave equation solver, with the adaptive Metropolis-Hastings algorithm. The speed of the field expansion method and its reduced parameterization allows us to perform the tens or hundreds of thousands of forward solves needed for non-parametric posterior estimations. We then migrate the observed data with the distribution of velocity models to generate uncertainty estimates of the resulting subsurface image. This procedure allows us to create both qualitative descriptions of seismic image uncertainty and put error bounds on quantities of interest such as the dip angle of a subduction slab or thickness of a stratigraphic layer.

A global food demand model for the assessment of complex human-earth systems

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

EDMONDS, JAMES A.; LINK, ROBERT; WALDHOFF, STEPHANIE T.

Demand for agricultural products is an important problem in climate change economics. Food consumption will shape and shaped by climate change and emissions mitigation policies through interactions with bioenergy and afforestation, two critical issues in meeting international climate goals such as two-degrees. We develop a model of food demand for staple and nonstaple commodities that evolves with changing incomes and prices. The model addresses a long-standing issue in estimating food demands, the evolution of demand relationships across large changes in income and prices. We discuss the model, some of its properties and limitations. We estimate parameter values using pooled cross-sectional-time-seriesmore » observations and the Metropolis Monte Carlo method and cross-validate the model by estimating parameters using a subset of the observations and test its ability to project into the unused observations. Finally, we apply bias correction techniques borrowed from the climate-modeling community and report results.« less

Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes.

PubMed

Balakrishnan, Narayanaswamy; Pal, Suvra

2016-08-01

Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell-Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence. © The Author(s) 2013.

An advection-diffusion-reaction size-structured fish population dynamics model combined with a statistical parameter estimation procedure: application to the Indian ocean skipjack tuna fishery.

PubMed

Faugeras, Blaise; Maury, Olivier

2005-10-01

We develop an advection-diffusion size-structured fish population dynamics model and apply it to simulate the skipjack tuna population in the Indian Ocean. The model is fully spatialized, and movements are parameterized with oceanographical and biological data; thus it naturally reacts to environment changes. We first formulate an initial-boundary value problem and prove existence of a unique positive solution. We then discuss the numerical scheme chosen for the integration of the simulation model. In a second step we address the parameter estimation problem for such a model. With the help of automatic differentiation, we derive the adjoint code which is used to compute the exact gradient of a Bayesian cost function measuring the distance between the outputs of the model and catch and length frequency data. A sensitivity analysis shows that not all parameters can be estimated from the data. Finally twin experiments in which pertubated parameters are recovered from simulated data are successfully conducted.

Population Estimates for Chum Salmon Spawning in the Mainstem Columbia River, 2002 Technical Report.

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

Rawding, Dan; Hillson, Todd D.

2003-11-15

Accurate and precise population estimates of chum salmon (Oncorhynchus keta) spawning in the mainstem Columbia River are needed to provide a basis for informed water allocation decisions, to determine the status of chum salmon listed under the Endangered Species Act, and to evaluate the contribution of the Duncan Creek re-introduction program to mainstem spawners. Currently, mark-recapture experiments using the Jolly-Seber model provide the only framework for this type of estimation. In 2002, a study was initiated to estimate mainstem Columbia River chum salmon populations using seining data collected while capturing broodstock as part of the Duncan Creek re-introduction. The fivemore » assumptions of the Jolly-Seber model were examined using hypothesis testing within a statistical framework, including goodness of fit tests and secondary experiments. We used POPAN 6, an integrated computer system for the analysis of capture-recapture data, to obtain maximum likelihood estimates of standard model parameters, derived estimates, and their precision. A more parsimonious final model was selected using Akaike Information Criteria. Final chum salmon escapement estimates and (standard error) from seining data for the Ives Island, Multnomah, and I-205 sites are 3,179 (150), 1,269 (216), and 3,468 (180), respectively. The Ives Island estimate is likely lower than the total escapement because only the largest two of four spawning sites were sampled. The accuracy and precision of these estimates would improve if seining was conducted twice per week instead of weekly, and by incorporating carcass recoveries into the analysis. Population estimates derived from seining mark-recapture data were compared to those obtained using the current mainstem Columbia River salmon escapement methodologies. The Jolly-Seber population estimate from carcass tagging in the Ives Island area was 4,232 adults with a standard error of 79. This population estimate appears reasonable and precise but batch marks and lack of secondary studies made it difficult to test Jolly-Seber assumptions, necessary for unbiased estimates. We recommend that individual tags be applied to carcasses to provide a statistical basis for goodness of fit tests and ultimately model selection. Secondary or double marks should be applied to assess tag loss and male and female chum salmon carcasses should be enumerated separately. Carcass tagging population estimates at the two other sites were biased low due to limited sampling. The Area-Under-the-Curve escapement estimates at all three sites were 36% to 76% of Jolly-Seber estimates. Area-Under-the Curve estimates are likely biased low because previous assumptions that observer efficiency is 100% and residence time is 10 days proved incorrect. If managers continue to rely on Area-Under-the-Curve to estimate mainstem Columbia River spawners, a methodology is provided to develop annual estimates of observer efficiency and residence time, and to incorporate uncertainty into the Area-Under-the-Curve escapement estimate.« less

Nonlinear pharmacokinetics of visnagin in rats after intravenous bolus administration.

PubMed

Haug, Karin G; Weber, Benjamin; Hochhaus, Guenther; Butterweck, Veronika

2012-01-23

Ammi visnaga L. (syn. Khella, Apiaceae) preparations have traditionally been used in the Middle East for the treatment of kidney stone disease. Visnagin, a furanocoumarin derivative, is one of the main compounds of Ammi visnaga with potential effects on kidney stone prevention. To date, no information is available about the pharmacokinetic (PK) properties of visnagin. It was the aim of the study to characterize the PK properties of visnagin after intravenous (i.v.) bolus administration in rats and to develop an adequate model for the description of the observed data, including model parameter estimates. Therefore, three doses of visnagin (1.25, 2.5, and 5mg/kg) solubilized in 25% Captisol® were administered by i.v. bolus injection to male Sprague-Dawley rats. Plasma samples were extracted and subsequently analyzed using a validated LC-MS/MS method. Both non-compartmental and compartmental PK analyses were performed. A stepwise model building approach was applied including nonlinear mixed effect modeling for final model selection and to obtain final model estimates in NONMEM VI. The average areas under the curve (AUC(0-last)) after doses of 1.25, 2.5, and 5mg/kg were 1.03, 3.61, and 12.6 mg *h/l, respectively. The shape of the plasma concentration-time profiles and the observed disproportionate increase in AUC(0-last) with increasing dose suggested nonlinearity in the elimination of visnagin. A two-compartment Michaelis-Menten model provided the best fit with following typical values of the parameter estimates: 2.09 mg/(l*h) (V(max)), 0.08 mg/l (K(M)), 0.175 l (V(C)), 1.0 h⁻¹ (k₁₂), and 1.22 h⁻¹ (k₂₁). Associated inter-subject variability estimates (% CV) for V(max), K(M) and V(C) were 21.8, 70.9, and 9.2, respectively. Intra-subject variability (constant CV error model) was estimated to be 7.0%. The results suggest the involvement of a saturable process in the elimination of visnagin, possibly an enzyme or transporter system. Copyright © 2011 Elsevier B.V. All rights reserved.

A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1

NASA Astrophysics Data System (ADS)

Rabin, Sam S.; Ward, Daniel S.; Malyshev, Sergey L.; Magi, Brian I.; Shevliakova, Elena; Pacala, Stephen W.

2018-03-01

This study describes and evaluates the Fire Including Natural & Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001-2009 (global totals: 0.434×106 and 2.02×106 km2 yr-1 modeled, 0.454×106 and 2.04×106 km2 yr-1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.295 and 0.706 PgC yr-1 modeled, 0.194 and 0.538 PgC yr-1 observed). The non-agricultural fire module underestimates global burned area (1.91×106 km2 yr-1 modeled, 2.44×106 km2 yr-1 observed) and carbon emissions (1.14 PgC yr-1 modeled, 1.84 PgC yr-1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, Central Asia, and Australia, whereas the boreal zone sees underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets. We include an in-depth discussion of the lessons learned from using the Levenberg-Marquardt algorithm in an interactive optimization for a dynamic global vegetation model.

Should Bouchet's hypothesis be taken into account in rainfall-runoff modelling? An assessment over 308 catchments

NASA Astrophysics Data System (ADS)

Oudin, Ludovic; Michel, Claude; Andréassian, Vazken; Anctil, François; Loumagne, Cécile

2005-12-01

An implementation of the complementary relationship hypothesis (Bouchet's hypothesis) for estimating regional evapotranspiration within two rainfall-runoff models is proposed and evaluated in terms of streamflow simulation efficiency over a large sample of 308 catchments located in Australia, France and the USA. Complementary relationship models are attractive approaches to estimating actual evapotranspiration because they rely solely on climatic variables. They are even more interesting since they are supported by a conceptual description underlying the interactions between the evapotranspirating surface and the atmospheric boundary layer, which was highlighted by Bouchet (1963). However, these approaches appear to be in contradiction with the methods prevailing in rainfall-runoff models, which compute actual evapotranspiration using soil moisture accounting procedures. The approach adopted in this article is to introduce the estimation of actual evapotranspiration provided by complementary relationship models (complementary relationship for areal evapotranspiration and advection aridity) into two rainfall-runoff models. Results show that directly using the complementary relationship approach to estimate actual evapotranspiration does not give better results than the soil moisture accounting procedures. Finally, we discuss feedback mechanisms between potential evapotranspiration and soil water availability, and their possible impact on rainfall-runoff modelling. Copyright

An introduction to multidimensional measurement using Rasch models.

PubMed

Briggs, Derek C; Wilson, Mark

2003-01-01

The act of constructing a measure requires a number of important assumptions. Principle among these assumptions is that the construct is unidimensional. In practice there are many instances when the assumption of unidimensionality does not hold, and where the application of a multidimensional measurement model is both technically appropriate and substantively advantageous. In this paper we illustrate the usefulness of a multidimensional approach to measurement with the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, an extension of the unidimensional Rasch model. An empirical example is taken from a collection of embedded assessments administered to 541 students enrolled in middle school science classes with a hands-on science curriculum. Student achievement on these assessments are multidimensional in nature, but can also be treated as consecutive unidimensional estimates, or as is most common, as a composite unidimensional estimate. Structural parameters are estimated for each model using ConQuest, and model fit is compared. Student achievement in science is also compared across models. The multidimensional approach has the best fit to the data, and provides more reliable estimates of student achievement than under the consecutive unidimensional approach. Finally, at an interpretational level, the multidimensional approach may well provide richer information to the classroom teacher about the nature of student achievement.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

NASA Astrophysics Data System (ADS)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

2014-12-01

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Dengue and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

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

Samat, N. A.; Ma'arof, S. H. Mohd Imam

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Denguemore » and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.« less

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

DOE PAGES

DeChant, Lawrence; Ray, Jaideep; Lefantzi, Sophia; ...

2017-06-09

The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnelmore » data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.« less

The relationship between cost estimates reliability and BIM adoption: SEM analysis

NASA Astrophysics Data System (ADS)

Ismail, N. A. A.; Idris, N. H.; Ramli, H.; Rooshdi, R. R. Raja Muhammad; Sahamir, S. R.

2018-02-01

This paper presents the usage of Structural Equation Modelling (SEM) approach in analysing the effects of Building Information Modelling (BIM) technology adoption in improving the reliability of cost estimates. Based on the questionnaire survey results, SEM analysis using SPSS-AMOS application examined the relationships between BIM-improved information and cost estimates reliability factors, leading to BIM technology adoption. Six hypotheses were established prior to SEM analysis employing two types of SEM models, namely the Confirmatory Factor Analysis (CFA) model and full structural model. The SEM models were then validated through the assessment on their uni-dimensionality, validity, reliability, and fitness index, in line with the hypotheses tested. The final SEM model fit measures are: P-value=0.000, RMSEA=0.079<0.08, GFI=0.824, CFI=0.962>0.90, TLI=0.956>0.90, NFI=0.935>0.90 and ChiSq/df=2.259; indicating that the overall index values achieved the required level of model fitness. The model supports all the hypotheses evaluated, confirming that all relationship exists amongst the constructs are positive and significant. Ultimately, the analysis verified that most of the respondents foresee better understanding of project input information through BIM visualization, its reliable database and coordinated data, in developing more reliable cost estimates. They also perceive to accelerate their cost estimating task through BIM adoption.

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

Advanced Crash Avoidance Technologies (ACAT) Program - Final Report of the GM-VTTI Backing Crash Countermeasures Project

DOT National Transportation Integrated Search

2011-08-01

The Backing crash Countermeasures project, part of the U.S. Department of Transportation's Advanced Crash Avoidance Technologies (ACAT) program, developed a basic methodological framework and computerbased simulation model to estimate the effectiv...

Revised Planning Methodology For Signalized Intersections And Operational Analysis Of Exclusive Left-Turn Lanes, Part-II: Models And Procedures (Final Report)

DOT National Transportation Integrated Search

1996-04-01

THIS REPORT ALSO DESCRIBES THE PROCEDURES FOR DIRECT ESTIMATION OF INTERSECTION CAPACITY WITH SIMULATION, INCLUDING A SET OF RIGOROUS STATISTICAL TESTS FOR SIMULATION PARAMETER CALIBRATION FROM FIELD DATA.

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

DOE PAGES

Kim, Young-Min; Zhou, Ying; Gao, Yang; ...

2014-11-16

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

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

Kim, Young-Min; Zhou, Ying; Gao, Yang

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

A hierarchical estimator development for estimation of tire-road friction coefficient

PubMed Central

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. PMID:28178332

A hierarchical estimator development for estimation of tire-road friction coefficient.

PubMed

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

USGS Publications Warehouse

Thornton, P. K.; Bowen, W. T.; Ravelo, A.C.; Wilkens, P. W.; Farmer, G.; Brock, J.; Brink, J. E.

1997-01-01

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Tire-road friction coefficient estimation based on the resonance frequency of in-wheel motor drive system

NASA Astrophysics Data System (ADS)

Chen, Long; Bian, Mingyuan; Luo, Yugong; Qin, Zhaobo; Li, Keqiang

2016-01-01

In this paper, a resonance frequency-based tire-road friction coefficient (TRFC) estimation method is proposed by considering the dynamics performance of the in-wheel motor drive system under small slip ratio conditions. A frequency response function (FRF) is deduced for the drive system that is composed of a dynamic tire model and a simplified motor model. A linear relationship between the squared system resonance frequency and the TFRC is described with the FRF. Furthermore, the resonance frequency is identified by the Auto-Regressive eXogenous model using the information of the motor torque and the wheel speed, and the TRFC is estimated thereafter by a recursive least squares filter with the identified resonance frequency. Finally, the effectiveness of the proposed approach is demonstrated through simulations and experimental tests on different road surfaces.

Image informative maps for component-wise estimating parameters of signal-dependent noise

NASA Astrophysics Data System (ADS)

Uss, Mykhail L.; Vozel, Benoit; Lukin, Vladimir V.; Chehdi, Kacem

2013-01-01

We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramér-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

NASA Astrophysics Data System (ADS)

Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

2018-01-01

In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

Spatial estimation from remotely sensed data via empirical Bayes models

NASA Technical Reports Server (NTRS)

Hill, J. R.; Hinkley, D. V.; Kostal, H.; Morris, C. N.

1984-01-01

Multichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.

A Bayesian hierarchical latent trait model for estimating rater bias and reliability in large-scale performance assessment

PubMed Central

2018-01-01

We propose a novel approach to modelling rater effects in scoring-based assessment. The approach is based on a Bayesian hierarchical model and simulations from the posterior distribution. We apply it to large-scale essay assessment data over a period of 5 years. Empirical results suggest that the model provides a good fit for both the total scores and when applied to individual rubrics. We estimate the median impact of rater effects on the final grade to be ± 2 points on a 50 point scale, while 10% of essays would receive a score at least ± 5 different from their actual quality. Most of the impact is due to rater unreliability, not rater bias. PMID:29614129

The role of the uncertainty in assessing future scenarios of water shortage in alluvial aquifers

NASA Astrophysics Data System (ADS)

Romano, Emanuele; Camici, Stefania; Brocca, Luca; Moramarco, Tommaso; Guyennon, Nicolas; Preziosi, Elisabetta

2015-04-01

There are many evidences that the combined effects of variations in precipitation and temperature due to climate change can result in a significant change of the recharge to groundwater at different time scales. A possible reduction of effective infiltration can result in a significant decrease, temporary or permanent, of the availability of the resource and, consequently, the sustainable pumping rate should be reassessed. In addition to this, one should also consider the so called indirect impacts of climate change, resulting from human intervention (e.g. augmentation of abstractions) which are feared to be even more important than the direct ones in the medium term: thus, a possible increase of episodes of shortage (i.e. the inability of the groundwater system to completely supply the water demand) can result both from change in the climate forcing and change in the demand. In order to assess future scenarios of water shortage a modelling chain is often used. It includes: 1) the use of General Circulation Models to estimate changes in temperature and precipitation; 2) downscaling procedures to match modeling scenarios to the observed meteorological time series; 3) soil-atmosphere modelling to estimate the time variation of the recharge to the aquifer; 4) groundwater flow models to simulate the water budget and piezometric head evolution; 5) future scenarios of groundwater quantitative status that include scenarios of demand variation. It is well known that each of these processing steps is affected by an intrinsic uncertainty that propagates through the whole chain leading to a final uncertainty on the piezometric head scenarios. The estimate of such an uncertainty is a key point for a correct management of groundwater resources, in case of water shortage due to prolonged droughts as well as for planning purposes. This study analyzes the uncertainty of the processing chain from GCM scenarios to its impact on an alluvial aquifer in terms of exploitation sustainability. To this goal, three GCMs (ECHAM5, PCM and CCSM3) and two downscaling methods (Linear Rescaling and Quantile Mapping) are used to generate future scenarios of precipitation and temperature; the Thornthwaite-Mather soil water balance model is used to estimate the recharge to the aquifer; the evolution in time of the piezometric heads is estimated through a numerical model developed using the MODFLOW2005 code. Finally, different scenarios of water demand are applied. Final results show that the uncertainty due to the groundwater flow model calibration/validation in steady-state conditions is comparable to that arising from the whole processing chain from the GCM choice to the effective infiltration estimates. Simulations in transient conditions show the high impact of the uncertainty related to the calibration of the storage coefficient, that significantly drives the resilience of the system, thus the ability of the aquifer to sustain the demand during the periods of prolonged drought.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2014-11-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

The Theory and Practice of Estimating the Accuracy of Dynamic Flight-Determined Coefficients

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1981-01-01

Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.

Using a laboratory-based growth model to estimate mass- and temperature-dependent growth parameters across populations of juvenile Chinook Salmon

USGS Publications Warehouse

Perry, Russell W.; Plumb, John M.; Huntington, Charles

2015-01-01

To estimate the parameters that govern mass- and temperature-dependent growth, we conducted a meta-analysis of existing growth data from juvenile Chinook Salmon Oncorhynchus tshawytscha that were fed an ad libitum ration of a pelleted diet. Although the growth of juvenile Chinook Salmon has been well studied, research has focused on a single population, a narrow range of fish sizes, or a narrow range of temperatures. Therefore, we incorporated the Ratkowsky model for temperature-dependent growth into an allometric growth model; this model was then fitted to growth data from 11 data sources representing nine populations of juvenile Chinook Salmon. The model fit the growth data well, explaining 98% of the variation in final mass. The estimated allometric mass exponent (b) was 0.338 (SE = 0.025), similar to estimates reported for other salmonids. This estimate of b will be particularly useful for estimating mass-standardized growth rates of juvenile Chinook Salmon. In addition, the lower thermal limit, optimal temperature, and upper thermal limit for growth were estimated to be 1.8°C (SE = 0.63°C), 19.0°C (SE = 0.27°C), and 24.9°C (SE = 0.02°C), respectively. By taking a meta-analytical approach, we were able to provide a growth model that is applicable across populations of juvenile Chinook Salmon receiving an ad libitum ration of a pelleted diet.

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Riley, Richard D; Abrams, Keith R

2016-03-30

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Pulse Jet Mixing Tests With Noncohesive Solids

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

Meyer, Perry A.; Bamberger, Judith A.; Enderlin, Carl W.

2012-02-17

This report summarizes results from pulse jet mixing (PJM) tests with noncohesive solids in Newtonian liquid. The tests were conducted during FY 2007 and 2008 to support the design of mixing systems for the Hanford Waste Treatment and Immobilization Plant (WTP). Tests were conducted at three geometric scales using noncohesive simulants, and the test data were used to develop models predicting two measures of mixing performance for full-scale WTP vessels. The models predict the cloud height (the height to which solids will be lifted by the PJM action) and the critical suspension velocity (the minimum velocity needed to ensure allmore » solids are suspended off the floor, though not fully mixed). From the cloud height, the concentration of solids at the pump inlet can be estimated. The predicted critical suspension velocity for lifting all solids is not precisely the same as the mixing requirement for 'disturbing' a sufficient volume of solids, but the values will be similar and closely related. These predictive models were successfully benchmarked against larger scale tests and compared well with results from computational fluid dynamics simulations. The application of the models to assess mixing in WTP vessels is illustrated in examples for 13 distinct designs and selected operational conditions. The values selected for these examples are not final; thus, the estimates of performance should not be interpreted as final conclusions of design adequacy or inadequacy. However, this work does reveal that several vessels may require adjustments to design, operating features, or waste feed properties to ensure confidence in operation. The models described in this report will prove to be valuable engineering tools to evaluate options as designs are finalized for the WTP. Revision 1 refines data sets used for model development and summarizes models developed since the completion of Revision 0.« less

SEM Based CARMA Time Series Modeling for Arbitrary N.

PubMed

Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C

2018-01-01

This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.

Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

A brief review on key technologies in the battery management system of electric vehicles

NASA Astrophysics Data System (ADS)

Liu, Kailong; Li, Kang; Peng, Qiao; Zhang, Cheng

2018-04-01

Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

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

Zhou, Ping; Lv, Youbin; Wang, Hong

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation basedmore » robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.« less

Power Management and Distribution (PMAD) Model Development: Final Report

NASA Technical Reports Server (NTRS)

Metcalf, Kenneth J.

2011-01-01

Power management and distribution (PMAD) models were developed in the early 1990's to model candidate architectures for various Space Exploration Initiative (SEI) missions. They were used to generate "ballpark" component mass estimates to support conceptual PMAD system design studies. The initial set of models was provided to NASA Lewis Research Center (since renamed Glenn Research Center) in 1992. They were developed to estimate the characteristics of power conditioning components predicted to be available in the 2005 timeframe. Early 90's component and device designs and material technologies were projected forward to the 2005 timeframe, and algorithms reflecting those design and material improvements were incorporated into the models to generate mass, volume, and efficiency estimates for circa 2005 components. The models are about ten years old now and NASA GRC requested a review of them to determine if they should be updated to bring them into agreement with current performance projections or to incorporate unforeseen design or technology advances. This report documents the results of this review and the updated power conditioning models and new transmission line models generated to estimate post 2005 PMAD system masses and sizes. This effort continues the expansion and enhancement of a library of PMAD models developed to allow system designers to assess future power system architectures and distribution techniques quickly and consistently.

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

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

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi

2014-04-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m -2 yr -1 and total NPP in the range of 318–490more » Tg C yr -1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m -2 yr -1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m -2 yr -1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. Finally, we suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.« less

Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem

USGS Publications Warehouse

Schoenecker, Kathryn A.; Lubow, Bruce C.

2016-01-01

Accurately estimating the size of wildlife populations is critical to wildlife management and conservation of species. Raw counts or “minimum counts” are still used as a basis for wildlife management decisions. Uncorrected raw counts are not only negatively biased due to failure to account for undetected animals, but also provide no estimate of precision on which to judge the utility of counts. We applied a hybrid population estimation technique that combined sightability modeling, radio collar-based mark-resight, and simultaneous double count (double-observer) modeling to estimate the population size of elk in a high elevation desert ecosystem. Combining several models maximizes the strengths of each individual model while minimizing their singular weaknesses. We collected data with aerial helicopter surveys of the elk population in the San Luis Valley and adjacent mountains in Colorado State, USA in 2005 and 2007. We present estimates from 7 alternative analyses: 3 based on different methods for obtaining a raw count and 4 based on different statistical models to correct for sighting probability bias. The most reliable of these approaches is a hybrid double-observer sightability model (model MH), which uses detection patterns of 2 independent observers in a helicopter plus telemetry-based detections of radio collared elk groups. Data were fit to customized mark-resight models with individual sighting covariates. Error estimates were obtained by a bootstrapping procedure. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to double-observer modeling. The resulting population estimate corrected for multiple sources of undercount bias that, if left uncorrected, would have underestimated the true population size by as much as 22.9%. Our comparison of these alternative methods demonstrates how various components of our method contribute to improving the final estimate and demonstrates why each is necessary.

Models and analysis for multivariate failure time data

NASA Astrophysics Data System (ADS)

Shih, Joanna Huang

The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the performance of these two methods using actual and computer generated data.

Estimating Risk of Natural Gas Portfolios by Using GARCH-EVT-Copula Model.

PubMed

Tang, Jiechen; Zhou, Chao; Yuan, Xinyu; Sriboonchitta, Songsak

2015-01-01

This paper concentrates on estimating the risk of Title Transfer Facility (TTF) Hub natural gas portfolios by using the GARCH-EVT-copula model. We first use the univariate ARMA-GARCH model to model each natural gas return series. Second, the extreme value distribution (EVT) is fitted to the tails of the residuals to model marginal residual distributions. Third, multivariate Gaussian copula and Student t-copula are employed to describe the natural gas portfolio risk dependence structure. Finally, we simulate N portfolios and estimate value at risk (VaR) and conditional value at risk (CVaR). Our empirical results show that, for an equally weighted portfolio of five natural gases, the VaR and CVaR values obtained from the Student t-copula are larger than those obtained from the Gaussian copula. Moreover, when minimizing the portfolio risk, the optimal natural gas portfolio weights are found to be similar across the multivariate Gaussian copula and Student t-copula and different confidence levels.

Estimation Model of Spacecraft Parameters and Cost Based on a Statistical Analysis of COMPASS Designs

NASA Technical Reports Server (NTRS)

Gerberich, Matthew W.; Oleson, Steven R.

2013-01-01

The Collaborative Modeling for Parametric Assessment of Space Systems (COMPASS) team at Glenn Research Center has performed integrated system analysis of conceptual spacecraft mission designs since 2006 using a multidisciplinary concurrent engineering process. The set of completed designs was archived in a database, to allow for the study of relationships between design parameters. Although COMPASS uses a parametric spacecraft costing model, this research investigated the possibility of using a top-down approach to rapidly estimate the overall vehicle costs. This paper presents the relationships between significant design variables, including breakdowns of dry mass, wet mass, and cost. It also develops a model for a broad estimate of these parameters through basic mission characteristics, including the target location distance, the payload mass, the duration, the delta-v requirement, and the type of mission, propulsion, and electrical power. Finally, this paper examines the accuracy of this model in regards to past COMPASS designs, with an assessment of outlying spacecraft, and compares the results to historical data of completed NASA missions.

Scientific analysis is essential to assess biofuel policy effects: in response to the paper by Kim and Dale on "Indirect land use change for biofuels: Testing predictions and improving analytical methodologies"

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

Kline, Keith L; Oladosu, Gbadebo A; Dale, Virginia H

2011-01-01

Vigorous debate on the effects of biofuels derives largely from the changes in land use estimated using economic models designed mainly for the analysis of agricultural trade and markets. The models referenced for land-use change (LUC) analysis in the U.S. Environmental Protection Agency Final Rule on the Renewable Fuel Standard include GTAP, FAPRI-CARD, and FASOM. To address bioenergy impacts, these models were expanded and modified to facilitate simulations of hypothesized LUC. However, even when models use similar basic assumptions and data, the range of LUC results can vary by ten-fold or more. While the market dynamics simulated in these modelsmore » include processes that are important in estimating effects of biofuel policies, the models have not been validated for estimating land-use changes and employ crucial assumptions and simplifications that contradict empirical evidence.« less

Estimating Risk of Natural Gas Portfolios by Using GARCH-EVT-Copula Model

PubMed Central

Tang, Jiechen; Zhou, Chao; Yuan, Xinyu; Sriboonchitta, Songsak

2015-01-01

This paper concentrates on estimating the risk of Title Transfer Facility (TTF) Hub natural gas portfolios by using the GARCH-EVT-copula model. We first use the univariate ARMA-GARCH model to model each natural gas return series. Second, the extreme value distribution (EVT) is fitted to the tails of the residuals to model marginal residual distributions. Third, multivariate Gaussian copula and Student t-copula are employed to describe the natural gas portfolio risk dependence structure. Finally, we simulate N portfolios and estimate value at risk (VaR) and conditional value at risk (CVaR). Our empirical results show that, for an equally weighted portfolio of five natural gases, the VaR and CVaR values obtained from the Student t-copula are larger than those obtained from the Gaussian copula. Moreover, when minimizing the portfolio risk, the optimal natural gas portfolio weights are found to be similar across the multivariate Gaussian copula and Student t-copula and different confidence levels. PMID:26351652

An investigation into exoplanet transits and uncertainties

NASA Astrophysics Data System (ADS)

Ji, Y.; Banks, T.; Budding, E.; Rhodes, M. D.

2017-06-01

A simple transit model is described along with tests of this model against published results for 4 exoplanet systems (Kepler-1, 2, 8, and 77). Data from the Kepler mission are used. The Markov Chain Monte Carlo (MCMC) method is applied to obtain realistic error estimates. Optimisation of limb darkening coefficients is subject to data quality. It is more likely for MCMC to derive an empirical limb darkening coefficient for light curves with S/N (signal to noise) above 15. Finally, the model is applied to Kepler data for 4 Kepler candidate systems (KOI 760.01, 767.01, 802.01, and 824.01) with previously unpublished results. Error estimates for these systems are obtained via the MCMC method.

Software reliability perspectives

NASA Technical Reports Server (NTRS)

Wilson, Larry; Shen, Wenhui

1987-01-01

Software which is used in life critical functions must be known to be highly reliable before installation. This requires a strong testing program to estimate the reliability, since neither formal methods, software engineering nor fault tolerant methods can guarantee perfection. Prior to the final testing software goes through a debugging period and many models have been developed to try to estimate reliability from the debugging data. However, the existing models are poorly validated and often give poor performance. This paper emphasizes the fact that part of their failures can be attributed to the random nature of the debugging data given to these models as input, and it poses the problem of correcting this defect as an area of future research.

Cost Estimation Techniques for C3I System Software.

DTIC Science & Technology

1984-07-01

opment manmonth have been determined for maxi, midi , and mini .1 type computers. Small to median size timeshared developments used 0.2 to 1.5 hours...development schedule 1.23 1.00 1.10 2.1.3 Detailed Model The final codification of the COCOMO regressions was the development of separate effort...regardless of the software structure level being estimated: D8VC -- the expected development computer (maxi. midi . mini, micro) MODE -- the expected

Radiolytic and thermolytic bubble gas hydrogen composition

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

Woodham, W.

This report describes the development of a mathematical model for the estimation of the hydrogen composition of gas bubbles trapped in radioactive waste. The model described herein uses a material balance approach to accurately incorporate the rates of hydrogen generation by a number of physical phenomena and scale the aforementioned rates in a manner that allows calculation of the final hydrogen composition.

Modelling the standing timber volume of Baden-Württemberg-A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data

NASA Astrophysics Data System (ADS)

Maack, Joachim; Lingenfelder, Marcus; Weinacker, Holger; Koch, Barbara

2016-07-01

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.

FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS. (R826610)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

ESTIMATING EQUILIBRIUM MODELS OF LOCAL JURISDICTION. (R826609 AKA R828103)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

"A space-time ensemble Kalman filter for state and parameter estimation of groundwater transport models"

NASA Astrophysics Data System (ADS)

Briseño, Jessica; Herrera, Graciela S.

2010-05-01

Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one of the variables is used as the prior space-time estimate for the Kalman filter, and the space-time cross-covariance matrix of h-ln K-C as the prior estimate-error covariance-matrix. The synthetic example has a modeling area of 700 x 700 square meters; a triangular mesh model with 702 nodes and 1306 elements is used. A pumping well located in the central part of the study area is considered. For the contaminant transport model, a contaminant source area is present in the western part of the study area. The estimation points for hydraulic conductivity, hydraulic head and contaminant concentrations are located on a submesh of the model mesh (same location for h, ln K and c), composed by 48 nodes spread throughout the study area, with an approximately separation of 90 meters between nodes. The results analysis was done through the mean error, root mean square error, initial and final estimation maps of h, ln K and C at each time, and the initial and final variance maps of h, ln K and C. To obtain model convergence, 3000 realizations of ln K were required using SGSim, and only 1000 with LHC. The results show that for both alternatives, the Kalman filter estimates for h, ln K and C using h and C data, have errors which magnitudes decrease as data is added. HERRERA, G. S.(1998), Cost Effective Groundwater Quality Sampling Network Design. Ph. D. thesis, University of Vermont, Burlington, Vermont, 172 pp. HERRERA G., GUARNACCIA J., PINDER G. Y SIMUTA R.(2001),"Diseño de redes de monitoreo de la calidad del agua subterránea eficientes", Proceedings of the 2001 International Symposium on Environmental Hydraulics, Arizona, U.S.A. HERRERA G. S. and PINDER G.F. (2005), Space-time optimization of groundwater quality sampling networks Water Resour. Res., Vol. 41, No. 12, W12407, 10.1029/2004WR003626.

Decoupling Intensity Radiated by the Emitter in Distance Estimation from Camera to IR Emitter

PubMed Central

Cano-García, Angel E.; Galilea, José Luis Lázaro; Fernández, Pedro; Infante, Arturo Luis; Pompa-Chacón, Yamilet; Vázquez, Carlos Andrés Luna

2013-01-01

Various models using radiometric approach have been proposed to solve the problem of estimating the distance between a camera and an infrared emitter diode (IRED). They depend directly on the radiant intensity of the emitter, set by the IRED bias current. As is known, this current presents a drift with temperature, which will be transferred to the distance estimation method. This paper proposes an alternative approach to remove temperature drift in the distance estimation method by eliminating the dependence on radiant intensity. The main aim was to use the relative accumulated energy together with other defined models, such as the zeroth-frequency component of the FFT of the IRED image and the standard deviation of pixel gray level intensities in the region of interest containing the IRED image. By using the abovementioned models, an expression free of IRED radiant intensity was obtained. Furthermore, the final model permitted simultaneous estimation of the distance between the IRED and the camera and the IRED orientation angle. The alternative presented in this paper gave a 3% maximum relative error over a range of distances up to 3 m. PMID:23727954

An alternative empirical likelihood method in missing response problems and causal inference.

PubMed

Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao

2016-11-30

Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Single Tracking Location Acoustic Radiation Force Impulse Viscoelasticity Estimation (STL-VE): A Method for Measuring Tissue Viscoelastic Parameters

PubMed Central

Langdon, Jonathan H; Elegbe, Etana; McAleavey, Stephen A

2015-01-01

Single Tracking Location (STL) Shear wave Elasticity Imaging (SWEI) is a method for detecting elastic differences between tissues. It has the advantage of intrinsic speckle bias suppression compared to Multiple Tracking Location (MTL) variants of SWEI. However, the assumption of a linear model leads to an overestimation of the shear modulus in viscoelastic media. A new reconstruction technique denoted Single Tracking Location Viscosity Estimation (STL-VE) is introduced to correct for this overestimation. This technique utilizes the same raw data generated in STL-SWEI imaging. Here, the STL-VE technique is developed by way of a Maximum Likelihood Estimation (MLE) for general viscoelastic materials. The method is then implemented for the particular case of the Kelvin-Voigt Model. Using simulation data, the STL-VE technique is demonstrated and the performance of the estimator is characterized. Finally, the STL-VE method is used to estimate the viscoelastic parameters of ex-vivo bovine liver. We find good agreement between the STL-VE results and the simulation parameters as well as between the liver shear wave data and the modeled data fit. PMID:26168170

Improving PAGER's real-time earthquake casualty and loss estimation toolkit: a challenge

USGS Publications Warehouse

Jaiswal, K.S.; Wald, D.J.

2012-01-01

We describe the on-going developments of PAGER’s loss estimation models, and discuss value-added web content that can be generated related to exposure, damage and loss outputs for a variety of PAGER users. These developments include identifying vulnerable building types in any given area, estimating earthquake-induced damage and loss statistics by building type, and developing visualization aids that help locate areas of concern for improving post-earthquake response efforts. While detailed exposure and damage information is highly useful and desirable, significant improvements are still necessary in order to improve underlying building stock and vulnerability data at a global scale. Existing efforts with the GEM’s GED4GEM and GVC consortia will help achieve some of these objectives. This will benefit PAGER especially in regions where PAGER’s empirical model is less-well constrained; there, the semi-empirical and analytical models will provide robust estimates of damage and losses. Finally, we outline some of the challenges associated with rapid casualty and loss estimation that we experienced while responding to recent large earthquakes worldwide.

Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods

USGS Publications Warehouse

Conn, P.B.; Arthur, A.D.; Bailey, L.L.; Singleton, G.R.

2006-01-01

Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark?recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark?recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark?recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.

Performance and Weight Estimates for an Advanced Open Rotor Engine

NASA Technical Reports Server (NTRS)

Hendricks, Eric S.; Tong, Michael T.

2012-01-01

NASA s Environmentally Responsible Aviation Project and Subsonic Fixed Wing Project are focused on developing concepts and technologies which may enable dramatic reductions to the environmental impact of future generation subsonic aircraft. The open rotor concept (also historically referred to an unducted fan or advanced turboprop) may allow for the achievement of this objective by reducing engine fuel consumption. To evaluate the potential impact of open rotor engines, cycle modeling and engine weight estimation capabilities have been developed. The initial development of the cycle modeling capabilities in the Numerical Propulsion System Simulation (NPSS) tool was presented in a previous paper. Following that initial development, further advancements have been made to the cycle modeling and weight estimation capabilities for open rotor engines and are presented in this paper. The developed modeling capabilities are used to predict the performance of an advanced open rotor concept using modern counter-rotating propeller designs. Finally, performance and weight estimates for this engine are presented and compared to results from a previous NASA study of advanced geared and direct-drive turbofans.

Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; Huang, Jiaqi; Wei, Daozhi

2016-11-01

This paper investigates the design of a novel estimation-free prescribed performance non-affine control strategy for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) via back-stepping. The proposed control scheme is capable of guaranteeing tracking errors of velocity, altitude, flight-path angle, pitch angle and pitch rate with prescribed performance. By prescribed performance, we mean that the tracking error is limited to a predefined arbitrarily small residual set, with convergence rate no less than a certain constant, exhibiting maximum overshoot less than a given value. Unlike traditional back-stepping designs, there is no need of an affine model in this paper. Moreover, both the tedious analytic and numerical computations of time derivatives of virtual control laws are completely avoided. In contrast to estimation-based strategies, the presented estimation-free controller possesses much lower computational costs, while successfully eliminating the potential problem of parameter drifting. Owing to its independence on an accurate AHV model, the studied methodology exhibits excellent robustness against system uncertainties. Finally, simulation results from a fully nonlinear model clarify and verify the design.

The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion.

PubMed

Ehrenfeld, Stephan; Butz, Martin V

2013-02-01

Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.

Population pharmacokinetics and exposure-uric acid analyses after single and multiple doses of ABT-639, a calcium channel blocker, in healthy volunteers.

PubMed

An, Guohua; Liu, Wei; Duan, W Rachel; Nothaft, Wolfram; Awni, Walid; Dutta, Sandeep

2015-03-01

ABT-639 is a selective T-type calcium channel blocker with efficacy in a wide range of preclinical models of nociceptive and neuropathic pain. In the current first-in-human (FIH) study, the pharmacokinetics, tolerability, and safety of ABT-639 after single- (up to 170 mg) and multiple doses (up to 160 mg BID) were evaluated in healthy volunteers in a randomized, double-blinded, placebo-controlled manner. ABT-639 demonstrated acceptable safety and pharmacokinetic profiles in human. Results from assessment of the routine laboratory variables showed an unexpected statistically significant and clinically relevant decrease in blood uric acid with the increase in ABT-639 dose, which is possibly due to inhibition in URAT1 transporter. Pharmacokinetic/pharmacodynamic models were constructed to characterize the relationship between ABT-639 exposure and uric acid response. The final model was a mechanism-based indirect response pharmacodynamic model with the stimulation of uric acid elimination by ABT-639. The model estimated K in values in males and females were 10.2 and 7.13 μmol/h, respectively. The model estimated K out was 0.033 1/h. ABT-639 concentration that can produce 50% stimulation in uric acid elimination was estimated to be 8,070 ng/mL. Based on the final model, further simulations were conducted to predict the effect of ABT-639 on uric acid in gout patients. The simulation results indicated that, if the urate-lowering response to ABT-639 in gout patients is similar to that in healthy subjects, ABT-639 BID doses of 140 mg or higher would be expected to provide clinically meaningful lowering of blood uric acid levels below the 380 μmol/L solubility limit of monosodium urate.

State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter

NASA Astrophysics Data System (ADS)

Zheng, Yuejiu; Gao, Wenkai; Ouyang, Minggao; Lu, Languang; Zhou, Long; Han, Xuebing

2018-04-01

State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence.

Multiple indicators, multiple causes measurement error models

DOE PAGES

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.; ...

2014-06-25

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Multiple indicators, multiple causes measurement error models

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

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Final Report for Dynamic Models for Causal Analysis of Panel Data. Models for Change in Quantitative Variables, Part III: Estimation from Panel Data. Part II, Chapter 5.

ERIC Educational Resources Information Center

Hannan, Michael T.

This document is part of a series of chapters described in SO 011 759. Addressing the problems of studying change and the change process, the report argues that sociologists should study coupled changes in qualitative and quantitative outcomes (e.g., marital status and earnings). The author presents a model for sociological studies of change in…

Quality control of the RMS US flood model

NASA Astrophysics Data System (ADS)

Jankowfsky, Sonja; Hilberts, Arno; Mortgat, Chris; Li, Shuangcai; Rafique, Farhat; Rajesh, Edida; Xu, Na; Mei, Yi; Tillmanns, Stephan; Yang, Yang; Tian, Ye; Mathur, Prince; Kulkarni, Anand; Kumaresh, Bharadwaj Anna; Chaudhuri, Chiranjib; Saini, Vishal

2016-04-01

The RMS US flood model predicts the flood risk in the US with a 30 m resolution for different return periods. The model is designed for the insurance industry to estimate the cost of flood risk for a given location. Different statistical, hydrological and hydraulic models are combined to develop the flood maps for different return periods. A rainfall-runoff and routing model, calibrated with observed discharge data, is run with 10 000 years of stochastic simulated precipitation to create time series of discharge and surface runoff. The 100, 250 and 500 year events are extracted from these time series as forcing for a two-dimensional pluvial and fluvial inundation model. The coupling of all the different models which are run on the large area of the US implies a certain amount of uncertainty. Therefore, special attention is paid to the final quality control of the flood maps. First of all, a thorough quality analysis of the Digital Terrain model and the river network was done, as the final quality of the flood maps depends heavily on the DTM quality. Secondly, the simulated 100 year discharge in the major river network (600 000 km) is compared to the 100 year discharge derived using extreme value distribution of all USGS gauges with more than 20 years of peak values (around 11 000 gauges). Thirdly, for each gauge the modelled flood depth is compared to the depth derived from the USGS rating curves. Fourthly, the modelled flood depth is compared to the base flood elevation given in the FEMA flood maps. Fifthly, the flood extent is compared to the FEMA flood extent. Then, for historic events we compare flood extents and flood depths at given locations. Finally, all the data and spatial layers are uploaded on geoserver to facilitate the manual investigation of outliers. The feedback from the quality control is used to improve the model and estimate its uncertainty.

Top-of-Atmosphere Albedo Estimation from POLDER Multi-Angle Measurements: Evaluation of Water and Ice Cloud Radiative Transfer Models

NASA Technical Reports Server (NTRS)

Loeb, Norman G.

2005-01-01

This final grant report lists accomplishments from the reporting period, and includes citations and abstracts for published or submitted papers produced under the grant in which a co-investigator is the lead author.

ORTHONORMAL RESIDUALS IN GEOSTATISTICS: MODEL CRITICISM AND PARAMETER ESTIMATION. (R825689C037)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

A NEW VARIANCE ESTIMATOR FOR PARAMETERS OF SEMI-PARAMETRIC GENERALIZED ADDITIVE MODELS. (R829213)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Quantification for complex assessment: uncertainty estimation in final year project thesis assessment

NASA Astrophysics Data System (ADS)

Kim, Ho Sung

2013-12-01

A quantitative method for estimating an expected uncertainty (reliability and validity) in assessment results arising from the relativity between four variables, viz examiner's expertise, examinee's expertise achieved, assessment task difficulty and examinee's performance, was developed for the complex assessment applicable to final year project thesis assessment including peer assessment. A guide map can be generated by the method for finding expected uncertainties prior to the assessment implementation with a given set of variables. It employs a scale for visualisation of expertise levels, derivation of which is based on quantified clarities of mental images for levels of the examiner's expertise and the examinee's expertise achieved. To identify the relevant expertise areas that depend on the complexity in assessment format, a graphical continuum model was developed. The continuum model consists of assessment task, assessment standards and criterion for the transition towards the complex assessment owing to the relativity between implicitness and explicitness and is capable of identifying areas of expertise required for scale development.

Investigations of potential bias in the estimation of lambda using Pradel's (1996) model for capture-recapture data

USGS Publications Warehouse

Hines, J.E.; Nichols, J.D.

2002-01-01

Pradel's (1996) temporal symmetry model permitting direct estimation and modelling of population growth rate, lambda sub i provides a potentially useful tool for the study of population dynamics using marked animals. Because of its recent publication date, the approach has not seen much use, and there have been virtually no investigations directed at robustness of the resulting estimators. Here we consider several potential sources of bias, all motivated by specific uses of this estimation approach. We consider sampling situations in which the study area expands with time and present an analytic expression for the bias in lambda hat sub i. We next consider trap response in capture probabilities and heterogeneous capture probabilities and compute large-sample and simulation-based approximations of resulting bias in lambda hat sub i. These approximations indicate that trap response is an especially important assumption violation that can produce substantial bias. Finally, we consider losses on capture and emphasize the importance of selecting the estimator for lambda sub i that is appropriate to the question being addressed. For studies based on only sighting and resighting data, Pradel's (1996) lambda hat prime sub i is the appropriate estimator.

Modeling the Arrest of Tissue Growth in Epithelia

NASA Astrophysics Data System (ADS)

Golden, Alexander; Lubensky, David

The mechanisms of control and eventual arrest of growth of tissues is an area that has received considerable attention, both experimentally and in the development of quantitative models. In particular, the Drosophila wing disc epithelium appears to robustly arrive at a unique final size. One mechanism that has the potential to play a role in the eventual cessation of growth is mechanical feedback from stresses induced by nonuniform growth. There is experimental support for an effect on the tissue growth rate by such mechanical stresses, and a number of numerical or cell-based models have been proposed that show that the arrest of growth can be achieved by mechanical feedback. We introduce an analytic framework that allows us to understand different coarse-grained feedback mechanisms on the same terms. We use the framework to distinguish between families of models that do not have a unique final size and those that do and give rough estimates for how much variability in the eventual organ size can be expected in models that do not have a unique final size. NSF Grant DMR-1056456.

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong

2014-07-01

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Estimating the waiting time of multi-priority emergency patients with downstream blocking.

PubMed

Lin, Di; Patrick, Jonathan; Labeau, Fabrice

2014-03-01

To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital.

Parameter estimation using meta-heuristics in systems biology: a comprehensive review.

PubMed

Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie

2012-01-01

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination

NASA Astrophysics Data System (ADS)

Rosas, Pedro; Wagemans, Johan; Ernst, Marc O.; Wichmann, Felix A.

2005-05-01

A number of models of depth-cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum-variance unbiased estimator that can be constructed from the available information. Here we test such models by using visual and haptic depth information. Different texture types produce differences in slant-discrimination performance, thus providing a means for testing a reliability-sensitive cue-combination model with texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability but fell short of statistically optimal combination - we find reliability-based reweighting but not statistically optimal cue combination.

Analysis of the influence of advanced materials for aerospace products R&D and manufacturing cost

NASA Astrophysics Data System (ADS)

Shen, A. W.; Guo, J. L.; Wang, Z. J.

2015-12-01

In this paper, we pointed out the deficiency of traditional cost estimation model about aerospace products Research & Development (R&D) and manufacturing based on analyzing the widely use of advanced materials in aviation products. Then we put up with the estimating formulas of cost factor, which representing the influences of advanced materials on the labor cost rate and manufacturing materials cost rate. The values ranges of the common advanced materials such as composite materials, titanium alloy are present in the labor and materials two aspects. Finally, we estimate the R&D and manufacturing cost of F/A-18, F/A- 22, B-1B and B-2 aircraft based on the common DAPCA IV model and the modified model proposed by this paper. The calculation results show that the calculation precision improved greatly by the proposed method which considering advanced materials. So we can know the proposed method is scientific and reasonable.

Modelling supply and demand of bioenergy from short rotation coppice and Miscanthus in the UK.

PubMed

Bauen, A W; Dunnett, A J; Richter, G M; Dailey, A G; Aylott, M; Casella, E; Taylor, G

2010-11-01

Biomass from lignocellulosic energy crops can contribute to primary energy supply in the short term in heat and electricity applications and in the longer term in transport fuel applications. This paper estimates the optimal feedstock allocation of herbaceous and woody lignocellulosic energy crops for England and Wales based on empirical productivity models. Yield maps for Miscanthus, willow and poplar, constrained by climatic, soil and land use factors, are used to estimate the potential resource. An energy crop supply-cost curve is estimated based on the resource distribution and associated production costs. The spatial resource model is then used to inform the supply of biomass to geographically distributed demand centres, with co-firing plants used as an illustration. Finally, the potential contribution of energy crops to UK primary energy and renewable energy targets is discussed. Copyright 2010 Elsevier Ltd. All rights reserved.

Real-time and rapid GNSS solutions from the M8.2 September 2017 Tehuantepec Earthquake and implications for Earthquake and Tsunami Early Warning Systems

NASA Astrophysics Data System (ADS)

Mencin, D.; Hodgkinson, K. M.; Mattioli, G. S.

2017-12-01

In support of hazard research and Earthquake Early Warning (EEW) Systems UNAVCO operates approximately 800 RT-GNSS stations throughout western North America and Alaska (EarthScope Plate Boundary Observatory), Mexico (TLALOCNet), and the pan-Caribbean region (COCONet). Our system produces and distributes raw data (BINEX and RTCM3) and real-time Precise Point Positions via the Trimble PIVOT Platform (RTX). The 2017-09-08 earthquake M8.2 located 98 km SSW of Tres Picos, Mexico is the first great earthquake to occur within the UNAVCO RT-GNSS footprint, which allows for a rigorous analysis of our dynamic and static processing methods. The need for rapid geodetic solutions ranges from seconds (EEW systems) to several minutes (Tsunami Warning and NEIC moment tensor and finite fault models). Here, we compare and quantify the relative processing strategies for producing static offsets, moment tensors and geodetically determined finite fault models using data recorded during this event. We also compare the geodetic solutions with the USGS NEIC seismically derived moment tensors and finite fault models, including displacement waveforms generated from these models. We define kinematic post-processed solutions from GIPSY-OASISII (v6.4) with final orbits and clocks as a "best" case reference to evaluate the performance of our different processing strategies. We find that static displacements of a few centimeters or less are difficult to resolve in the real-time GNSS position estimates. The standard daily 24-hour solutions provide the highest-quality data-set to determine coseismic offsets, but these solutions are delayed by at least 48 hours after the event. Dynamic displacements, estimated in real-time, however, show reasonable agreement with final, post-processed position estimates, and while individual position estimates have large errors, the real-time solutions offer an excellent operational option for EEW systems, including the use of estimated peak-ground displacements or directly inverting for finite-fault solutions. In the near-field, we find that the geodetically-derived moment tensors and finite fault models differ significantly with seismically-derived models, highlighting the utility of using geodetic data in hazard applications.

Implementation of a mezzo-level HOV carpool model for Texas. Final report, September 1986-April 1990

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

Benson, J.D.; Mullins, J.A.; Stokes, R.W.

1989-11-01

The report presents the results of an evaluation and adaptation of three existing high-occupancy vehicle (HOV) lane carpool demand estimation models for possible use in Houston and other large Texas cities. These models use trip tables, networks and zone structures that are consistent with the regional travel demand modeling process currently in use in Texas. By implementing the HOV carpool models in a structure that is consistent with the regional travel demand modeling process, it is possible to estimate the carpool demand for an HOV facility and to evaluate the effects of the following changes in HOV lane configuration andmore » operating strategies: (1) Effects of additional and/or alternative access points; (2) Effects of extending and HOV lane; and (3) Effects of changing the definition of eligible HOV carpools. The models have produced promising results in test applications in Houston.« less

Spectral estimates of solar radiation intercepted by corn canopies

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Daughtry, C. S. T.; Gallo, K. P.

1982-01-01

Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.

Estimating recharge rates with analytic element models and parameter estimation

USGS Publications Warehouse

Dripps, W.R.; Hunt, R.J.; Anderson, M.P.

2006-01-01

Quantifying the spatial and temporal distribution of recharge is usually a prerequisite for effective ground water flow modeling. In this study, an analytic element (AE) code (GFLOW) was used with a nonlinear parameter estimation code (UCODE) to quantify the spatial and temporal distribution of recharge using measured base flows as calibration targets. The ease and flexibility of AE model construction and evaluation make this approach well suited for recharge estimation. An AE flow model of an undeveloped watershed in northern Wisconsin was optimized to match median annual base flows at four stream gages for 1996 to 2000 to demonstrate the approach. Initial optimizations that assumed a constant distributed recharge rate provided good matches (within 5%) to most of the annual base flow estimates, but discrepancies of >12% at certain gages suggested that a single value of recharge for the entire watershed is inappropriate. Subsequent optimizations that allowed for spatially distributed recharge zones based on the distribution of vegetation types improved the fit and confirmed that vegetation can influence spatial recharge variability in this watershed. Temporally, the annual recharge values varied >2.5-fold between 1996 and 2000 during which there was an observed 1.7-fold difference in annual precipitation, underscoring the influence of nonclimatic factors on interannual recharge variability for regional flow modeling. The final recharge values compared favorably with more labor-intensive field measurements of recharge and results from studies, supporting the utility of using linked AE-parameter estimation codes for recharge estimation. Copyright ?? 2005 The Author(s).

Human Pose Estimation from Monocular Images: A Comprehensive Survey

PubMed Central

Gong, Wenjuan; Zhang, Xuena; Gonzàlez, Jordi; Sobral, Andrews; Bouwmans, Thierry; Tu, Changhe; Zahzah, El-hadi

2016-01-01

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. PMID:27898003

Estimates of genetic parameters and environmental effects for measures of hunting performance in Finnish hounds.

PubMed

Liinamo, A E; Karjalainen, L; Ojala, M; Vilva, V

1997-03-01

Data from field trials of Finnish Hounds between 1988 and 1992 in Finland were used to estimate genetic parameters and environmental effects for measures of hunting performance using REML procedures and an animal model. The original data set included 28,791 field trial records from 5,666 dogs. Males and females had equal hunting performance, whereas experience acquired by age improved trial results compared with results for young dogs (P < .001). Results were mostly better on snow than on bare ground (P < .001), and testing areas, years, months, and their interactions affected results (P < .001). Estimates of heritabilities and repeatabilities were low for most of the 28 measures, mainly due to large residual variances. The highest heritabilities were for frequency of tonguing (h2 = .15), pursuit score (h2 = .13), tongue score (h2 = .13), ghost trailing score (h2 = .12), and merit and final score (both h2 = .11). Estimates of phenotypic and genetic correlations were positive and moderate or high for search scores, pursuit scores, and final scores but lower for other studied measures. The results suggest that, due to low heritabilities, evaluation of breeding values for Finnish Hounds with respect to their hunting ability should be based on animal model BLUP methods instead of mere performance testing. The evaluation system of field trials should also be revised for more reliability.

Evaluating the impacts of different measurement and model configurations on top-down estimates of UK methane emissions

NASA Astrophysics Data System (ADS)

Lunt, Mark; Rigby, Matt; Manning, Alistair; O'Doherty, Simon; Stavert, Ann; Stanley, Kieran; Young, Dickon; Pitt, Joseph; Bauguitte, Stephane; Allen, Grant; Helfter, Carole; Palmer, Paul

2017-04-01

The Greenhouse gAs Uk and Global Emissions (GAUGE) project aims to quantify the magnitude and uncertainty of key UK greenhouse gas emissions more robustly than previously achieved. Measurements of methane have been taken from a number of tall-tower and surface sites as well as mobile measurement platforms such as a research aircraft and a ferry providing regular transects off the east coast of the UK. Using the UK Met Office's atmospheric transport model, NAME, and a novel Bayesian inversion technique we present estimates of methane emissions from the UK from a number of different combinations of sites to show the robustness of the UK total emissions to network configuration. The impact on uncertainties will be discussed, focusing on the usefulness of the various measurement platforms for constraining UK emissions. We will examine the effects of observation selection and how a priori assumptions about model uncertainty can affect the emission estimates, even within a data-driven hierarchical inversion framework. Finally, we will show the impact of the resolution of the meteorology used to drive the NAME model on emissions estimates, and how to rationalise our understanding of the ability of transport models to represent reality.

Estimate carbon emissions from degraded permafrost with InSAR and a soil thermal model

NASA Astrophysics Data System (ADS)

Zhou, Z.; Liu, L.

2016-12-01

Climate warming, tundra fire over past decades has caused degradation in permafrost widely and quickly. Recent studies indicate that an increase in degradation could switch permafrost from a carbon sink to a source, with the potential of creating a positive feedback to anthropogenic climate warming. Unfortunately, Soil Organic Carbon (SOC) emissions from degraded permafrost unquantified, and limit our ability to understand SOC losses in arctic environments. This work will investigate recent 10 years of data already collected at the Anaktuvuk River fire (both ground and remote sensed), and will employ a soil thermal model to estimate SOC emission in this region. The model converts the increases in Active Layer Thickness (ALT), as measured by InSAR, to changes in Organic Layer Thickness (OLT), and SOC. ALOS-1/2 L-band SAR dataset will be used to produce the ATL changes over the study area. Soil prosperities (e.g. temperature at different depth, bulk density) will be used in the soil thermal model to estimate OLT changes and SOC losses. Ground measurement will validate the InSAR results and the soil thermal model. A final estimation of SOC emission will be produced in Anaktuvuk River region.

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference

PubMed Central

Karcher, Michael D.; Palacios, Julia A.; Bedford, Trevor; Suchard, Marc A.; Minin, Vladimir N.

2016-01-01

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals’ genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples. PMID:26938243

Consistency of different tropospheric models and mapping functions for precise GNSS processing

NASA Astrophysics Data System (ADS)

Graffigna, Victoria; Hernández-Pajares, Manuel; García-Rigo, Alberto; Gende, Mauricio

2017-04-01

The TOmographic Model of the IONospheric electron content (TOMION) software implements a simultaneous precise geodetic and ionospheric modeling, which can be used to test new approaches for real-time precise GNSS modeling (positioning, ionospheric and tropospheric delays, clock errors, among others). In this work, the software is used to estimate the Zenith Tropospheric Delay (ZTD) emulating real time and its performance is evaluated through a comparative analysis with a built-in GIPSY estimation and IGS final troposphere product, exemplified in a two-day experiment performed in East Australia. Furthermore, the troposphere mapping function was upgraded from Niell to Vienna approach. On a first scenario, only forward processing was activated and the coordinates of the Wide Area GNSS network were loosely constrained, without fixing the carrier phase ambiguities, for both reference and rover receivers. On a second one, precise point positioning (PPP) was implemented, iterating for a fixed coordinates set for the second day. Comparisons between TOMION, IGS and GIPSY estimates have been performed and for the first one, IGS clocks and orbits were considered. The agreement with GIPSY results seems to be 10 times better than with the IGS final ZTD product, despite having considered IGS products for the computations. Hence, the subsequent analysis was carried out with respect to the GIPSY computations. The estimates show a typical bias of 2cm for the first strategy and of 7mm for PPP, in the worst cases. Moreover, Vienna mapping function showed in general a fairly better agreement than Niell one for both strategies. The RMS values' were found to be around 1cm for all studied situations, with a slightly fitter performance for the Niell one. Further improvement could be achieved for such estimations with coefficients for the Vienna mapping function calculated from raytracing as well as integrating meteorological comparative parameters.

Robust estimation of cerebral hemodynamics in neonates using multilayered diffusion model for normal and oblique incidences

NASA Astrophysics Data System (ADS)

Steinberg, Idan; Harbater, Osnat; Gannot, Israel

2014-07-01

The diffusion approximation is useful for many optical diagnostics modalities, such as near-infrared spectroscopy. However, the simple normal incidence, semi-infinite layer model may prove lacking in estimation of deep-tissue optical properties such as required for monitoring cerebral hemodynamics, especially in neonates. To answer this need, we present an analytical multilayered, oblique incidence diffusion model. Initially, the model equations are derived in vector-matrix form to facilitate fast and simple computation. Then, the spatiotemporal reflectance predicted by the model for a complex neonate head is compared with time-resolved Monte Carlo (TRMC) simulations under a wide range of physiologically feasible parameters. The high accuracy of the multilayer model is demonstrated in that the deviation from TRMC simulations is only a few percent even under the toughest conditions. We then turn to solve the inverse problem and estimate the oxygen saturation of deep brain tissues based on the temporal and spatial behaviors of the reflectance. Results indicate that temporal features of the reflectance are more sensitive to deep-layer optical parameters. The accuracy of estimation is shown to be more accurate and robust than the commonly used single-layer diffusion model. Finally, the limitations of such approaches are discussed thoroughly.

A System for Video Surveillance and Monitoring CMU VSAM Final Report

DTIC Science & Technology

1999-11-30

motion-based skeletonization, neural network , spatio-temporal salience Patterns inside image chips, spurious motion rejection, model -based... network of sensors with respect to the model coordinate system, computation of 3D geolocation estimates, and graphical display of object hypotheses...rithms have been developed. The first uses view dependent visual properties to train a neural network classifier to recognize four classes: single

First photometric study of two southern eclipsing binaries IS Tel and DW Aps

NASA Astrophysics Data System (ADS)

Özer, S.; Sürgit, D.; Erdem, A.; Öztürk, O.

2017-02-01

The paper presents the first photometric analysis of two southern eclipsing binary stars, IS Tel and DW Aps. Their V light curves from the All Sky Automated Survey were modelled by using Wilson-Devinney method. The final models give these two Algol-like binary stars as having detached configurations. Absolute parameters of the components of the systems were also estimated.

ISC-GEM: Global Instrumental Earthquake Catalogue (1900-2009), III. Re-computed MS and mb, proxy MW, final magnitude composition and completeness assessment

NASA Astrophysics Data System (ADS)

Di Giacomo, Domenico; Bondár, István; Storchak, Dmitry A.; Engdahl, E. Robert; Bormann, Peter; Harris, James

2015-02-01

This paper outlines the re-computation and compilation of the magnitudes now contained in the final ISC-GEM Reference Global Instrumental Earthquake Catalogue (1900-2009). The catalogue is available via the ISC website (http://www.isc.ac.uk/iscgem/). The available re-computed MS and mb provided an ideal basis for deriving new conversion relationships to moment magnitude MW. Therefore, rather than using previously published regression models, we derived new empirical relationships using both generalized orthogonal linear and exponential non-linear models to obtain MW proxies from MS and mb. The new models were tested against true values of MW, and the newly derived exponential models were then preferred to the linear ones in computing MW proxies. For the final magnitude composition of the ISC-GEM catalogue, we preferred directly measured MW values as published by the Global CMT project for the period 1976-2009 (plus intermediate-depth earthquakes between 1962 and 1975). In addition, over 1000 publications have been examined to obtain direct seismic moment M0 and, therefore, also MW estimates for 967 large earthquakes during 1900-1978 (Lee and Engdahl, 2015) by various alternative methods to the current GCMT procedure. In all other instances we computed MW proxy values by converting our re-computed MS and mb values into MW, using the newly derived non-linear regression models. The final magnitude composition is an improvement in terms of magnitude homogeneity compared to previous catalogues. The magnitude completeness is not homogeneous over the 110 years covered by the ISC-GEM catalogue. Therefore, seismicity rate estimates may be strongly affected without a careful time window selection. In particular, the ISC-GEM catalogue appears to be complete down to MW 5.6 starting from 1964, whereas for the early instrumental period the completeness varies from ∼7.5 to 6.2. Further time and resources would be necessary to homogenize the magnitude of completeness over the entire catalogue length.

Loss Estimation Modeling Of Scenario Lahars From Mount Rainier, Washington State, Using HAZUS-MH

NASA Astrophysics Data System (ADS)

Walsh, T. J.; Cakir, R.

2011-12-01

We have adapted lahar hazard zones developed by Hoblitt and others (1998) and converted to digital data by Schilling and others (2008) into the appropriate format for HAZUS-MH, which is FEMA's loss estimation model. We assume that structures engulfed by cohesive lahars will suffer complete loss, and structures affected by post-lahar flooding will be appropriately modeled by the HAZUS-MH flood model. Another approach investigated is to estimate the momentum of lahars, calculate a lateral force, and apply the earthquake model, substituting the lahar lateral force for PGA. Our initial model used the HAZUS default data, which include estimates of building type and value from census data. This model estimated a loss of about 12 billion for a repeat lahar similar to the Electron Mudflow down the Puyallup River. Because HAZUS data are based on census tracts, this estimated damage includes everything in the census tract, even buildings outside of the lahar hazard zone. To correct this, we acquired assessors data from all of the affected counties and converted them into HAZUS format. We then clipped it to the boundaries of the lahar hazard zone to more precisely delineate those properties actually at risk in each scenario. This refined our initial loss estimate to about 6 billion with exclusion of building content values. We are also investigating rebuilding the lahar hazard zones applying Lahar-Z to a more accurate topographic grid derived from recent Lidar data acquired from the Puget Sound Lidar Consortium and Mount Rainier National Park. Final results of these models for the major drainages of Mount Rainier will be posted to the Washington Interactive Geologic Map (http://www.dnr.wa.gov/ResearchScience/Topics/GeosciencesData/Pages/geology_portal.aspx).

Regression estimators for generic health-related quality of life and quality-adjusted life years.

PubMed

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding

PubMed Central

Tosteson, Tor D.; Morden, Nancy E.; Stukel, Therese A.; O'Malley, A. James

2014-01-01

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival. PMID:25506259

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.

PubMed

MacKenzie, Todd A; Tosteson, Tor D; Morden, Nancy E; Stukel, Therese A; O'Malley, A James

2014-06-01

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival.

Development and Implementation of an Empirical Ionosphere Variability Model

NASA Technical Reports Server (NTRS)

Minow, Joesph I.; Almond, Deborah (Technical Monitor)

2002-01-01

Spacecraft designers and operations support personnel involved in space environment analysis for low Earth orbit missions require ionospheric specification and forecast models that provide not only average ionospheric plasma parameters for a given set of geophysical conditions but the statistical variations about the mean as well. This presentation describes the development of a prototype empirical model intended for use with the International Reference Ionosphere (IRI) to provide ionospheric Ne and Te variability. We first describe the database of on-orbit observations from a variety of spacecraft and ground based radars over a wide range of latitudes and altitudes used to obtain estimates of the environment variability. Next, comparison of the observations with the IRI model provide estimates of the deviations from the average model as well as the range of possible values that may correspond to a given IRI output. Options for implementation of the statistical variations in software that can be run with the IRI model are described. Finally, we provide example applications including thrust estimates for tethered satellites and specification of sunrise Ne, Te conditions required to support spacecraft charging issues for satellites with high voltage solar arrays.

A Spanish model for quantification and management of construction waste.

PubMed

Solís-Guzmán, Jaime; Marrero, Madelyn; Montes-Delgado, Maria Victoria; Ramírez-de-Arellano, Antonio

2009-09-01

Currently, construction and demolition waste (C&D waste) is a worldwide issue that concerns not only governments but also the building actors involved in construction activity. In Spain, a new national decree has been regulating the production and management of C&D waste since February 2008. The present work describes the waste management model that has inspired this decree: the Alcores model implemented with good results in Los Alcores Community (Seville, Spain). A detailed model is also provided to estimate the volume of waste that is expected to be generated on the building site. The quantification of C&D waste volume, from the project stage, is essential for the building actors to properly plan and control its disposal. This quantification model has been developed by studying 100 dwelling projects, especially their bill of quantities, and defining three coefficients to estimate the demolished volume (CT), the wreckage volume (CR) and the packaging volume (CE). Finally, two case studies are included to illustrate the usefulness of the model to estimate C&D waste volume in both new construction and demolition projects.

Addressing Angular Single-Event Effects in the Estimation of On-Orbit Error Rates

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

Lee, David S.; Swift, Gary M.; Wirthlin, Michael J.

2015-12-01

Our study describes complications introduced by angular direct ionization events on space error rate predictions. In particular, prevalence of multiple-cell upsets and a breakdown in the application of effective linear energy transfer in modern-scale devices can skew error rates approximated from currently available estimation models. Moreover, this paper highlights the importance of angular testing and proposes a methodology to extend existing error estimation tools to properly consider angular strikes in modern-scale devices. Finally, these techniques are illustrated with test data provided from a modern 28 nm SRAM-based device.

Analyzing developmental processes on an individual level using nonstationary time series modeling.

PubMed

Molenaar, Peter C M; Sinclair, Katerina O; Rovine, Michael J; Ram, Nilam; Corneal, Sherry E

2009-01-01

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data.

A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA.

PubMed

Fong, Duncan K H; Kim, Sunghoon; Chen, Zhe; DeSarbo, Wayne S

2016-03-01

A new Bayesian multinomial probit model is proposed for the analysis of panel choice data. Using a parameter expansion technique, we are able to devise a Markov Chain Monte Carlo algorithm to compute our Bayesian estimates efficiently. We also show that the proposed procedure enables the estimation of individual level coefficients for the single-period multinomial probit model even when the available prior information is vague. We apply our new procedure to consumer purchase data and reanalyze a well-known scanner panel dataset that reveals new substantive insights. In addition, we delineate a number of advantageous features of our proposed procedure over several benchmark models. Finally, through a simulation analysis employing a fractional factorial design, we demonstrate that the results from our proposed model are quite robust with respect to differing factors across various conditions.

Estimation of available global solar radiation using sunshine duration over South Korea

NASA Astrophysics Data System (ADS)

Das, Amrita; Park, Jin-ki; Park, Jong-hwa

2015-11-01

Besides designing a solar energy system, accurate insolation data is also a key component for many biological and atmospheric studies. But solar radiation stations are not widely available due to financial and technical limitations; this insufficient number affects the spatial resolution whenever an attempt is made to construct a solar radiation map. There are several models in literature for estimating incoming solar radiation using sunshine fraction. Seventeen of such models among which 6 are linear and 11 non-linear, have been chosen for studying and estimating solar radiation on a horizontal surface over South Korea. The better performance of a non-linear model signifies the fact that the relationship between sunshine duration and clearness index does not follow a straight line. With such a model solar radiation over 79 stations measuring sunshine duration is computed and used as input for spatial interpolation. Finally monthly solar radiation maps are constructed using the Ordinary Kriging method. The cross validation results show good agreement between observed and predicted data.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

PubMed

Wang, Yan-Cang; Yang, Gui-Jun; Zhu, Jin-Shan; Gu, Xiao-He; Xu, Peng; Liao, Qin-Hong

2014-07-01

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data.

PubMed

Onojima, Takayuki; Goto, Takahiro; Mizuhara, Hiroaki; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results.

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

PubMed

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile.

PubMed

Corvalán, Roberto M; Osses, Mauricio; Urrutia, Cristian M

2002-02-01

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Estimation of the Characterized Tsunami Source Model considering the Complicated Shape of Tsunami Source by Using the observed waveforms of GPS Buoys in the Nankai Trough

NASA Astrophysics Data System (ADS)

Seto, S.; Takahashi, T.

2017-12-01

In the 2011 Tohoku earthquake tsunami disaster, the delay of understanding damage situation increased the human damage. To solve this problem, it is important to search the severe damaged areas. The tsunami numerical modeling is useful to estimate damages and the accuracy of simulation depends on the tsunami source. Seto and Takahashi (2017) proposed a method to estimate the characterized tsunami source model by using the limited observed data of GPS buoys. The model consists of Large slip zone (LSZ), Super large slip zone (SLSZ) and background rupture zone (BZ) as the Cabinet Office, Government of Japan (below COGJ) reported after the Tohoku tsunami. At the beginning of this method, the rectangular fault model is assumed based on the seismic magnitude and hypocenter reported right after an earthquake. By using the fault model, tsunami propagation is simulated numerically, and the fault model is improved after comparing the computed data with the observed data repeatedly. In the comparison, correlation coefficient and regression coefficient are used as indexes. They are calculated with the observed and the computed tsunami wave profiles. This repetition is conducted to get the two coefficients close to 1.0, which makes the precise of the fault model higher. However, it was indicated as the improvement that the model did not examine a complicated shape of tsunami source. In this study, we proposed an improved model to examine the complicated shape. COGJ(2012) assumed that possible tsunami source region in the Nankai trough consisted of the several thousands small faults. And, we use these small faults to estimate the targeted tsunami source in this model. Therefore, we can estimate the complicated tsunami source by using these small faults. The estimation of BZ is carried out as a first step, and LSZ and SLSZ are estimated next as same as the previous model. The proposed model by using GPS buoy was applied for a tsunami scenario in the Nankai Trough. As a result, the final estimated location of LSZ and SLSZ in BZ are estimated well.

Population pharmacokinetic-pharmacodynamic modelling of mycophenolic acid in paediatric renal transplant recipients in the early post-transplant period.

PubMed

Dong, Min; Fukuda, Tsuyoshi; Cox, Shareen; de Vries, Marij T; Hooper, David K; Goebel, Jens; Vinks, Alexander A

2014-11-01

The purpose of this study was to develop a population pharmacokinetic and pharmacodynamic (PK-PD) model for mycophenolic acid (MPA) in paediatric renal transplant recipients in the early post-transplant period. A total of 214 MPA plasma concentrations-time data points from 24 patients were available for PK model development. In 17 out of a total of 24 patients, inosine monophosphate dehydrogenase (IMPDH) enzyme activity measurements (n = 97) in peripheral blood mononuclear cells were available for PK-PD modelling. The PK-PD model was developed using non-linear mixed effects modelling sequentially by 1) developing a population PK model and 2) incorporating IMPDH activity into a PK-PD model using post hoc Bayesian PK parameter estimates. Covariate analysis included patient demographics, co-medication and clinical laboratory data. Non-parametric bootstrapping and prediction-corrected visual predictive checks were performed to evaluate the final models. A two compartment model with a transit compartment absorption best described MPA PK. A non-linear relationship between dose and MPA exposure was observed and was described by a power function in the model. The final population PK parameter estimates (and their 95% confidence intervals) were CL/F, 22 (14.8, 25.2) l h(-1) 70 kg(-1) ; Vc /F, 45.4 (29.6, 55.6) l; Vp /F, 411 (152.6, 1472.6)l; Q/F, 22.4 (16.0, 32.5) l h(-1) ; Ka , 2.5 (1.45, 4.93) h(-1) . Covariate analysis in the PK study identified body weight to be significantly correlated with CL/F. A simplified inhibitory Emax model adequately described the relationship between MPA concentration and IMPDH activity. The final population PK-PD parameter estimates (and their 95% confidence intervals) were: E0 , 3.45 (2.61, 4.56) nmol h(-1)  mg(-1) protein and EC50 , 1.73 (1.16, 3.01) mg l(-1) . Emax was fixed to 0. There were two African-American patients in our study cohorts and both had low IMPDH baseline activities (E0 ) compared with Caucasian patients (mean value 2.13 mg l(-1) vs. 3.86 mg l(-1) ). An integrated population PK-PD model of MPA has been developed in paediatric renal transplant recipients. The current model provides information that will facilitate future studies and may be implemented in a Bayesian algorithm to allow a PK-PD guided therapeutic drug monitoring strategy. © 2014 The British Pharmacological Society.

Muscle parameters estimation based on biplanar radiography.

PubMed

Dubois, G; Rouch, P; Bonneau, D; Gennisson, J L; Skalli, W

2016-11-01

The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography.

Non-Cooperative Target Imaging and Parameter Estimation with Narrowband Radar Echoes.

PubMed

Yeh, Chun-mao; Zhou, Wei; Lu, Yao-bing; Yang, Jian

2016-01-20

This study focuses on the rotating target imaging and parameter estimation with narrowband radar echoes, which is essential for radar target recognition. First, a two-dimensional (2D) imaging model with narrowband echoes is established in this paper, and two images of the target are formed on the velocity-acceleration plane at two neighboring coherent processing intervals (CPIs). Then, the rotating velocity (RV) is proposed to be estimated by utilizing the relationship between the positions of the scattering centers among two images. Finally, the target image is rescaled to the range-cross-range plane with the estimated rotational parameter. The validity of the proposed approach is confirmed using numerical simulations.

Improving Water Balance Estimation in the Nile by Combining Remote Sensing and Hydrological Modelling: a Template for Ungauged Basins

NASA Astrophysics Data System (ADS)

Gleason, C. J.; Wada, Y.; Wang, J.

2017-12-01

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally, especially in international river basins. Remote sensing and water balance modelling are frequently cited as a potential solutions, but these techniques largely rely on the same in decline gauge data to constrain or parameterize discharge estimates, thus creating a circular approach to estimating discharge inapplicable to ungauged basins. To address this, we here combine a discontinued gauge, remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and Landsat data, and the PCR-GLOBWB hydrological model to estimate discharge for an ungauged time period for the Lower Nile (1978-present). Specifically, we first estimate initial discharges from 86 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the hydrologic model. Our tuning methodology is purposefully simple and can be easily applied to any model without the need for calibration/parameterization. The resulting tuned modelled hydrograph shows large improvement in flow magnitude over previous modelled hydrographs, and validation of tuned monthly model output flows against the historical gauge yields an RMSE of 343 m3/s (33.7%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: modelled flows have a one-to two-month wet season lag and a negative bias. More sophisticated model calibration and training (e.g. data assimilation) is needed to improve upon our results, however, our results achieved by coupling physical models and remote sensing is a promising first step and proof of concept toward future modelling of ungauged flows. This is especially true as massive cloud computing via Google Earth Engine makes our method easily applicable to any basin without current gauges. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water.

The episodic random utility model unifies time trade-off and discrete choice approaches in health state valuation

PubMed Central

Craig, Benjamin M; Busschbach, Jan JV

2009-01-01

Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation. Methods First, we introduce two alternative random utility models (RUMs) for health preferences: the episodic RUM and the more common instant RUM. For the interpretation of time trade-off (TTO) responses, we show that the episodic model implies a coefficient estimator, and the instant model implies a mean slope estimator. Secondly, we demonstrate these estimators and the differences between the estimates for 42 health states using TTO responses from the seminal Measurement and Valuation in Health (MVH) study conducted in the United Kingdom. Mean slopes are estimates with and without Dolan's transformation of worse-than-death (WTD) responses. Finally, we demonstrate an exploded probit estimator, an extension of the coefficient estimator for discrete choice data that accommodates both TTO and rank responses. Results By construction, mean slopes are less than or equal to coefficients, because slopes are fractions and, therefore, magnify downward errors in WTD responses. The Dolan transformation of WTD responses causes mean slopes to increase in similarity to coefficient estimates, yet they are not equivalent (i.e., absolute mean difference = 0.179). Unlike mean slopes, coefficient estimates demonstrate strong concordance with rank-based predictions (Lin's rho = 0.91). Combining TTO and rank responses under the exploded probit model improves the identification of health state values, decreasing the average width of confidence intervals from 0.057 to 0.041 compared to TTO only results. Conclusion The episodic RUM expands upon the theoretical framework underlying health state valuation and contributes to health econometrics by motivating the selection of coefficient and exploded probit estimators for the analysis of TTO and rank responses. In future MVH surveys, sample size requirements may be reduced through the incorporation of multiple responses under a single estimator. PMID:19144115

An object-based approach for areal rainfall estimation and validation of atmospheric models

NASA Astrophysics Data System (ADS)

Troemel, Silke; Simmer, Clemens

2010-05-01

An object-based approach for areal rainfall estimation is applied to pseudo-radar data simulated of a weatherforecast model as well as to real radar volume data. The method aims at an as fully as possible exploitation of three-dimensional radar signals produced by precipitation generating systems during their lifetime to enhance areal rainfall estimation. Therefore tracking of radar-detected precipitation-centroids is performed and rain events are investigated using so-called Integral Radar Volume Descriptors (IRVD) containing relevant information of the underlying precipitation process. Some investigated descriptors are statistical quantities from the radar reflectivities within the boundary of a tracked rain cell like the area mean reflectivity or the compactness of a cell; others evaluate the mean vertical structure during the tracking period at the near surface reflectivity-weighted center of the cell like the mean effective efficiency or the mean echo top height. The stage of evolution of a system is given by the trend in the brightband fraction or related quantities. Furthermore, two descriptors not directly derived from radar data are considered: the mean wind shear and an orographic rainfall amplifier. While in case of pseudo-radar data a model based on a small set of IRVDs alone provides rainfall estimates of high accuracy, the application of such a model to the real world remains within the accuracies achievable with a constant Z-R-relationship. However, a combined model based on single IRVDs and the Marshall-Palmer Z-R-estimator already provides considerable enhancements even though the resolution of the data base used has room for improvement. The mean echo top height, the mean effective efficiency, the empirical standard deviation and the Marshall-Palmer estimator are detected for the final rainfall estimator. High correlations between storm height and rain rates, a shift of the probability distribution to higher values with increasing effective efficiency, and the possibility to classify continental and maritime systems using the effective efficiency confirm the informative value of the qualified descriptors. The IRVDs especially correct for the underestimation in case of intense rain events, and the information content of descriptors is most likely higher than demonstrated so far. We used quite sparse information about meteorological variables needed for the calculation of some IRVDs from single radiosoundings, and several descriptors suffered from the range-dependent vertical resolution of the reflectivity profile. Inclusion of neighbouring radars and assimilation runs of weather forecasting models will further enhance the accuracy of rainfall estimates. Finally, the clear difference between the IRVD selection from the pseudo-radar data and from the real world data hint to a new object-based avenue for the validation of higher resolution atmospheric models and for evaluating their potential to digest radar observations in data assimilation schemes.

A Hybrid of Optical Remote Sensing and Hydrological Modeling Improves Water Balance Estimation

NASA Astrophysics Data System (ADS)

Gleason, Colin J.; Wada, Yoshihide; Wang, Jida

2018-01-01

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally. Remote sensing and water balance modeling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates. A different approach is therefore needed, and we here combine remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and the PCR-GLOBWB hydrological model to estimate discharge over the Lower Nile. Specifically, we first estimate initial discharges from 87 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the model, all without using gauge data. The resulting tuned modeled hydrograph shows a large improvement in flow magnitude: validation of the tuned monthly hydrograph against a historical gauge (1978-1984) yields an RMSE of 439 m3/s (40.8%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: tuned flows have a 1-2 month wet season lag and a negative base flow bias. Accounting for this 2 month lag yields a hydrograph RMSE of 270 m3/s (25.7%). Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modeling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water.

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.

PubMed

Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing

2015-01-01

Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.

Screening level risk assessment model for chemical fate and effects in the environment.

PubMed

Arnot, Jon A; Mackay, Don; Webster, Eva; Southwood, Jeanette M

2006-04-01

A screening level risk assessment model is developed and described to assess and prioritize chemicals by estimating environmental fate and transport, bioaccumulation, and exposure to humans and wildlife for a unit emission rate. The most sensitive risk endpoint is identified and a critical emission rate is then calculated as a result of that endpoint being reached. Finally, this estimated critical emission rate is compared with the estimated actual emission rate as a risk assessment factor. This "back-tracking" process avoids the use of highly uncertain emission rate data as model input. The application of the model is demonstrated in detail for three diverse chemicals and in less detail for a group of 70 chemicals drawn from the Canadian Domestic Substances List. The simple Level II and the more complex Level III fate calculations are used to "bin" substances into categories of similar probable risk. The essential role of the model is to synthesize information on chemical and environmental properties within a consistent mass balance framework to yield an overall estimate of screening level risk with respect to the defined endpoint. The approach may be useful to identify and prioritize those chemicals of commerce that are of greatest potential concern and require more comprehensive modeling and monitoring evaluations in actual regional environments and food webs.

Models for Predicting the Biomass of Cunninghamialanceolata Trees and Stands in Southeastern China

PubMed Central

Saeed, Sajjad

2017-01-01

Using existing equations to estimate the biomass of a single tree or a forest stand still involves large uncertainties. In this study, we developed individual-tree biomass models for Chinese Fir (Cunninghamia lanceolata.) stands in Fujian Province, southeast China, by using 74 previously established models that have been most commonly used to estimate tree biomass. We selected the best fit models and modified them. The results showed that the published model ln(B(Biomass)) = a + b * ln(D) + c * (ln(H))2 + d * (ln(H))3 + e * ln(WD) had the best fit for estimating the tree biomass of Chinese Fir stands. Furthermore, we observed that variables D(diameter at breast height), H (height), and WD(wood density)were significantly correlated with the total tree biomass estimation model. As a result, a natural logarithm structure gave the best estimates for the tree biomass structure. Finally, when a multi-step improvement on tree biomass model was performed, the tree biomass model with Tree volume(TV), WD and biomass wood density conversion factor (BECF),achieved the highest simulation accuracy, expressed as ln(TB) = −0.0703 + 0.9780 * ln(TV) + 0.0213 * ln(WD) + 1.0166 * ln(BECF). Therefore, when TV, WD and BECF were combined with tree biomass volume coefficient bi for Chinese Fir, the stand biomass (SB)model included both volume(SV) and coefficient bi variables of the stand as follows: bi = Exp(−0.0703+0.9780*ln(TV)+0.0213 * ln(WD)+1.0166*ln(BECF)). The stand biomass model is SB = SV/TV * bi. PMID:28095512

Models for Predicting the Biomass of Cunninghamialanceolata Trees and Stands in Southeastern China.

PubMed

Guangyi, Mei; Yujun, Sun; Saeed, Sajjad

2017-01-01

Using existing equations to estimate the biomass of a single tree or a forest stand still involves large uncertainties. In this study, we developed individual-tree biomass models for Chinese Fir (Cunninghamia lanceolata.) stands in Fujian Province, southeast China, by using 74 previously established models that have been most commonly used to estimate tree biomass. We selected the best fit models and modified them. The results showed that the published model ln(B(Biomass)) = a + b * ln(D) + c * (ln(H))2 + d * (ln(H))3 + e * ln(WD) had the best fit for estimating the tree biomass of Chinese Fir stands. Furthermore, we observed that variables D(diameter at breast height), H (height), and WD(wood density)were significantly correlated with the total tree biomass estimation model. As a result, a natural logarithm structure gave the best estimates for the tree biomass structure. Finally, when a multi-step improvement on tree biomass model was performed, the tree biomass model with Tree volume(TV), WD and biomass wood density conversion factor (BECF),achieved the highest simulation accuracy, expressed as ln(TB) = -0.0703 + 0.9780 * ln(TV) + 0.0213 * ln(WD) + 1.0166 * ln(BECF). Therefore, when TV, WD and BECF were combined with tree biomass volume coefficient bi for Chinese Fir, the stand biomass (SB)model included both volume(SV) and coefficient bi variables of the stand as follows: bi = Exp(-0.0703+0.9780*ln(TV)+0.0213 * ln(WD)+1.0166*ln(BECF)). The stand biomass model is SB = SV/TV * bi.

The use of cluster analysis techniques in spaceflight project cost risk estimation

NASA Technical Reports Server (NTRS)

Fox, G.; Ebbeler, D.; Jorgensen, E.

2003-01-01

Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.

NEW PROCEDURES TO ESTIMATE WATER TEMPERATURES AND WATER DEPTHS FOR APPLICATION IN CLIMATE-DENGUE MODELING. (R824995)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

ESTIMATION AND WELFARE CALCULATIONS IN A GENERALIZED CORNER SOLUTION MODEL WITH AN APPLICATION TO RECREATION DEMAND. (R826615)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

ESTIMATING GROUND LEVEL PM 2.5 IN THE EASTERN UNITED STATES USING SATELLITE REMOTE SENSING

EPA Science Inventory

An empirical model based on the regression between daily average final particle (PM2.5) concentrations and aerosol optical thickness (AOT) measurements from the Multi-angle Imaging SpectroRadiometer (MISR) was developed and tested using data from the eastern United States during ...

A MECHANISTIC MODEL FOR ESTIMATING VOC EMISSIONS FROM INDUSTRIAL PROCESS DRAINS. II: THE WATER SEAL. (R823335)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

CHARACTERIZATION OF TRAVELING WAVES AND ANALYTICAL ESTIMATION OF POLLUTANT REMOVAL IN ONE-DIMENSIONAL SUBSURFACE BIOREMEDIATION MODELING. (R824785)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Research on Estimates of Xi’an City Life Garbage Pay-As-You-Throw Based on Two-part Tariff method

NASA Astrophysics Data System (ADS)

Yaobo, Shi; Xinxin, Zhao; Fuli, Zheng

2017-05-01

Domestic waste whose pricing can’t be separated from the pricing of public economics category is quasi public goods. Based on Two-part Tariff method on urban public utilities, this paper designs the pricing model in order to match the charging method and estimates the standard of pay-as-you-throw using data of the past five years in Xi’an. Finally, this paper summarizes the main results and proposes corresponding policy recommendations.

Identifiability of PBPK Models with Applications to ...

EPA Pesticide Factsheets

Any statistical model should be identifiable in order for estimates and tests using it to be meaningful. We consider statistical analysis of physiologically-based pharmacokinetic (PBPK) models in which parameters cannot be estimated precisely from available data, and discuss different types of identifiability that occur in PBPK models and give reasons why they occur. We particularly focus on how the mathematical structure of a PBPK model and lack of appropriate data can lead to statistical models in which it is impossible to estimate at least some parameters precisely. Methods are reviewed which can determine whether a purely linear PBPK model is globally identifiable. We propose a theorem which determines when identifiability at a set of finite and specific values of the mathematical PBPK model (global discrete identifiability) implies identifiability of the statistical model. However, we are unable to establish conditions that imply global discrete identifiability, and conclude that the only safe approach to analysis of PBPK models involves Bayesian analysis with truncated priors. Finally, computational issues regarding posterior simulations of PBPK models are discussed. The methodology is very general and can be applied to numerous PBPK models which can be expressed as linear time-invariant systems. A real data set of a PBPK model for exposure to dimethyl arsinic acid (DMA(V)) is presented to illustrate the proposed methodology. We consider statistical analy

Nonspinning numerical relativity waveform surrogates: assessing the model

NASA Astrophysics Data System (ADS)

Field, Scott; Blackman, Jonathan; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel

2015-04-01

Recently, multi-modal gravitational waveform surrogate models have been built directly from data numerically generated by the Spectral Einstein Code (SpEC). I will describe ways in which the surrogate model error can be quantified. This task, in turn, requires (i) characterizing differences between waveforms computed by SpEC with those predicted by the surrogate model and (ii) estimating errors associated with the SpEC waveforms from which the surrogate is built. Both pieces can have numerous sources of numerical and systematic errors. We make an attempt to study the most dominant error sources and, ultimately, the surrogate model's fidelity. These investigations yield information about the surrogate model's uncertainty as a function of time (or frequency) and parameter, and could be useful in parameter estimation studies which seek to incorporate model error. Finally, I will conclude by comparing the numerical relativity surrogate model to other inspiral-merger-ringdown models. A companion talk will cover the building of multi-modal surrogate models.

Microstructure and hydrogen bonding in water-acetonitrile mixtures.

PubMed

Mountain, Raymond D

2010-12-16

The connection of hydrogen bonding between water and acetonitrile in determining the microheterogeneity of the liquid mixture is examined using NPT molecular dynamics simulations. Mixtures for six, rigid, three-site models for acetonitrile and one water model (SPC/E) were simulated to determine the amount of water-acetonitrile hydrogen bonding. Only one of the six acetonitrile models (TraPPE-UA) was able to reproduce both the liquid density and the experimental estimates of hydrogen bonding derived from Raman scattering of the CN stretch band or from NMR quadrupole relaxation measurements. A simple modification of the acetonitrile model parameters for the models that provided poor estimates produced hydrogen-bonding results consistent with experiments for two of the models. Of these, only one of the modified models also accurately determined the density of the mixtures. The self-diffusion coefficient of liquid acetonitrile provided a final winnowing of the modified model and the successful, unmodified model. The unmodified model is provisionally recommended for simulations of water-acetonitrile mixtures.

Accuracy Assessment of the Precise Point Positioning for Different Troposphere Models

NASA Astrophysics Data System (ADS)

Oguz Selbesoglu, Mahmut; Gurturk, Mert; Soycan, Metin

2016-04-01

This study investigates the accuracy and repeatability of PPP technique at different latitudes by using different troposphere delay models. Nine IGS stations were selected between 00-800 latitudes at northern hemisphere and southern hemisphere. Coordinates were obtained for 7 days at 1 hour intervals in summer and winter. At first, the coordinates were estimated by using Niell troposphere delay model with and without including north and east gradients in order to investigate the contribution of troposphere delay gradients to the positioning . Secondly, Saastamoinen model was used to eliminate troposphere path delays by using standart atmosphere parameters were extrapolated for all station levels. Finally, coordinates were estimated by using RTCA-MOPS empirical troposphere delay model. Results demonstrate that Niell troposphere delay model with horizontal gradients has better mean values of rms errors 0.09 % and 65 % than the Niell troposphere model without horizontal gradients and RTCA-MOPS model, respectively. Saastamoinen model mean values of rms errors were obtained approximately 4 times bigger than the Niell troposphere delay model with horizontal gradients.

A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-06-01

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

Characterizing white matter tissue in large strain via asymmetric indentation and inverse finite element modeling.

PubMed

Feng, Yuan; Lee, Chung-Hao; Sun, Lining; Ji, Songbai; Zhao, Xuefeng

2017-01-01

Characterizing the mechanical properties of white matter is important to understand and model brain development and injury. With embedded aligned axonal fibers, white matter is typically modeled as a transversely isotropic material. However, most studies characterize the white matter tissue using models with a single anisotropic invariant or in a small-strain regime. In this study, we combined a single experimental procedure - asymmetric indentation - with inverse finite element (FE) modeling to estimate the nearly incompressible transversely isotropic material parameters of white matter. A minimal form comprising three parameters was employed to simulate indentation responses in the large-strain regime. The parameters were estimated using a global optimization procedure based on a genetic algorithm (GA). Experimental data from two indentation configurations of porcine white matter, parallel and perpendicular to the axonal fiber direction, were utilized to estimate model parameters. Results in this study confirmed a strong mechanical anisotropy of white matter in large strain. Further, our results suggested that both indentation configurations are needed to estimate the parameters with sufficient accuracy, and that the indenter-sample friction is important. Finally, we also showed that the estimated parameters were consistent with those previously obtained via a trial-and-error forward FE method in the small-strain regime. These findings are useful in modeling and parameterization of white matter, especially under large deformation, and demonstrate the potential of the proposed asymmetric indentation technique to characterize other soft biological tissues with transversely isotropic properties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brute force meets Bruno force in parameter optimisation: introduction of novel constraints for parameter accuracy improvement by symbolic computation.

PubMed

Nakatsui, M; Horimoto, K; Lemaire, F; Ürgüplü, A; Sedoglavic, A; Boulier, F

2011-09-01

Recent remarkable advances in computer performance have enabled us to estimate parameter values by the huge power of numerical computation, the so-called 'Brute force', resulting in the high-speed simultaneous estimation of a large number of parameter values. However, these advancements have not been fully utilised to improve the accuracy of parameter estimation. Here the authors review a novel method for parameter estimation using symbolic computation power, 'Bruno force', named after Bruno Buchberger, who found the Gröbner base. In the method, the objective functions combining the symbolic computation techniques are formulated. First, the authors utilise a symbolic computation technique, differential elimination, which symbolically reduces an equivalent system of differential equations to a system in a given model. Second, since its equivalent system is frequently composed of large equations, the system is further simplified by another symbolic computation. The performance of the authors' method for parameter accuracy improvement is illustrated by two representative models in biology, a simple cascade model and a negative feedback model in comparison with the previous numerical methods. Finally, the limits and extensions of the authors' method are discussed, in terms of the possible power of 'Bruno force' for the development of a new horizon in parameter estimation.

Dynamic coordinated control during mode transition process for a compound power-split hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Su, Yanzhao; Hu, Minghui; Su, Ling; Qin, Datong; Zhang, Tong; Fu, Chunyun

2018-07-01

The fuel economy of the hybrid electric vehicles (HEVs) can be effectively improved by the mode transition (MT). However, for a power-split powertrain whose power-split transmission is directly connected to the engine, the engine ripple torque (ERT), inconsistent dynamic characteristics (IDC) of engine and motors, model estimation inaccuracies (MEI), system parameter uncertainties (SPU) can cause jerk and vibration of transmission system during the MT process, which will reduce the driving comfort and the life of the drive parts. To tackle these problems, a dynamic coordinated control strategy (DCCS), including a staged engine torque feedforward and feedback estimation (ETFBC) and an active damping feedback compensation (ADBC) based on drive shaft torque estimation (DSTE), is proposed. And the effectiveness of this strategy is verified using a plant model. Firstly, the powertrain plant model is established, and the MT process and problems are analyzed. Secondly, considering the characteristics of the engine torque estimation (ETE) model before and after engine ignition, a motor torque compensation control based on the staged ERT estimation is developed. Then, considering the MEI, SPU and the load change, an ADBC based on a real-time nonlinear reduced-order robust observer of the DSTE is designed. Finally, the simulation results show that the proposed DCCS can effectively improve the driving comfort.

Surrogacy assessment using principal stratification with multivariate normal and Gaussian copula models.

PubMed

Taylor, Jeremy M G; Conlon, Anna S C; Elliott, Michael R

2015-08-01

The validation of intermediate markers as surrogate markers (S) for the true outcome of interest (T) in clinical trials offers the possibility for trials to be run more quickly and cheaply by using the surrogate endpoint in place of the true endpoint. Working within a principal stratification framework, we propose causal quantities to evaluate surrogacy using a Gaussian copula model for an ordinal surrogate and time-to-event final outcome. The methods are applied to data from four colorectal cancer clinical trials, where S is tumor response and T is overall survival. For the Gaussian copula model, a Bayesian estimation strategy is used and, as some parameters are not identifiable from the data, we explore the use of informative priors that are consistent with reasonable assumptions in the surrogate marker setting to aid in estimation. While there is some bias in the estimation of the surrogacy quantities of interest, the estimation procedure does reasonably well at distinguishing between poor and good surrogate markers. Some of the parameters of the proposed model are not identifiable from the data, and therefore, assumptions must be made in order to aid in their estimation. The proposed quantities can be used in combination to provide evidence about the validity of S as a surrogate marker for T. © The Author(s) 2014.

Statistical Models and Inference Procedures for Structural and Materials Reliability

DTIC Science & Technology

1990-12-01

as an official Department of the Army positio~n, policy, or decision, unless sD designated by other documentazion. 12a. DISTRIBUTION /AVAILABILITY...Some general stress-strength models were also developed and applied to the failure of systems subject to cyclic loading. Involved in the failure of...process control ideas and sequential design and analysis methods. Finally, smooth nonparametric quantile .wJ function estimators were studied. All of

An Econometric Analysis of the Unemployment Insurance System in a Local Urban Labor Market. Final Report for September 1, 1973--September 30, 1974.

ERIC Educational Resources Information Center

Marston, Stephen Tilney

The study derives a model of the unemployment insurance (UI) system and its relationship to the labor market, estimates it with data from the Detroit Standard Metropolitan Statistical Area, and evaluates its potential use to forecast UI benefit amounts, UI insured unemployment, and UI exhaustions. It further uses the model to analyze policy issues…

The Economic Effects of Reduced Defense Spending

DTIC Science & Technology

1992-02-01

Macroeconomics Annual 1987 (Cambridge, Mass.: MIT Press), pp. 263-315; and Leonardo Leiderman and Mario Blejer, " Modeling and Testing Ricardian ... model ), converting LIFT’s 78-sector forecasts of final demand by product to estimates for some 420 producing sectors. Unlike LIFT, DOM does not...24s in production in Fort Worth, Texas, during World War II. Photo is courtesy of Gen- eral Dynamics Corporation. Preface The past three years

Renal Function Descriptors in Neonates: Which Creatinine-Based Formula Best Describes Vancomycin Clearance?

PubMed

Bhongsatiern, Jiraganya; Stockmann, Chris; Yu, Tian; Constance, Jonathan E; Moorthy, Ganesh; Spigarelli, Michael G; Desai, Pankaj B; Sherwin, Catherine M T

2016-05-01

Growth and maturational changes have been identified as significant covariates in describing variability in clearance of renally excreted drugs such as vancomycin. Because of immaturity of clearance mechanisms, quantification of renal function in neonates is of importance. Several serum creatinine (SCr)-based renal function descriptors have been developed in adults and children, but none are selectively derived for neonates. This review summarizes development of the neonatal kidney and discusses assessment of the renal function regarding estimation of glomerular filtration rate using renal function descriptors. Furthermore, identification of the renal function descriptors that best describe the variability of vancomycin clearance was performed in a sample study of a septic neonatal cohort. Population pharmacokinetic models were developed applying a combination of age-weight, renal function descriptors, or SCr alone. In addition to age and weight, SCr or renal function descriptors significantly reduced variability of vancomycin clearance. The population pharmacokinetic models with Léger and modified Schwartz formulas were selected as the optimal final models, although the other renal function descriptors and SCr provided reasonably good fit to the data, suggesting further evaluation of the final models using external data sets and cross validation. The present study supports incorporation of renal function descriptors in the estimation of vancomycin clearance in neonates. © 2015, The American College of Clinical Pharmacology.

Uncertainty importance analysis using parametric moment ratio functions.

PubMed

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

Genetic parameter estimation for pre- and post-weaning traits in Brahman cattle in Brazil.

PubMed

Vargas, Giovana; Buzanskas, Marcos Eli; Guidolin, Diego Gomes Freire; Grossi, Daniela do Amaral; Bonifácio, Alexandre da Silva; Lôbo, Raysildo Barbosa; da Fonseca, Ricardo; Oliveira, João Ademir de; Munari, Danísio Prado

2014-10-01

Beef cattle producers in Brazil use body weight traits as breeding program selection criteria due to their great economic importance. The objectives of this study were to evaluate different animal models, estimate genetic parameters, and define the most fitting model for Brahman cattle body weight standardized at 120 (BW120), 210 (BW210), 365 (BW365), 450 (BW450), and 550 (BW550) days of age. To estimate genetic parameters, single-, two-, and multi-trait analyses were performed using the animal model. The likelihood ratio test was verified between all models. For BW120 and BW210, additive direct genetic, maternal genetic, maternal permanent environment, and residual effects were considered, while for BW365 and BW450, additive direct genetic, maternal genetic, and residual effects were considered. Finally, for BW550, additive direct genetic and residual effects were considered. Estimates of direct heritability for BW120 were similar in all analyses; however, for the other traits, multi-trait analysis resulted in higher estimates. The maternal heritability and proportion of maternal permanent environmental variance to total variance were minimal in multi-trait analyses. Genetic, environmental, and phenotypic correlations were of high magnitude between all traits. Multi-trait analyses would aid in the parameter estimation for body weight at older ages because they are usually affected by a lower number of animals with phenotypic information due to culling and mortality.

Estimating the effect of a rare time-dependent treatment on the recurrent event rate.

PubMed

Smith, Abigail R; Zhu, Danting; Goodrich, Nathan P; Merion, Robert M; Schaubel, Douglas E

2018-05-30

In many observational studies, the objective is to estimate the effect of treatment or state-change on the recurrent event rate. If treatment is assigned after the start of follow-up, traditional methods (eg, adjustment for baseline-only covariates or fully conditional adjustment for time-dependent covariates) may give biased results. We propose a two-stage modeling approach using the method of sequential stratification to accurately estimate the effect of a time-dependent treatment on the recurrent event rate. At the first stage, we estimate the pretreatment recurrent event trajectory using a proportional rates model censored at the time of treatment. Prognostic scores are estimated from the linear predictor of this model and used to match treated patients to as yet untreated controls based on prognostic score at the time of treatment for the index patient. The final model is stratified on matched sets and compares the posttreatment recurrent event rate to the recurrent event rate of the matched controls. We demonstrate through simulation that bias due to dependent censoring is negligible, provided the treatment frequency is low, and we investigate a threshold at which correction for dependent censoring is needed. The method is applied to liver transplant (LT), where we estimate the effect of development of post-LT End Stage Renal Disease (ESRD) on rate of days hospitalized. Copyright © 2018 John Wiley & Sons, Ltd.

Estimation of 3D reconstruction errors in a stereo-vision system

NASA Astrophysics Data System (ADS)

Belhaoua, A.; Kohler, S.; Hirsch, E.

2009-06-01

The paper presents an approach for error estimation for the various steps of an automated 3D vision-based reconstruction procedure of manufactured workpieces. The process is based on a priori planning of the task and built around a cognitive intelligent sensory system using so-called Situation Graph Trees (SGT) as a planning tool. Such an automated quality control system requires the coordination of a set of complex processes performing sequentially data acquisition, its quantitative evaluation and the comparison with a reference model (e.g., CAD object model) in order to evaluate quantitatively the object. To ensure efficient quality control, the aim is to be able to state if reconstruction results fulfill tolerance rules or not. Thus, the goal is to evaluate independently the error for each step of the stereo-vision based 3D reconstruction (e.g., for calibration, contour segmentation, matching and reconstruction) and then to estimate the error for the whole system. In this contribution, we analyze particularly the segmentation error due to localization errors for extracted edge points supposed to belong to lines and curves composing the outline of the workpiece under evaluation. The fitting parameters describing these geometric features are used as quality measure to determine confidence intervals and finally to estimate the segmentation errors. These errors are then propagated through the whole reconstruction procedure, enabling to evaluate their effect on the final 3D reconstruction result, specifically on position uncertainties. Lastly, analysis of these error estimates enables to evaluate the quality of the 3D reconstruction, as illustrated by the shown experimental results.

Medicare and Medicaid Programs; CY 2018 Home Health Prospective Payment System Rate Update and CY 2019 Case-Mix Adjustment Methodology Refinements; Home Health Value-Based Purchasing Model; and Home Health Quality Reporting Requirements. Final rule.

PubMed

2017-11-07

This final rule updates the home health prospective payment system (HH PPS) payment rates, including the national, standardized 60-day episode payment rates, the national per-visit rates, and the non-routine medical supply (NRS) conversion factor, effective for home health episodes of care ending on or after January 1, 2018. This rule also: Updates the HH PPS case-mix weights using the most current, complete data available at the time of rulemaking; implements the third year of a 3-year phase-in of a reduction to the national, standardized 60-day episode payment to account for estimated case-mix growth unrelated to increases in patient acuity (that is, nominal case-mix growth) between calendar year (CY) 2012 and CY 2014; and discusses our efforts to monitor the potential impacts of the rebasing adjustments that were implemented in CY 2014 through CY 2017. In addition, this rule finalizes changes to the Home Health Value-Based Purchasing (HHVBP) Model and to the Home Health Quality Reporting Program (HH QRP). We are not finalizing the implementation of the Home Health Groupings Model (HHGM) in this final rule.

Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

NASA Astrophysics Data System (ADS)

Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

2018-02-01

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

Estimating the decline in excess risk of chronic obstructive pulmonary disease following quitting smoking - a systematic review based on the negative exponential model.

PubMed

Lee, Peter N; Fry, John S; Forey, Barbara A

2014-03-01

We quantified the decline in COPD risk following quitting using the negative exponential model, as previously carried out for other smoking-related diseases. We identified 14 blocks of RRs (from 11 studies) comparing current smokers, former smokers (by time quit) and never smokers, some studies providing sex-specific blocks. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We estimated the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block, except for one where no decline with quitting was evident, and H was not estimable. For the remaining 13 blocks, goodness-of-fit to the model was generally adequate, the combined estimate of H being 13.32 (95% CI 11.86-14.96) years. There was no heterogeneity in H, overall or by various studied sources. Sensitivity analyses allowing for reverse causation or different assumed times for the final quitting period little affected the results. The model summarizes quitting data well. The estimate of 13.32years is substantially larger than recent estimates of 4.40years for ischaemic heart disease and 4.78years for stroke, and also larger than the 9.93years for lung cancer. Heterogeneity was unimportant for COPD, unlike for the other three diseases. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger

NASA Astrophysics Data System (ADS)

Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.

2016-12-01

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator.

PubMed

King, A P; Buerger, C; Tsoumpas, C; Marsden, P K; Schaeffter, T

2012-01-01

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition. Copyright © 2011 Elsevier B.V. All rights reserved.

Dislocation model for aseismic fault slip in the transverse ranges of Southern California

NASA Technical Reports Server (NTRS)

Cheng, A.; Jackson, D. D.; Matsuura, M.

1985-01-01

Geodetic data at a plate boundary can reveal the pattern of subsurface displacements that accompany plate motion. These displacements are modelled as the sum of rigid block motion and the elastic effects of frictional interaction between blocks. The frictional interactions are represented by uniform dislocation on each of several rectangular fault patches. The block velocities and fault parameters are then estimated from geodetic data. Bayesian inversion procedure employs prior estimates based on geological and seismological data. The method is applied to the Transverse Ranges, using prior geological and seismological data and geodetic data from the USGS trilateration networks. Geodetic data imply a displacement rate of about 20 mm/yr across the San Andreas Fault, while the geologic estimates exceed 30 mm/yr. The prior model and the final estimates both imply about 10 mm/yr crustal shortening normal to the trend of the San Andreas Fault. Aseismic fault motion is a major contributor to plate motion. The geodetic data can help to identify faults that are suffering rapid stress accumulation; in the Transverse Ranges those faults are the San Andreas and the Santa Susana.

Slope Estimation in Noisy Piecewise Linear Functions✩

PubMed Central

Ingle, Atul; Bucklew, James; Sethares, William; Varghese, Tomy

2014-01-01

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure. PMID:25419020

Slope Estimation in Noisy Piecewise Linear Functions.

PubMed

Ingle, Atul; Bucklew, James; Sethares, William; Varghese, Tomy

2015-03-01

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure.

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

Polarimetric scattering model for estimation of above ground biomass of multilayer vegetation using ALOS-PALSAR quad-pol data

NASA Astrophysics Data System (ADS)

Sai Bharadwaj, P.; Kumar, Shashi; Kushwaha, S. P. S.; Bijker, Wietske

Forests are important biomes covering a major part of the vegetation on the Earth, and as such account for seventy percent of the carbon present in living beings. The value of a forest's above ground biomass (AGB) is considered as an important parameter for the estimation of global carbon content. In the present study, the quad-pol ALOS-PALSAR data was used for the estimation of AGB for the Dudhwa National Park, India. For this purpose, polarimetric decomposition components and an Extended Water Cloud Model (EWCM) were used. The PolSAR data orientation angle shifts were compensated for before the polarimetric decomposition. The scattering components obtained from the polarimetric decomposition were used in the Water Cloud Model (WCM). The WCM was extended for higher order interactions like double bounce scattering. The parameters of the EWCM were retrieved using the field measurements and the decomposition components. Finally, the relationship between the estimated AGB and measured AGB was assessed. The coefficient of determination (R2) and root mean square error (RMSE) were 0.4341 and 119 t/ha respectively.

Estimating Soil Cation Exchange Capacity from Soil Physical and Chemical Properties

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Emamgholizadeh, S.; Shahsavani, D.

2014-12-01

The soil Cation Exchange Capacity (CEC) is an important soil characteristic that has many applications in soil science and environmental studies. For example, CEC influences soil fertility by controlling the exchange of ions in the soil. Measurement of CEC is costly and difficult. Consequently, several studies attempted to obtain CEC from readily measurable soil physical and chemical properties such as soil pH, organic matter, soil texture, bulk density, and particle size distribution. These studies have often used multiple regression or artificial neural network models. Regression-based models cannot capture the intricate relationship between CEC and soil physical and chemical attributes and provide inaccurate CEC estimates. Although neural network models perform better than regression methods, they act like a black-box and cannot generate an explicit expression for retrieval of CEC from soil properties. In a departure with regression and neural network models, this study uses Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) to estimate CEC from easily measurable soil variables such as clay, pH, and OM. CEC estimates from GEP and MARS are compared with measurements at two field sites in Iran. Results show that GEP and MARS can estimate CEC accurately. Also, the MARS model performs slightly better than GEP. Finally, a sensitivity test indicates that organic matter and pH have respectively the least and the most significant impact on CEC.

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

PubMed

Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

2014-03-01

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

Estimation of energetic efficiency of heat supply in front of the aircraft at supersonic accelerated flight. Part 1. Mathematical models

NASA Astrophysics Data System (ADS)

Latypov, A. F.

2008-12-01

Fuel economy at boost trajectory of the aerospace plane was estimated during energy supply to the free stream. Initial and final flight velocities were specified. The model of a gliding flight above cold air in an infinite isobaric thermal wake was used. The fuel consumption rates were compared at optimal trajectory. The calculations were carried out using a combined power plant consisting of ramjet and liquid-propellant engine. An exergy model was built in the first part of the paper to estimate the ramjet thrust and specific impulse. A quadratic dependence on aerodynamic lift was used to estimate the aerodynamic drag of aircraft. The energy for flow heating was obtained at the expense of an equivalent reduction of the exergy of combustion products. The dependencies were obtained for increasing the range coefficient of cruise flight for different Mach numbers. The second part of the paper presents a mathematical model for the boost interval of the aircraft flight trajectory and the computational results for the reduction of fuel consumption at the boost trajectory for a given value of the energy supplied in front of the aircraft.

Estimation of energetic efficiency of heat supply in front of the aircraft at supersonic accelerated flight. Part II. Mathematical model of the trajectory boost part and computational results

NASA Astrophysics Data System (ADS)

Latypov, A. F.

2009-03-01

The fuel economy was estimated at boost trajectory of aerospace plane during energy supply to the free stream. Initial and final velocities of the flight were given. A model of planning flight above cold air in infinite isobaric thermal wake was used. The comparison of fuel consumption was done at optimal trajectories. The calculations were done using a combined power plant consisting of ramjet and liquid-propellant engine. An exergy model was constructed in the first part of the paper for estimating the ramjet thrust and specific impulse. To estimate the aerodynamic drag of aircraft a quadratic dependence on aerodynamic lift is used. The energy for flow heating is obtained at the sacrifice of an equivalent decrease of exergy of combustion products. The dependencies are obtained for increasing the range coefficient of cruise flight at different Mach numbers. In the second part of the paper, a mathematical model is presented for the boost part of the flight trajectory of the flying vehicle and computational results for reducing the fuel expenses at the boost trajectory at a given value of the energy supplied in front of the aircraft.

Heritability, assortative mating and gender differences in violent crime: results from a total population sample using twin, adoption, and sibling models.

PubMed

Frisell, Thomas; Pawitan, Yudi; Långström, Niklas; Lichtenstein, Paul

2012-01-01

Research addressing genetic and environmental determinants to antisocial behaviour suggests substantial variability across studies. Likewise, evidence for etiologic gender differences is mixed, and estimates might be biased due to assortative mating. We used longitudinal Swedish total population registers to estimate the heritability of objectively measured violent offending (convictions) in classic twin (N = 36,877 pairs), adoptee-parent (N = 5,068 pairs), adoptee-sibling (N = 10,610 pairs), and sibling designs (N = 1,521,066 pairs). Type and degree of assortative mating were calculated from comparisons between spouses of siblings and half-siblings, and across consecutive spouses. Heritability estimates for the liability of violent offending agreed with previously reported heritability for self-reported antisocial behaviour. While the sibling model yielded estimates similar to the twin model (A ≈ 55%, C ≈ 13%), adoptee-models appeared to underestimate familial effects (A ≈ 20-30%, C ≈ 0%). Assortative mating was moderate to strong (r (spouse) = 0.4), appeared to result from both phenotypic assortment and social homogamy, but had only minor effect on variance components. Finally, we found significant gender differences in the etiology of violent crime.

Three Different Methods of Estimating LAI in a Small Watershed

NASA Astrophysics Data System (ADS)

Speckman, H. N.; Ewers, B. E.; Beverly, D.

2015-12-01

Leaf area index (LAI) is a critical input of models that improve predictive understanding of ecology, hydrology, and climate change. Multiple techniques exist to quantify LAI, most of which are labor intensive, and all often fail to converge on similar estimates. . Recent large-scale bark beetle induced mortality greatly altered LAI, which is now dominated by younger and more metabolically active trees compared to the pre-beetle forest. Tree mortality increases error in optical LAI estimates due to the lack of differentiation between live and dead branches in dense canopy. Our study aims to quantify LAI using three different LAI methods, and then to compare the techniques to each other and topographic drivers to develop an effective predictive model of LAI. This study focuses on quantifying LAI within a small (~120 ha) beetle infested watershed in Wyoming's Snowy Range Mountains. The first technique estimated LAI using in-situ hemispherical canopy photographs that were then analyzed with Hemisfer software. The second LAI estimation technique was use of the Kaufmann 1982 allometrerics from forest inventories conducted throughout the watershed, accounting for stand basal area, species composition, and the extent of bark beetle driven mortality. The final technique used airborne light detection and ranging (LIDAR) first DMS returns, which were used to estimating canopy heights and crown area. LIDAR final returns provided topographical information and were then ground-truthed during forest inventories. Once data was collected, a fractural analysis was conducted comparing the three methods. Species composition was driven by slope position and elevation Ultimately the three different techniques provided very different estimations of LAI, but each had their advantage: estimates from hemisphere photos were well correlated with SWE and snow depth measurements, forest inventories provided insight into stand health and composition, and LIDAR were able to quickly and efficiently cover a very large area.

Use of an ecosystem model for testing ecosystem response to inaccuracies of root and microflora productivity estimates

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

Petersen, H.; O'Neill, R.V.; Gardner, R.H.

1984-01-01

A seventy-compartment model for a Danish beech forest ecosystem is described in outline. The unmodified model predicts considerable accumulation of wood litter and decreasing accumulation through secondary to final decomposition products. Increment rates are similar for all components of the detritus based food chain. Modification of fine root production rate produces strong, positive response for root litter, and less, but still significant, response for detritus, humus and the components of the decomposer food chain. Increase of microbial biomass with adjustments of metabolism and production causes reduced accumulation of detritus and humus. The soil organisms respond according to food source. Themore » use of the model for testing the sensitivity of the ecosystem to inaccuracies of rroot- and microflora estimates is discussed. 21 references, 3 figures, 1 table.« less

Identification of the focal plane wavefront control system using E-M algorithm

NASA Astrophysics Data System (ADS)

Sun, He; Kasdin, N. Jeremy; Vanderbei, Robert

2017-09-01

In a typical focal plane wavefront control (FPWC) system, such as the adaptive optics system of NASA's WFIRST mission, the efficient controllers and estimators in use are usually model-based. As a result, the modeling accuracy of the system influences the ultimate performance of the control and estimation. Currently, a linear state space model is used and calculated based on lab measurements using Fourier optics. Although the physical model is clearly defined, it is usually biased due to incorrect distance measurements, imperfect diagnoses of the optical aberrations, and our lack of knowledge of the deformable mirrors (actuator gains and influence functions). In this paper, we present a new approach for measuring/estimating the linear state space model of a FPWC system using the expectation-maximization (E-M) algorithm. Simulation and lab results in the Princeton's High Contrast Imaging Lab (HCIL) show that the E-M algorithm can well handle both the amplitude and phase errors and accurately recover the system. Using the recovered state space model, the controller creates dark holes with faster speed. The final accuracy of the model depends on the amount of data used for learning.

Battery Pack Life Estimation through Cell Degradation Data and Pack Thermal Modeling for BAS+ Li-Ion Batteries. Cooperative Research and Development Final Report, CRADA Number CRD-12-489

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

Smith, Kandler

Battery Life estimation is one of the key inputs required for Hybrid applications for all GM Hybrid/EV/EREV/PHEV programs. For each Hybrid vehicle program, GM has instituted multi-parameter Design of Experiments generating test data at Cell level and also Pack level on a reduced basis. Based on experience, generating test data on a pack level is found to be very expensive, resource intensive and sometimes less reliable. The proposed collaborative project will focus on a methodology to estimate Battery life based on cell degradation data combined with pack thermal modeling. NREL has previously developed cell-level battery aging models and pack-level thermal/electricalmore » network models, though these models are currently not integrated. When coupled together, the models are expected to describe pack-level thermal and aging response of individual cells. GM and NREL will use data collected for GM's Bas+ battery system for evaluation of the proposed methodology and assess to what degree these models can replace pack-level aging experiments in the future.« less

The linear transformation model with frailties for the analysis of item response times.

PubMed

Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey A

2013-02-01

The item response times (RTs) collected from computerized testing represent an underutilized source of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. In this paper, we propose a semi-parametric model for RTs, the linear transformation model with a latent speed covariate, which combines the flexibility of non-parametric modelling and the brevity as well as interpretability of parametric modelling. In this new model, the RTs, after some non-parametric monotone transformation, become a linear model with latent speed as covariate plus an error term. The distribution of the error term implicitly defines the relationship between the RT and examinees' latent speeds; whereas the non-parametric transformation is able to describe various shapes of RT distributions. The linear transformation model represents a rich family of models that includes the Cox proportional hazards model, the Box-Cox normal model, and many other models as special cases. This new model is embedded in a hierarchical framework so that both RTs and responses are modelled simultaneously. A two-stage estimation method is proposed. In the first stage, the Markov chain Monte Carlo method is employed to estimate the parametric part of the model. In the second stage, an estimating equation method with a recursive algorithm is adopted to estimate the non-parametric transformation. Applicability of the new model is demonstrated with a simulation study and a real data application. Finally, methods to evaluate the model fit are suggested. © 2012 The British Psychological Society.

The rate of equilibration of viscous aerosol particles

NASA Astrophysics Data System (ADS)

O'Meara, Simon; Topping, David O.; McFiggans, Gordon

2016-04-01

The proximity of atmospheric aerosol particles to equilibrium with their surrounding condensable vapours can substantially impact their transformations, fate and impacts and is the subject of vibrant research activity. In this study we first compare equilibration timescales estimated by three different models for diffusion through aerosol particles to assess any sensitivity to choice of model framework. Equilibration times for diffusion coefficients with varying dependencies on composition are compared for the first time. We show that even under large changes in the saturation ratio of a semi-volatile component (es) of 1-90 % predicted equilibration timescales are in agreement, including when diffusion coefficients vary with composition. For condensing water and a diffusion coefficient dependent on composition, a plasticising effect is observed, leading to a decreased estimated equilibration time with increasing final es. Above 60 % final es maximum equilibration times of around 1 s are estimated for comparatively large particles (10 µm) containing a relatively low diffusivity component (1 × 10-25 m2 s-1 in pure form). This, as well as other results here, questions whether particle-phase diffusion through water-soluble particles can limit hygroscopic growth in the ambient atmosphere. In the second part of this study, we explore sensitivities associated with the use of particle radius measurements to infer diffusion coefficient dependencies on composition using a diffusion model. Given quantified similarities between models used in this study, our results confirm considerations that must be taken into account when designing such experiments. Although quantitative agreement of equilibration timescales between models is found, further work is necessary to determine their suitability for assessing atmospheric impacts, such as their inclusion in polydisperse aerosol simulations.

How social information can improve estimation accuracy in human groups.

PubMed

Jayles, Bertrand; Kim, Hye-Rin; Escobedo, Ramón; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-11-21

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. Copyright © 2017 the Author(s). Published by PNAS.

How social information can improve estimation accuracy in human groups

PubMed Central

Jayles, Bertrand; Kim, Hye-rin; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-01-01

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects’ sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. PMID:29118142

Orbit/attitude estimation with LANDSAT Landmark data

NASA Technical Reports Server (NTRS)

Hall, D. L.; Waligora, S.

1979-01-01

The use of LANDSAT landmark data for orbit/attitude and camera bias estimation was studied. The preliminary results of these investigations are presented. The Goddard Trajectory Determination System (GTDS) error analysis capability was used to perform error analysis studies. A number of questions were addressed including parameter observability and sensitivity, effects on the solve-for parameter errors of data span, density, and distribution an a priori covariance weighting. The use of the GTDS differential correction capability with acutal landmark data was examined. The rms line and element observation residuals were studied as a function of the solve-for parameter set, a priori covariance weighting, force model, attitude model and data characteristics. Sample results are presented. Finally, verfication and preliminary system evaluation of the LANDSAT NAVPAK system for sequential (extended Kalman Filter) estimation of orbit, and camera bias parameters is given.

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

PubMed Central

Vrancken, Bram; Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Longdon, Ben; Günthard, Huldrych F.; Suchard, Marc A.

2014-01-01

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel’s λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution. PMID:25780554

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Essays in energy economics: The electricity industry

NASA Astrophysics Data System (ADS)

Martinez-Chombo, Eduardo

Electricity demand analysis using cointegration and error-correction models with time varying parameters: The Mexican case. In this essay we show how some flexibility can be allowed in modeling the parameters of the electricity demand function by employing the time varying coefficient (TVC) cointegrating model developed by Park and Hahn (1999). With the income elasticity of electricity demand modeled as a TVC, we perform tests to examine the adequacy of the proposed model against the cointegrating regression with fixed coefficients, as well as against the spuriousness of the regression with TVC. The results reject the specification of the model with fixed coefficients and favor the proposed model. We also show how some flexibility is gained in the specification of the error correction model based on the proposed TVC cointegrating model, by including more lags of the error correction term as predetermined variables. Finally, we present the results of some out-of-sample forecast comparison among competing models. Electricity demand and supply in Mexico. In this essay we present a simplified model of the Mexican electricity transmission network. We use the model to approximate the marginal cost of supplying electricity to consumers in different locations and at different times of the year. We examine how costs and system operations will be affected by proposed investments in generation and transmission capacity given a forecast of growth in regional electricity demands. Decomposing electricity prices with jumps. In this essay we propose a model that decomposes electricity prices into two independent stochastic processes: one that represents the "normal" pattern of electricity prices and the other that captures temporary shocks, or "jumps", with non-lasting effects in the market. Each contains specific mean reverting parameters to estimate. In order to identify such components we specify a state-space model with regime switching. Using Kim's (1994) filtering algorithm we estimate the parameters of the model, the transition probabilities and the unobservable components for the mean adjusted series of New South Wales' electricity prices. Finally, bootstrap simulations were performed to estimate the expected contribution of each of the components in the overall electricity prices.

Estimating Pressure Reactivity Using Noninvasive Doppler-Based Systolic Flow Index.

PubMed

Zeiler, Frederick A; Smielewski, Peter; Donnelly, Joseph; Czosnyka, Marek; Menon, David K; Ercole, Ari

2018-04-05

The study objective was to derive models that estimate the pressure reactivity index (PRx) using the noninvasive transcranial Doppler (TCD) based systolic flow index (Sx_a) and mean flow index (Mx_a), both based on mean arterial pressure, in traumatic brain injury (TBI). Using a retrospective database of 347 patients with TBI with intracranial pressure and TCD time series recordings, we derived PRx, Sx_a, and Mx_a. We first derived the autocorrelative structure of PRx based on: (A) autoregressive integrative moving average (ARIMA) modeling in representative patients, and (B) within sequential linear mixed effects (LME) models with various embedded ARIMA error structures for PRx for the entire population. Finally, we performed sequential LME models with embedded PRx ARIMA modeling to find the best model for estimating PRx using Sx_a and Mx_a. Model adequacy was assessed via normally distributed residual density. Model superiority was assessed via Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log likelihood (LL), and analysis of variance testing between models. The most appropriate ARIMA structure for PRx in this population was (2,0,2). This was applied in sequential LME modeling. Two models were superior (employing random effects in the independent variables and intercept): (A) PRx ∼ Sx_a, and (B) PRx ∼ Sx_a + Mx_a. Correlation between observed and estimated PRx with these two models was: (A) 0.794 (p < 0.0001, 95% confidence interval (CI) = 0.788-0.799), and (B) 0.814 (p < 0.0001, 95% CI = 0.809-0.819), with acceptable agreement on Bland-Altman analysis. Through using linear mixed effects modeling and accounting for the ARIMA structure of PRx, one can estimate PRx using noninvasive TCD-based indices. We have described our first attempts at such modeling and PRx estimation, establishing the strong link between two aspects of cerebral autoregulation: measures of cerebral blood flow and those of pulsatile cerebral blood volume. Further work is required to validate.

A multivariate method for estimating mortality rates among children under 5 years from health and social indicators in Iraq.

PubMed

Garfield, R; Leu, C S

2000-06-01

Many reports on Iraq suggest that a rise in rates of death and disease have occurred since the Gulf War of January/February 1991 and the economic sanctions that followed it. Four preliminary models, based on unadjusted projections, were developed. A logistic regression model was then developed on the basis of six social variables in Iraq and comparable information from countries in the State of the World's Children report. Missing data were estimated for this model by a multiple imputation procedure. The final model depends on three socio-medical indicators: adult literacy, nutritional stunting of children under 5 years, and access to piped water. The model successfully predicted both the mortality rate in 1990, under stable conditions, and in 1991, following the Gulf War. For 1996, after 5 years of sanctions and prior to receipt of humanitarian food via the oil for food programme, this model shows mortality among children under 5 to have reached an estimated 87 per 1000, a rate last experienced more than 30 years ago. Accurate and timely estimates of mortality levels in developing countries are costly and require considerable methodological expertise. A rapid estimation technique like the one developed here may be a useful tool for quick and efficient estimation of mortality rates among under 5 year olds in countries where good mortality data are not routinely available. This is especially true for countries with complex humanitarian emergencies where information on mortality changes can guide interventions and the social stability to use standard demographic methods does not exist.

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV-infected infants and toddlers.

PubMed

Zhao, Wei; Cella, Massimo; Della Pasqua, Oscar; Burger, David; Jacqz-Aigrain, Evelyne

2012-04-01

Abacavir is used to treat HIV infection in both adults and children. The recommended paediatric dose is 8 mg kg(-1) twice daily up to a maximum of 300 mg twice daily. Weight was identified as the central covariate influencing pharmacokinetics of abacavir in children. A population pharmacokinetic model was developed to describe both once and twice daily pharmacokinetic profiles of abacavir in infants and toddlers. Standard dosage regimen is associated with large interindividual variability in abacavir concentrations. A maximum a posteriori probability Bayesian estimator of AUC(0-) (t) based on three time points (0, 1 or 2, and 3 h) is proposed to support area under the concentration-time curve (AUC) targeted individualized therapy in infants and toddlers. To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration-time curve (AUC) targeted dosage and individualize therapy. The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation-estimation method. The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 () h−1 (RSE 6.3%), apparent central volume of distribution 4.94 () (RSE 28.7%), apparent peripheral volume of distribution 8.12 () (RSE14.2%), apparent intercompartment clearance 1.25 () h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t. The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC(0-) (t) was developed from the final model and can be used routinely to optimize individual dosing. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Advances and applications of occupancy models

USGS Publications Warehouse

Bailey, Larissa; MacKenzie, Darry I.; Nichols, James D.

2013-01-01

Summary: The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. Unlike many mark–recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute ‘detection’ of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian–pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

The Width of a Solar Coronal Mass Ejection and the Source of the Driving Magnetic Explosion: A Test of the Standard Scenario for CME Production

NASA Technical Reports Server (NTRS)

Moore, Ronald L.; Sterling, Alphonse C.; Suess, Steven T.

2007-01-01

We show that the strength (B(sub F1are)) of the magnetic field in the area covered by the flare arcade following a CME-producing ejective solar eruption can be estimated from the final angular width (Final Theta(sub CME)) of the CME in the outer corona and the final angular width (Theta(sub Flare)) of the flare arcade: B(sub Flare) approx. equals 1.4[(Final Theta(sub CME)/Theta(sub Flare)] (exp 2)G. We assume (1) the flux-rope plasmoid ejected from the flare site becomes the interior of the CME plasmoid; (2) in the outer corona (R > 2 (solar radius)) the CME is roughly a "spherical plasmoid with legs" shaped like a lightbulb; and (3) beyond some height in or below the outer corona the CME plasmoid is in lateral pressure balance with the surrounding magnetic field. The strength of the nearly radial magnetic field in the outer corona is estimated from the radial component of the interplanetary magnetic field measured by Ulysses. We apply this model to three well-observed CMEs that exploded from flare regions of extremely different size and magnetic setting. One of these CMEs was an over-and-out CME, that is, in the outer corona the CME was laterally far offset from the flare-marked source of the driving magnetic explosion. In each event, the estimated source-region field strength is appropriate for the magnetic setting of the flare. This agreement (1) indicates that CMEs are propelled by the magnetic field of the CME plasmoid pushing against the surrounding magnetic field; (2) supports the magnetic-arch-blowout scenario for over-and-out CMEs; and (3) shows that a CME's final angular width in the outer corona can be estimated from the amount of magnetic flux covered by the source-region flare arcade.

Effects of sample size on estimates of population growth rates calculated with matrix models.

PubMed

Fiske, Ian J; Bruna, Emilio M; Bolker, Benjamin M

2008-08-28

Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (lambda) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of lambda-Jensen's Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of lambda due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of lambda. Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating lambda for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of lambda with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography. We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities.

Development of GP and GEP models to estimate an environmental issue induced by blasting operation.

PubMed

Faradonbeh, Roohollah Shirani; Hasanipanah, Mahdi; Amnieh, Hassan Bakhshandeh; Armaghani, Danial Jahed; Monjezi, Masoud

2018-05-21

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.

Stable boundary conditions and difference schemes for Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Dutt, P.

1985-01-01

The Navier-Stokes equations can be viewed as an incompletely elliptic perturbation of the Euler equations. By using the entropy function for the Euler equations as a measure of energy for the Navier-Stokes equations, it was possible to obtain nonlinear energy estimates for the mixed initial boundary value problem. These estimates are used to derive boundary conditions which guarantee L2 boundedness even when the Reynolds number tends to infinity. Finally, a new difference scheme for modelling the Navier-Stokes equations in multidimensions for which it is possible to obtain discrete energy estimates exactly analogous to those we obtained for the differential equation was proposed.

Modeling phytoplankton community in reservoirs. A comparison between taxonomic and functional groups-based models.

PubMed

Di Maggio, Jimena; Fernández, Carolina; Parodi, Elisa R; Diaz, M Soledad; Estrada, Vanina

2016-01-01

In this paper we address the formulation of two mechanistic water quality models that differ in the way the phytoplankton community is described. We carry out parameter estimation subject to differential-algebraic constraints and validation for each model and comparison between models performance. The first approach aggregates phytoplankton species based on their phylogenetic characteristics (Taxonomic group model) and the second one, on their morpho-functional properties following Reynolds' classification (Functional group model). The latter approach takes into account tolerance and sensitivity to environmental conditions. The constrained parameter estimation problems are formulated within an equation oriented framework, with a maximum likelihood objective function. The study site is Paso de las Piedras Reservoir (Argentina), which supplies water for consumption for 450,000 population. Numerical results show that phytoplankton morpho-functional groups more closely represent each species growth requirements within the group. Each model performance is quantitatively assessed by three diagnostic measures. Parameter estimation results for seasonal dynamics of the phytoplankton community and main biogeochemical variables for a one-year time horizon are presented and compared for both models, showing the functional group model enhanced performance. Finally, we explore increasing nutrient loading scenarios and predict their effect on phytoplankton dynamics throughout a one-year time horizon. Copyright © 2015 Elsevier Ltd. All rights reserved.

Calibration of mass transfer-based models to predict reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Valipour, Mohammad

2017-05-01

The present study aims to compare mass transfer-based models to determine the best model under different weather conditions. The results showed that the Penman model estimates reference crop evapotranspiration better than other models in most provinces of Iran (15 provinces). However, the values of R 2 were less than 0.90 for 24 provinces of Iran. Therefore, the models were calibrated, and precision of estimation was increased (the values of R 2 were less than 0.90 for only ten provinces in the modified models). The mass transfer-based models estimated reference crop evapotranspiration in the northern (near the Caspian Sea) and southern (near the Persian Gulf) Iran (annual relative humidity more than 65 %) better than other provinces. The best values of R 2 were 0.96 and 0.98 for the Trabert and Rohwer models in Ardabil (AR) and Mazandaran (MZ) provinces before and after calibration, respectively. Finally, a list of the best performances of each model was presented to use other regions and next studies according to values of mean, maximum, and minimum temperature, relative humidity, and wind speed. The best weather conditions to use mass transfer-based equations are 8-18 °C (with the exception of Ivanov), <25.5 °C, <15 °C, >55 % for mean, maximum, and minimum temperature, and relative humidity, respectively.

Robust estimation of simulated urinary volume from camera images under bathroom illumination.

PubMed

Honda, Chizuru; Bhuiyan, Md Shoaib; Kawanaka, Haruki; Watanabe, Eiichi; Oguri, Koji

2016-08-01

General uroflowmetry method involves the risk of nosocomial infections or time and effort of the recording. Medical institutions, therefore, need to measure voided volume simply and hygienically. Multiple cylindrical model that can estimate the fluid flow rate from the photographed image using camera has been proposed in an earlier study. This study implemented a flow rate estimation by using a general-purpose camera system (Raspberry Pi Camera Module) and the multiple cylindrical model. However, large amounts of noise in extracting liquid region are generated by the variation of the illumination when performing measurements in the bathroom. So the estimation error gets very large. In other words, the specifications of the previous study's camera setup regarding the shutter type and the frame rate was too strict. In this study, we relax the specifications to achieve a flow rate estimation using a general-purpose camera. In order to determine the appropriate approximate curve, we propose a binarizing method using background subtraction at each scanning row and a curve approximation method using RANSAC. Finally, by evaluating the estimation accuracy of our experiment and by comparing it with the earlier study's results, we show the effectiveness of our proposed method for flow rate estimation.

[Survival analysis with competing risks: estimating failure probability].

PubMed

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Northwest Energy Policy Project. Energy conservation policy evaluation: study module IA. Final report. Volume I. Summary report

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

Berney, R.E.; Butcher, W.R.; Carter, L.F.

1977-01-01

This summary report identifies potential energy conservation measures and evaluates measures and policy options not likely to be suitable for adoption in the Pacific Northwest. Potential and incentives for energy conservation are specifically identified in the residential, commercial, industrial, and transportation sectors and through urban design. Selection of preferable policies and estimation of response to policies are next discussed. Finally, a computer impact model for calculating energy savings and changes in the levels of residuals resulting from energy conservation activities is discussed. (MCW)

Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

NASA Astrophysics Data System (ADS)

Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.

2018-04-01

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.

Delineating parameter unidentifiabilities in complex models

NASA Astrophysics Data System (ADS)

Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis

2017-03-01

Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.

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

Sisniega, A.; Vaquero, J. J., E-mail: juanjose.vaquero@uc3m.es; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid ES28007

Purpose: The availability of accurate and simple models for the estimation of x-ray spectra is of great importance for system simulation, optimization, or inclusion of photon energy information into data processing. There is a variety of publicly available tools for estimation of x-ray spectra in radiology and mammography. However, most of these models cannot be used directly for modeling microfocus x-ray sources due to differences in inherent filtration, energy range and/or anode material. For this reason the authors propose in this work a new model for the simulation of microfocus spectra based on existing models for mammography and radiology, modifiedmore » to compensate for the effects of inherent filtration and energy range. Methods: The authors used the radiology and mammography versions of an existing empirical model [tungsten anode spectral model interpolating polynomials (TASMIP)] as the basis of the microfocus model. First, the authors estimated the inherent filtration included in the radiology model by comparing the shape of the spectra with spectra from the mammography model. Afterwards, the authors built a unified spectra dataset by combining both models and, finally, they estimated the parameters of the new version of TASMIP for microfocus sources by calibrating against experimental exposure data from a microfocus x-ray source. The model was validated by comparing estimated and experimental exposure and attenuation data for different attenuating materials and x-ray beam peak energy values, using two different x-ray tubes. Results: Inherent filtration for the radiology spectra from TASMIP was found to be equivalent to 1.68 mm Al, as compared to spectra obtained from the mammography model. To match the experimentally measured exposure data the combined dataset required to apply a negative filtration of about 0.21 mm Al and an anode roughness of 0.003 mm W. The validation of the model against real acquired data showed errors in exposure and attenuation in line with those reported for other models for radiology or mammography. Conclusions: A new version of the TASMIP model for the estimation of x-ray spectra in microfocus x-ray sources has been developed and validated experimentally. Similarly to other versions of TASMIP, the estimation of spectra is very simple, involving only the evaluation of polynomial expressions.« less

Effects of linear trends on estimation of noise in GNSS position time-series

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

Dmitrieva, K.; Segall, P.; Bradley, A. M.

A thorough understanding of time-dependent noise in Global Navigation Satellite System (GNSS) position time-series is necessary for computing uncertainties in any signals found in the data. However, estimation of time-correlated noise is a challenging task and is complicated by the difficulty in separating noise from signal, the features of greatest interest in the time-series. In this study, we investigate how linear trends affect the estimation of noise in daily GNSS position time-series. We use synthetic time-series to study the relationship between linear trends and estimates of time-correlated noise for the six most commonly cited noise models. We find that themore » effects of added linear trends, or conversely de-trending, vary depending on the noise model. The commonly adopted model of random walk (RW), flicker noise (FN) and white noise (WN) is the most severely affected by de-trending, with estimates of low-amplitude RW most severely biased. FN plus WN is least affected by adding or removing trends. Non-integer power-law noise estimates are also less affected by de-trending, but are very sensitive to the addition of trend when the spectral index is less than one. We derive an analytical relationship between linear trends and the estimated RW variance for the special case of pure RW noise. Finally, overall, we find that to ascertain the correct noise model for GNSS position time-series and to estimate the correct noise parameters, it is important to have independent constraints on the actual trends in the data.« less

Effects of linear trends on estimation of noise in GNSS position time-series

DOE PAGES

Dmitrieva, K.; Segall, P.; Bradley, A. M.

2016-10-20

A thorough understanding of time-dependent noise in Global Navigation Satellite System (GNSS) position time-series is necessary for computing uncertainties in any signals found in the data. However, estimation of time-correlated noise is a challenging task and is complicated by the difficulty in separating noise from signal, the features of greatest interest in the time-series. In this study, we investigate how linear trends affect the estimation of noise in daily GNSS position time-series. We use synthetic time-series to study the relationship between linear trends and estimates of time-correlated noise for the six most commonly cited noise models. We find that themore » effects of added linear trends, or conversely de-trending, vary depending on the noise model. The commonly adopted model of random walk (RW), flicker noise (FN) and white noise (WN) is the most severely affected by de-trending, with estimates of low-amplitude RW most severely biased. FN plus WN is least affected by adding or removing trends. Non-integer power-law noise estimates are also less affected by de-trending, but are very sensitive to the addition of trend when the spectral index is less than one. We derive an analytical relationship between linear trends and the estimated RW variance for the special case of pure RW noise. Finally, overall, we find that to ascertain the correct noise model for GNSS position time-series and to estimate the correct noise parameters, it is important to have independent constraints on the actual trends in the data.« less

Numerical Demons in Monte Carlo Estimation of Bayesian Model Evidence with Application to Soil Respiration Models

NASA Astrophysics Data System (ADS)

Elshall, A. S.; Ye, M.; Niu, G. Y.; Barron-Gafford, G.

2016-12-01

Bayesian multimodel inference is increasingly being used in hydrology. Estimating Bayesian model evidence (BME) is of central importance in many Bayesian multimodel analysis such as Bayesian model averaging and model selection. BME is the overall probability of the model in reproducing the data, accounting for the trade-off between the goodness-of-fit and the model complexity. Yet estimating BME is challenging, especially for high dimensional problems with complex sampling space. Estimating BME using the Monte Carlo numerical methods is preferred, as the methods yield higher accuracy than semi-analytical solutions (e.g. Laplace approximations, BIC, KIC, etc.). However, numerical methods are prone the numerical demons arising from underflow of round off errors. Although few studies alluded to this issue, to our knowledge this is the first study that illustrates these numerical demons. We show that the precision arithmetic can become a threshold on likelihood values and Metropolis acceptance ratio, which results in trimming parameter regions (when likelihood function is less than the smallest floating point number that a computer can represent) and corrupting of the empirical measures of the random states of the MCMC sampler (when using log-likelihood function). We consider two of the most powerful numerical estimators of BME that are the path sampling method of thermodynamic integration (TI) and the importance sampling method of steppingstone sampling (SS). We also consider the two most widely used numerical estimators, which are the prior sampling arithmetic mean (AS) and posterior sampling harmonic mean (HM). We investigate the vulnerability of these four estimators to the numerical demons. Interesting, the most biased estimator, namely the HM, turned out to be the least vulnerable. While it is generally assumed that AM is a bias-free estimator that will always approximate the true BME by investing in computational effort, we show that arithmetic underflow can hamper AM resulting in severe underestimation of BME. TI turned out to be the most vulnerable, resulting in BME overestimation. Finally, we show how SS can be largely invariant to rounding errors, yielding the most accurate and computational efficient results. These research results are useful for MC simulations to estimate Bayesian model evidence.

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

DOE PAGES

Ray, J.; Lee, J.; Yadav, V.; ...

2015-04-29

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

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

Ray, J.; Lee, J.; Yadav, V.

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

PubMed

Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

2011-12-01

The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

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

Li, Nan; Carmona, Ruben; Sirak, Igor

Purpose: To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. Methods and Materials: We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volumemore » histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V{sub 10} (percentage volume of PBM receiving at least 10 Gy dose) and V{sub 20} (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V{sub 40} (absolute volume of bowel receiving at least 40 Gy dose) and V{sub 45} (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBP{sub FINAL}), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBP{sub FINAL} plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. Results: KBP{sub FINAL} plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBP{sub FINAL} versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBP{sub FINAL} plans, indicating lowered probability of hematologic toxicity. Conclusions: This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.« less

Linear theory for filtering nonlinear multiscale systems with model error

PubMed Central

Berry, Tyrus; Harlim, John

2014-01-01

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline. PMID:25002829

Framework for Uncertainty Assessment - Hanford Site-Wide Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Bergeron, M. P.; Cole, C. R.; Murray, C. J.; Thorne, P. D.; Wurstner, S. K.

2002-05-01

Pacific Northwest National Laboratory is in the process of development and implementation of an uncertainty estimation methodology for use in future site assessments that addresses parameter uncertainty as well as uncertainties related to the groundwater conceptual model. The long-term goals of the effort are development and implementation of an uncertainty estimation methodology for use in future assessments and analyses being made with the Hanford site-wide groundwater model. The basic approach in the framework developed for uncertainty assessment consists of: 1) Alternate conceptual model (ACM) identification to identify and document the major features and assumptions of each conceptual model. The process must also include a periodic review of the existing and proposed new conceptual models as data or understanding become available. 2) ACM development of each identified conceptual model through inverse modeling with historical site data. 3) ACM evaluation to identify which of conceptual models are plausible and should be included in any subsequent uncertainty assessments. 4) ACM uncertainty assessments will only be carried out for those ACMs determined to be plausible through comparison with historical observations and model structure identification measures. The parameter uncertainty assessment process generally involves: a) Model Complexity Optimization - to identify the important or relevant parameters for the uncertainty analysis; b) Characterization of Parameter Uncertainty - to develop the pdfs for the important uncertain parameters including identification of any correlations among parameters; c) Propagation of Uncertainty - to propagate parameter uncertainties (e.g., by first order second moment methods if applicable or by a Monte Carlo approach) through the model to determine the uncertainty in the model predictions of interest. 5)Estimation of combined ACM and scenario uncertainty by a double sum with each component of the inner sum (an individual CCDF) representing parameter uncertainty associated with a particular scenario and ACM and the outer sum enumerating the various plausible ACM and scenario combinations in order to represent the combined estimate of uncertainty (a family of CCDFs). A final important part of the framework includes identification, enumeration, and documentation of all the assumptions, which include those made during conceptual model development, required by the mathematical model, required by the numerical model, made during the spatial and temporal descretization process, needed to assign the statistical model and associated parameters that describe the uncertainty in the relevant input parameters, and finally those assumptions required by the propagation method. Pacific Northwest National Laboratory is operated for the U.S. Department of Energy under Contract DE-AC06-76RL01830.

Revised Planning Methodology For Signalized Intersections And Operational Analysis Of Exclusive Left-Turn Lanes, A Simulation-Based Method, Part - I: Literature Review (Final Report)

DOT National Transportation Integrated Search

1996-04-01

THE STUDY INVESTIGATES THE APPLICATION OF SIMULATION ALONG WITH FIELD OBSERVATIONS FOR ESTIMATION OF EXCLUSIVE LEFT-TURN SATURATION FLOW RATE AND CAPACITY. THE ENTIRE RESEARCH HAS COVERED THE FOLLOWING PRINCIPAL SUBJECTS: (1) A SATURATION FLOW MODEL ...

TOWARD DEVELOPMENT OF A COMMON SOFTWARE APPLICATION PROGRAMMING INTERFACE (API) FOR UNCERTAINTY, SENSITIVITY, AND PARAMETER ESTIMATION METHODS AND TOOLS

EPA Science Inventory

The final session of the workshop considered the subject of software technology and how it might be better constructed to support those who develop, evaluate, and apply multimedia environmental models. Two invited presentations were featured along with an extended open discussio...

Emerging accounting trends accounting for leases.

PubMed

Valletta, Robert; Huggins, Brian

2010-12-01

A new model for lease accounting can have a significant impact on hospitals and healthcare organizations. The new approach proposes a "right-of-use" model that involves complex estimates and significant administrative burden. Hospitals and health systems that draw heavily on lease arrangements should start preparing for the new approach now even though guidance and a final rule are not expected until mid-2011. This article highlights a number of considerations from the lessee point of view.

Numerical trials of HISSE

NASA Technical Reports Server (NTRS)

Peters, C.; Kampe, F. (Principal Investigator)

1980-01-01

The mathematical description and implementation of the statistical estimation procedure known as the Houston integrated spatial/spectral estimator (HISSE) is discussed. HISSE is based on a normal mixture model and is designed to take advantage of spectral and spatial information of LANDSAT data pixels, utilizing the initial classification and clustering information provided by the AMOEBA algorithm. The HISSE calculates parametric estimates of class proportions which reduce the error inherent in estimates derived from typical classify and count procedures common to nonparametric clustering algorithms. It also singles out spatial groupings of pixels which are most suitable for labeling classes. These calculations are designed to aid the analyst/interpreter in labeling patches with a crop class label. Finally, HISSE's initial performance on an actual LANDSAT agricultural ground truth data set is reported.

Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

PubMed

Park, Byung-Jung; Lord, Dominique; Wu, Lingtao

2016-10-28

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Rotor Position Sensorless Control and Its Parameter Sensitivity of Permanent Magnet Motor Based on Model Reference Adaptive System

NASA Astrophysics Data System (ADS)

Ohara, Masaki; Noguchi, Toshihiko

This paper describes a new method for a rotor position sensorless control of a surface permanent magnet synchronous motor based on a model reference adaptive system (MRAS). This method features the MRAS in a current control loop to estimate a rotor speed and position by using only current sensors. This method as well as almost all the conventional methods incorporates a mathematical model of the motor, which consists of parameters such as winding resistances, inductances, and an induced voltage constant. Hence, the important thing is to investigate how the deviation of these parameters affects the estimated rotor position. First, this paper proposes a structure of the sensorless control applied in the current control loop. Next, it proves the stability of the proposed method when motor parameters deviate from the nominal values, and derives the relationship between the estimated position and the deviation of the parameters in a steady state. Finally, some experimental results are presented to show performance and effectiveness of the proposed method.

Quantile Regression Models for Current Status Data

PubMed Central

Ou, Fang-Shu; Zeng, Donglin; Cai, Jianwen

2016-01-01

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging. PMID:27994307

Large historical growth in global terrestrial gross primary production

DOE PAGES

Campbell, J. E.; Berry, J. A.; Seibt, U.; ...

2017-04-05

Growth in terrestrial gross primary production (GPP) may provide a negative feedback for climate change. It remains uncertain, however, to what extent biogeochemical processes can suppress global GPP growth. In consequence, model estimates of terrestrial carbon storage and carbon cycle –climate feedbacks remain poorly constrained. Here we present a global, measurement-based estimate of GPP growth during the twentieth century based on long-term atmospheric carbonyl sulphide (COS) records derived from ice core, firn, and ambient air samples. Here, we interpret these records using a model that simulates changes in COS concentration due to changes in its sources and sinks, including amore » large sink that is related to GPP. We find that the COS record is most consistent with climate-carbon cycle model simulations that assume large GPP growth during the twentieth century (31% ± 5%; mean ± 95% confidence interval). Finally, while this COS analysis does not directly constrain estimates of future GPP growth it provides a global-scale benchmark for historical carbon cycle simulations.« less

Verification of a model for the detection of intrauterine growth restriction (IUGR) by receiver operating characteristics (ROC)

NASA Astrophysics Data System (ADS)

Liu, Pengbo; Mongelli, Max; Mondry, Adrian

2004-07-01

The purpose of this study is to verify by Receiver Operating Characteristics (ROC) a mathematical model supporting the hypothesis that IUGR can be diagnosed by estimating growth velocity. The ROC compare computerized simulation results with clinical data from 325 pregnant British women. Each patient had 6 consecutive ultrasound examinations for fetal abdominal circumference (fac). Customized and un-customized fetal weights were calculated according to Hadlock"s formula. IUGR was diagnosed by the clinical standard, i.e. estimated weight below the tenth percentile. Growth velocity was estimated by calculating the changes of fac (Dzfac/dt) at various time intervals from 3 to 10 weeks. Finally, ROC was used to compare the methods. At 3~4 weeks scan interval, the area under the ROC curve is 0.68 for customized data and 0.66 for the uncustomized data with 95% confidence interval. Comparison between simulation data and real pregnancies verified that the model is clinically acceptable.

Large historical growth in global terrestrial gross primary production

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

Campbell, J. E.; Berry, J. A.; Seibt, U.

Growth in terrestrial gross primary production (GPP) may provide a negative feedback for climate change. It remains uncertain, however, to what extent biogeochemical processes can suppress global GPP growth. In consequence, model estimates of terrestrial carbon storage and carbon cycle –climate feedbacks remain poorly constrained. Here we present a global, measurement-based estimate of GPP growth during the twentieth century based on long-term atmospheric carbonyl sulphide (COS) records derived from ice core, firn, and ambient air samples. Here, we interpret these records using a model that simulates changes in COS concentration due to changes in its sources and sinks, including amore » large sink that is related to GPP. We find that the COS record is most consistent with climate-carbon cycle model simulations that assume large GPP growth during the twentieth century (31% ± 5%; mean ± 95% confidence interval). Finally, while this COS analysis does not directly constrain estimates of future GPP growth it provides a global-scale benchmark for historical carbon cycle simulations.« less

Incorporating parametric uncertainty into population viability analysis models

USGS Publications Warehouse

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

Assessing the limitations of the Banister model in monitoring training

PubMed Central

Hellard, Philippe; Avalos, Marta; Lacoste, Lucien; Barale, Frédéric; Chatard, Jean-Claude; Millet, Grégoire P.

2006-01-01

The aim of this study was to carry out a statistical analysis of the Banister model to verify how useful it is in monitoring the training programmes of elite swimmers. The accuracy, the ill-conditioning and the stability of this model were thus investigated. Training loads of nine elite swimmers, measured over one season, were related to performances with the Banister model. Firstly, to assess accuracy, the 95% bootstrap confidence interval (95% CI) of parameter estimates and modelled performances were calculated. Secondly, to study ill-conditioning, the correlation matrix of parameter estimates was computed. Finally, to analyse stability, iterative computation was performed with the same data but minus one performance, chosen randomly. Performances were significantly related to training loads in all subjects (R2= 0.79 ± 0.13, P < 0.05) and the estimation procedure seemed to be stable. Nevertheless, the 95% CI of the most useful parameters for monitoring training were wide τa =38 (17, 59), τf =19 (6, 32), tn =19 (7, 35), tg =43 (25, 61). Furthermore, some parameters were highly correlated making their interpretation worthless. The study suggested possible ways to deal with these problems and reviewed alternative methods to model the training-performance relationships. PMID:16608765

Field size dependent mapping of medical linear accelerator radiation leakage

NASA Astrophysics Data System (ADS)

Vũ Bezin, Jérémi; Veres, Attila; Lefkopoulos, Dimitri; Chavaudra, Jean; Deutsch, Eric; de Vathaire, Florent; Diallo, Ibrahima

2015-03-01

The purpose of this study was to investigate the suitability of a graphics library based model for the assessment of linear accelerator radiation leakage. Transmission through the shielding elements was evaluated using the build-up factor corrected exponential attenuation law and the contribution from the electron guide was estimated using the approximation of a linear isotropic radioactive source. Model parameters were estimated by a fitting series of thermoluminescent dosimeter leakage measurements, achieved up to 100 cm from the beam central axis along three directions. The distribution of leakage data at the patient plane reflected the architecture of the shielding elements. Thus, the maximum leakage dose was found under the collimator when only one jaw shielded the primary beam and was about 0.08% of the dose at isocentre. Overall, we observe that the main contributor to leakage dose according to our model was the electron beam guide. Concerning the discrepancies between the measurements used to calibrate the model and the calculations from the model, the average difference was about 7%. Finally, graphics library modelling is a readily and suitable way to estimate leakage dose distribution on a personal computer. Such data could be useful for dosimetric evaluations in late effect studies.

A new estimator method for GARCH models

NASA Astrophysics Data System (ADS)

Onody, R. N.; Favaro, G. M.; Cazaroto, E. R.

2007-06-01

The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a central role in empirical finance. The Markovian GARCH (1, 1) model has only 3 control parameters and a much discussed question is how to estimate them when a series of some financial asset is given. Besides the maximum likelihood estimator technique, there is another method which uses the variance, the kurtosis and the autocorrelation time to determine them. We propose here to use the standardized 6th moment. The set of parameters obtained in this way produces a very good probability density function and a much better time autocorrelation function. This is true for both studied indexes: NYSE Composite and FTSE 100. The probability of return to the origin is investigated at different time horizons for both Gaussian and Laplacian GARCH models. In spite of the fact that these models show almost identical performances with respect to the final probability density function and to the time autocorrelation function, their scaling properties are, however, very different. The Laplacian GARCH model gives a better scaling exponent for the NYSE time series, whereas the Gaussian dynamics fits better the FTSE scaling exponent.

Forecasting of cereals yields in a semi-arid area using the agrometeorological model «SAFY» combined to optical SPOT/HRV images

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra; Mougenot, Bernard

2015-10-01

In semi-arid areas, an operational grain yield forecasting system, which could help decision-makers to plan annual imports, is needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Remote sensing has demonstrated its strong potential for the monitoring of the vegetation's dynamics and temporal variations. Through the use of a rich database, acquired over a period of two years for more than 60 test fields, and from 20 optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two approaches to estimate the dynamics and yields of cereals in the context of semi-arid, low productivity regions in North Africa. The first approach is based on the application of the semi-empirical growth model SAFY "Simple Algorithm For Yield estimation", developed to simulate the dynamics of the leaf area index and the grain yield, at the field scale. The model is able to reproduce the time evolution of the LAI of all fields. However, the yields are under-estimated. Therefore, we developed a new approach to improve the SAFY model. The grain yield is function of LAI area in the growth period between 25 March and 5 April. This approach is robust, the measured and estimated grain yield are well correlated. Finally, this model is used in combination with remotely sensed LAI measurements to estimate yield for the entire studied site.

An integrative modeling approach for the efficient estimation of cross sectional tibial stresses during locomotion.

PubMed

Derrick, Timothy R; Edwards, W Brent; Fellin, Rebecca E; Seay, Joseph F

2016-02-08

The purpose of this research was to utilize a series of models to estimate the stress in a cross section of the tibia, located 62% from the proximal end, during walking. Twenty-eight male, active duty soldiers walked on an instrumented treadmill while external force data and kinematics were recorded. A rigid body model was used to estimate joint moments and reaction forces. A musculoskeletal model was used to gather muscle length, muscle velocity, moment arm and orientation information. Optimization procedures were used to estimate muscle forces and finally internal bone forces and moments were applied to an inhomogeneous, subject specific bone model obtained from CT scans to estimate stress in the bone cross section. Validity was assessed by comparison to stresses calculated from strain gage data in the literature and sensitivity was investigated using two simplified versions of the bone model-a homogeneous model and an ellipse approximation. Peak compressive stress occurred on the posterior aspect of the cross section (-47.5 ± 14.9 MPa). Peak tensile stress occurred on the anterior aspect (27.0 ± 11.7 MPa) while the location of peak shear was variable between subjects (7.2 ± 2.4 MPa). Peak compressive, tensile and shear stresses were within 0.52 MPa, 0.36 MPa and 3.02 MPa respectively of those calculated from the converted strain gage data. Peak values from a inhomogeneous model of the bone correlated well with homogeneous model (normal: 0.99; shear: 0.94) as did the normal ellipse model (r=0.89-0.96). However, the relationship between shear stress in the inhomogeneous model and ellipse model was less accurate (r=0.64). The procedures detailed in this paper provide a non-invasive and relatively quick method of estimating cross sectional stress that holds promise for assessing injury and osteogenic stimulus in bone during normal physical activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bayesian Non-Stationary Flood Frequency Estimation at Ungauged Basins Using Climate Information and a Scaling Model

NASA Astrophysics Data System (ADS)

Lima, C. H.; Lall, U.

2010-12-01

Flood frequency statistical analysis most often relies on stationary assumptions, where distribution moments (e.g. mean, standard deviation) and associated flood quantiles do not change over time. In this sense, one expects that flood magnitudes and their frequency of occurrence will remain constant as observed in the historical information. However, evidence of inter-annual and decadal climate variability and anthropogenic change as well as an apparent increase in the number and magnitude of flood events across the globe have made the stationary assumption questionable. Here, we show how to estimate flood quantiles (e.g. 100-year flood) at ungauged basins without needing to consider stationarity. A statistical model based on the well known flow-area scaling law is proposed to estimate flood flows at ungauged basins. The slope and intercept scaling law coefficients are assumed time varying and a hierarchical Bayesian model is used to include climate information and reduce parameter uncertainties. Cross-validated results from 34 streamflow gauges located in a nested Basin in Brazil show that the proposed model is able to estimate flood quantiles at ungauged basins with remarkable skills compared with data based estimates using the full record. The model as developed in this work is also able to simulate sequences of flood flows considering global climate changes provided an appropriate climate index developed from the General Circulation Model is used as a predictor. The time varying flood frequency estimates can be used for pricing insurance models, and in a forecast mode for preparations for flooding, and finally, for timing infrastructure investments and location. Non-stationary 95% interval estimation for the 100-year Flood (shaded gray region) and 95% interval for the 100-year flood estimated from data (horizontal dashed and solid lines). The average distribution of the 100-year flood is shown in green in the right side.

Comparison of two perturbation methods to estimate the land surface modeling uncertainty

NASA Astrophysics Data System (ADS)

Su, H.; Houser, P.; Tian, Y.; Kumar, S.; Geiger, J.; Belvedere, D.

2007-12-01

In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.

On the distinguishability of HRF models in fMRI.

PubMed

Rosa, Paulo N; Figueiredo, Patricia; Silvestre, Carlos J

2015-01-01

Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.

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.

Gauging the cosmic acceleration with recent type Ia supernovae data sets

NASA Astrophysics Data System (ADS)

Velten, Hermano; Gomes, Syrios; Busti, Vinicius C.

2018-04-01

We revisit a model-independent estimator for cosmic acceleration based on type Ia supernovae distance measurements. This approach does not rely on any specific theory for gravity, energy content, nor parametrization for the scale factor or deceleration parameter and is based on falsifying the null hypothesis that the Universe never expanded in an accelerated way. By generating mock catalogs of known cosmologies, we test the robustness of this estimator, establishing its limits of applicability. We detail the pros and cons of such an approach. For example, we find that there are specific counterexamples in which the estimator wrongly provides evidence against acceleration in accelerating cosmologies. The dependence of the estimator on the H0 value is also discussed. Finally, we update the evidence for acceleration using the recent UNION2.1 and Joint Light-Curve Analysis samples. Contrary to recent claims, available data strongly favor an accelerated expansion of the Universe in complete agreement with the standard Λ CDM model.

The Yale-Brown Obsessive Compulsive Scale: A Reliability Generalization Meta-Analysis.

PubMed

López-Pina, José Antonio; Sánchez-Meca, Julio; López-López, José Antonio; Marín-Martínez, Fulgencio; Núñez-Núñez, Rosa Maria; Rosa-Alcázar, Ana I; Gómez-Conesa, Antonia; Ferrer-Requena, Josefa

2015-10-01

The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) is the most frequently applied test to assess obsessive compulsive symptoms. We conducted a reliability generalization meta-analysis on the Y-BOCS to estimate the average reliability, examine the variability among the reliability estimates, search for moderators, and propose a predictive model that researchers and clinicians can use to estimate the expected reliability of the Y-BOCS. We included studies where the Y-BOCS was applied to a sample of adults and reliability estimate was reported. Out of the 11,490 references located, 144 studies met the selection criteria. For the total scale, the mean reliability was 0.866 for coefficients alpha, 0.848 for test-retest correlations, and 0.922 for intraclass correlations. The moderator analyses led to a predictive model where the standard deviation of the total test and the target population (clinical vs. nonclinical) explained 38.6% of the total variability among coefficients alpha. Finally, clinical implications of the results are discussed. © The Author(s) 2014.

An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise.

PubMed

Le Montagner, Yoann; Angelini, Elsa D; Olivo-Marin, Jean-Christophe

2014-03-01

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.

A new parametric method to smooth time-series data of metabolites in metabolic networks.

PubMed

Miyawaki, Atsuko; Sriyudthsak, Kansuporn; Hirai, Masami Yokota; Shiraishi, Fumihide

2016-12-01

Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches.

PubMed

Chan, Jennifer S K

2016-05-01

Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Structural Inference in the Art of Violin Making.

NASA Astrophysics Data System (ADS)

Morse-Fortier, Leonard Joseph

The "secrets" of success of early Italian violins have long been sought. Among their many efforts to reproduce the results of Stradiveri, Guarneri, and Amati, luthiers have attempted to order and match natural resonant frequencies in the free violin plates. This tap-tone plate tuning technique is simply an eigenvalue extraction scheme. In the final stages of carving, the violin maker complements considerable intuitive knowledge of violin plate structure and of modal attributes with tap-tone frequency estimates to better understand plate structure and to inform decisions about plate carving and completeness. Examining the modal attributes of violin plates, this work develops and incorporates an impulse-response scheme for modal inference, measures resonant frequencies and modeshapes for a pair of violin plates, and presents modeshapes through a unique computer visualization scheme developed specifically for this purpose. The work explores, through simple examples questions of how plate modal attributes reflect underlying structure, and questions about the so -called evolution of modeshapes and frequencies through assembly of the violin. Separately, the work develops computer code for a carved, anisotropic, plate/shell finite element. Solutions are found to the static displacement and free-vibration eigenvalue problems for an orthotropic plate, and used to verify element accuracy. Finally, a violin back plate is modelled with full consideration of plate thickness and arching. Model estimates for modal attributes compare very well against experimentally acquired values. Finally, the modal synthesis technique is applied to predicting the modal attributes of the violin top plate with ribs attached from those of the top plate alone, and with an estimate of rib mass and stiffness. This last analysis serves to verify the modal synthesis method, and to quantify its limits of applicability in attempting to solve problems with severe structural modification. Conclusions emphasize the importance of better understanding the underlying structure, improved understanding of its relationship to modal attributes, and better estimates of wood elasticity.

Variability and predictability of finals times of elite rowers.

PubMed

Smith, Tiaki Brett; Hopkins, Will G

2011-11-01

Little is known about the competitive performance characteristics of elite rowers. We report here analyses of performance times for finalists in world-class regattas from 1999 to 2009. The data were official race times for the 10 men's and 7 women's single and crewed boat classes, each with ∼ 200-300 different boats competing in 1-33 of the 46 regattas at 18 venues. A linear mixed model of race times for each boat class provided estimates of variability as coefficients of variation after adjustment for means of calendar year, level of competition (Olympics, world championship, World Cup), venue, and level of final (A, B, C, …). Mean performance was substantially slower between consecutive levels of competition (1.5%, 2.7%) and consecutive levels of finals (∼ 1%-2%). Differences in the effects of venue and of environmental conditions, estimated as variability in mean race time between venues and finals, were extremely large (∼ 3.0%). Within-boat race-to-race variability for A finalists was 1.1% for single sculls and 0.9% for crewed boats, with little difference between men and women and only a small increase in lower-level finalists. Predictability of performance, expressed as intraclass correlation coefficients, showed considerable differences between boat classes, but the mean was high (∼ 0.63), with little difference between crewed and single boats, between men and women, and between within and between years. The race-to-race variability of boat times of ∼ 1.0% is similar to that in comparable endurance sports performed against water or air resistance. Estimates of the smallest important performance enhancement (∼ 0.3%) and the effects of level of competition, level of final, venue, environment, and boat class will help inform investigations of factors affecting elite competitive rowing performance.

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias

NASA Astrophysics Data System (ADS)

Nüske, Feliks; Wu, Hao; Prinz, Jan-Hendrik; Wehmeyer, Christoph; Clementi, Cecilia; Noé, Frank

2017-03-01

Many state-of-the-art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ensembles of short simulations, provided that these short simulations are in "local equilibrium" within the MSM states. However, over the last 15 years since the inception of MSMs, it has been controversially discussed and not yet been answered how deviations from local equilibrium can be detected, whether these deviations induce a practical bias in MSM estimation, and how to correct for them. In this paper, we address these issues: We systematically analyze the estimation of MSMs from short non-equilibrium simulations, and we provide an expression for the error between unbiased transition probabilities and the expected estimate from many short simulations. We show that the unbiased MSM estimate can be obtained even from relatively short non-equilibrium simulations in the limit of long lag times and good discretization. Further, we exploit observable operator model (OOM) theory to derive an unbiased estimator for the MSM transition matrix that corrects for the effect of starting out of equilibrium, even when short lag times are used. Finally, we show how the OOM framework can be used to estimate the exact eigenvalues or relaxation time scales of the system without estimating an MSM transition matrix, which allows us to practically assess the discretization quality of the MSM. Applications to model systems and molecular dynamics simulation data of alanine dipeptide are included for illustration. The improved MSM estimator is implemented in PyEMMA of version 2.3.

Hierarchical Bayesian modeling of ionospheric TEC disturbances as non-stationary processes

NASA Astrophysics Data System (ADS)

Seid, Abdu Mohammed; Berhane, Tesfahun; Roininen, Lassi; Nigussie, Melessew

2018-03-01

We model regular and irregular variation of ionospheric total electron content as stationary and non-stationary processes, respectively. We apply the method developed to SCINDA GPS data set observed at Bahir Dar, Ethiopia (11.6 °N, 37.4 °E) . We use hierarchical Bayesian inversion with Gaussian Markov random process priors, and we model the prior parameters in the hyperprior. We use Matérn priors via stochastic partial differential equations, and use scaled Inv -χ2 hyperpriors for the hyperparameters. For drawing posterior estimates, we use Markov Chain Monte Carlo methods: Gibbs sampling and Metropolis-within-Gibbs for parameter and hyperparameter estimations, respectively. This allows us to quantify model parameter estimation uncertainties as well. We demonstrate the applicability of the method proposed using a synthetic test case. Finally, we apply the method to real GPS data set, which we decompose to regular and irregular variation components. The result shows that the approach can be used as an accurate ionospheric disturbance characterization technique that quantifies the total electron content variability with corresponding error uncertainties.

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data

PubMed Central

Goto, Takahiro; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results. PMID:29337999

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

PubMed Central

Li, Degui; Li, Runze

2016-01-01

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894

Latent degradation indicators estimation and prediction: A Monte Carlo approach

NASA Astrophysics Data System (ADS)

Zhou, Yifan; Sun, Yong; Mathew, Joseph; Wolff, Rodney; Ma, Lin

2011-01-01

Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

Factoring out nondecision time in choice reaction time data: Theory and implications.

PubMed

Verdonck, Stijn; Tuerlinckx, Francis

2016-03-01

Choice reaction time (RT) experiments are an invaluable tool in psychology and neuroscience. A common assumption is that the total choice response time is the sum of a decision and a nondecision part (time spent on perceptual and motor processes). While the decision part is typically modeled very carefully (commonly with diffusion models), a simple and ad hoc distribution (mostly uniform) is assumed for the nondecision component. Nevertheless, it has been shown that the misspecification of the nondecision time can severely distort the decision model parameter estimates. In this article, we propose an alternative approach to the estimation of choice RT models that elegantly bypasses the specification of the nondecision time distribution by means of an unconventional convolution of data and decision model distributions (hence called the D*M approach). Once the decision model parameters have been estimated, it is possible to compute a nonparametric estimate of the nondecision time distribution. The technique is tested on simulated data, and is shown to systematically remove traditional estimation bias related to misspecified nondecision time, even for a relatively small number of observations. The shape of the actual underlying nondecision time distribution can also be recovered. Next, the D*M approach is applied to a selection of existing diffusion model application articles. For all of these studies, substantial quantitative differences with the original analyses are found. For one study, these differences radically alter its final conclusions, underlining the importance of our approach. Additionally, we find that strongly right skewed nondecision time distributions are not at all uncommon. (c) 2016 APA, all rights reserved).

An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data

PubMed Central

Yao, Jiayun; Henderson, Sarah B

2014-01-01

Exposure to forest fire smoke (FFS) is associated with a range of adverse health effects. The British Columbia Asthma Medication Surveillance (BCAMS) product was developed to detect potential impacts from FFS in British Columbia (BC), Canada. However, it has been a challenge to estimate FFS exposure with sufficient spatial coverage for the provincial population. We constructed an empirical model to estimate FFS-related fine particulate matter (PM2.5) for all populated areas of BC using data from the most extreme FFS days in 2003 through 2012. The input data included PM2.5 measurements on the previous day, remotely sensed aerosols, remotely sensed fires, hand-drawn tracings of smoke plumes from satellite images, fire danger ratings, and the atmospheric venting index. The final model explained 71% of the variance in PM2.5 observations. Model performance was tested in days with high, moderate, and low levels of FFS, resulting in correlations from 0.57 to 0.83. We also developed a method to assign the model estimates to geographical local health areas for use in BCAMS. The simplicity of the model allows easy application in time-constrained public health surveillance, and its sufficient spatial coverage suggests utility as an exposure assessment tool for epidemiologic studies on FFS exposure. PMID:24301352

Improved quantitative analysis of spectra using a new method of obtaining derivative spectra based on a singular perturbation technique.

PubMed

Li, Zhigang; Wang, Qiaoyun; Lv, Jiangtao; Ma, Zhenhe; Yang, Linjuan

2015-06-01

Spectroscopy is often applied when a rapid quantitative analysis is required, but one challenge is the translation of raw spectra into a final analysis. Derivative spectra are often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress unwanted spectral features that arise due to non-ideal instrument and sample properties. In this study, to improve quantitative analysis of near-infrared spectra, derivatives of noisy raw spectral data need to be estimated with high accuracy. A new spectral estimator based on singular perturbation technique, called the singular perturbation spectra estimator (SPSE), is presented, and the stability analysis of the estimator is given. Theoretical analysis and simulation experimental results confirm that the derivatives can be estimated with high accuracy using this estimator. Furthermore, the effectiveness of the estimator for processing noisy infrared spectra is evaluated using the analysis of beer spectra. The derivative spectra of the beer and the marzipan are used to build the calibration model using partial least squares (PLS) modeling. The results show that the PLS based on the new estimator can achieve better performance compared with the Savitzky-Golay algorithm and can serve as an alternative choice for quantitative analytical applications.

LACIE performance predictor final operational capability program description, volume 3

NASA Technical Reports Server (NTRS)

1976-01-01

The requirements and processing logic for the LACIE Error Model program (LEM) are described. This program is an integral part of the Large Area Crop Inventory Experiment (LACIE) system. LEM is that portion of the LPP (LACIE Performance Predictor) which simulates the sample segment classification, strata yield estimation, and production aggregation. LEM controls repetitive Monte Carlo trials based on input error distributions to obtain statistical estimates of the wheat area, yield, and production at different levels of aggregation. LEM interfaces with the rest of the LPP through a set of data files.

Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year

USGS Publications Warehouse

Saad, David A.; Benoy, Glenn A.; Robertson, Dale M.

2018-05-11

Streamflow and nutrient concentration data needed to compute nitrogen and phosphorus loads were compiled from Federal, State, Provincial, and local agency databases and also from selected university databases. The nitrogen and phosphorus loads are necessary inputs to Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models are a way to estimate the distribution, sources, and transport of nutrients in streams throughout the Midcontinental region of Canada and the United States. After screening the data, approximately 1,500 sites sampled by 34 agencies were identified as having suitable data for calculating the long-term mean-annual nutrient loads required for SPARROW model calibration. These final sites represent a wide range in watershed sizes, types of nutrient sources, and land-use and watershed characteristics in the Midcontinental region of Canada and the United States.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data

DOE PAGES

Stewart, Robert N.; Urban, Marie L.; Duchscherer, Samantha E.; ...

2016-01-01

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the artmore » by introducing the Population Data Tables (PDT), a Bayesian based informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the Population Data Tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.« less

Estimation of contour motion and deformation for nonrigid object tracking

NASA Astrophysics Data System (ADS)

Shao, Jie; Porikli, Fatih; Chellappa, Rama

2007-08-01

We present an algorithm for nonrigid contour tracking in heavily cluttered background scenes. Based on the properties of nonrigid contour movements, a sequential framework for estimating contour motion and deformation is proposed. We solve the nonrigid contour tracking problem by decomposing it into three subproblems: motion estimation, deformation estimation, and shape regulation. First, we employ a particle filter to estimate the global motion parameters of the affine transform between successive frames. Then we generate a probabilistic deformation map to deform the contour. To improve robustness, multiple cues are used for deformation probability estimation. Finally, we use a shape prior model to constrain the deformed contour. This enables us to retrieve the occluded parts of the contours and accurately track them while allowing shape changes specific to the given object types. Our experiments show that the proposed algorithm significantly improves the tracker performance.

Estimating the diversity of dinosaurs

NASA Astrophysics Data System (ADS)

Wang, Steve C.; Dodson, Peter

2006-09-01

Despite current interest in estimating the diversity of fossil and extant groups, little effort has been devoted to estimating the diversity of dinosaurs. Here we estimate the diversity of nonavian dinosaurs at ≈1,850 genera, including those that remain to be discovered. With 527 genera currently described, at least 71% of dinosaur genera thus remain unknown. Although known diversity declined in the last stage of the Cretaceous, estimated diversity was steady, suggesting that dinosaurs as a whole were not in decline in the 10 million years before their ultimate extinction. We also show that known diversity is biased by the availability of fossiliferous rock outcrop. Finally, by using a logistic model, we predict that 75% of discoverable genera will be known within 60-100 years and 90% within 100-140 years. Because of nonrandom factors affecting the process of fossil discovery (which preclude the possibility of computing realistic confidence bounds), our estimate of diversity is likely to be a lower bound.

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

PubMed Central

Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

2015-01-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

PubMed

Schnitzer, Mireille E; Lok, Judith J; Gruber, Susan

2016-05-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data.

PubMed

Ding, Aidong Adam; Hsieh, Jin-Jian; Wang, Weijing

2015-01-01

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.

Joint Estimation of the Epoch of Reionization Power Spectrum and Foregrounds

NASA Astrophysics Data System (ADS)

Sims, Peter; Pober, Jonathan

2018-01-01

Bright astrophysical foregrounds present a significant impediment to the detection of redshifted 21-cm emission from the Epoch of Reionization on large spatial scales. In this talk I present a framework for the joint modeling of the power spectral contamination by astrophysical foregrounds and the power spectrum of the Epoch of Reionization. I show how informative priors on the power spectral contamination by astrophysical foregrounds at high redshifts, where emission from both the Epoch of Reionization and its foregrounds is present in the data, can be obtained through analysis of foreground-only emission at lower redshifts. Finally, I demonstrate how, by using such informative foreground priors, joint modeling can be employed to mitigate bias in estimates of the power spectrum of the Epoch of Reionization signal and, in particular, to enable recovery of more robust power spectral estimates on large spatial scales.

Scale of reference bias and the evolution of health.

PubMed

Groot, Wim

2003-09-01

The analysis of subjective measures of well-being-such as self-reports by individuals about their health status is frequently hampered by the problem of scale of reference bias. A particular form of scale of reference bias is age norming. In this study we corrected for scale of reference bias by allowing for individual specific effects in an equation on subjective health. A random effects ordered response model was used to analyze scale of reference bias in self-reported health measures. The results indicate that if we do not control for unobservable individual specific effects, the response to a subjective health state measure suffers from age norming. Age norming can be controlled for by a random effects estimation technique using longitudinal data. Further, estimates are presented on the rate of depreciation of health. Finally, simulations of life expectancy indicate that the estimated model provides a reasonably good fit of the true life expectancy.

Lifetime prediction and reliability estimation methodology for Stirling-type pulse tube refrigerators by gaseous contamination accelerated degradation testing

NASA Astrophysics Data System (ADS)

Wan, Fubin; Tan, Yuanyuan; Jiang, Zhenhua; Chen, Xun; Wu, Yinong; Zhao, Peng

2017-12-01

Lifetime and reliability are the two performance parameters of premium importance for modern space Stirling-type pulse tube refrigerators (SPTRs), which are required to operate in excess of 10 years. Demonstration of these parameters provides a significant challenge. This paper proposes a lifetime prediction and reliability estimation method that utilizes accelerated degradation testing (ADT) for SPTRs related to gaseous contamination failure. The method was experimentally validated via three groups of gaseous contamination ADT. First, the performance degradation model based on mechanism of contamination failure and material outgassing characteristics of SPTRs was established. Next, a preliminary test was performed to determine whether the mechanism of contamination failure of the SPTRs during ADT is consistent with normal life testing. Subsequently, the experimental program of ADT was designed for SPTRs. Then, three groups of gaseous contamination ADT were performed at elevated ambient temperatures of 40 °C, 50 °C, and 60 °C, respectively and the estimated lifetimes of the SPTRs under normal condition were obtained through acceleration model (Arrhenius model). The results show good fitting of the degradation model with the experimental data. Finally, we obtained the reliability estimation of SPTRs through using the Weibull distribution. The proposed novel methodology enables us to take less than one year time to estimate the reliability of the SPTRs designed for more than 10 years.

Simulating the Refractive Index Structure Constant ({C}_{n}^{2}) in the Surface Layer at Antarctica with a Mesoscale Model

NASA Astrophysics Data System (ADS)

Qing, Chun; Wu, Xiaoqing; Li, Xuebin; Tian, Qiguo; Liu, Dong; Rao, Ruizhong; Zhu, Wenyue

2018-01-01

In this paper, we introduce an approach wherein the Weather Research and Forecasting (WRF) model is coupled with the bulk aerodynamic method to estimate the surface layer refractive index structure constant (C n 2) above Taishan Station in Antarctica. First, we use the measured meteorological parameters to estimate C n 2 using the bulk aerodynamic method, and second, we use the WRF model output parameters to estimate C n 2 using the bulk aerodynamic method. Finally, the corresponding C n 2 values from the micro-thermometer are compared with the C n 2 values estimated using the WRF model coupled with the bulk aerodynamic method. We analyzed the statistical operators—the bias, root mean square error (RMSE), bias-corrected RMSE (σ), and correlation coefficient (R xy )—in a 20 day data set to assess how this approach performs. In addition, we employ contingency tables to investigate the estimation quality of this approach, which provides complementary key information with respect to the bias, RMSE, σ, and R xy . The quantitative results are encouraging and permit us to confirm the fine performance of this approach. The main conclusions of this study tell us that this approach provides a positive impact on optimizing the observing time in astronomical applications and provides complementary key information for potential astronomical sites.

Near-Infrared (0.67-4.7 microns) Optical Constants Estimated for Montmorillonite

NASA Technical Reports Server (NTRS)

Roush, T. L.

2005-01-01

Various models of the reflectance from particulate surfaces are used for interpretation of remote sensing data of solar system objects. These models rely upon the real (n) and imaginary (k) refractive indices of the materials. Such values are limited for commonly encountered silicates at visual and near-infrared wavelengths (lambda, 0.4-5 microns). Availability of optical constants for candidate materials allows more thorough modeling of the observations obtained by Earth-based telescopes and spacecraft. Two approaches for determining the absorption coefficient (alpha=2pik/lambda) from reflectance measurements of particulates have been described; one relies upon Kubelka-Munk theory and the other Hapke theory. Both have been applied to estimate alpha and k for various materials. Neither enables determination of the wavelength dependence of n, n=f(lambda). Thus, a mechanism providing this ability is desirable. Using Hapke-theory to estimate k from reflectance measurements requires two additional quantities be known or assumed: 1) n=f(lambda) and 2) d, the sample particle diameter. Typically n is assumed constant (c) or modestly varying with lambda; referred to here as n(sub 0). Assuming n(sub 0), at each lambda an estimate of k is used to calculate the reflectance and is iteratively adjusted until the difference between the model and measured reflectance is minimized. The estimated k's (k(sub 1)) are the final results, and this concludes the typical analysis.

Receiver function analysis applied to refraction survey data

NASA Astrophysics Data System (ADS)

Subaru, T.; Kyosuke, O.; Hitoshi, M.

2008-12-01

For the estimation of the thickness of oceanic crust or petrophysical investigation of subsurface material, refraction or reflection seismic exploration is one of the methods frequently practiced. These explorations use four-component (x,y,z component of acceleration and pressure) seismometer, but only compressional wave or vertical component of seismometers tends to be used in the analyses. Hence, it is needed to use shear wave or lateral component of seismograms for more precise investigation to estimate the thickness of oceanic crust. Receiver function is a function at a place that can be used to estimate the depth of velocity interfaces by receiving waves from teleseismic signal including shear wave. Receiver function analysis uses both vertical and horizontal components of seismograms and deconvolves the horizontal with the vertical to estimate the spectral difference of P-S converted waves arriving after the direct P wave. Once the phase information of the receiver function is obtained, then one can estimate the depth of the velocity interface. This analysis has advantage in the estimation of the depth of velocity interface including Mohorovicic discontinuity using two components of seismograms when P-to-S converted waves are generated at the interface. Our study presents results of the preliminary study using synthetic seismograms. First, we use three types of geological models that are composed of a single sediment layer, a crust layer, and a sloped Moho, respectively, for underground sources. The receiver function can estimate the depth and shape of Moho interface precisely for the three models. Second, We applied this method to synthetic refraction survey data generated not by earthquakes but by artificial sources on the ground or sea surface. Compressional seismic waves propagate under the velocity interface and radiate converted shear waves as well as at the other deep underground layer interfaces. However, the receiver function analysis applied to the second model cannot clearly estimate the velocity interface behind S-P converted wave or multi-reflected waves in a sediment layer. One of the causes is that the incidence angles of upcoming waves are too large compared to the underground source model due to the slanted interface. As a result, incident converted shear waves have non-negligible energy contaminating the vertical component of seismometers. Therefore, recorded refraction waves need to be transformed from depth-lateral coordinate into radial-tangential coordinate, and then Ps converted waves can be observed clearly. Finally, we applied the receiver function analysis to a more realistic model. This model has not only similar sloping Mohorovicic discontinuity and surface source locations as second model but the surface water layer. Receivers are aligned on the sea bottom (OBS; Ocean Bottom Seismometer survey case) Due to intricately bounced reflections, simulated seismic section becomes more complex than the other previously-mentioned models. In spite of the complexity in the seismic records, we could pick up the refraction waves from Moho interface, after stacking more than 20 receiver functions independently produced from each shot gather. After these processing, the receiver function analysis is justified as a method to estimate the depths of velocity interfaces and would be the applicable method for refraction wave analysis. The further study will be conducted for more realistic model that contain inhomogeneous sediment model, for example, and finally used in the inversion of the depth of velocity interfaces like Moho.

Transient Inverse Calibration of Site-Wide Groundwater Model to Hanford Operational Impacts from 1943 to 1996--Alternative Conceptual Model Considering Interaction with Uppermost Basalt Confined Aquifer

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

Vermeul, Vincent R.; Cole, Charles R.; Bergeron, Marcel P.

2001-08-29

The baseline three-dimensional transient inverse model for the estimation of site-wide scale flow parameters, including their uncertainties, using data on the transient behavior of the unconfined aquifer system over the entire historical period of Hanford operations, has been modified to account for the effects of basalt intercommunication between the Hanford unconfined aquifer and the underlying upper basalt confined aquifer. Both the baseline and alternative conceptual models (ACM-1) considered only the groundwater flow component and corresponding observational data in the 3-Dl transient inverse calibration efforts. Subsequent efforts will examine both groundwater flow and transport. Comparisons of goodness of fit measures andmore » parameter estimation results for the ACM-1 transient inverse calibrated model with those from previous site-wide groundwater modeling efforts illustrate that the new 3-D transient inverse model approach will strengthen the technical defensibility of the final model(s) and provide the ability to incorporate uncertainty in predictions related to both conceptual model and parameter uncertainty. These results, however, indicate that additional improvements are required to the conceptual model framework. An investigation was initiated at the end of this basalt inverse modeling effort to determine whether facies-based zonation would improve specific yield parameter estimation results (ACM-2). A description of the justification and methodology to develop this zonation is discussed.« less

Bottom friction optimization for a better barotropic tide modelling

NASA Astrophysics Data System (ADS)

Boutet, Martial; Lathuilière, Cyril; Son Hoang, Hong; Baraille, Rémy

2015-04-01

At a regional scale, barotropic tides are the dominant source of variability of currents and water heights. A precise representation of these processes is essential because of their great impacts on human activities (submersion risks, marine renewable energies, ...). Identified sources of error for tide modelling at a regional scale are the followings: bathymetry, boundary forcing and dissipation due to bottom friction. Nevertheless, bathymetric databases are nowadays known with a good accuracy, especially over shelves, and global tide models performances are better than ever. The most promising improvement is thus the bottom friction representation. The method used to estimate bottom friction is the simultaneous perturbation stochastic approximation (SPSA) which consists in the approximation of the gradient based on a fixed number of cost function measurements, regardless of the dimension of the vector to be estimated. Indeed, each cost function measurement is obtained by randomly perturbing every component of the parameter vector. An important feature of SPSA is its relative ease of implementation. In particular, the method does not require the development of tangent linear and adjoint version of the circulation model. Experiments are carried out to estimate bottom friction with the HYbrid Coordinate Ocean Model (HYCOM) in barotropic mode (one isopycnal layer). The study area is the Northeastern Atlantic margin which is characterized by strong currents and an intense dissipation. Bottom friction is parameterized with a quadratic term and friction coefficient is computed with the water height and the bottom roughness. The latter parameter is the one to be estimated. Assimilated data are the available tide gauge observations. First, the bottom roughness is estimated taking into account bottom sediment natures and bathymetric ranges. Then, it is estimated with geographical degrees of freedom. Finally, the impact of the estimation of a mixed quadratic/linear friction is evaluated.

Variable-period surface-wave magnitudes: A rapid and robust estimator of seismic moments

USGS Publications Warehouse

Bonner, J.; Herrmann, R.; Benz, H.

2010-01-01

We demonstrate that surface-wave magnitudes (Ms), measured at local, regional, and teleseismic distances, can be used as a rapid and robust estimator of seismic moment magnitude (Mw). We used the Russell (2006) variable-period surface-wave magnitude formula, henceforth called Ms(VMAX), to estimate the Ms for 165 North American events with 3.2

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

PubMed

Feng, Lei; Peng, Fuduan; Li, Shanfei; Jiang, Li; Sun, Hui; Ji, Anquan; Zeng, Changqing; Li, Caixia; Liu, Fan

2018-03-23

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R 2 ) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R 2  = 0.95, and validation set 2, N = 62, MAD = 3.36, R 2  = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R 2  = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R 2  = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimizing Constrained Single Period Problem under Random Fuzzy Demand

NASA Astrophysics Data System (ADS)

Taleizadeh, Ata Allah; Shavandi, Hassan; Riazi, Afshin

2008-09-01

In this paper, we consider the multi-product multi-constraint newsboy problem with random fuzzy demands and total discount. The demand of the products is often stochastic in the real word but the estimation of the parameters of distribution function may be done by fuzzy manner. So an appropriate option to modeling the demand of products is using the random fuzzy variable. The objective function of proposed model is to maximize the expected profit of newsboy. We consider the constraints such as warehouse space and restriction on quantity order for products, and restriction on budget. We also consider the batch size for products order. Finally we introduce a random fuzzy multi-product multi-constraint newsboy problem (RFM-PM-CNP) and it is changed to a multi-objective mixed integer nonlinear programming model. Furthermore, a hybrid intelligent algorithm based on genetic algorithm, Pareto and TOPSIS is presented for the developed model. Finally an illustrative example is presented to show the performance of the developed model and algorithm.

On the Effectiveness of Security Countermeasures for Critical Infrastructures.

PubMed

Hausken, Kjell; He, Fei

2016-04-01

A game-theoretic model is developed where an infrastructure of N targets is protected against terrorism threats. An original threat score is determined by the terrorist's threat against each target and the government's inherent protection level and original protection. The final threat score is impacted by the government's additional protection. We investigate and verify the effectiveness of countermeasures using empirical data and two methods. The first is to estimate the model's parameter values to minimize the sum of the squared differences between the government's additional resource investment predicted by the model and the empirical data. The second is to develop a multivariate regression model where the final threat score varies approximately linearly relative to the original threat score, sectors, and threat scenarios, and depends nonlinearly on the additional resource investment. The model and method are offered as tools, and as a way of thinking, to determine optimal resource investments across vulnerable targets subject to terrorism threats. © 2014 Society for Risk Analysis.

Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models

PubMed Central

2016-01-01

Studies of audiovisual perception of distance are rare. Here, visual and auditory cue interactions in distance are tested against several multisensory models, including a modified causal inference model. In this causal inference model predictions of estimate distributions are included. In our study, the audiovisual perception of distance was overall better explained by Bayesian causal inference than by other traditional models, such as sensory dominance and mandatory integration, and no interaction. Causal inference resolved with probability matching yielded the best fit to the data. Finally, we propose that sensory weights can also be estimated from causal inference. The analysis of the sensory weights allows us to obtain windows within which there is an interaction between the audiovisual stimuli. We find that the visual stimulus always contributes by more than 80% to the perception of visual distance. The visual stimulus also contributes by more than 50% to the perception of auditory distance, but only within a mobile window of interaction, which ranges from 1 to 4 m. PMID:27959919

A real-time ionospheric model based on GNSS Precise Point Positioning

NASA Astrophysics Data System (ADS)

Tu, Rui; Zhang, Hongping; Ge, Maorong; Huang, Guanwen

2013-09-01

This paper proposes a method of real-time monitoring and modeling the ionospheric Total Electron Content (TEC) by Precise Point Positioning (PPP). Firstly, the ionospheric TEC and receiver’s Differential Code Biases (DCB) are estimated with the undifferenced raw observation in real-time, then the ionospheric TEC model is established based on the Single Layer Model (SLM) assumption and the recovered ionospheric TEC. In this study, phase observations with high precision are directly used instead of phase smoothed code observations. In addition, the DCB estimation is separated from the establishment of the ionospheric model which will limit the impacts of the SLM assumption impacts. The ionospheric model is established at every epoch for real time application. The method is validated with three different GNSS networks on a local, regional, and global basis. The results show that the method is feasible and effective, the real-time ionosphere and DCB results are very consistent with the IGS final products, with a bias of 1-2 TECU and 0.4 ns respectively.

Epidemiological characteristics of reported sporadic and outbreak cases of E. coli O157 in people from Alberta, Canada (2000-2002): methodological challenges of comparing clustered to unclustered data.

PubMed

Pearl, D L; Louie, M; Chui, L; Doré, K; Grimsrud, K M; Martin, S W; Michel, P; Svenson, L W; McEwen, S A

2008-04-01

Using multivariable models, we compared whether there were significant differences between reported outbreak and sporadic cases in terms of their sex, age, and mode and site of disease transmission. We also determined the potential role of administrative, temporal, and spatial factors within these models. We compared a variety of approaches to account for clustering of cases in outbreaks including weighted logistic regression, random effects models, general estimating equations, robust variance estimates, and the random selection of one case from each outbreak. Age and mode of transmission were the only epidemiologically and statistically significant covariates in our final models using the above approaches. Weighing observations in a logistic regression model by the inverse of their outbreak size appeared to be a relatively robust and valid means for modelling these data. Some analytical techniques, designed to account for clustering, had difficulty converging or producing realistic measures of association.

Measuring global monopole velocities, one by one

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

Lopez-Eiguren, Asier; Urrestilla, Jon; Achúcarro, Ana, E-mail: asier.lopez@ehu.eus, E-mail: jon.urrestilla@ehu.eus, E-mail: achucar@lorentz.leidenuniv.nl

We present an estimation of the average velocity of a network of global monopoles in a cosmological setting using large numerical simulations. In order to obtain the value of the velocity, we improve some already known methods, and present a new one. This new method estimates individual global monopole velocities in a network, by means of detecting each monopole position in the lattice and following the path described by each one of them. Using our new estimate we can settle an open question previously posed in the literature: velocity-dependent one-scale (VOS) models for global monopoles predict two branches of scalingmore » solutions, one with monopoles moving at subluminal speeds and one with monopoles moving at luminal speeds. Previous attempts to estimate monopole velocities had large uncertainties and were not able to settle that question. Our simulations find no evidence of a luminal branch. We also estimate the values of the parameters of the VOS model. With our new method we can also study the microphysics of the complicated dynamics of individual monopoles. Finally we use our large simulation volume to compare the results from the different estimator methods, as well as to asses the validity of the numerical approximations made.« less

Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography.

PubMed

Rüdt, Matthias; Gillet, Florian; Heege, Stefanie; Hitzler, Julian; Kalbfuss, Bernd; Guélat, Bertrand

2015-09-25

Application of model-based design is appealing to support the development of protein chromatography in the biopharmaceutical industry. However, the required efforts for parameter estimation are frequently perceived as time-consuming and expensive. In order to speed-up this work, a new parameter estimation approach for modelling ion-exchange chromatography in linear conditions was developed. It aims at reducing the time and protein demand for the model calibration. The method combines the estimation of kinetic and thermodynamic parameters based on the simultaneous variation of the gradient slope and the residence time in a set of five linear gradient elutions. The parameters are estimated from a Yamamoto plot and a gradient-adjusted Van Deemter plot. The combined approach increases the information extracted per experiment compared to the individual methods. As a proof of concept, the combined approach was successfully applied for a monoclonal antibody on a cation-exchanger and for a Fc-fusion protein on an anion-exchange resin. The individual parameter estimations for the mAb confirmed that the new approach maintained the accuracy of the usual Yamamoto and Van Deemter plots. In the second case, offline size-exclusion chromatography was performed in order to estimate the thermodynamic parameters of an impurity (high molecular weight species) simultaneously with the main product. Finally, the parameters obtained from the combined approach were used in a lumped kinetic model to simulate the chromatography runs. The simulated chromatograms obtained for a wide range of gradient lengths and residence times showed only small deviations compared to the experimental data. Copyright © 2015 Elsevier B.V. All rights reserved.

Rasch Model Analysis with the BICAL Computer Program

DTIC Science & Technology

1976-09-01

and persons which lead to measures that persist from trial to trial . The measurement model is essential in this process because it provides a framework...and his students. Section 15 two derives the estimating equations for the Bernoulli (i.e. one trial per task) form : " and then generalizes to the...Binomial form (several trials per task). Finall) goodness of fit tests are presented for assessing the adequacy of the calibration. t { ) I I I 41 CHAPTER

Carbon emissions risk map from deforestation in the tropical Amazon

NASA Astrophysics Data System (ADS)

Ometto, J.; Soler, L. S.; Assis, T. D.; Oliveira, P. V.; Aguiar, A. P.

2011-12-01

Assis, Pedro Valle This work aims to estimate the carbon emissions from tropical deforestation in the Brazilian Amazon associated to the risk assessment of future land use change. The emissions are estimated by incorporating temporal deforestation dynamics, accounting for the biophysical and socioeconomic heterogeneity in the region, as well secondary forest growth dynamic in abandoned areas. The land cover change model that supported the risk assessment of deforestation, was run based on linear regressions. This method takes into account spatial heterogeneity of deforestation as the spatial variables adopted to fit the final regression model comprise: environmental aspects, economic attractiveness, accessibility and land tenure structure. After fitting a suitable regression models for each land cover category, the potential of each cell to be deforested (25x25km and 5x5 km of resolution) in the near future was used to calculate the risk assessment of land cover change. The carbon emissions model combines high-resolution new forest clear-cut mapping and four alternative sources of spatial information on biomass distribution for different vegetation types. The risk assessment map of CO2 emissions, was obtained by crossing the simulation results of the historical land cover changes to a map of aboveground biomass contained in the remaining forest. This final map represents the risk of CO2 emissions at 25x25km and 5x5 km until 2020, under a scenario of carbon emission reduction target.

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

Murray, S. G.; Trott, C. M.; Jordan, C. H.

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions formore » the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.« less

Loss estimates for a Puente Hills blind-thrust earthquake in Los Angeles, California

USGS Publications Warehouse

Field, E.H.; Seligson, H.A.; Gupta, N.; Gupta, V.; Jordan, T.H.; Campbell, K.W.

2005-01-01

Based on OpenSHA and HAZUS-MH, we present loss estimates for an earthquake rupture on the recently identified Puente Hills blind-thrust fault beneath Los Angeles. Given a range of possible magnitudes and ground motion models, and presuming a full fault rupture, we estimate the total economic loss to be between $82 and $252 billion. This range is not only considerably higher than a previous estimate of $69 billion, but also implies the event would be the costliest disaster in U.S. history. The analysis has also provided the following predictions: 3,000-18,000 fatalities, 142,000-735,000 displaced households, 42,000-211,000 in need of short-term public shelter, and 30,000-99,000 tons of debris generated. Finally, we show that the choice of ground motion model can be more influential than the earthquake magnitude, and that reducing this epistemic uncertainty (e.g., via model improvement and/or rejection) could reduce the uncertainty of the loss estimates by up to a factor of two. We note that a full Puente Hills fault rupture is a rare event (once every ???3,000 years), and that other seismic sources pose significant risk as well. ?? 2005, Earthquake Engineering Research Institute.

Spectroscopic analysis of soil metal contamination around a derelict mine site in the Blue Mountains, Australia

NASA Astrophysics Data System (ADS)

Shamsoddini, A.; Raval, S.; Taplin, R.

2014-09-01

Abandoned mine sites pose the potential threat of the heavy metal pollution spread through streams and via runoff leading to contamination of soil and water in their surrounding areas. Regular monitoring of these areas is critical to minimise impacts on water resources, flora and fauna. Conventional ground based monitoring is expensive and sometimes impractical; spectroscopic methods have been emerged as a reliable alternative for this purpose. In this study, the capabilities of the spectroscopy method were examined for modelling soil contamination from around the abandoned silver-zinc mine located at Yerranderie, NSW Australia. The diagnostic characteristics of the original reflectance data were compared with models derived from first and second derivatives of the reflectance data. The results indicate that the models derived from the first derivative of the reflectance data estimate heavy metals significantly more accurately than model derived from the original reflectance. It was also found in this study that there is no need to use second derivative for modelling heavy metal soil contamination. Finally, the results indicate that estimates were of greater accuracy for arsenic and lead compared to other heavy metals, while the estimation for silver was found to be the most erroneous.

A numerical model for the whole Wadden Sea: results on the hydrodynamics

NASA Astrophysics Data System (ADS)

Gräwe, Ulf; Duran-Matute, Matias; Gerkema, Theo; Flöser, Götz; Burchard, Hans

2015-04-01

A high-resolution baroclinic three-dimensional numerical model for the entire Wadden Sea of the German Bight in the southern North Sea is first validated against field data for surface elevation, current velocity, temperature and salinity at selected stations and then used to calculate fluxes of volume, heat and salt inside the Wadden Sea and the exchange between the Wadden Sea and the adjacent North Sea through the major tidal inlets. The General Estuarine Transport Model (GETM) is simulating the reference years 2009-2011. The numerical grid has a resolution of 200x200m and 30 adaptive vertical layers. It is the final stage of a multi-nested setup, starting from the North Atlantic. The atmospheric forcing is taken from the operational forecast of the German Weather Service. Additionally, the freshwater discharge of 23 local rivers and creeks are included. For validation, we use observations from a ship of opportunity measuring sea surface properties, tidal gauge stations, high frequency of salinity and volume transport estimates for the Mardiep and Spiekeroog inlet. Finally, the estuarine overturning circulation in three tidal gulleys is quantified. Regional differences between the gullies are assessed and drivers of the estuarine circulation are identified. Moreover, we will give a consistent estimate of the tidal prisms for all tidal inlets in the entire Wadden Sea.

The depth estimation of 3D face from single 2D picture based on manifold learning constraints

NASA Astrophysics Data System (ADS)

Li, Xia; Yang, Yang; Xiong, Hailiang; Liu, Yunxia

2018-04-01

The estimation of depth is virtual important in 3D face reconstruction. In this paper, we propose a t-SNE based on manifold learning constraints and introduce K-means method to divide the original database into several subset, and the selected optimal subset to reconstruct the 3D face depth information can greatly reduce the computational complexity. Firstly, we carry out the t-SNE operation to reduce the key feature points in each 3D face model from 1×249 to 1×2. Secondly, the K-means method is applied to divide the training 3D database into several subset. Thirdly, the Euclidean distance between the 83 feature points of the image to be estimated and the feature point information before the dimension reduction of each cluster center is calculated. The category of the image to be estimated is judged according to the minimum Euclidean distance. Finally, the method Kong D will be applied only in the optimal subset to estimate the depth value information of 83 feature points of 2D face images. Achieving the final depth estimation results, thus the computational complexity is greatly reduced. Compared with the traditional traversal search estimation method, although the proposed method error rate is reduced by 0.49, the number of searches decreases with the change of the category. In order to validate our approach, we use a public database to mimic the task of estimating the depth of face images from 2D images. The average number of searches decreased by 83.19%.

The beta Burr type X distribution properties with application.

PubMed

Merovci, Faton; Khaleel, Mundher Abdullah; Ibrahim, Noor Akma; Shitan, Mahendran

2016-01-01

We develop a new continuous distribution called the beta-Burr type X distribution that extends the Burr type X distribution. The properties provide a comprehensive mathematical treatment of this distribution. Further more, various structural properties of the new distribution are derived, that includes moment generating function and the rth moment thus generalizing some results in the literature. We also obtain expressions for the density, moment generating function and rth moment of the order statistics. We consider the maximum likelihood estimation to estimate the parameters. Additionally, the asymptotic confidence intervals for the parameters are derived from the Fisher information matrix. Finally, simulation study is carried at under varying sample size to assess the performance of this model. Illustration the real dataset indicates that this new distribution can serve as a good alternative model to model positive real data in many areas.

Low-cost silicon solar array project environmental hail model for assessing risk to solar collectors

NASA Technical Reports Server (NTRS)

Gonzalez, C.

1977-01-01

The probability of solar arrays being struck by hailstones of various sizes as a function of geographic location and service life was assessed. The study complements parallel studies of solar array sensitivity to hail damage, the final objective being an estimate of the most cost effective level for solar array hail protection.

An Empirical Study of Decisions Involving Post-Secondary Vocational School Training. Volume II--Technical Report. Final Report.

ERIC Educational Resources Information Center

Olson, Lawrence S.

A study examined decisions involving private, postsecondary vocational schooling using three large, national sets of data (from the National Longitudinal Surveys) on males. Particular attention is paid to three target groups: inner-city and rural individuals and dropouts. Various equations were estimated using a life-cycle model of time allocation…

An Empirical Study of Decisions Involving Post-Secondary Vocational School Training. Volume I--Executive Summary. Final Report.

ERIC Educational Resources Information Center

Olson, Lawrence S.

A study examined decisions involving private, postsecondary vocational schooling using three large, national sets of data (from the National Longitudinal Surveys) on males. Particular attention is paid to three target groups--inner-city and rural individuals and dropouts. Various equations were estimated using a life-cycle model of time allocation…

A Procedure for Estimating a Criterion-Referenced Standard to Identify Educationally Deprived Children for Title I Services. Final Report.

ERIC Educational Resources Information Center

Ziomek, Robert L.; Wright, Benjamin D.

Techniques such as the norm-referenced and average score techniques, commonly used in the identification of educationally disadvantaged students, are critiqued. This study applied latent trait theory, specifically the Rasch Model, along with teacher judgments relative to the mastery of instructional/test decisions, to derive a standard setting…

Feasibility of developing LSI microcircuit reliability prediction models

NASA Technical Reports Server (NTRS)

Ryerson, C. M.

1972-01-01

In the proposed modeling approach, when any of the essential key factors are not known initially, they can be approximated in various ways with a known impact on the accuracy of the final predictions. For example, on any program where reliability predictions are started at interim states of project completion, a-priori approximate estimates of the key factors are established for making preliminary predictions. Later these are refined for greater accuracy as subsequent program information of a more definitive nature becomes available. Specific steps to develop, validate and verify these new models are described.

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

NASA Technical Reports Server (NTRS)

Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.

1985-01-01

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a Landsat MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95 percent of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50 percent of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73 percent of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

NASA Technical Reports Server (NTRS)

Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.

1984-01-01

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a LANDSAT MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95% of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Essays on variational approximation techniques for stochastic optimization problems

NASA Astrophysics Data System (ADS)

Deride Silva, Julio A.

This dissertation presents five essays on approximation and modeling techniques, based on variational analysis, applied to stochastic optimization problems. It is divided into two parts, where the first is devoted to equilibrium problems and maxinf optimization, and the second corresponds to two essays in statistics and uncertainty modeling. Stochastic optimization lies at the core of this research as we were interested in relevant equilibrium applications that contain an uncertain component, and the design of a solution strategy. In addition, every stochastic optimization problem relies heavily on the underlying probability distribution that models the uncertainty. We studied these distributions, in particular, their design process and theoretical properties such as their convergence. Finally, the last aspect of stochastic optimization that we covered is the scenario creation problem, in which we described a procedure based on a probabilistic model to create scenarios for the applied problem of power estimation of renewable energies. In the first part, Equilibrium problems and maxinf optimization, we considered three Walrasian equilibrium problems: from economics, we studied a stochastic general equilibrium problem in a pure exchange economy, described in Chapter 3, and a stochastic general equilibrium with financial contracts, in Chapter 4; finally from engineering, we studied an infrastructure planning problem in Chapter 5. We stated these problems as belonging to the maxinf optimization class and, in each instance, we provided an approximation scheme based on the notion of lopsided convergence and non-concave duality. This strategy is the foundation of the augmented Walrasian algorithm, whose convergence is guaranteed by lopsided convergence, that was implemented computationally, obtaining numerical results for relevant examples. The second part, Essays about statistics and uncertainty modeling, contains two essays covering a convergence problem for a sequence of estimators, and a problem for creating probabilistic scenarios on renewable energies estimation. In Chapter 7 we re-visited one of the "folk theorems" in statistics, where a family of Bayes estimators under 0-1 loss functions is claimed to converge to the maximum a posteriori estimator. This assertion is studied under the scope of the hypo-convergence theory, and the density functions are included in the class of upper semicontinuous functions. We conclude this chapter with an example in which the convergence does not hold true, and we provided sufficient conditions that guarantee convergence. The last chapter, Chapter 8, addresses the important topic of creating probabilistic scenarios for solar power generation. Scenarios are a fundamental input for the stochastic optimization problem of energy dispatch, especially when incorporating renewables. We proposed a model designed to capture the constraints induced by physical characteristics of the variables based on the application of an epi-spline density estimation along with a copula estimation, in order to account for partial correlations between variables.

Error analysis of leaf area estimates made from allometric regression models

NASA Technical Reports Server (NTRS)

Feiveson, A. H.; Chhikara, R. S.

1986-01-01

Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Examining the effect of initialization strategies on the performance of Gaussian mixture modeling.

PubMed

Shireman, Emilie; Steinley, Douglas; Brusco, Michael J

2017-02-01

Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (EM) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the EM algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.

White paper updating conclusions of 1998 ILAW performance assessment

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

MANN, F.M.

The purpose of this document is to provide a comparison of the estimated immobilized low-activity waste (LAW) disposal system performance against established performance objectives using the beat estimates for parameters and models to describe the system. The principal advances in knowledge since the last performance assessment (known as the 1998 ILAW PA [Mann 1998a]) have been in site specific information and data on the waste form performance for BNFL, Inc. relevant glass formulations. The white paper also estimates the maximum release rates for technetium and other key radionuclides and chemicals from the waste form. Finally, this white paper provides limitedmore » information on the impact of changes in waste form loading.« less

Electromagnetic wave scattering from some vegetation samples

NASA Technical Reports Server (NTRS)

Karam, Mostafa A.; Fung, Adrian K.; Antar, Yahia M.

1988-01-01

For an incident plane wave, the field inside a thin scatterer (disk and needle) is estimated by the generalized Rayleigh-Gans (GRG) approximation. This leads to a scattering amplitude tensor equal to that obtained via the Rayleigh approximation (dipole term) with a modifying function. For a finite-length cylinder the inner field is estimated by the corresponding field for the same cylinder of infinite lenght. The effects of different approaches in estimating the field inside the scatterer on the backscattering cross section are illustrated numerically for a circular disk, a needle, and a finite-length cylinder as a function of the wave number and the incidence angle. Finally, the modeling predictions are compared with measurements.

Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

PubMed

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

Uranium resource assessment through statistical analysis of exploration geochemical and other data. Final report. [Codes EVAL, SURE

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

Koch, G.S. Jr.; Howarth, R.J.; Schuenemeyer, J.H.

1981-02-01

We have developed a procedure that can help quadrangle evaluators to systematically summarize and use hydrogeochemical and stream sediment reconnaissance (HSSR) and occurrence data. Although we have not provided an independent estimate of uranium endowment, we have devised a methodology that will provide this independent estimate when additional calibration is done by enlarging the study area. Our statistical model for evaluation (system EVAL) ranks uranium endowment for each quadrangle. Because using this model requires experience in geology, statistics, and data analysis, we have also devised a simplified model, presented in the package SURE, a System for Uranium Resource Evaluation. Wemore » have developed and tested these models for the four quadrangles in southern Colorado that comprise the study area; to investigate their generality, the models should be applied to other quandrangles. Once they are calibrated with accepted uranium endowments for several well-known quadrangles, the models can be used to give independent estimates for less-known quadrangles. The point-oriented models structure the objective comparison of the quandrangles on the bases of: (1) Anomalies (a) derived from stream sediments, (b) derived from waters (stream, well, pond, etc.), (2) Geology (a) source rocks, as defined by the evaluator, (b) host rocks, as defined by the evaluator, and (3) Aerial radiometric anomalies.« less

The Width of a CME and the Source of the Driving Magnetic Explosion

NASA Technical Reports Server (NTRS)

Moore, R. L.; Sterling, A. C.; Suess, S. T.

2007-01-01

We show that the strength of the magnetic field in the area covered by the flare arcade following a CME-producing ejective solar eruption can be estimated from the final angular width of the CME in the outer corona and the final angular width of the flare arcade. We assume (1) the flux-rope plasmoid ejected from the flare site becomes the interior of the CME plasmoid, (2) in the outer corona the CME is roughly a "spherical plasmoid with legs" shaped like a light bulb, and (3) beyond some height in or below the outer corona the CME plasmoid is in lateral pressure balance with the surrounding magnetic field. The strength of the nearly radial magnetic field in the outer corona is estimated from the radial component of the interplanetary magnetic field measured by Ulysses. We apply this model to three well-observed CMEs that exploded from flare regions of extremely different size and magnetic setting. In each event, the estimated source-region field strength is appropriate for the magnetic setting of the flare. This agreement indicates via the model that CMEs (1) are propelled by the magnetic field of the CME plasmoid pushing against the surrounding magnetic field, and (2) can explode from flare regions that are laterally far offset from the radial path of the CME in the outer corona.

Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands

PubMed Central

Aynekulu, Ermias; Pitkänen, Sari; Packalen, Petteri

2016-01-01

It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance. PMID:27367857

A Comparison of the Cheater Detection and the Unrelated Question Models: A Randomized Response Survey on Physical and Cognitive Doping in Recreational Triathletes

PubMed Central

Schröter, Hannes; Studzinski, Beatrix; Dietz, Pavel; Ulrich, Rolf; Striegel, Heiko; Simon, Perikles

2016-01-01

Purpose This study assessed the prevalence of physical and cognitive doping in recreational triathletes with two different randomized response models, that is, the Cheater Detection Model (CDM) and the Unrelated Question Model (UQM). Since both models have been employed in assessing doping, the major objective of this study was to investigate whether the estimates of these two models converge. Material and Methods An anonymous questionnaire was distributed to 2,967 athletes at two triathlon events (Frankfurt and Wiesbaden, Germany). Doping behavior was assessed either with the CDM (Frankfurt sample, one Wiesbaden subsample) or the UQM (one Wiesbaden subsample). A generalized likelihood-ratio test was employed to check whether the prevalence estimates differed significantly between models. In addition, we compared the prevalence rates of the present survey with those of a previous study on a comparable sample. Results After exclusion of incomplete questionnaires and outliers, the data of 2,017 athletes entered the final data analysis. Twelve-month prevalence for physical doping ranged from 4% (Wiesbaden, CDM and UQM) to 12% (Frankfurt CDM), and for cognitive doping from 1% (Wiesbaden, CDM) to 9% (Frankfurt CDM). The generalized likelihood-ratio test indicated no differences in prevalence rates between the two methods. Furthermore, there were no significant differences in prevalences between the present (undertaken in 2014) and the previous survey (undertaken in 2011), although the estimates tended to be smaller in the present survey. Discussion The results suggest that the two models can provide converging prevalence estimates. The high rate of cheaters estimated by the CDM, however, suggests that the present results must be seen as a lower bound and that the true prevalence of doping might be considerably higher. PMID:27218830

Effect of naringenin in Qianggu capsule on population pharmacokinetics in Chinese women with primary osteoporosis.

PubMed

Jiannong, Wang; Junjie, Jiang; Yanming, Xie; Xu, Wei; Jianpeng, Li; Jingli, Duan; Xin, Xiong

2015-04-01

To characterize naringenin (NAR) pop- ulation pharmacokinetics (PPK) in Chinese women with primary osteoporosis. Ninety-eight female patients with primary osteoporosis from the Jingshan, Beixinqiao, Jiaodaokou, Chaoyangmen, and Donghuamen communities in Beijing, China, aged 40 to 80 years, re- ceived oral Qianggu capsules (250 mg). Blood samples were collected before and at 0.5, 1, 2, 3, 4, 6, 8, 10, 12, and 24 h after administration. The concentration of NAR in the blood samples was measured using high performance liquid chromatography-tandem mass spectrometry. PPK analyses were performed with nonlinear mixed-effect modeling software (version 7.1.2, PsN3.2.12). The clearance (C1), central distribution volume (V), absorption rate constant (Ka1), peripheral distribution volume (VII), and inter-compartmental clearance (CLII) were set as parameters and estimated by the base model, covariate model, and final model. Kidney-Yang deficiency [Shenyangxu (SYAX)] and liver-kidney-Yin deficiency (Ganshenyinxu) are patterns of symptoms in Traditional Chinese Medicine that were set as covariates, along with age, height, blood urea nitrogen, serum creatinine, alanine transaminase, aspartate transaminase, and hyperlipidemia. Both stepwise forward and backward procedures were accomplished to build models. The final model was evaluated by internal and external validation, visual predictive check, bootstrap, and leverage analysis. A one compartment open model with first order degradation was the best fitted to the concentration-time profiles following oral administration of NAR. The mean of population parameters of the final model, C1, SYAX on C1, V, Ka1, CLII, and VII, were measured to be 37.6 L/h, 0.427 L, 123 L/h, 0.12/h, 0.3056, and 1.446, respectively. Inter-individual variability was estimated and SYAX was identified as a significant covariate. The population pharmacokinetic model described in this study could effectively characterize the pharmacokinetic profile of NAR following administration of a single dose of oral Qianggu capsules in Chinese women with primary osteoporosis. Among the tested covariates, only SYAX was a significant factor.

Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

NASA Astrophysics Data System (ADS)

Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri

2018-03-01

Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.

On the development of a model predicting the recrystallization texture of aluminum using the Taylor model for rolling textures and the coincidence lattice site theory

NASA Astrophysics Data System (ADS)

T, Morimoto; F, Yoshida; A, Yanagida; J, Yanagimoto

2015-04-01

First, hardening model in f.c.c. metals was formulated with collinear interactions slips, Hirth slips and Lomer-Cottrell slips. Using the Taylor and the Sachs rolling texture prediction model, the residual dislocation densities of cold-rolled commercial pure aluminum were estimated. Then, coincidence site lattice grains were investigated from observed cold rolling texture. Finally, on the basis of oriented nucleation theory and coincidence site lattice theory, the recrystallization texture of commercial pure aluminum after low-temperature annealing was predicted.

The challenges associated with applying global models in heterogeneous landscapes: A case study using MOD17 GPP estimates in Hawaii

NASA Astrophysics Data System (ADS)

Kimball, H.; Selmants, P. C.; Running, S. W.; Moreno, A.; Giardina, C. P.

2016-12-01

In this study we evaluate the influence of spatial data product accuracy and resolution on the application of global models for smaller scale heterogeneous landscapes. In particular, we assess the influence of locally specific land cover and high-resolution climate data products on estimates of Gross Primary Production (GPP) for the Hawaiian Islands using the MOD17 model. The MOD17 GPP algorithm uses a measure of the fraction of absorbed photosynthetically active radiation from the National Aeronautics and Space Administration's Earth Observation System. This direct measurement is combined with global land cover (500-m resolution) and climate models ( 1/2-degree resolution) to estimate GPP. We first compared the alignment between the global land cover model used in MOD17 with a Hawaii specific land cover data product. We found that there was a 51.6% overall agreement between the two land cover products. We then compared four MOD17 GPP models: A global model that used the global land cover and low-resolution global climate data products, a model produced using the Hawaii specific land cover and low-resolution global climate data products, a model with global land cover and high-resolution climate data products, and finally, a model using both Hawaii specific land cover and high-resolution climate data products. We found that including either the Hawaii specific land cover or the high-resolution Hawaii climate data products with MOD17 reduced overall estimates of GPP by 8%. When both were used, GPP estimates were reduced by 16%. The reduction associated with land cover is explained by a reduction of the total area designated as evergreen broad leaf forest and an increase in the area designated as barren or sparsely vegetated in the Hawaii land cover product as compared to the global product. The climate based reduction is explained primarily by the spatial resolution and distribution of solar radiation in the Hawaiian Islands. This study highlights the importance of accuracy and resolution when applying global models to highly variable landscapes and provides an estimate of the influence of land cover and climate data products on estimates of GPP using MOD17.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

PubMed

Sun, Yu; Reynolds, Hayley M; Wraith, Darren; Williams, Scott; Finnegan, Mary E; Mitchell, Catherine; Murphy, Declan; Haworth, Annette

2018-04-26

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10 3 cells/mm 2 and a relative deviation of 13.3 ± 0.8%. Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Learn-as-you-go acceleration of cosmological parameter estimates

NASA Astrophysics Data System (ADS)

Aslanyan, Grigor; Easther, Richard; Price, Layne C.

2015-09-01

Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly.

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

Ghosh, Vinita J.; Schaefer, Charles; Kahnhauser, Henry

The National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory was shut down in September 2014. Lead bricks used as radiological shadow shielding within the accelerator were exposed to stray radiation fields during normal operations. The FLUKA code, a fully integrated Monte Carlo simulation package for the interaction and transport of particles and nuclei in matter, was used to estimate induced radioactivity in this shielding and stainless steel beam pipe from known beam losses. The FLUKA output was processed using MICROSHIELD® to estimate on-contact exposure rates with individually exposed bricks to help design and optimize the radiological survey process. Thismore » entire process can be modeled using FLUKA, but use of MICROSHIELD® as a secondary method was chosen because of the project’s resource constraints. Due to the compressed schedule and lack of shielding configuration data, simple FLUKA models were developed in this paper. FLUKA activity estimates for stainless steel were compared with sampling data to validate results, which show that simple FLUKA models and irradiation geometries can be used to predict radioactivity inventories accurately in exposed materials. During decommissioning 0.1% of the lead bricks were found to have measurable levels of induced radioactivity. Finally, post-processing with MICROSHIELD® provides an acceptable secondary method of estimating residual exposure rates.« less

Estimating the periodic components of a biomedical signal through inverse problem modelling and Bayesian inference with sparsity enforcing prior

NASA Astrophysics Data System (ADS)

Dumitru, Mircea; Djafari, Ali-Mohammad

2015-01-01

The recent developments in chronobiology need a periodic components variation analysis for the signals expressing the biological rhythms. A precise estimation of the periodic components vector is required. The classical approaches, based on FFT methods, are inefficient considering the particularities of the data (short length). In this paper we propose a new method, using the sparsity prior information (reduced number of non-zero values components). The considered law is the Student-t distribution, viewed as a marginal distribution of a Infinite Gaussian Scale Mixture (IGSM) defined via a hidden variable representing the inverse variances and modelled as a Gamma Distribution. The hyperparameters are modelled using the conjugate priors, i.e. using Inverse Gamma Distributions. The expression of the joint posterior law of the unknown periodic components vector, hidden variables and hyperparameters is obtained and then the unknowns are estimated via Joint Maximum A Posteriori (JMAP) and Posterior Mean (PM). For the PM estimator, the expression of the posterior law is approximated by a separable one, via the Bayesian Variational Approximation (BVA), using the Kullback-Leibler (KL) divergence. Finally we show the results on synthetic data in cancer treatment applications.

Learn-as-you-go acceleration of cosmological parameter estimates

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

Aslanyan, Grigor; Easther, Richard; Price, Layne C., E-mail: g.aslanyan@auckland.ac.nz, E-mail: r.easther@auckland.ac.nz, E-mail: lpri691@aucklanduni.ac.nz

2015-09-01

Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitlymore » describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly.« less

An efficiency-decay model for Lumen maintenance

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

Bobashev, Georgiy; Baldasaro, Nicholas G.; Mills, Karmann C.

Proposed is a multicomponent model for the estimation of light-emitting diode (LED) lumen maintenance using test data that were acquired in accordance with the test standards of the Illumination Engineering Society of North America, i.e., LM-80-08. Lumen maintenance data acquired with this test do not always follow exponential decay, particularly data collected in the first 1000 h or under low-stress (e.g., low temperature) conditions. This deviation from true exponential behavior makes it difficult to use the full data set in models for the estimation of lumen maintenance decay coefficient. As a result, critical information that is relevant to the earlymore » life or low-stress operation of LED light sources may be missed. We present an efficiency-decay model approach, where all lumen maintenance data can be used to provide an alternative estimate of the decay rate constant. The approach considers a combined model wherein one part describes an initial “break-in” period and another part describes the decay in lumen maintenance. During the break-in period, several mechanisms within the LED can act to produce a small (typically <; 10%) increase in luminous flux. The effect of the break-in period and its longevity is more likely to be present at low-ambient temperatures and currents, where the discrepancy between a standard TM-21 approach and our proposed model is the largest. For high temperatures and currents, the difference between the estimates becomes nonsubstantial. Finally, our approach makes use of all the collected data and avoids producing unrealistic estimates of the decay coefficient.« less

An efficiency-decay model for Lumen maintenance

DOE PAGES

Bobashev, Georgiy; Baldasaro, Nicholas G.; Mills, Karmann C.; ...

2016-08-25

Proposed is a multicomponent model for the estimation of light-emitting diode (LED) lumen maintenance using test data that were acquired in accordance with the test standards of the Illumination Engineering Society of North America, i.e., LM-80-08. Lumen maintenance data acquired with this test do not always follow exponential decay, particularly data collected in the first 1000 h or under low-stress (e.g., low temperature) conditions. This deviation from true exponential behavior makes it difficult to use the full data set in models for the estimation of lumen maintenance decay coefficient. As a result, critical information that is relevant to the earlymore » life or low-stress operation of LED light sources may be missed. We present an efficiency-decay model approach, where all lumen maintenance data can be used to provide an alternative estimate of the decay rate constant. The approach considers a combined model wherein one part describes an initial “break-in” period and another part describes the decay in lumen maintenance. During the break-in period, several mechanisms within the LED can act to produce a small (typically <; 10%) increase in luminous flux. The effect of the break-in period and its longevity is more likely to be present at low-ambient temperatures and currents, where the discrepancy between a standard TM-21 approach and our proposed model is the largest. For high temperatures and currents, the difference between the estimates becomes nonsubstantial. Finally, our approach makes use of all the collected data and avoids producing unrealistic estimates of the decay coefficient.« less

Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review.

PubMed

Camomilla, Valentina; Cereatti, Andrea; Cutti, Andrea Giovanni; Fantozzi, Silvia; Stagni, Rita; Vannozzi, Giuseppe

2017-08-18

Quantitative gait analysis can provide a description of joint kinematics and dynamics, and it is recognized as a clinically useful tool for functional assessment, diagnosis and intervention planning. Clinically interpretable parameters are estimated from quantitative measures (i.e. ground reaction forces, skin marker trajectories, etc.) through biomechanical modelling. In particular, the estimation of joint moments during motion is grounded on several modelling assumptions: (1) body segmental and joint kinematics is derived from the trajectories of markers and by modelling the human body as a kinematic chain; (2) joint resultant (net) loads are, usually, derived from force plate measurements through a model of segmental dynamics. Therefore, both measurement errors and modelling assumptions can affect the results, to an extent that also depends on the characteristics of the motor task analysed (i.e. gait speed). Errors affecting the trajectories of joint centres, the orientation of joint functional axes, the joint angular velocities, the accuracy of inertial parameters and force measurements (concurring to the definition of the dynamic model), can weigh differently in the estimation of clinically interpretable joint moments. Numerous studies addressed all these methodological aspects separately, but a critical analysis of how these aspects may affect the clinical interpretation of joint dynamics is still missing. This article aims at filling this gap through a systematic review of the literature, conducted on Web of Science, Scopus and PubMed. The final objective is hence to provide clear take-home messages to guide laboratories in the estimation of joint moments for the clinical practice.

Multi-Scale Soil Moisture Monitoring and Modeling at ARS Watersheds for NASA's Soil Moisture Active Passive (SMAP) Calibration/Validation Mission

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Cosh, M. H.

2014-12-01

NASA's SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks. This can be achieved via the integration of NLDAS precipitation data to perform calibration of models at each ­in-situ gauge. In turn, these models and the gauges' volumetric estimations are used to generate soil moisture estimates at a 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography. Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach. The South Fork watershed in Iowa represents the simplest case - the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation. The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically simple), while the Little River watershed in Georgia incorporates topographic classification. Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates. Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.

The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2

NASA Astrophysics Data System (ADS)

Basu, Sourish; Baker, David F.; Chevallier, Frédéric; Patra, Prabir K.; Liu, Junjie; Miller, John B.

2018-05-01

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.

A laboratory-calibrated model of coho salmon growth with utility for ecological analyses

USGS Publications Warehouse

Manhard, Christopher V.; Som, Nicholas A.; Perry, Russell W.; Plumb, John M.

2018-01-01

We conducted a meta-analysis of laboratory- and hatchery-based growth data to estimate broadly applicable parameters of mass- and temperature-dependent growth of juvenile coho salmon (Oncorhynchus kisutch). Following studies of other salmonid species, we incorporated the Ratkowsky growth model into an allometric model and fit this model to growth observations from eight studies spanning ten different populations. To account for changes in growth patterns with food availability, we reparameterized the Ratkowsky model to scale several of its parameters relative to ration. The resulting model was robust across a wide range of ration allocations and experimental conditions, accounting for 99% of the variation in final body mass. We fit this model to growth data from coho salmon inhabiting tributaries and constructed ponds in the Klamath Basin by estimating habitat-specific indices of food availability. The model produced evidence that constructed ponds provided higher food availability than natural tributaries. Because of their simplicity (only mass and temperature are required as inputs) and robustness, ration-varying Ratkowsky models have utility as an ecological tool for capturing growth in freshwater fish populations.

Model parameter estimation approach based on incremental analysis for lithium-ion batteries without using open circuit voltage

NASA Astrophysics Data System (ADS)

Wu, Hongjie; Yuan, Shifei; Zhang, Xi; Yin, Chengliang; Ma, Xuerui

2015-08-01

To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage.

[A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

PubMed

Zhonggang, Liang; Hong, Yan

2006-10-01

A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.

An Improved Statistical Point-source Foreground Model for the Epoch of Reionization

NASA Astrophysics Data System (ADS)

Murray, S. G.; Trott, C. M.; Jordan, C. H.

2017-08-01

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power-spectral modes in 21 cm EoR experiments. The former we generalize to an arbitrarily broken power law, and the latter to an arbitrary isotropically correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window that was previously unaccounted for. Failure to include this contribution can yield bias in the final power-spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.

Estimation et validation des derivees de stabilite et controle du modele dynamique non-lineaire d'un drone a voilure fixe

NASA Astrophysics Data System (ADS)

Courchesne, Samuel

Knowledge of the dynamic characteristics of a fixed-wing UAV is necessary to design flight control laws and to conceive a high quality flight simulator. The basic features of a flight mechanic model include the properties of mass, inertia and major aerodynamic terms. They respond to a complex process involving various numerical analysis techniques and experimental procedures. This thesis focuses on the analysis of estimation techniques applied to estimate problems of stability and control derivatives from flight test data provided by an experimental UAV. To achieve this objective, a modern identification methodology (Quad-M) is used to coordinate the processing tasks from multidisciplinary fields, such as parameter estimation modeling, instrumentation, the definition of flight maneuvers and validation. The system under study is a non-linear model with six degrees of freedom with a linear aerodynamic model. The time domain techniques are used for identification of the drone. The first technique, the equation error method is used to determine the structure of the aerodynamic model. Thereafter, the output error method and filter error method are used to estimate the aerodynamic coefficients values. The Matlab scripts for estimating the parameters obtained from the American Institute of Aeronautics and Astronautics (AIAA) are used and modified as necessary to achieve the desired results. A commendable effort in this part of research is devoted to the design of experiments. This includes an awareness of the system data acquisition onboard and the definition of flight maneuvers. The flight tests were conducted under stable flight conditions and with low atmospheric disturbance. Nevertheless, the identification results showed that the filter error method is most effective for estimating the parameters of the drone due to the presence of process noise and measurement. The aerodynamic coefficients are validated using a numerical analysis of the vortex method. In addition, a simulation model incorporating the estimated parameters is used to compare the behavior of states measured. Finally, a good correspondence between the results is demonstrated despite a limited number of flight data. Keywords: drone, identification, estimation, nonlinear, flight test, system, aerodynamic coefficient.

Estimation of seismic quality factor: Artificial neural networks and current approaches

NASA Astrophysics Data System (ADS)

Yıldırım, Eray; Saatçılar, Ruhi; Ergintav, Semih

2017-01-01

The aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yeniköy (Arnavutköy, İstanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise-free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated practically and quickly by processing the traces with the recommended ANN model. Consequently, the ANN method could be used for estimating Q value from seismic data.

Estimation of the reproduction number of dengue fever from spatial epidemic data.

PubMed

Chowell, G; Diaz-Dueñas, P; Miller, J C; Alcazar-Velazco, A; Hyman, J M; Fenimore, P W; Castillo-Chavez, C

2007-08-01

Dengue, a vector-borne disease, thrives in tropical and subtropical regions worldwide. A retrospective analysis of the 2002 dengue epidemic in Colima located on the Mexican central Pacific coast is carried out. We estimate the reproduction number from spatial epidemic data at the level of municipalities using two different methods: (1) Using a standard dengue epidemic model and assuming pure exponential initial epidemic growth and (2) Fitting a more realistic epidemic model to the initial phase of the dengue epidemic curve. Using Method I, we estimate an overall mean reproduction number of 3.09 (95% CI: 2.34,3.84) as well as local reproduction numbers whose values range from 1.24 (1.15,1.33) to 4.22 (2.90,5.54). Using Method II, the overall mean reproduction number is estimated to be 2.0 (1.75,2.23) and local reproduction numbers ranging from 0.49 (0.0,1.0) to 3.30 (1.63,4.97). Method I systematically overestimates the reproduction number relative to the refined Method II, and hence it would overestimate the intensity of interventions required for containment. Moreover, optimal intervention with defined resources demands different levels of locally tailored mitigation. Local epidemic peaks occur between the 24th and 35th week of the year, and correlate positively with the final local epidemic sizes (rho=0.92, P-value<0.001). Moreover, final local epidemic sizes are found to be linearly related to the local population size (P-value<0.001). This observation supports a roughly constant number of female mosquitoes per person across urban and rural regions.

Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

PubMed

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation

PubMed Central

Chen, Rongda; Wang, Ze

2013-01-01

Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

A Simple Model Predicting Individual Weight Change in Humans

PubMed Central

Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

2010-01-01

Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

Decision Support System For Management Of Low-Level Radioactive Waste Disposal At The Nevada Test Site

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

Shott, G.; Yucel, V.; Desotell, L.

2006-07-01

The long-term safety of U.S. Department of Energy (DOE) low-level radioactive disposal facilities is assessed by conducting a performance assessment -- a systematic analysis that compares estimated risks to the public and the environment with performance objectives contained in DOE Manual 435.1-1, Radioactive Waste Management Manual. Before site operations, facilities design features such as final inventory, waste form characteristics, and closure cover design may be uncertain. Site operators need a modeling tool that can be used throughout the operational life of the disposal site to guide decisions regarding the acceptance of problematic waste streams, new disposal cell design, environmental monitoringmore » program design, and final site closure. In response to these needs the National Nuclear Security Administration Nevada Site Office (NNSA/NSO) has developed a decision support system for the Area 5 Radioactive Waste Management Site in Frenchman Flat on the Nevada Test Site. The core of the system is a probabilistic inventory and performance assessment model implemented in the GoldSim{sup R} simulation platform. The modeling platform supports multiple graphic capabilities that allow clear documentation of the model data sources, conceptual model, mathematical implementation, and results. The combined models have the capability to estimate disposal site inventory, contaminant concentrations in environmental media, and radiological doses to members of the public engaged in various activities at multiple locations. The model allows rapid assessment and documentation of the consequences of waste management decisions using the most current site characterization information, radionuclide inventory, and conceptual model. The model is routinely used to provide annual updates of site performance, evaluate the consequences of disposal of new waste streams, develop waste concentration limits, optimize the design of new disposal cells, and assess the adequacy of environmental monitoring programs. (authors)« less

Modeling and validating the cost and clinical pathway of colorectal cancer.

PubMed

Joranger, Paal; Nesbakken, Arild; Hoff, Geir; Sorbye, Halfdan; Oshaug, Arne; Aas, Eline

2015-02-01

Cancer is a major cause of morbidity and mortality, and colorectal cancer (CRC) is the third most common cancer in the world. The estimated costs of CRC treatment vary considerably, and if CRC costs in a model are based on empirically estimated total costs of stage I, II, III, or IV treatments, then they lack some flexibility to capture future changes in CRC treatment. The purpose was 1) to describe how to model CRC costs and survival and 2) to validate the model in a transparent and reproducible way. We applied a semi-Markov model with 70 health states and tracked age and time since specific health states (using tunnels and 3-dimensional data matrix). The model parameters are based on an observational study at Oslo University Hospital (2049 CRC patients), the National Patient Register, literature, and expert opinion. The target population was patients diagnosed with CRC. The model followed the patients diagnosed with CRC from the age of 70 until death or 100 years. The study focused on the perspective of health care payers. The model was validated for face validity, internal and external validity, and cross-validity. The validation showed a satisfactory match with other models and empirical estimates for both cost and survival time, without any preceding calibration of the model. The model can be used to 1) address a range of CRC-related themes (general model) like survival and evaluation of the cost of treatment and prevention measures; 2) make predictions from intermediate to final outcomes; 3) estimate changes in resource use and costs due to changing guidelines; and 4) adjust for future changes in treatment and trends over time. The model is adaptable to other populations. © The Author(s) 2014.

Modification of the TASMIP x-ray spectral model for the simulation of microfocus x-ray sources

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

Sisniega, A.; Vaquero, J. J., E-mail: juanjose.vaquero@uc3m.es; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid ES28007

2014-01-15

Purpose: The availability of accurate and simple models for the estimation of x-ray spectra is of great importance for system simulation, optimization, or inclusion of photon energy information into data processing. There is a variety of publicly available tools for estimation of x-ray spectra in radiology and mammography. However, most of these models cannot be used directly for modeling microfocus x-ray sources due to differences in inherent filtration, energy range and/or anode material. For this reason the authors propose in this work a new model for the simulation of microfocus spectra based on existing models for mammography and radiology, modifiedmore » to compensate for the effects of inherent filtration and energy range. Methods: The authors used the radiology and mammography versions of an existing empirical model [tungsten anode spectral model interpolating polynomials (TASMIP)] as the basis of the microfocus model. First, the authors estimated the inherent filtration included in the radiology model by comparing the shape of the spectra with spectra from the mammography model. Afterwards, the authors built a unified spectra dataset by combining both models and, finally, they estimated the parameters of the new version of TASMIP for microfocus sources by calibrating against experimental exposure data from a microfocus x-ray source. The model was validated by comparing estimated and experimental exposure and attenuation data for different attenuating materials and x-ray beam peak energy values, using two different x-ray tubes. Results: Inherent filtration for the radiology spectra from TASMIP was found to be equivalent to 1.68 mm Al, as compared to spectra obtained from the mammography model. To match the experimentally measured exposure data the combined dataset required to apply a negative filtration of about 0.21 mm Al and an anode roughness of 0.003 mm W. The validation of the model against real acquired data showed errors in exposure and attenuation in line with those reported for other models for radiology or mammography. Conclusions: A new version of the TASMIP model for the estimation of x-ray spectra in microfocus x-ray sources has been developed and validated experimentally. Similarly to other versions of TASMIP, the estimation of spectra is very simple, involving only the evaluation of polynomial expressions.« less

Covariance specification and estimation to improve top-down Green House Gas emission estimates

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Whetstone, J. R.

2015-12-01

The National Institute of Standards and Technology (NIST) operates the North-East Corridor (NEC) project and the Indianapolis Flux Experiment (INFLUX) in order to develop measurement methods to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties in urban domains using a top down inversion method. Top down inversion updates prior knowledge using observations in a Bayesian way. One primary consideration in a Bayesian inversion framework is the covariance structure of (1) the emission prior residuals and (2) the observation residuals (i.e. the difference between observations and model predicted observations). These covariance matrices are respectively referred to as the prior covariance matrix and the model-data mismatch covariance matrix. It is known that the choice of these covariances can have large effect on estimates. The main objective of this work is to determine the impact of different covariance models on inversion estimates and their associated uncertainties in urban domains. We use a pseudo-data Bayesian inversion framework using footprints (i.e. sensitivities of tower measurements of GHGs to surface emissions) and emission priors (based on Hestia project to quantify fossil-fuel emissions) to estimate posterior emissions using different covariance schemes. The posterior emission estimates and uncertainties are compared to the hypothetical truth. We find that, if we correctly specify spatial variability and spatio-temporal variability in prior and model-data mismatch covariances respectively, then we can compute more accurate posterior estimates. We discuss few covariance models to introduce space-time interacting mismatches along with estimation of the involved parameters. We then compare several candidate prior spatial covariance models from the Matern covariance class and estimate their parameters with specified mismatches. We find that best-fitted prior covariances are not always best in recovering the truth. To achieve accuracy, we perform a sensitivity study to further tune covariance parameters. Finally, we introduce a shrinkage based sample covariance estimation technique for both prior and mismatch covariances. This technique allows us to achieve similar accuracy nonparametrically in a more efficient and automated way.

Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

NASA Astrophysics Data System (ADS)

Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea

2014-05-01

Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.

Researches of fruit quality prediction model based on near infrared spectrum

NASA Astrophysics Data System (ADS)

Shen, Yulin; Li, Lian

2018-04-01

With the improvement in standards for food quality and safety, people pay more attention to the internal quality of fruits, therefore the measurement of fruit internal quality is increasingly imperative. In general, nondestructive soluble solid content (SSC) and total acid content (TAC) analysis of fruits is vital and effective for quality measurement in global fresh produce markets, so in this paper, we aim at establishing a novel fruit internal quality prediction model based on SSC and TAC for Near Infrared Spectrum. Firstly, the model of fruit quality prediction based on PCA + BP neural network, PCA + GRNN network, PCA + BP adaboost strong classifier, PCA + ELM and PCA + LS_SVM classifier are designed and implemented respectively; then, in the NSCT domain, the median filter and the SavitzkyGolay filter are used to preprocess the spectral signal, Kennard-Stone algorithm is used to automatically select the training samples and test samples; thirdly, we achieve the optimal models by comparing 15 kinds of prediction model based on the theory of multi-classifier competition mechanism, specifically, the non-parametric estimation is introduced to measure the effectiveness of proposed model, the reliability and variance of nonparametric estimation evaluation of each prediction model to evaluate the prediction result, while the estimated value and confidence interval regard as a reference, the experimental results demonstrate that this model can better achieve the optimal evaluation of the internal quality of fruit; finally, we employ cat swarm optimization to optimize two optimal models above obtained from nonparametric estimation, empirical testing indicates that the proposed method can provide more accurate and effective results than other forecasting methods.

Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S., Final Report

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

Beverly E. Law

2011-10-05

As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 inmore » the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.« less

Hydrological Relevant Parameters from Remote Sensing - Spatial Modelling Input and Validation Basis

NASA Astrophysics Data System (ADS)

Hochschild, V.

2012-12-01

This keynote paper will demonstrate how multisensoral remote sensing data is used as spatial input for mesoscale hydrological modeling as well as for sophisticated validation purposes. The tasks of Water Resources Management are subject as well as the role of remote sensing in regional catchment modeling. Parameters derived from remote sensing discussed in this presentation will be land cover, topographical information from digital elevation models, biophysical vegetation parameters, surface soil moisture, evapotranspiration estimations, lake level measurements, determination of snow covered area, lake ice cycles, soil erosion type, mass wasting monitoring, sealed area, flash flood estimation. The actual possibilities of recent satellite and airborne systems are discussed, as well as the data integration into GIS and hydrological modeling, scaling issues and quality assessment will be mentioned. The presentation will provide an overview of own research examples from Germany, Tibet and Africa (Ethiopia, South Africa) as well as other international research activities. Finally the paper gives an outlook on upcoming sensors and concludes the possibilities of remote sensing in hydrology.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

PubMed

Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

2016-09-01

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Estimation of real-time runway surface contamination using flight data recorder parameters

NASA Astrophysics Data System (ADS)

Curry, Donovan

Within this research effort, the development of an analytic process for friction coefficient estimation is presented. Under static equilibrium, the sum of forces and moments acting on the aircraft, in the aircraft body coordinate system, while on the ground at any instant is equal to zero. Under this premise the longitudinal, lateral and normal forces due to landing are calculated along with the individual deceleration components existent when an aircraft comes to a rest during ground roll. In order to validate this hypothesis a six degree of freedom aircraft model had to be created and landing tests had to be simulated on different surfaces. The simulated aircraft model includes a high fidelity aerodynamic model, thrust model, landing gear model, friction model and antiskid model. Three main surfaces were defined in the friction model; dry, wet and snow/ice. Only the parameters recorded by an FDR are used directly from the aircraft model all others are estimated or known a priori. The estimation of unknown parameters is also presented in the research effort. With all needed parameters a comparison and validation with simulated and estimated data, under different runway conditions, is performed. Finally, this report presents results of a sensitivity analysis in order to provide a measure of reliability of the analytic estimation process. Linear and non-linear sensitivity analysis has been performed in order to quantify the level of uncertainty implicit in modeling estimated parameters and how they can affect the calculation of the instantaneous coefficient of friction. Using the approach of force and moment equilibrium about the CG at landing to reconstruct the instantaneous coefficient of friction appears to be a reasonably accurate estimate when compared to the simulated friction coefficient. This is also true when the FDR and estimated parameters are introduced to white noise and when crosswind is introduced to the simulation. After the linear analysis the results show the minimum frequency at which the algorithm still provides moderately accurate data is at 2Hz. In addition, the linear analysis shows that with estimated parameters increased and decreased up to 25% at random, high priority parameters have to be accurate to within at least +/-5% to have an effect of less than 1% change in the average coefficient of friction. Non-linear analysis results show that the algorithm can be considered reasonably accurate for all simulated cases when inaccuracies in the estimated parameters vary randomly and simultaneously up to +/-27%. At worst-case the maximum percentage change in average coefficient of friction is less than 10% for all surfaces.

Assessment of Methods for Estimating Risk to Birds from Ingestion of Contaminated Grit Particles (Final Report)

EPA Science Inventory

The U.S. EPA Ecological Risk Assessment Support Center (ERASC) announced the release of the final report entitled, Assessment of Methods for Estimating Risk to Birds from Ingestion of Contaminated Grit Particles. This report evaluates approaches for estimating the probabi...

Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies and Incidence Time Series

PubMed Central

Li, Lucy M.; Grassly, Nicholas C.; Fraser, Christophe

2017-01-01

Abstract Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter k of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate k. Incorporating phylogenetic analysis can help to estimate k concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate k and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter k to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, k was most accurately estimated using pathogen phylogeny alone. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates. PMID:28981709

Doubly robust matching estimators for high dimensional confounding adjustment.

PubMed

Antonelli, Joseph; Cefalu, Matthew; Palmer, Nathan; Agniel, Denis

2018-05-11

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data. © 2018, The International Biometric Society.

Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles

NASA Astrophysics Data System (ADS)

Chen, Te; Xu, Xing; Chen, Long; Jiang, Haobing; Cai, Yingfeng; Li, Yong

2018-02-01

Accurate estimation of longitudinal force, lateral vehicle speed and yaw rate is of great significance to torque allocation and stability control for four-wheel independent driven electric vehicle (4WID-EVs). A fusion method is proposed to estimate the longitudinal force, lateral vehicle speed and yaw rate for 4WID-EVs. The electric driving wheel model (EDWM) is introduced into the longitudinal force estimation, the longitudinal force observer (LFO) is designed firstly based on the adaptive high-order sliding mode observer (HSMO), and the convergence of LFO is analyzed and proved. Based on the estimated longitudinal force, an estimation strategy is then presented in which the strong tracking filter (STF) is used to estimate lateral vehicle speed and yaw rate simultaneously. Finally, co-simulation via Carsim and Matlab/Simulink is carried out to demonstrate the effectiveness of the proposed method. The performance of LFO in practice is verified by the experiment on chassis dynamometer bench.

Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement.

PubMed

Roy, Venkat; Simonetto, Andrea; Leus, Geert

2018-06-01

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.

System Reliability-Based Design Optimization Under Input and Model Uncertainties

DTIC Science & Technology

2014-02-02

Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 W911NF-09- 1 -0250 319-335-5684 Final Report 56025-NS.40 a. REPORT 14. ABSTRACT 16...resource. In such cases the inaccuracy and uncertainty of the surrogate model needs to 1 . REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the

Mechanical Properties of Uranium Silicides by Nanoindentation and Finite Elements Modeling

NASA Astrophysics Data System (ADS)

Carvajal-Nunez, U.; Elbakhshwan, M. S.; Mara, N. A.; White, J. T.; Nelson, A. T.

2018-02-01

Three methods were used to measure the mechanical properties of {U}3{Si}, {U}_3{Si}2, and USi. Quasi-static and continuous stiffness measurement nanoindentation were used to determine hardness and Young's modulus, and microindentation was used to evaluate the bulk hardness. Hardness and Young's modulus of the three U-Si compounds were both observed to increase with Si content. Finally, finite elements modelling was used to validate the nanoindentation data calculated for {U}3{Si}2 and estimate its yield strength.

Theoretical and Experimental Studies of the Electro-Optic Effect: Toward a Microscopic Understanding.

DTIC Science & Technology

1981-08-01

electro - optic effect is investigated both theoretically and experimentally. The theoretical approach is based upon W.A. Harrison’s ’Bond-Orbital Model’. The separate electronic and lattice contributions to the second-order, electro - optic susceptibility are examined within the context of this model and formulae which can accommodate any crystal structure are presented. In addition, a method for estimating the lattice response to a low frequency (dc) electric field is outlined. Finally, experimental measurements of the electro -