Transformation of Galilean satellite parameters to J2000

NASA Astrophysics Data System (ADS)

Lieske, J. H.

1998-09-01

The so-called galsat software has the capability of computing Earth-equatorial coordinates of Jupiter's Galilean satellies in an arbitrary reference frame, not just that of B1950. The 50 parameters which define the theory of motion of the Galilean satellites (Lieske 1977, Astron. Astrophys. 56, 333--352) could also be transformed in a manner such that the same galsat computer program can be employed to compute rectangular coordinates with their values being in the J2000 system. One of the input parameters (varepsilon_ {27}) is related to the obliquity of the ecliptic and its value is normally zero in the B1950 frame. If that parameter is changed from 0 to -0.0002771, and if other input parameters are changed in a prescribed manner, then the same galsat software can be employed to produce ephemerides on the J2000 system for any of the ephemerides which employ the galsat parameters, such as those of Arlot (1982), Vasundhara (1994) and Lieske. In this paper we present the parameters whose values must be altered in order for the software to produce coordinates directly in the J2000 system.

Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

PubMed

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.

Dual-input two-compartment pharmacokinetic model of dynamic contrast-enhanced magnetic resonance imaging in hepatocellular carcinoma.

PubMed

Yang, Jian-Feng; Zhao, Zhen-Hua; Zhang, Yu; Zhao, Li; Yang, Li-Ming; Zhang, Min-Ming; Wang, Bo-Yin; Wang, Ting; Lu, Bao-Chun

2016-04-07

To investigate the feasibility of a dual-input two-compartment tracer kinetic model for evaluating tumorous microvascular properties in advanced hepatocellular carcinoma (HCC). From January 2014 to April 2015, we prospectively measured and analyzed pharmacokinetic parameters [transfer constant (Ktrans), plasma flow (Fp), permeability surface area product (PS), efflux rate constant (kep), extravascular extracellular space volume ratio (ve), blood plasma volume ratio (vp), and hepatic perfusion index (HPI)] using dual-input two-compartment tracer kinetic models [a dual-input extended Tofts model and a dual-input 2-compartment exchange model (2CXM)] in 28 consecutive HCC patients. A well-known consensus that HCC is a hypervascular tumor supplied by the hepatic artery and the portal vein was used as a reference standard. A paired Student's t-test and a nonparametric paired Wilcoxon rank sum test were used to compare the equivalent pharmacokinetic parameters derived from the two models, and Pearson correlation analysis was also applied to observe the correlations among all equivalent parameters. The tumor size and pharmacokinetic parameters were tested by Pearson correlation analysis, while correlations among stage, tumor size and all pharmacokinetic parameters were assessed by Spearman correlation analysis. The Fp value was greater than the PS value (FP = 1.07 mL/mL per minute, PS = 0.19 mL/mL per minute) in the dual-input 2CXM; HPI was 0.66 and 0.63 in the dual-input extended Tofts model and the dual-input 2CXM, respectively. There were no significant differences in the kep, vp, or HPI between the dual-input extended Tofts model and the dual-input 2CXM (P = 0.524, 0.569, and 0.622, respectively). All equivalent pharmacokinetic parameters, except for ve, were correlated in the two dual-input two-compartment pharmacokinetic models; both Fp and PS in the dual-input 2CXM were correlated with Ktrans derived from the dual-input extended Tofts model (P = 0.002, r = 0.566; P = 0.002, r = 0.570); kep, vp, and HPI between the two kinetic models were positively correlated (P = 0.001, r = 0.594; P = 0.0001, r = 0.686; P = 0.04, r = 0.391, respectively). In the dual input extended Tofts model, ve was significantly less than that in the dual input 2CXM (P = 0.004), and no significant correlation was seen between the two tracer kinetic models (P = 0.156, r = 0.276). Neither tumor size nor tumor stage was significantly correlated with any of the pharmacokinetic parameters obtained from the two models (P > 0.05). A dual-input two-compartment pharmacokinetic model (a dual-input extended Tofts model and a dual-input 2CXM) can be used in assessing the microvascular physiopathological properties before the treatment of advanced HCC. The dual-input extended Tofts model may be more stable in measuring the ve; however, the dual-input 2CXM may be more detailed and accurate in measuring microvascular permeability.

Sediment residence times constrained by uranium-series isotopes: A critical appraisal of the comminution approach

NASA Astrophysics Data System (ADS)

Handley, Heather K.; Turner, Simon; Afonso, Juan C.; Dosseto, Anthony; Cohen, Tim

2013-02-01

Quantifying the rates of landscape evolution in response to climate change is inhibited by the difficulty of dating the formation of continental detrital sediments. We present uranium isotope data for Cooper Creek palaeochannel sediments from the Lake Eyre Basin in semi-arid South Australia in order to attempt to determine the formation ages and hence residence times of the sediments. To calculate the amount of recoil loss of 234U, a key input parameter used in the comminution approach, we use two suggested methods (weighted geometric and surface area measurement with an incorporated fractal correction) and typical assumed input parameter values found in the literature. The calculated recoil loss factors and comminution ages are highly dependent on the method of recoil loss factor determination used and the chosen assumptions. To appraise the ramifications of the assumptions inherent in the comminution age approach and determine individual and combined comminution age uncertainties associated to each variable, Monte Carlo simulations were conducted for a synthetic sediment sample. Using a reasonable associated uncertainty for each input factor and including variations in the source rock and measured (234U/238U) ratios, the total combined uncertainty on comminution age in our simulation (for both methods of recoil loss factor estimation) can amount to ±220-280 ka. The modelling shows that small changes in assumed input values translate into large effects on absolute comminution age. To improve the accuracy of the technique and provide meaningful absolute comminution ages, much tighter constraints are required on the assumptions for input factors such as the fraction of α-recoil lost 234Th and the initial (234U/238U) ratio of the source material. In order to be able to directly compare calculated comminution ages produced by different research groups, the standardisation of pre-treatment procedures, recoil loss factor estimation and assumed input parameter values is required. We suggest a set of input parameter values for such a purpose. Additional considerations for calculating comminution ages of sediments deposited within large, semi-arid drainage basins are discussed.

Summary of longitudinal stability and control parameters as determined from Space Shuttle Challenger flight test data

NASA Technical Reports Server (NTRS)

Suit, William T.

1989-01-01

Estimates of longitudinal stability and control parameters for the space shuttle were determined by applying a maximum likelihood parameter estimation technique to Challenger flight test data. The parameters for pitching moment coefficient, C(m sub alpha), (at different angles of attack), pitching moment coefficient, C(m sub delta e), (at different elevator deflections) and the normal force coefficient, C(z sub alpha), (at different angles of attack) describe 90 percent of the response to longitudinal inputs during Space Shuttle Challenger flights with C(m sub delta e) being the dominant parameter. The values of C(z sub alpha) were found to be input dependent for these tests. However, when C(z sub alpha) was set at preflight predictions, the values determined for C(m sub delta e) changed less than 10 percent from the values obtained when C(z sub alpha) was estimated as well. The preflight predictions for C(z sub alpha) and C(m sub alpha) are acceptable values, while the values of C(z sub delta e) should be about 30 percent less negative than the preflight predictions near Mach 1, and 10 percent less negative, otherwise.

Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

NASA Astrophysics Data System (ADS)

Kavimani, V.; Prakash, K. Soorya

2017-11-01

This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

Comparisons of Solar Wind Coupling Parameters with Auroral Energy Deposition Rates

NASA Technical Reports Server (NTRS)

Elsen, R.; Brittnacher, M. J.; Fillingim, M. O.; Parks, G. K.; Germany G. A.; Spann, J. F., Jr.

1997-01-01

Measurement of the global rate of energy deposition in the ionosphere via auroral particle precipitation is one of the primary goals of the Polar UVI program and is an important component of the ISTP program. The instantaneous rate of energy deposition for the entire month of January 1997 has been calculated by applying models to the UVI images and is presented by Fillingim et al. In this session. A number of parameters that predict the rate of coupling of solar wind energy into the magnetosphere have been proposed in the last few decades. Some of these parameters, such as the epsilon parameter of Perrault and Akasofu, depend on the instantaneous values in the solar wind. Other parameters depend on the integrated values of solar wind parameters, especially IMF Bz, e.g. applied flux which predicts the net transfer of magnetic flux to the tail. While these parameters have often been used successfully with substorm studies, their validity in terms of global energy input has not yet been ascertained, largely because data such as that supplied by the ISTP program was lacking. We have calculated these and other energy coupling parameters for January 1997 using solar wind data provided by WIND and other solar wind monitors. The rates of energy input predicted by these parameters are compared to those measured through UVI data and correlations are sought. Whether these parameters are better at providing an instantaneous rate of energy input or an average input over some time period is addressed. We also study if either type of parameter may provide better correlations if a time delay is introduced; if so, this time delay may provide a characteristic time for energy transport in the coupled solar wind-magnetosphere-ionosphere system.

Extension of the PC version of VEPFIT with input and output routines running under Windows

NASA Astrophysics Data System (ADS)

Schut, H.; van Veen, A.

1995-01-01

The fitting program VEPFIT has been extended with applications running under the Microsoft-Windows environment facilitating the input and output of the VEPFIT fitting module. We have exploited the Microsoft-Windows graphical users interface by making use of dialog windows, scrollbars, command buttons, etc. The user communicates with the program simply by clicking and dragging with the mouse pointing device. Keyboard actions are limited to a minimum. Upon changing one or more input parameters the results of the modeling of the S-parameter and Ps fractions versus positron implantation energy are updated and displayed. This action can be considered as the first step in the fitting procedure upon which the user can decide to further adapt the input parameters or to forward these parameters as initial values to the fitting routine. The modeling step has proven to be helpful for designing positron beam experiments.

Guidance for Selecting Input Parameters in Modeling the Environmental Fate and Transport of Pesticides

EPA Pesticide Factsheets

Guidance to select and prepare input values for OPP's aquatic exposure models. Intended to improve the consistency in modeling the fate of pesticides in the environment and quality of OPP's aquatic risk assessments.

Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model

NASA Astrophysics Data System (ADS)

Touhidul Mustafa, Syed Md.; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

2017-04-01

Transient numerical groundwater flow models have been used to understand and forecast groundwater flow systems under anthropogenic and climatic effects, but the reliability of the predictions is strongly influenced by different sources of uncertainty. Hence, researchers in hydrological sciences are developing and applying methods for uncertainty quantification. Nevertheless, spatially distributed flow models pose significant challenges for parameter and spatially distributed input estimation and uncertainty quantification. In this study, we present a general and flexible approach for input and parameter estimation and uncertainty analysis of groundwater models. The proposed approach combines a fully distributed groundwater flow model (MODFLOW) with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. To avoid over-parameterization, the uncertainty of the spatially distributed model input has been represented by multipliers. The posterior distributions of these multipliers and the regular model parameters were estimated using DREAM. The proposed methodology has been applied in an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results confirm that input uncertainty does have a considerable effect on the model predictions and parameter distributions. Additionally, our approach also provides a new way to optimize the spatially distributed recharge and pumping data along with the parameter values under uncertain input conditions. It can be concluded from our approach that considering model input uncertainty along with parameter uncertainty is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.

Computing the structural influence matrix for biological systems.

PubMed

Giordano, Giulia; Cuba Samaniego, Christian; Franco, Elisa; Blanchini, Franco

2016-06-01

We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.

Support vector machines-based modelling of seismic liquefaction potential

NASA Astrophysics Data System (ADS)

Pal, Mahesh

2006-08-01

This paper investigates the potential of support vector machines (SVM)-based classification approach to assess the liquefaction potential from actual standard penetration test (SPT) and cone penetration test (CPT) field data. SVMs are based on statistical learning theory and found to work well in comparison to neural networks in several other applications. Both CPT and SPT field data sets is used with SVMs for predicting the occurrence and non-occurrence of liquefaction based on different input parameter combination. With SPT and CPT test data sets, highest accuracy of 96 and 97%, respectively, was achieved with SVMs. This suggests that SVMs can effectively be used to model the complex relationship between different soil parameter and the liquefaction potential. Several other combinations of input variable were used to assess the influence of different input parameters on liquefaction potential. Proposed approach suggest that neither normalized cone resistance value with CPT data nor the calculation of standardized SPT value is required with SPT data. Further, SVMs required few user-defined parameters and provide better performance in comparison to neural network approach.

Application of Artificial Neural Network to Optical Fluid Analyzer

NASA Astrophysics Data System (ADS)

Kimura, Makoto; Nishida, Katsuhiko

1994-04-01

A three-layer artificial neural network has been applied to the presentation of optical fluid analyzer (OFA) raw data, and the accuracy of oil fraction determination has been significantly improved compared to previous approaches. To apply the artificial neural network approach to solving a problem, the first step is training to determine the appropriate weight set for calculating the target values. This involves using a series of data sets (each comprising a set of input values and an associated set of output values that the artificial neural network is required to determine) to tune artificial neural network weighting parameters so that the output of the neural network to the given set of input values is as close as possible to the required output. The physical model used to generate the series of learning data sets was the effective flow stream model, developed for OFA data presentation. The effectiveness of the training was verified by reprocessing the same input data as were used to determine the weighting parameters and then by comparing the results of the artificial neural network to the expected output values. The standard deviation of the expected and obtained values was approximately 10% (two sigma).

Application and optimization of input parameter spaces in mass flow modelling: a case study with r.randomwalk and r.ranger

NASA Astrophysics Data System (ADS)

Krenn, Julia; Zangerl, Christian; Mergili, Martin

2017-04-01

r.randomwalk is a GIS-based, multi-functional, conceptual open source model application for forward and backward analyses of the propagation of mass flows. It relies on a set of empirically derived, uncertain input parameters. In contrast to many other tools, r.randomwalk accepts input parameter ranges (or, in case of two or more parameters, spaces) in order to directly account for these uncertainties. Parameter spaces represent a possibility to withdraw from discrete input values which in most cases are likely to be off target. r.randomwalk automatically performs multiple calculations with various parameter combinations in a given parameter space, resulting in the impact indicator index (III) which denotes the fraction of parameter value combinations predicting an impact on a given pixel. Still, there is a need to constrain the parameter space used for a certain process type or magnitude prior to performing forward calculations. This can be done by optimizing the parameter space in terms of bringing the model results in line with well-documented past events. As most existing parameter optimization algorithms are designed for discrete values rather than for ranges or spaces, the necessity for a new and innovative technique arises. The present study aims at developing such a technique and at applying it to derive guiding parameter spaces for the forward calculation of rock avalanches through back-calculation of multiple events. In order to automatize the work flow we have designed r.ranger, an optimization and sensitivity analysis tool for parameter spaces which can be directly coupled to r.randomwalk. With r.ranger we apply a nested approach where the total value range of each parameter is divided into various levels of subranges. All possible combinations of subranges of all parameters are tested for the performance of the associated pattern of III. Performance indicators are the area under the ROC curve (AUROC) and the factor of conservativeness (FoC). This strategy is best demonstrated for two input parameters, but can be extended arbitrarily. We use a set of small rock avalanches from western Austria, and some larger ones from Canada and New Zealand, to optimize the basal friction coefficient and the mass-to-drag ratio of the two-parameter friction model implemented with r.randomwalk. Thereby we repeat the optimization procedure with conservative and non-conservative assumptions of a set of complementary parameters and with different raster cell sizes. Our preliminary results indicate that the model performance in terms of AUROC achieved with broad parameter spaces is hardly surpassed by the performance achieved with narrow parameter spaces. However, broad spaces may result in very conservative or very non-conservative predictions. Therefore, guiding parameter spaces have to be (i) broad enough to avoid the risk of being off target; and (ii) narrow enough to ensure a reasonable level of conservativeness of the results. The next steps will consist in (i) extending the study to other types of mass flow processes in order to support forward calculations using r.randomwalk; and (ii) in applying the same strategy to the more complex, dynamic model r.avaflow.

Update on ɛK with lattice QCD inputs

NASA Astrophysics Data System (ADS)

Jang, Yong-Chull; Lee, Weonjong; Lee, Sunkyu; Leem, Jaehoon

2018-03-01

We report updated results for ɛK, the indirect CP violation parameter in neutral kaons, which is evaluated directly from the standard model with lattice QCD inputs. We use lattice QCD inputs to fix B\\hatk,|Vcb|,ξ0,ξ2,|Vus|, and mc(mc). Since Lattice 2016, the UTfit group has updated the Wolfenstein parameters in the angle-only-fit method, and the HFLAV group has also updated |Vcb|. Our results show that the evaluation of ɛK with exclusive |Vcb| (lattice QCD inputs) has 4.0σ tension with the experimental value, while that with inclusive |Vcb| (heavy quark expansion based on OPE and QCD sum rules) shows no tension.

Distribution Development for STORM Ingestion Input Parameters

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

Fulton, John

The Sandia-developed Transport of Radioactive Materials (STORM) code suite is used as part of the Radioisotope Power System Launch Safety (RPSLS) program to perform statistical modeling of the consequences due to release of radioactive material given a launch accident. As part of this modeling, STORM samples input parameters from probability distributions with some parameters treated as constants. This report described the work done to convert four of these constant inputs (Consumption Rate, Average Crop Yield, Cropland to Landuse Database Ratio, and Crop Uptake Factor) to sampled values. Consumption rate changed from a constant value of 557.68 kg / yr tomore » a normal distribution with a mean of 102.96 kg / yr and a standard deviation of 2.65 kg / yr. Meanwhile, Average Crop Yield changed from a constant value of 3.783 kg edible / m 2 to a normal distribution with a mean of 3.23 kg edible / m 2 and a standard deviation of 0.442 kg edible / m 2 . The Cropland to Landuse Database ratio changed from a constant value of 0.0996 (9.96%) to a normal distribution with a mean value of 0.0312 (3.12%) and a standard deviation of 0.00292 (0.29%). Finally the crop uptake factor changed from a constant value of 6.37e -4 (Bq crop /kg)/(Bq soil /kg) to a lognormal distribution with a geometric mean value of 3.38e -4 (Bq crop /kg)/(Bq soil /kg) and a standard deviation value of 3.33 (Bq crop /kg)/(Bq soil /kg)« less

Can Whole-Body Cryotherapy with Subsequent Kinesiotherapy Procedures in Closed Type Cryogenic Chamber Improve BASDAI, BASFI, and Some Spine Mobility Parameters and Decrease Pain Intensity in Patients with Ankylosing Spondylitis?

PubMed

Stanek, Agata; Cholewka, Armand; Gadula, Jolanta; Drzazga, Zofia; Sieron, Aleksander; Sieron-Stoltny, Karolina

2015-01-01

The present study investigated whether whole-body cryotherapy (WBC) procedures could potentially have more beneficial effects on index of BASDAI and BASFI, pain intensity, and spine mobility parameters: Ott test, modified Schober test, chest expansion in ankylosing spondylitis (AS) patients, than kinesiotherapy procedures used separately. AS patients were exposed to a cycle of WBC procedures lasting 3 minutes a day, with a subsequent 60 minutes of kinesiotherapy or 60 minutes of kinesiotherapy only, for 10 consecutive days excluding weekend. After the completion of the cycle of WBC procedures with subsequent kinesiotherapy in the AS patients, BASDAI index decreased about 40% in comparison with the input value, whereas in the group of patients who received only kinesiotherapy it decreased only about 15% in comparison with the input value. After the completion of the treatment in the WBC group, BASFI index decreased about 30% in comparison with the input value, whereas in the kinesiotherapy group it only decreased about 16% in comparison with the input value. The important conclusion was that, in WBC group with subsequent kinesiotherapy, we observed on average about twice better results than in the group treated only by kinesiotherapy.

Can Whole-Body Cryotherapy with Subsequent Kinesiotherapy Procedures in Closed Type Cryogenic Chamber Improve BASDAI, BASFI, and Some Spine Mobility Parameters and Decrease Pain Intensity in Patients with Ankylosing Spondylitis?

PubMed Central

Stanek, Agata; Cholewka, Armand; Gadula, Jolanta; Drzazga, Zofia; Sieron, Aleksander; Sieron-Stoltny, Karolina

2015-01-01

The present study investigated whether whole-body cryotherapy (WBC) procedures could potentially have more beneficial effects on index of BASDAI and BASFI, pain intensity, and spine mobility parameters: Ott test, modified Schober test, chest expansion in ankylosing spondylitis (AS) patients, than kinesiotherapy procedures used separately. AS patients were exposed to a cycle of WBC procedures lasting 3 minutes a day, with a subsequent 60 minutes of kinesiotherapy or 60 minutes of kinesiotherapy only, for 10 consecutive days excluding weekend. After the completion of the cycle of WBC procedures with subsequent kinesiotherapy in the AS patients, BASDAI index decreased about 40% in comparison with the input value, whereas in the group of patients who received only kinesiotherapy it decreased only about 15% in comparison with the input value. After the completion of the treatment in the WBC group, BASFI index decreased about 30% in comparison with the input value, whereas in the kinesiotherapy group it only decreased about 16% in comparison with the input value. The important conclusion was that, in WBC group with subsequent kinesiotherapy, we observed on average about twice better results than in the group treated only by kinesiotherapy. PMID:26273618

Uncertainty Quantification of Equilibrium Climate Sensitivity in CCSM4

NASA Astrophysics Data System (ADS)

Covey, C. C.; Lucas, D. D.; Tannahill, J.; Klein, R.

2013-12-01

Uncertainty in the global mean equilibrium surface warming due to doubled atmospheric CO2, as computed by a "slab ocean" configuration of the Community Climate System Model version 4 (CCSM4), is quantified using 1,039 perturbed-input-parameter simulations. The slab ocean configuration reduces the model's e-folding time when approaching an equilibrium state to ~5 years. This time is much less than for the full ocean configuration, consistent with the shallow depth of the upper well-mixed layer of the ocean represented by the "slab." Adoption of the slab ocean configuration requires the assumption of preset values for the convergence of ocean heat transport beneath the upper well-mixed layer. A standard procedure for choosing these values maximizes agreement with the full ocean version's simulation of the present-day climate when input parameters assume their default values. For each new set of input parameter values, we computed the change in ocean heat transport implied by a "Phase 1" model run in which sea surface temperatures and sea ice concentrations were set equal to present-day values. The resulting total ocean heat transport (= standard value + change implied by Phase 1 run) was then input into "Phase 2" slab ocean runs with varying values of atmospheric CO2. Our uncertainty estimate is based on Latin Hypercube sampling over expert-provided uncertainty ranges of N = 36 adjustable parameters in the atmosphere (CAM4) and sea ice (CICE4) components of CCSM4. Two-dimensional projections of our sampling distribution for the N(N-1)/2 possible pairs of input parameters indicate full coverage of the N-dimensional parameter space, including edges. We used a machine learning-based support vector regression (SVR) statistical model to estimate the probability density function (PDF) of equilibrium warming. This fitting procedure produces a PDF that is qualitatively consistent with the raw histogram of our CCSM4 results. Most of the values from the SVR statistical model are within ~0.1 K of the raw results, well below the inter-decile range inferred below. Independent validation of the fit indicates residual errors that are distributed about zero with a standard deviation of 0.17 K. Analysis of variance shows that the equilibrium warming in CCSM4 is mainly linear in parameter changes. Thus, in accord with the Central Limit Theorem of statistics, the PDF of the warming is approximately Gaussian, i.e. symmetric about its mean value (3.0 K). Since SVR allows for highly nonlinear fits, the symmetry is not an artifact of the fitting procedure. The 10-90 percentile range of the PDF is 2.6-3.4 K, consistent with earlier estimates from CCSM4 but narrower than estimates from other models, which sometimes produce a high-temperature asymmetric tail in the PDF. This work was performed under auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was funded by LLNL's Uncertainty Quantification Strategic Initiative (Laboratory Directed Research and Development Project 10-SI-013).

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Simulation tests of the optimization method of Hopfield and Tank using neural networks

NASA Technical Reports Server (NTRS)

Paielli, Russell A.

1988-01-01

The method proposed by Hopfield and Tank for using the Hopfield neural network with continuous valued neurons to solve the traveling salesman problem is tested by simulation. Several researchers have apparently been unable to successfully repeat the numerical simulation documented by Hopfield and Tank. However, as suggested to the author by Adams, it appears that the reason for those difficulties is that a key parameter value is reported erroneously (by four orders of magnitude) in the original paper. When a reasonable value is used for that parameter, the network performs generally as claimed. Additionally, a new method of using feedback to control the input bias currents to the amplifiers is proposed and successfully tested. This eliminates the need to set the input currents by trial and error.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Impact of Rainfall Data Source on Hydrologic and Water Quality Model Response of a Coastal Plain Watershed

USDA-ARS?s Scientific Manuscript database

Hydrologic and water quality models are very sensitive to input parameter values, especially precipitation input data. With several different sources of precipitation data now available, it is quite difficult to determine which source is most appropriate under various circumstances. We used several ...

MODFLOW-2000, the U.S. Geological Survey modular ground-water model; user guide to the observation, sensitivity, and parameter-estimation processes and three post-processing programs

USGS Publications Warehouse

Hill, Mary C.; Banta, E.R.; Harbaugh, A.W.; Anderman, E.R.

2000-01-01

This report documents the Observation, Sensitivity, and Parameter-Estimation Processes of the ground-water modeling computer program MODFLOW-2000. The Observation Process generates model-calculated values for comparison with measured, or observed, quantities. A variety of statistics is calculated to quantify this comparison, including a weighted least-squares objective function. In addition, a number of files are produced that can be used to compare the values graphically. The Sensitivity Process calculates the sensitivity of hydraulic heads throughout the model with respect to specified parameters using the accurate sensitivity-equation method. These are called grid sensitivities. If the Observation Process is active, it uses the grid sensitivities to calculate sensitivities for the simulated values associated with the observations. These are called observation sensitivities. Observation sensitivities are used to calculate a number of statistics that can be used (1) to diagnose inadequate data, (2) to identify parameters that probably cannot be estimated by regression using the available observations, and (3) to evaluate the utility of proposed new data. The Parameter-Estimation Process uses a modified Gauss-Newton method to adjust values of user-selected input parameters in an iterative procedure to minimize the value of the weighted least-squares objective function. Statistics produced by the Parameter-Estimation Process can be used to evaluate estimated parameter values; statistics produced by the Observation Process and post-processing program RESAN-2000 can be used to evaluate how accurately the model represents the actual processes; statistics produced by post-processing program YCINT-2000 can be used to quantify the uncertainty of model simulated values. Parameters are defined in the Ground-Water Flow Process input files and can be used to calculate most model inputs, such as: for explicitly defined model layers, horizontal hydraulic conductivity, horizontal anisotropy, vertical hydraulic conductivity or vertical anisotropy, specific storage, and specific yield; and, for implicitly represented layers, vertical hydraulic conductivity. In addition, parameters can be defined to calculate the hydraulic conductance of the River, General-Head Boundary, and Drain Packages; areal recharge rates of the Recharge Package; maximum evapotranspiration of the Evapotranspiration Package; pumpage or the rate of flow at defined-flux boundaries of the Well Package; and the hydraulic head at constant-head boundaries. The spatial variation of model inputs produced using defined parameters is very flexible, including interpolated distributions that require the summation of contributions from different parameters. Observations can include measured hydraulic heads or temporal changes in hydraulic heads, measured gains and losses along head-dependent boundaries (such as streams), flows through constant-head boundaries, and advective transport through the system, which generally would be inferred from measured concentrations. MODFLOW-2000 is intended for use on any computer operating system. The program consists of algorithms programmed in Fortran 90, which efficiently performs numerical calculations and is fully compatible with the newer Fortran 95. The code is easily modified to be compatible with FORTRAN 77. Coordination for multiple processors is accommodated using Message Passing Interface (MPI) commands. The program is designed in a modular fashion that is intended to support inclusion of new capabilities.

An improved state-parameter analysis of ecosystem models using data assimilation

USGS Publications Warehouse

Chen, M.; Liu, S.; Tieszen, L.L.; Hollinger, D.Y.

2008-01-01

Much of the effort spent in developing data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be lowered if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) by combining ensemble Kalman filter with kernel smoothing technique. SEnKF has following characteristics: (1) to estimate simultaneously the model states and parameters through concatenating unknown parameters and state variables into a joint state vector; (2) to mitigate dramatic, sudden changes of parameter values in parameter sampling and parameter evolution process, and control narrowing of parameter variance which results in filter divergence through adjusting smoothing factor in kernel smoothing algorithm; (3) to assimilate recursively data into the model and thus detect possible time variation of parameters; and (4) to address properly various sources of uncertainties stemming from input, output and parameter uncertainties. The SEnKF is tested by assimilating observed fluxes of carbon dioxide and environmental driving factor data from an AmeriFlux forest station located near Howland, Maine, USA, into a partition eddy flux model. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and others, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce the variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving the understanding and quantification of carbon cycle parameters and processes. ?? 2008 Elsevier B.V.

Modeling clear-sky solar radiation across a range of elevations in Hawai‘i: Comparing the use of input parameters at different temporal resolutions

NASA Astrophysics Data System (ADS)

Longman, Ryan J.; Giambelluca, Thomas W.; Frazier, Abby G.

2012-01-01

Estimates of clear sky global solar irradiance using the parametric model SPCTRAL2 were tested against clear sky radiation observations at four sites in Hawai`i using daily, mean monthly, and 1 year mean model parameter settings. Atmospheric parameters in SPCTRAL2 and similar models are usually set at site-specific values and are not varied to represent the effects of fluctuating humidity, aerosol amount and type, or ozone concentration, because time-dependent atmospheric parameter estimates are not available at most sites of interest. In this study, we sought to determine the added value of using time dependent as opposed to fixed model input parameter settings. At the AERONET site, Mauna Loa Observatory (MLO) on the island of Hawai`i, where daily measurements of atmospheric optical properties and hourly solar radiation observations are available, use of daily rather than 1 year mean aerosol parameter values reduced mean bias error (MBE) from 18 to 10 W m-2 and root mean square error from 25 to 17 W m-2. At three stations in the HaleNet climate network, located at elevations of 960, 1640, and 2590 m on the island of Maui, where aerosol-related parameter settings were interpolated from observed values for AERONET sites at MLO (3397 m) and Lāna`i (20 m), and precipitable water was estimated using radiosonde-derived humidity profiles from nearby Hilo, the model performed best when using constant 1 year mean parameter values. At HaleNet Station 152, for example, MBE was 18, 10, and 8 W m-2 for daily, monthly, and 1 year mean parameters, respectively.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Impact of the hard-coded parameters on the hydrologic fluxes of the land surface model Noah-MP

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Attinger, Sabine; Thober, Stephan

2016-04-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The process descriptions contain a number of parameters that can be soil or plant type dependent and are typically read from tabulated input files. Land surface models may have, however, process descriptions that contain fixed, hard-coded numbers in the computer code, which are not identified as model parameters. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the importance of the fixed values on restricting the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options, which are mostly spatially constant values. This is in addition to the 71 standard parameters of Noah-MP, which mostly get distributed spatially by given vegetation and soil input maps. We performed a Sobol' global sensitivity analysis of Noah-MP to variations of the standard and hard-coded parameters for a specific set of process options. 42 standard parameters and 75 hard-coded parameters were active with the chosen process options. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated. These sensitivities were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities towards standard and hard-coded parameters in Noah-MP because of their tight coupling via the water balance. It should therefore be comparable to calibrate Noah-MP either against latent heat observations or against river runoff data. Latent heat and total runoff are sensitive to both, plant and soil parameters. Calibrating only a parameter sub-set of only soil parameters, for example, thus limits the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Reliability of system for precise cold forging

NASA Astrophysics Data System (ADS)

Krušič, Vid; Rodič, Tomaž

2017-07-01

The influence of scatter of principal input parameters of the forging system on the dimensional accuracy of product and on the tool life for closed-die forging process is presented in this paper. Scatter of the essential input parameters for the closed-die upsetting process was adjusted to the maximal values that enabled the reliable production of a dimensionally accurate product at optimal tool life. An operating window was created in which exists the maximal scatter of principal input parameters for the closed-die upsetting process that still ensures the desired dimensional accuracy of the product and the optimal tool life. Application of the adjustment of the process input parameters is shown on the example of making an inner race of homokinetic joint from mass production. High productivity in manufacture of elements by cold massive extrusion is often achieved by multiple forming operations that are performed simultaneously on the same press. By redesigning the time sequences of forming operations at multistage forming process of starter barrel during the working stroke the course of the resultant force is optimized.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Recovering stellar population parameters via two full-spectrum fitting algorithms in the absence of model uncertainties

NASA Astrophysics Data System (ADS)

Ge, Junqiang; Yan, Renbin; Cappellari, Michele; Mao, Shude; Li, Hongyu; Lu, Youjun

2018-05-01

Using mock spectra based on Vazdekis/MILES library fitted within the wavelength region 3600-7350Å, we analyze the bias and scatter on the resulting physical parameters induced by the choice of fitting algorithms and observational uncertainties, but avoid effects of those model uncertainties. We consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for stellar population age, metallicity, mass-to-light ratio, and dust extinction. With pPXF we find that both the bias μ in the population parameters and the scatter σ in the recovered logarithmic values follows the expected trend μ ∝ σ ∝ 1/(S/N). The bias increases for younger ages and systematically makes recovered ages older, M*/Lr larger and metallicities lower than the true values. For reference, at S/N=30, and for the worst case (t = 108yr), the bias is 0.06 dex in M/Lr, 0.03 dex in both age and [M/H]. There is no significant dependence on either E(B-V) or the shape of the error spectrum. Moreover, the results are consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we find trends similar to pPXF, when the input E(B-V)<0.2 mag. However, with larger input E(B-V), the biases of the output parameter do not converge to zero even at the highest S/N and are strongly affected by the shape of the error spectra. This effect is particularly dramatic for youngest age (t = 108yr), for which all population parameters can be strongly different from the input values, with significantly underestimated dust extinction and [M/H], and larger ages and M*/Lr. Results degrade when moving from our 1-SSP to the 2-SSP tests. The STARLIGHT convergence to the true values can be improved by increasing Markov Chains and annealing loops to the "slow mode". For the same input spectrum, pPXF is about two order of magnitudes faster than STARLIGHT's "default mode" and about three order of magnitude faster than STARLIGHT's "slow mode".

Optimisation of process parameters on thin shell part using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Faiz, J. M.; Shayfull, Z.; Nasir, S. M.; Fathullah, M.; Rashidi, M. M.

2017-09-01

This study is carried out to focus on optimisation of process parameters by simulation using Autodesk Moldflow Insight (AMI) software. The process parameters are taken as the input in order to analyse the warpage value which is the output in this study. There are some significant parameters that have been used which are melt temperature, mould temperature, packing pressure, and cooling time. A plastic part made of Polypropylene (PP) has been selected as the study part. Optimisation of process parameters is applied in Design Expert software with the aim to minimise the obtained warpage value. Response Surface Methodology (RSM) has been applied in this study together with Analysis of Variance (ANOVA) in order to investigate the interactions between parameters that are significant to the warpage value. Thus, the optimised warpage value can be obtained using the model designed using RSM due to its minimum error value. This study comes out with the warpage value improved by using RSM.

WE-FG-206-06: Dual-Input Tracer Kinetic Modeling and Its Analog Implementation for Dynamic Contrast-Enhanced (DCE-) MRI of Malignant Mesothelioma (MPM)

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

Lee, S; Rimner, A; Hayes, S

Purpose: To use dual-input tracer kinetic modeling of the lung for mapping spatial heterogeneity of various kinetic parameters in malignant MPM Methods: Six MPM patients received DCE-MRI as part of their radiation therapy simulation scan. 5 patients had the epitheloid subtype of MPM, while one was biphasic. A 3D fast-field echo sequence with TR/TE/Flip angle of 3.62ms/1.69ms/15° was used for DCE-MRI acquisition. The scan was collected for 5 minutes with a temporal resolution of 5-9 seconds depending on the spatial extent of the tumor. A principal component analysis-based groupwise deformable registration was used to co-register all the DCE-MRI series formore » motion compensation. All the images were analyzed using five different dual-input tracer kinetic models implemented in analog continuous-time formalism: the Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. The following parameters were computed for each model: total blood flow (BF), pulmonary flow fraction (γ), pulmonary blood flow (BF-pa), systemic blood flow (BF-a), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (vi), extraction fraction (E), volume transfer constant (Ktrans) and efflux rate constant (kep). Results: Although the majority of patients had epitheloid histologies, kinetic parameter values varied across different models. One patient showed a higher total BF value in all models among the epitheloid histologies, although the γ value was varying among these different models. In one tumor with a large area of necrosis, the TK and ETK models showed higher E, Ktrans, and kep values and lower interstitial volume as compared to AATH and DP and 2CX models. Kinetic parameters such as BF-pa, BF-a, PS, Ktrans values were higher in surviving group compared to non-surviving group across most models. Conclusion: Dual-input tracer kinetic modeling is feasible in determining micro-vascular characteristics of MPM. This project was supported from Cycle for Survival and MSK Imaging and radiation science (IMRAS) grants.« less

Geochemical Data Package for Performance Assessment Calculations Related to the Savannah River Site

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

Kaplan, Daniel I.

The Savannah River Site (SRS) disposes of low-level radioactive waste (LLW) and stabilizes high-level radioactive waste (HLW) tanks in the subsurface environment. Calculations used to establish the radiological limits of these facilities are referred to as Performance Assessments (PA), Special Analyses (SA), and Composite Analyses (CA). The objective of this document is to revise existing geochemical input values used for these calculations. This work builds on earlier compilations of geochemical data (2007, 2010), referred to a geochemical data packages. This work is being conducted as part of the on-going maintenance program of the SRS PA programs that periodically updates calculationsmore » and data packages when new information becomes available. Because application of values without full understanding of their original purpose may lead to misuse, this document also provides the geochemical conceptual model, the approach used for selecting the values, the justification for selecting data, and the assumptions made to assure that the conceptual and numerical geochemical models are reasonably conservative (i.e., bias the recommended input values to reflect conditions that will tend to predict the maximum risk to the hypothetical recipient). This document provides 1088 input parameters for geochemical parameters describing transport processes for 64 elements (>740 radioisotopes) potentially occurring within eight subsurface disposal or tank closure areas: Slit Trenches (ST), Engineered Trenches (ET), Low Activity Waste Vault (LAWV), Intermediate Level (ILV) Vaults, Naval Reactor Component Disposal Areas (NRCDA), Components-in-Grout (CIG) Trenches, Saltstone Facility, and Closed Liquid Waste Tanks. The geochemical parameters described here are the distribution coefficient, Kd value, apparent solubility concentration, k s value, and the cementitious leachate impact factor.« less

Modification of a rainfall-runoff model for distributed modeling in a GIS and its validation

NASA Astrophysics Data System (ADS)

Nyabeze, W. R.

A rainfall-runoff model, which can be inter-faced with a Geographical Information System (GIS) to integrate definition, measurement, calculating parameter values for spatial features, presents considerable advantages. The modification of the GWBasic Wits Rainfall-Runoff Erosion Model (GWBRafler) to enable parameter value estimation in a GIS (GISRafler) is presented in this paper. Algorithms are applied to estimate parameter values reducing the number of input parameters and the effort to populate them. The use of a GIS makes the relationship between parameter estimates and cover characteristics more evident. This paper has been produced as part of research to generalize the GWBRafler on a spatially distributed basis. Modular data structures are assumed and parameter values are weighted relative to the module area and centroid properties. Modifications to the GWBRafler enable better estimation of low flows, which are typical in drought conditions.

Determination of stability and control parameters of a light airplane from flight data using two estimation methods. [equation error and maximum likelihood methods

NASA Technical Reports Server (NTRS)

Klein, V.

1979-01-01

Two identification methods, the equation error method and the output error method, are used to estimate stability and control parameter values from flight data for a low-wing, single-engine, general aviation airplane. The estimated parameters from both methods are in very good agreement primarily because of sufficient accuracy of measured data. The estimated static parameters also agree with the results from steady flights. The effect of power different input forms are demonstrated. Examination of all results available gives the best values of estimated parameters and specifies their accuracies.

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

Parameter extraction with neural networks

NASA Astrophysics Data System (ADS)

Cazzanti, Luca; Khan, Mumit; Cerrina, Franco

1998-06-01

In semiconductor processing, the modeling of the process is becoming more and more important. While the ultimate goal is that of developing a set of tools for designing a complete process (Technology CAD), it is also necessary to have modules to simulate the various technologies and, in particular, to optimize specific steps. This need is particularly acute in lithography, where the continuous decrease in CD forces the technologies to operate near their limits. In the development of a 'model' for a physical process, we face several levels of challenges. First, it is necessary to develop a 'physical model,' i.e. a rational description of the process itself on the basis of know physical laws. Second, we need an 'algorithmic model' to represent in a virtual environment the behavior of the 'physical model.' After a 'complete' model has been developed and verified, it becomes possible to do performance analysis. In many cases the input parameters are poorly known or not accessible directly to experiment. It would be extremely useful to obtain the values of these 'hidden' parameters from experimental results by comparing model to data. This is particularly severe, because the complexity and costs associated with semiconductor processing make a simple 'trial-and-error' approach infeasible and cost- inefficient. Even when computer models of the process already exists, obtaining data through simulations may be time consuming. Neural networks (NN) are powerful computational tools to predict the behavior of a system from an existing data set. They are able to adaptively 'learn' input/output mappings and to act as universal function approximators. In this paper we use artificial neural networks to build a mapping from the input parameters of the process to output parameters which are indicative of the performance of the process. Once the NN has been 'trained,' it is also possible to observe the process 'in reverse,' and to extract the values of the inputs which yield outputs with desired characteristics. Using this method, we can extract optimum values for the parameters and determine the process latitude very quickly.

Musings on cosmological relaxation and the hierarchy problem

NASA Astrophysics Data System (ADS)

Jaeckel, Joerg; Mehta, Viraf M.; Witkowski, Lukas T.

2016-03-01

Recently Graham, Kaplan and Rajendran proposed cosmological relaxation as a mechanism for generating a hierarchically small Higgs vacuum expectation value. Inspired by this we collect some thoughts on steps towards a solution to the electroweak hierarchy problem and apply them to the original model of cosmological relaxation [Phys. Rev. Lett. 115, 221801 (2015)]. To do so, we study the dynamics of the model and determine the relation between the fundamental input parameters and the electroweak vacuum expectation value. Depending on the input parameters the model exhibits three qualitatively different regimes, two of which allow for hierarchically small Higgs vacuum expectation values. One leads to standard electroweak symmetry breaking whereas in the other regime electroweak symmetry is mainly broken by a Higgs source term. While the latter is not acceptable in a model based on the QCD axion, in non-QCD models this may lead to new and interesting signatures in Higgs observables. Overall, we confirm that cosmological relaxation can successfully give rise to a hierarchically small Higgs vacuum expectation value if (at least) one model parameter is chosen sufficiently small. However, we find that the required level of tuning for achieving this hierarchy in relaxation models can be much more severe than in the Standard Model.

Approximate, computationally efficient online learning in Bayesian spiking neurons.

PubMed

Kuhlmann, Levin; Hauser-Raspe, Michael; Manton, Jonathan H; Grayden, David B; Tapson, Jonathan; van Schaik, André

2014-03-01

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases

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

Snyder, Sandra F.; Arimescu, Carmen; Napier, Bruce A.

The GENII v2 code is used to estimate dose to individuals or populations from the release of radioactive materials into air or water. Numerous parameter values are required for input into this code. User-defined parameters cover the spectrum from chemical data, meteorological data, agricultural data, and behavioral data. This document is a summary of parameter values that reflect conditions in the United States. Reasonable regional and age-dependent data is summarized. Data availability and quality varies. The set of parameters described address scenarios for chronic air emissions or chronic releases to public waterways. Considerations for the special tritium and carbon-14 modelsmore » are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.« less

Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2015-12-01

For computationally expensive climate models, Monte-Carlo approaches of exploring the input parameter space are often prohibitive due to slow convergence with respect to ensemble size. To alleviate this, we build inexpensive surrogates using uncertainty quantification (UQ) methods employing Polynomial Chaos (PC) expansions that approximate the input-output relationships using as few model evaluations as possible. However, when many uncertain input parameters are present, such UQ studies suffer from the curse of dimensionality. In particular, for 50-100 input parameters non-adaptive PC representations have infeasible numbers of basis terms. To this end, we develop and employ Weighted Iterative Bayesian Compressive Sensing to learn the most important input parameter relationships for efficient, sparse PC surrogate construction with posterior uncertainty quantified due to insufficient data. Besides drastic dimensionality reduction, the uncertain surrogate can efficiently replace the model in computationally intensive studies such as forward uncertainty propagation and variance-based sensitivity analysis, as well as design optimization and parameter estimation using observational data. We applied the surrogate construction and variance-based uncertainty decomposition to Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal.

PubMed

Karmakar, Chandan; Udhayakumar, Radhagayathri K; Li, Peng; Venkatesh, Svetha; Palaniswami, Marimuthu

2017-01-01

Distribution entropy ( DistEn ) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters-the embedding dimension m , and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy ( ApEn ) and sample entropy ( SampEn ) measures. The performance of DistEn can also be affected by the data length N . In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter ( m or M ) or combination of two parameters ( N and M ). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn . The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series.

Quantification of the Effect of Shuttling on Computed Tomography Perfusion Parameters by Investigation of Aortic Inputs on Different Table Positions From Shuttle-Mode Scans of Lung and Liver Tumors.

PubMed

Ghosh, Payel; Chandler, Adam G; Hobbs, Brian P; Sun, Jia; Rong, John; Hong, David; Subbiah, Vivek; Janku, Filip; Naing, Aung; Hwu, Wen-Jen; Ng, Chaan S

The aim of this study was to quantify the effect of shuttling on computed tomography perfusion (CTp) parameters derived from shuttle-mode body CT images using aortic inputs from different table positions. Axial shuttle-mode CT scans were acquired from 6 patients (10 phases, 2 nonoverlapping table positions 1.4 seconds apart) after contrast agent administration. Artifacts resulting from the shuttling motion were corrected with nonrigid registration before computing CTp maps from 4 aortic levels chosen from the most superior and inferior slices of each table position scan. The effect of shuttling on CTp parameters was estimated by mean differences in mappings obtained from aortic inputs in different table positions. Shuttling effect was also quantified using 95% limits of agreement of CTp parameter differences within-table and between-table aortic positions from the interaortic mean CTp values. Blood flow, permeability surface, and hepatic arterial fraction differences were insignificant (P > 0.05) for both within-table and between-table comparisons. The 95% limits of agreement for within-table blood volume (BV) value deviations obtained from lung tumor regions were less than 4.7% (P = 0.18) compared with less than 12.2% (P = 0.003) for between-table BV value deviations. The 95% limits of agreement of within-table deviations for liver tumor regions were less than 1.9% (P = 0.55) for BV and less than 3.2% (P = 0.23) for mean transit time, whereas between-table BV and mean transit time deviations were less than 11.7% (P < 0.01) and less than 14.6% (P < 0.01), respectively. Values for normal liver tissue regions were concordant. Computed tomography perfusion parameters acquired from aortic levels within-table positions generally yielded higher agreement than mappings obtained from aortic levels between-table positions indicating differences due to shuttling effect.

Sensitivity analysis of periodic errors in heterodyne interferometry

NASA Astrophysics Data System (ADS)

Ganguly, Vasishta; Kim, Nam Ho; Kim, Hyo Soo; Schmitz, Tony

2011-03-01

Periodic errors in heterodyne displacement measuring interferometry occur due to frequency mixing in the interferometer. These nonlinearities are typically characterized as first- and second-order periodic errors which cause a cyclical (non-cumulative) variation in the reported displacement about the true value. This study implements an existing analytical periodic error model in order to identify sensitivities of the first- and second-order periodic errors to the input parameters, including rotational misalignments of the polarizing beam splitter and mixing polarizer, non-orthogonality of the two laser frequencies, ellipticity in the polarizations of the two laser beams, and different transmission coefficients in the polarizing beam splitter. A local sensitivity analysis is first conducted to examine the sensitivities of the periodic errors with respect to each input parameter about the nominal input values. Next, a variance-based approach is used to study the global sensitivities of the periodic errors by calculating the Sobol' sensitivity indices using Monte Carlo simulation. The effect of variation in the input uncertainty on the computed sensitivity indices is examined. It is seen that the first-order periodic error is highly sensitive to non-orthogonality of the two linearly polarized laser frequencies, while the second-order error is most sensitive to the rotational misalignment between the laser beams and the polarizing beam splitter. A particle swarm optimization technique is finally used to predict the possible setup imperfections based on experimentally generated values for periodic errors.

Modeling polyvinyl chloride Plasma Modification by Neural Networks

NASA Astrophysics Data System (ADS)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

Evaluation of the geomorphometric results and residual values of a robust plane fitting method applied to different DTMs of various scales and accuracy

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Székely, Balázs; Dorninger, Peter; Kovács, Gábor

2013-04-01

Due to the need for quantitative analysis of various geomorphological landforms, the importance of fast and effective automatic processing of the different kind of digital terrain models (DTMs) is increasing. The robust plane fitting (segmentation) method, developed at the Institute of Photogrammetry and Remote Sensing at Vienna University of Technology, allows the processing of large 3D point clouds (containing millions of points), performs automatic detection of the planar elements of the surface via parameter estimation, and provides a considerable data reduction for the modeled area. Its geoscientific application allows the modeling of different landforms with the fitted planes as planar facets. In our study we aim to analyze the accuracy of the resulting set of fitted planes in terms of accuracy, model reliability and dependence on the input parameters. To this end we used DTMs of different scales and accuracy: (1) artificially generated 3D point cloud model with different magnitudes of error; (2) LiDAR data with 0.1 m error; (3) SRTM (Shuttle Radar Topography Mission) DTM database with 5 m accuracy; (4) DTM data from HRSC (High Resolution Stereo Camera) of the planet Mars with 10 m error. The analysis of the simulated 3D point cloud with normally distributed errors comprised different kinds of statistical tests (for example Chi-square and Kolmogorov-Smirnov tests) applied on the residual values and evaluation of dependence of the residual values on the input parameters. These tests have been repeated on the real data supplemented with the categorization of the segmentation result depending on the input parameters, model reliability and the geomorphological meaning of the fitted planes. The simulation results show that for the artificially generated data with normally distributed errors the null hypothesis can be accepted based on the residual value distribution being also normal, but in case of the test on the real data the residual value distribution is often mixed or unknown. The residual values are found to be dependent on two input parameters (standard deviation and maximum point-plane distance both defining distance thresholds for assigning points to a segment) mainly and the curvature of the surface affected mostly the distributions. The results of the analysis helped to decide which parameter set is the best for further modelling and provides the highest accuracy. With these results in mind the success of quasi-automatic modelling of the planar (for example plateau-like) features became more successful and often provided more accuracy. These studies were carried out partly in the framework of TMIS.ascrea project (Nr. 2001978) financed by the Austrian Research Promotion Agency (FFG); the contribution of ZsK was partly funded by Campus Hungary Internship TÁMOP-424B1.

Method of validating measurement data of a process parameter from a plurality of individual sensor inputs

DOEpatents

Scarola, Kenneth; Jamison, David S.; Manazir, Richard M.; Rescorl, Robert L.; Harmon, Daryl L.

1998-01-01

A method for generating a validated measurement of a process parameter at a point in time by using a plurality of individual sensor inputs from a scan of said sensors at said point in time. The sensor inputs from said scan are stored and a first validation pass is initiated by computing an initial average of all stored sensor inputs. Each sensor input is deviation checked by comparing each input including a preset tolerance against the initial average input. If the first deviation check is unsatisfactory, the sensor which produced the unsatisfactory input is flagged as suspect. It is then determined whether at least two of the inputs have not been flagged as suspect and are therefore considered good inputs. If two or more inputs are good, a second validation pass is initiated by computing a second average of all the good sensor inputs, and deviation checking the good inputs by comparing each good input including a present tolerance against the second average. If the second deviation check is satisfactory, the second average is displayed as the validated measurement and the suspect sensor as flagged as bad. A validation fault occurs if at least two inputs are not considered good, or if the second deviation check is not satisfactory. In the latter situation the inputs from each of all the sensors are compared against the last validated measurement and the value from the sensor input that deviates the least from the last valid measurement is displayed.

HEAT INPUT AND POST WELD HEAT TREATMENT EFFECTS ON REDUCED-ACTIVATION FERRITIC/MARTENSITIC STEEL FRICTION STIR WELDS

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

Tang, Wei; Chen, Gaoqiang; Chen, Jian

Reduced-activation ferritic/martensitic (RAFM) steels are an important class of structural materials for fusion reactor internals developed in recent years because of their improved irradiation resistance. However, they can suffer from welding induced property degradations. In this paper, a solid phase joining technology friction stir welding (FSW) was adopted to join a RAFM steel Eurofer 97 and different FSW parameters/heat input were chosen to produce welds. FSW response parameters, joint microstructures and microhardness were investigated to reveal relationships among welding heat input, weld structure characterization and mechanical properties. In general, FSW heat input results in high hardness inside the stir zonemore » mostly due to a martensitic transformation. It is possible to produce friction stir welds similar to but not with exactly the same base metal hardness when using low power input because of other hardening mechanisms. Further, post weld heat treatment (PWHT) is a very effective way to reduce FSW stir zone hardness values.« less

A statistical survey of heat input parameters into the cusp thermosphere

NASA Astrophysics Data System (ADS)

Moen, J. I.; Skjaeveland, A.; Carlson, H. C.

2017-12-01

Based on three winters of observational data, we present those ionosphere parameters deemed most critical to realistic space weather ionosphere and thermosphere representation and prediction, in regions impacted by variability in the cusp. The CHAMP spacecraft revealed large variability in cusp thermosphere densities, measuring frequent satellite drag enhancements, up to doublings. The community recognizes a clear need for more realistic representation of plasma flows and electron densities near the cusp. Existing average-value models produce order of magnitude errors in these parameters, resulting in large under estimations of predicted drag. We fill this knowledge gap with statistics-based specification of these key parameters over their range of observed values. The EISCAT Svalbard Radar (ESR) tracks plasma flow Vi , electron density Ne, and electron, ion temperatures Te, Ti , with consecutive 2-3 minute windshield-wipe scans of 1000x500 km areas. This allows mapping the maximum Ti of a large area within or near the cusp with high temporal resolution. In magnetic field-aligned mode the radar can measure high-resolution profiles of these plasma parameters. By deriving statistics for Ne and Ti , we enable derivation of thermosphere heating deposition under background and frictional-drag-dominated magnetic reconnection conditions. We separate our Ne and Ti profiles into quiescent and enhanced states, which are not closely correlated due to the spatial structure of the reconnection foot point. Use of our data-based parameter inputs can make order of magnitude corrections to input data driving thermosphere models, enabling removal of previous two fold drag errors.

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, Chi Yung

1998-01-01

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, C.Y.

1998-11-24

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

Input Uncertainty and its Implications on Parameter Assessment in Hydrologic and Hydroclimatic Modelling Studies

NASA Astrophysics Data System (ADS)

Chowdhury, S.; Sharma, A.

2005-12-01

Hydrological model inputs are often derived from measurements at point locations taken at discrete time steps. The nature of uncertainty associated with such inputs is thus a function of the quality and number of measurements available in time. A change in these characteristics (such as a change in the number of rain-gauge inputs used to derive spatially averaged rainfall) results in inhomogeneity in the associated distributional profile. Ignoring such uncertainty can lead to models that aim to simulate based on the observed input variable instead of the true measurement, resulting in a biased representation of the underlying system dynamics as well as an increase in both bias and the predictive uncertainty in simulations. This is especially true of cases where the nature of uncertainty likely in the future is significantly different to that in the past. Possible examples include situations where the accuracy of the catchment averaged rainfall has increased substantially due to an increase in the rain-gauge density, or accuracy of climatic observations (such as sea surface temperatures) increased due to the use of more accurate remote sensing technologies. We introduce here a method to ascertain the true value of parameters in the presence of additive uncertainty in model inputs. This method, known as SIMulation EXtrapolation (SIMEX, [Cook, 1994]) operates on the basis of an empirical relationship between parameters and the level of additive input noise (or uncertainty). The method starts with generating a series of alternate realisations of model inputs by artificially adding white noise in increasing multiples of the known error variance. The alternate realisations lead to alternate sets of parameters that are increasingly biased with respect to the truth due to the increased variability in the inputs. Once several such realisations have been drawn, one is able to formulate an empirical relationship between the parameter values and the level of additive noise present. SIMEX is based on theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. We illustrate the utility of SIMEX in a synthetic rainfall-runoff modelling scenario and an application to study the dependence of uncertain distributed sea surface temperature anomalies with an indicator of the El Nino Southern Oscillation, the Southern Oscillation Index (SOI). The errors in rainfall data and its affect is explored using Sacramento rainfall runoff model. The rainfall uncertainty is assumed to be multiplicative and temporally invariant. The model used to relate the sea surface temperature anomalies (SSTA) to the SOI is assumed to be of a linear form. The nature of uncertainty in the SSTA is additive and varies with time. The SIMEX framework allows assessment of the relationship between the error free inputs and response. Cook, J.R., Stefanski, L. A., Simulation-Extrapolation Estimation in Parametric Measurement Error Models, Journal of the American Statistical Association, 89 (428), 1314-1328, 1994.

Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal

PubMed Central

Karmakar, Chandan; Udhayakumar, Radhagayathri K.; Li, Peng; Venkatesh, Svetha; Palaniswami, Marimuthu

2017-01-01

Distribution entropy (DistEn) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters—the embedding dimension m, and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy (ApEn) and sample entropy (SampEn) measures. The performance of DistEn can also be affected by the data length N. In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter (m or M) or combination of two parameters (N and M). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn. The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series. PMID:28979215

A Novel Degradation Identification Method for Wind Turbine Pitch System

NASA Astrophysics Data System (ADS)

Guo, Hui-Dong

2018-04-01

It’s difficult for traditional threshold value method to identify degradation of operating equipment accurately. An novel degradation evaluation method suitable for wind turbine condition maintenance strategy implementation was proposed in this paper. Based on the analysis of typical variable-speed pitch-to-feather control principle and monitoring parameters for pitch system, a multi input multi output (MIMO) regression model was applied to pitch system, where wind speed, power generation regarding as input parameters, wheel rotation speed, pitch angle and motor driving currency for three blades as output parameters. Then, the difference between the on-line measurement and the calculated value from the MIMO regression model applying least square support vector machines (LSSVM) method was defined as the Observed Vector of the system. The Gaussian mixture model (GMM) was applied to fitting the distribution of the multi dimension Observed Vectors. Applying the model established, the Degradation Index was calculated using the SCADA data of a wind turbine damaged its pitch bearing retainer and rolling body, which illustrated the feasibility of the provided method.

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-01-01

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R 2 =0.82), but it was not robust in extrapolation (R 2 =0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermomechanical conditions and stresses on the friction stir welding tool

NASA Astrophysics Data System (ADS)

Atthipalli, Gowtam

Friction stir welding has been commercially used as a joining process for aluminum and other soft materials. However, the use of this process in joining of hard alloys is still developing primarily because of the lack of cost effective, long lasting tools. Here I have developed numerical models to understand the thermo mechanical conditions experienced by the FSW tool and to improve its reusability. A heat transfer and visco-plastic flow model is used to calculate the torque, and traverse force on the tool during FSW. The computed values of torque and traverse force are validated using the experimental results for FSW of AA7075, AA2524, AA6061 and Ti-6Al-4V alloys. The computed torque components are used to determine the optimum tool shoulder diameter based on the maximum use of torque and maximum grip of the tool on the plasticized workpiece material. The estimation of the optimum tool shoulder diameter for FSW of AA6061 and AA7075 was verified with experimental results. The computed values of traverse force and torque are used to calculate the maximum shear stress on the tool pin to determine the load bearing ability of the tool pin. The load bearing ability calculations are used to explain the failure of H13 steel tool during welding of AA7075 and commercially pure tungsten during welding of L80 steel. Artificial neural network (ANN) models are developed to predict the important FSW output parameters as function of selected input parameters. These ANN consider tool shoulder radius, pin radius, pin length, welding velocity, tool rotational speed and axial pressure as input parameters. The total torque, sliding torque, sticking torque, peak temperature, traverse force, maximum shear stress and bending stress are considered as the output for ANN models. These output parameters are selected since they define the thermomechanical conditions around the tool during FSW. The developed ANN models are used to understand the effect of various input parameters on the total torque and traverse force during FSW of AA7075 and 1018 mild steel. The ANN models are also used to determine tool safety factor for wide range of input parameters. A numerical model is developed to calculate the strain and strain rates along the streamlines during FSW. The strain and strain rate values are calculated for FSW of AA2524. Three simplified models are also developed for quick estimation of output parameters such as material velocity field, torque and peak temperature. The material velocity fields are computed by adopting an analytical method of calculating velocities for flow of non-compressible fluid between two discs where one is rotating and other is stationary. The peak temperature is estimated based on a non-dimensional correlation with dimensionless heat input. The dimensionless heat input is computed using known welding parameters and material properties. The torque is computed using an analytical function based on shear strength of the workpiece material. These simplified models are shown to be able to predict these output parameters successfully.

TU-H-207A-02: Relative Importance of the Various Factors Influencing the Accuracy of Monte Carlo Simulated CT Dose Index

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

Marous, L; Muryn, J; Liptak, C

2016-06-15

Purpose: Monte Carlo simulation is a frequently used technique for assessing patient dose in CT. The accuracy of a Monte Carlo program is often validated using the standard CT dose index (CTDI) phantoms by comparing simulated and measured CTDI{sub 100}. To achieve good agreement, many input parameters in the simulation (e.g., energy spectrum and effective beam width) need to be determined. However, not all the parameters have equal importance. Our aim was to assess the relative importance of the various factors that influence the accuracy of simulated CTDI{sub 100}. Methods: A Monte Carlo program previously validated for a clinical CTmore » system was used to simulate CTDI{sub 100}. For the standard CTDI phantoms (32 and 16 cm in diameter), CTDI{sub 100} values from central and four peripheral locations at 70 and 120 kVp were first simulated using a set of reference input parameter values (treated as the truth). To emulate the situation in which the input parameter values used by the researcher may deviate from the truth, additional simulations were performed in which intentional errors were introduced into the input parameters, the effects of which on simulated CTDI{sub 100} were analyzed. Results: At 38.4-mm collimation, errors in effective beam width up to 5.0 mm showed negligible effects on simulated CTDI{sub 100} (<1.0%). Likewise, errors in acrylic density of up to 0.01 g/cm{sup 3} resulted in small CTDI{sub 100} errors (<2.5%). In contrast, errors in spectral HVL produced more significant effects: slight deviations (±0.2 mm Al) produced errors up to 4.4%, whereas more extreme deviations (±1.4 mm Al) produced errors as high as 25.9%. Lastly, ignoring the CT table introduced errors up to 13.9%. Conclusion: Monte Carlo simulated CTDI{sub 100} is insensitive to errors in effective beam width and acrylic density. However, they are sensitive to errors in spectral HVL. To obtain accurate results, the CT table should not be ignored. This work was supported by a Faculty Research and Development Award from Cleveland State University.« less

The Use of the Nelder-Mead Method in Determining Projection Parameters for Globe Photographs

NASA Astrophysics Data System (ADS)

Gede, M.

2009-04-01

A photo of a terrestrial or celestial globe can be handled as a map. The only hard issue is its projection: the so-called Tilted Perspective Projection which, if the optical axis of the photo intersects the globe's centre, is simplified to the Vertical Near-Side Perspective Projection. When georeferencing such a photo, the exact parameters of the projections are also needed. These parameters depend on the position of the viewpoint of the camera. Several hundreds of globe photos had to be georeferenced during the Virtual Globes Museum project, which made necessary to automatize the calculation of the projection parameters. The author developed a program for this task which uses the Nelder-Mead Method in order to find the optimum parameters when a set of control points are given as input. The Nelder-Mead method is a numerical algorithm for minimizing a function in a many-dimensional space. The function in the present application is the average error of the control points calculated from the actual values of parameters. The parameters are the geographical coordinates of the projection centre, the image coordinates of the same point, the rotation of the projection, the height of the perspective point and the scale of the photo (calculated in pixels/km). The program reads the Global Mappers Ground Control Point (.GCP) file format as input and creates projection description files (.PRJ) for the same software. The initial values of the geographical coordinates of the projection centre are calculated as the average of the control points, while the other parameters are set to experimental values which represent the most common circumstances of taking a globe photograph. The algorithm runs until the change of the parameters sinks below a pre-defined limit. The minimum search can be refined by using the previous result parameter set as new initial values. This paper introduces the calculation mechanism and examples of the usage. Other possible other usages of the method are also discussed.

A dual theory of price and value in a meso-scale economic model with stochastic profit rate

NASA Astrophysics Data System (ADS)

Greenblatt, R. E.

2014-12-01

The problem of commodity price determination in a market-based, capitalist economy has a long and contentious history. Neoclassical microeconomic theories are based typically on marginal utility assumptions, while classical macroeconomic theories tend to be value-based. In the current work, I study a simplified meso-scale model of a commodity capitalist economy. The production/exchange model is represented by a network whose nodes are firms, workers, capitalists, and markets, and whose directed edges represent physical or monetary flows. A pair of multivariate linear equations with stochastic input parameters represent physical (supply/demand) and monetary (income/expense) balance. The input parameters yield a non-degenerate profit rate distribution across firms. Labor time and price are found to be eigenvector solutions to the respective balance equations. A simple relation is derived relating the expected value of commodity price to commodity labor content. Results of Monte Carlo simulations are consistent with the stochastic price/labor content relation.

Local Sensitivity of Predicted CO 2 Injectivity and Plume Extent to Model Inputs for the FutureGen 2.0 site

DOE PAGES

Zhang, Z. Fred; White, Signe K.; Bonneville, Alain; ...

2014-12-31

Numerical simulations have been used for estimating CO2 injectivity, CO2 plume extent, pressure distribution, and Area of Review (AoR), and for the design of CO2 injection operations and monitoring network for the FutureGen project. The simulation results are affected by uncertainties associated with numerous input parameters, the conceptual model, initial and boundary conditions, and factors related to injection operations. Furthermore, the uncertainties in the simulation results also vary in space and time. The key need is to identify those uncertainties that critically impact the simulation results and quantify their impacts. We introduce an approach to determine the local sensitivity coefficientmore » (LSC), defined as the response of the output in percent, to rank the importance of model inputs on outputs. The uncertainty of an input with higher sensitivity has larger impacts on the output. The LSC is scalable by the error of an input parameter. The composite sensitivity of an output to a subset of inputs can be calculated by summing the individual LSC values. We propose a local sensitivity coefficient method and applied it to the FutureGen 2.0 Site in Morgan County, Illinois, USA, to investigate the sensitivity of input parameters and initial conditions. The conceptual model for the site consists of 31 layers, each of which has a unique set of input parameters. The sensitivity of 11 parameters for each layer and 7 inputs as initial conditions is then investigated. For CO2 injectivity and plume size, about half of the uncertainty is due to only 4 or 5 of the 348 inputs and 3/4 of the uncertainty is due to about 15 of the inputs. The initial conditions and the properties of the injection layer and its neighbour layers contribute to most of the sensitivity. Overall, the simulation outputs are very sensitive to only a small fraction of the inputs. However, the parameters that are important for controlling CO2 injectivity are not the same as those controlling the plume size. The three most sensitive inputs for injectivity were the horizontal permeability of Mt Simon 11 (the injection layer), the initial fracture-pressure gradient, and the residual aqueous saturation of Mt Simon 11, while those for the plume area were the initial salt concentration, the initial pressure, and the initial fracture-pressure gradient. The advantages of requiring only a single set of simulation results, scalability to the proper parameter errors, and easy calculation of the composite sensitivities make this approach very cost-effective for estimating AoR uncertainty and guiding cost-effective site characterization, injection well design, and monitoring network design for CO2 storage projects.« less

Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation

NASA Astrophysics Data System (ADS)

Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten

2015-04-01

Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.

Quantitative Microbial Risk Assessment Tutorial - SDMProjectBuilder: Import Local Data Files to Identify and Modify Contamination Sources and Input ParametersUpdated 2017

EPA Science Inventory

Twelve example local data support files are automatically downloaded when the SDMProjectBuilder is installed on a computer. They allow the user to modify values to parameters that impact the release, migration, fate, and transport of microbes within a watershed, and control delin...

Quantitative Microbial Risk Assessment Tutorial – SDMProjectBuilder: Import Local Data Files to Identify and Modify Contamination Sources and Input Parameters

EPA Science Inventory

Twelve example local data support files are automatically downloaded when the SDMProjectBuilder is installed on a computer. They allow the user to modify values to parameters that impact the release, migration, fate, and transport of microbes within a watershed, and control delin...

Computer programs for computing particle-size statistics of fluvial sediments

USGS Publications Warehouse

Stevens, H.H.; Hubbell, D.W.

1986-01-01

Two versions of computer programs for inputing data and computing particle-size statistics of fluvial sediments are presented. The FORTRAN 77 language versions are for use on the Prime computer, and the BASIC language versions are for use on microcomputers. The size-statistics program compute Inman, Trask , and Folk statistical parameters from phi values and sizes determined for 10 specified percent-finer values from inputed size and percent-finer data. The program also determines the percentage gravel, sand, silt, and clay, and the Meyer-Peter effective diameter. Documentation and listings for both versions of the programs are included. (Author 's abstract)

Translating landfill methane generation parameters among first-order decay models.

PubMed

Krause, Max J; Chickering, Giles W; Townsend, Timothy G

2016-11-01

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L 0 ) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia's Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k c ) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models. Translating multiphase first-order decay model input parameters by weighted average shows that single-phase models can predict cumulative methane generation within the level of uncertainty of many of the input parameters as defined by the Intergovernmental Panel on Climate Change (IPCC), which indicates that decreasing the uncertainty of the input parameters will make the model more accurate rather than adding multiple phases or input parameters.

Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

NASA Astrophysics Data System (ADS)

Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.

2016-09-01

Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas.

PubMed

Nguyen, T B; Cron, G O; Perdrizet, K; Bezzina, K; Torres, C H; Chakraborty, S; Woulfe, J; Jansen, G H; Sinclair, J; Thornhill, R E; Foottit, C; Zanette, B; Cameron, I G

2015-11-01

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas. This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis. Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters. In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV. © 2015 by American Journal of Neuroradiology.

Gaussian beam profile shaping apparatus, method therefor and evaluation thereof

DOEpatents

Dickey, Fred M.; Holswade, Scott C.; Romero, Louis A.

1999-01-01

A method and apparatus maps a Gaussian beam into a beam with a uniform irradiance profile by exploiting the Fourier transform properties of lenses. A phase element imparts a design phase onto an input beam and the output optical field from a lens is then the Fourier transform of the input beam and the phase function from the phase element. The phase element is selected in accordance with a dimensionless parameter which is dependent upon the radius of the incoming beam, the desired spot shape, the focal length of the lens and the wavelength of the input beam. This dimensionless parameter can also be used to evaluate the quality of a system. In order to control the radius of the incoming beam, optics such as a telescope can be employed. The size of the target spot and the focal length can be altered by exchanging the transform lens, but the dimensionless parameter will remain the same. The quality of the system, and hence the value of the dimensionless parameter, can be altered by exchanging the phase element. The dimensionless parameter provides design guidance, system evaluation, and indication as to how to improve a given system.

Gaussian beam profile shaping apparatus, method therefore and evaluation thereof

DOEpatents

Dickey, F.M.; Holswade, S.C.; Romero, L.A.

1999-01-26

A method and apparatus maps a Gaussian beam into a beam with a uniform irradiance profile by exploiting the Fourier transform properties of lenses. A phase element imparts a design phase onto an input beam and the output optical field from a lens is then the Fourier transform of the input beam and the phase function from the phase element. The phase element is selected in accordance with a dimensionless parameter which is dependent upon the radius of the incoming beam, the desired spot shape, the focal length of the lens and the wavelength of the input beam. This dimensionless parameter can also be used to evaluate the quality of a system. In order to control the radius of the incoming beam, optics such as a telescope can be employed. The size of the target spot and the focal length can be altered by exchanging the transform lens, but the dimensionless parameter will remain the same. The quality of the system, and hence the value of the dimensionless parameter, can be altered by exchanging the phase element. The dimensionless parameter provides design guidance, system evaluation, and indication as to how to improve a given system. 27 figs.

Fuzzy logic modeling of the resistivity parameter and topography features for aquifer assessment in hydrogeological investigation of a crystalline basement complex

NASA Astrophysics Data System (ADS)

Adabanija, M. A.; Omidiora, E. O.; Olayinka, A. I.

2008-05-01

A linguistic fuzzy logic system (LFLS)-based expert system model has been developed for the assessment of aquifers for the location of productive water boreholes in a crystalline basement complex. The model design employed a multiple input/single output (MISO) approach with geoelectrical parameters and topographic features as input variables and control crisp value as the output. The application of the method to the data acquired in Khondalitic terrain, a basement complex in Vizianagaram District, south India, shows that potential groundwater resource zones that have control output values in the range 0.3295-0.3484 have a yield greater than 6,000 liters per hour (LPH). The range 0.3174-0.3226 gives a yield less than 4,000 LPH. The validation of the control crisp value using data acquired from Oban Massif, a basement complex in southeastern Nigeria, indicates a yield less than 3,000 LPH for control output values in the range 0.2938-0.3065. This validation corroborates the ability of control output values to predict a yield, thereby vindicating the applicability of linguistic fuzzy logic system in siting productive water boreholes in a basement complex.

Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

NASA Astrophysics Data System (ADS)

Rong, Youmin; Zhang, Zhen; Zhang, Guojun; Yue, Chen; Gu, Yafei; Huang, Yu; Wang, Chunming; Shao, Xinyu

2015-04-01

The laser brazing (LB) is widely used in the automotive industry due to the advantages of high speed, small heat affected zone, high quality of welding seam, and low heat input. Welding parameters play a significant role in determining the bead geometry and hence quality of the weld joint. This paper addresses the optimization of the seam shape in LB process with welding crimping butt of 0.8 mm thickness using back propagation neural network (BPNN) and genetic algorithm (GA). A 3-factor, 5-level welding experiment is conducted by Taguchi L25 orthogonal array through the statistical design method. Then, the input parameters are considered here including welding speed, wire speed rate, and gap with 5 levels. The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead. The experiment results are embed into the BPNN network to establish relationship between the input and output variables. The predicted results of the BPNN are fed to GA algorithm that optimizes the process parameters subjected to the objectives. Then, the effects of welding speed (WS), wire feed rate (WF), and gap (GAP) on the sum values of bead geometry is discussed. Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable. On the whole, the proposed hybrid method, BPNN-GA, can be used to guide the actual work and improve the efficiency and stability of LB process.

Method for analyzing the chemical composition of liquid effluent from a direct contact condenser

DOEpatents

Bharathan, Desikan; Parent, Yves; Hassani, A. Vahab

2001-01-01

A computational modeling method for predicting the chemical, physical, and thermodynamic performance of a condenser using calculations based on equations of physics for heat, momentum and mass transfer and equations of equilibrium thermodynamics to determine steady state profiles of parameters throughout the condenser. The method includes providing a set of input values relating to a condenser including liquid loading, vapor loading, and geometric characteristics of the contact medium in the condenser. The geometric and packing characteristics of the contact medium include the dimensions and orientation of a channel in the contact medium. The method further includes simulating performance of the condenser using the set of input values to determine a related set of output values such as outlet liquid temperature, outlet flow rates, pressures, and the concentration(s) of one or more dissolved noncondensable gas species in the outlet liquid. The method may also include iteratively performing the above computation steps using a plurality of sets of input values and then determining whether each of the resulting output values and performance profiles satisfies acceptance criteria.

Averages of $b$-hadron, $c$-hadron, and $$\\tau$$-lepton properties as of summer 2014

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

Amhis, Y.; et al.

2014-12-23

This article reports world averages of measurements ofmore » $b$-hadron, $c$-hadron, and $$\\tau$$-lepton properties obtained by the Heavy Flavor Averaging Group (HFAG) using results available through summer 2014. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, $CP$ violation parameters, parameters of semileptonic decays and CKM matrix elements.« less

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Evaluation of seasonal and spatial variations of lumped water balance model sensitivity to precipitation data errors

NASA Astrophysics Data System (ADS)

Xu, Chong-yu; Tunemar, Liselotte; Chen, Yongqin David; Singh, V. P.

2006-06-01

Sensitivity of hydrological models to input data errors have been reported in the literature for particular models on a single or a few catchments. A more important issue, i.e. how model's response to input data error changes as the catchment conditions change has not been addressed previously. This study investigates the seasonal and spatial effects of precipitation data errors on the performance of conceptual hydrological models. For this study, a monthly conceptual water balance model, NOPEX-6, was applied to 26 catchments in the Mälaren basin in Central Sweden. Both systematic and random errors were considered. For the systematic errors, 5-15% of mean monthly precipitation values were added to the original precipitation to form the corrupted input scenarios. Random values were generated by Monte Carlo simulation and were assumed to be (1) independent between months, and (2) distributed according to a Gaussian law of zero mean and constant standard deviation that were taken as 5, 10, 15, 20, and 25% of the mean monthly standard deviation of precipitation. The results show that the response of the model parameters and model performance depends, among others, on the type of the error, the magnitude of the error, physical characteristics of the catchment, and the season of the year. In particular, the model appears less sensitive to the random error than to the systematic error. The catchments with smaller values of runoff coefficients were more influenced by input data errors than were the catchments with higher values. Dry months were more sensitive to precipitation errors than were wet months. Recalibration of the model with erroneous data compensated in part for the data errors by altering the model parameters.

Watershed-based Morphometric Analysis: A Review

NASA Astrophysics Data System (ADS)

Sukristiyanti, S.; Maria, R.; Lestiana, H.

2018-02-01

Drainage basin/watershed analysis based on morphometric parameters is very important for watershed planning. Morphometric analysis of watershed is the best method to identify the relationship of various aspects in the area. Despite many technical papers were dealt with in this area of study, there is no particular standard classification and implication of each parameter. It is very confusing to evaluate a value of every morphometric parameter. This paper deals with the meaning of values of the various morphometric parameters, with adequate contextual information. A critical review is presented on each classification, the range of values, and their implications. Besides classification and its impact, the authors also concern about the quality of input data, either in data preparation or scale/the detail level of mapping. This review paper hopefully can give a comprehensive explanation to assist the upcoming research dealing with morphometric analysis.

Preliminary results of neural networks and zernike polynomials for classification of videokeratography maps.

PubMed

Carvalho, Luis Alberto

2005-02-01

Our main goal in this work was to develop an artificial neural network (NN) that could classify specific types of corneal shapes using Zernike coefficients as input. Other authors have implemented successful NN systems in the past and have demonstrated their efficiency using different parameters. Our claim is that, given the increasing popularity of Zernike polynomials among the eye care community, this may be an interesting choice to add complementing value and precision to existing methods. By using a simple and well-documented corneal surface representation scheme, which relies on corneal elevation information, one can generate simple NN input parameters that are independent of curvature definition and that are also efficient. We have used the Matlab Neural Network Toolbox (MathWorks, Natick, MA) to implement a three-layer feed-forward NN with 15 inputs and 5 outputs. A database from an EyeSys System 2000 (EyeSys Vision, Houston, TX) videokeratograph installed at the Escola Paulista de Medicina-Sao Paulo was used. This database contained an unknown number of corneal types. From this database, two specialists selected 80 corneas that could be clearly classified into five distinct categories: (1) normal, (2) with-the-rule astigmatism, (3) against-the-rule astigmatism, (4) keratoconus, and (5) post-laser-assisted in situ keratomileusis. The corneal height (SAG) information of the 80 data files was fit with the first 15 Vision Science and it Applications (VSIA) standard Zernike coefficients, which were individually used to feed the 15 neurons of the input layer. The five output neurons were associated with the five typical corneal shapes. A group of 40 cases was randomly selected from the larger group of 80 corneas and used as the training set. The NN responses were statistically analyzed in terms of sensitivity [true positive/(true positive + false negative)], specificity [true negative/(true negative + false positive)], and precision [(true positive + true negative)/total number of cases]. The mean values for these parameters were, respectively, 78.75, 97.81, and 94%. Although we have used a relatively small training and testing set, results presented here should be considered promising. They are certainly an indication of the potential of Zernike polynomials as reliable parameters, at least in the cases presented here, as input data for artificial intelligence automation of the diagnosis process of videokeratography examinations. This technique should facilitate the implementation and add value to the classification methods already available. We also discuss briefly certain special properties of Zernike polynomials that are what we think make them suitable as NN inputs for this type of application.

Estimating the uncertainty in thermochemical calculations for oxygen-hydrogen combustors

NASA Astrophysics Data System (ADS)

Sims, Joseph David

The thermochemistry program CEA2 was combined with the statistical thermodynamics program PAC99 in a Monte Carlo simulation to determine the uncertainty in several CEA2 output variables due to uncertainty in thermodynamic reference values for the reactant and combustion species. In all, six typical performance parameters were examined, along with the required intermediate calculations (five gas properties and eight stoichiometric coefficients), for three hydrogen-oxygen combustors: a main combustor, an oxidizer preburner and a fuel preburner. The three combustors were analyzed in two different modes: design mode, where, for the first time, the uncertainty in thermodynamic reference values---taken from the literature---was considered (inputs to CEA2 were specified and so had no uncertainty); and data reduction mode, where inputs to CEA2 did have uncertainty. The inputs to CEA2 were contrived experimental measurements that were intended to represent the typical combustor testing facility. In design mode, uncertainties in the performance parameters were on the order of 0.1% for the main combustor, on the order of 0.05% for the oxidizer preburner and on the order of 0.01% for the fuel preburner. Thermodynamic reference values for H2O were the dominant sources of uncertainty, as was the assigned enthalpy for liquid oxygen. In data reduction mode, uncertainties in performance parameters increased significantly as a result of the uncertainties in experimental measurements compared to uncertainties in thermodynamic reference values. Main combustor and fuel preburner theoretical performance values had uncertainties of about 0.5%, while the oxidizer preburner had nearly 2%. Associated experimentally-determined performance values for all three combustors were 3% to 4%. The dominant sources of uncertainty in this mode were the propellant flowrates. These results only apply to hydrogen-oxygen combustors and should not be generalized to every propellant combination. Species for a hydrogen-oxygen system are relatively simple, thereby resulting in low thermodynamic reference value uncertainties. Hydrocarbon combustors, solid rocket motors and hybrid rocket motors have combustion gases containing complex molecules that will likely have thermodynamic reference values with large uncertainties. Thus, every chemical system should be analyzed in a similar manner as that shown in this work.

Parameter Estimation for Thurstone Choice Models

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

Vojnovic, Milan; Yun, Seyoung

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one ormore » more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.« less

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Validation and upgrading of physically based mathematical models

NASA Technical Reports Server (NTRS)

Duval, Ronald

1992-01-01

The validation of the results of physically-based mathematical models against experimental results was discussed. Systematic techniques are used for: (1) isolating subsets of the simulator mathematical model and comparing the response of each subset to its experimental response for the same input conditions; (2) evaluating the response error to determine whether it is the result of incorrect parameter values, incorrect structure of the model subset, or unmodeled external effects of cross coupling; and (3) modifying and upgrading the model and its parameter values to determine the most physically appropriate combination of changes.

Optimization of Dimensional accuracy in plasma arc cutting process employing parametric modelling approach

NASA Astrophysics Data System (ADS)

Naik, Deepak kumar; Maity, K. P.

2018-03-01

Plasma arc cutting (PAC) is a high temperature thermal cutting process employed for the cutting of extensively high strength material which are difficult to cut through any other manufacturing process. This process involves high energized plasma arc to cut any conducting material with better dimensional accuracy in lesser time. This research work presents the effect of process parameter on to the dimensional accuracy of PAC process. The input process parameters were selected as arc voltage, standoff distance and cutting speed. A rectangular plate of 304L stainless steel of 10 mm thickness was taken for the experiment as a workpiece. Stainless steel is very extensively used material in manufacturing industries. Linear dimension were measured following Taguchi’s L16 orthogonal array design approach. Three levels were selected to conduct the experiment for each of the process parameter. In all experiments, clockwise cut direction was followed. The result obtained thorough measurement is further analyzed. Analysis of variance (ANOVA) and Analysis of means (ANOM) were performed to evaluate the effect of each process parameter. ANOVA analysis reveals the effect of input process parameter upon leaner dimension in X axis. The results of the work shows that the optimal setting of process parameter values for the leaner dimension on the X axis. The result of the investigations clearly show that the specific range of input process parameter achieved the improved machinability.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Uncertainty Quantification in Multi-Scale Coronary Simulations Using Multi-resolution Expansion

NASA Astrophysics Data System (ADS)

Tran, Justin; Schiavazzi, Daniele; Ramachandra, Abhay; Kahn, Andrew; Marsden, Alison

2016-11-01

Computational simulations of coronary flow can provide non-invasive information on hemodynamics that can aid in surgical planning and research on disease propagation. In this study, patient-specific geometries of the aorta and coronary arteries are constructed from CT imaging data and finite element flow simulations are carried out using the open source software SimVascular. Lumped parameter networks (LPN), consisting of circuit representations of vascular hemodynamics and coronary physiology, are used as coupled boundary conditions for the solver. The outputs of these simulations depend on a set of clinically-derived input parameters that define the geometry and boundary conditions, however their values are subjected to uncertainty. We quantify the effects of uncertainty from two sources: uncertainty in the material properties of the vessel wall and uncertainty in the lumped parameter models whose values are estimated by assimilating patient-specific clinical and literature data. We use a generalized multi-resolution chaos approach to propagate the uncertainty. The advantages of this approach lies in its ability to support inputs sampled from arbitrary distributions and its built-in adaptivity that efficiently approximates stochastic responses characterized by steep gradients.

Option B+ for the prevention of mother-to-child transmission of HIV infection in developing countries: a review of published cost-effectiveness analyses.

PubMed

Karnon, Jonathan; Orji, Nneka

2016-10-01

To review the published literature on the cost effectiveness of Option B+ (lifelong antiretroviral therapy) for preventing mother-to-child transmission (PMTCT) of HIV during pregnancy and breastfeeding to inform decision making in low- and middle-income countries. PubMed, Scopus, Google scholar and Medline were searched to identify studies of the cost effectiveness of the World Health Organization (WHO) treatment guidelines for PMTCT. Study quality was appraised using the consolidated health economic evaluation reporting standards checklist. Eligible studies were reviewed in detail to assess the relevance and impact of alternative evaluation frameworks, assumptions and input parameter values. Five published cost effectiveness analyses of Option B+ for the PMTCT of HIV were identified. The reported cost-effectiveness of Option B+ varies substantially, with the results of different studies implying that Option B+ is dominant (lower costs, greater benefits), cost-effective (additional benefits at acceptable additional costs) or not cost-effective (additional benefits at unacceptable additional costs). This variation is due to significant differences in model structures and input parameter values. Structural differences were observed around the estimation of programme effects on infants, HIV-infected mothers and their HIV negative partners, over multiple pregnancies, as well assumptions regarding routine access to antiretroviral therapies. Significant differences in key input parameters were observed in transmission rates, intervention costs and effects and downstream cost savings. Across five model-based cost-effectiveness analyses of strategies for the PMTCT of HIV, the most comprehensive analysis reported that option B+ is highly likely to be cost-effective. This evaluation may have been overly favourable towards option B+ with respect to some input parameter values, but potentially important additional benefits were omitted. Decision makers might be best advised to review this analysis, with a view to requesting additional analyses of the model to inform local funding decisions around alternative strategies for the PMTCT of HIV. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Determination of ionospheric electron density profiles from satellite UV (Ultraviolet) emission measurements, fiscal year 1984

NASA Astrophysics Data System (ADS)

Daniell, R. E.; Strickland, D. J.; Decker, D. T.; Jasperse, J. R.; Carlson, H. C., Jr.

1985-04-01

The possible use of satellite ultraviolet measurements to deduce the ionospheric electron density profile (EDP) on a global basis is discussed. During 1984 comparisons were continued between the hybrid daytime ionospheric model and the experimental observations. These comparison studies indicate that: (1) the essential features of the EDP and certain UV emissions can be modelled; (2) the models are sufficiently sensitive to input parameters to yield poor agreement with observations when typical input values are used; (3) reasonable adjustments of the parameters can produce excellent agreement between theory and data for either EDP or airglow but not both; and (4) the qualitative understanding of the relationship between two input parameters (solar flux and neutral densities) and the model EDP and airglow features has been verified. The development of a hybrid dynamic model for the nighttime midlatitude ionosphere has been initiated. This model is similar to the daytime hybrid model, but uses the sunset EDP as an initial value and calculates the EDP as a function of time through the night. In addition, a semiempirical model has been developed, based on the assumption that the nighttime EDP is always well described by a modified Chapman function. This model has great simplicity and allows the EDP to be inferred in a straightforward manner from optical observations. Comparisons with data are difficult, however, because of the low intensity of the nightglow.

Defining a procedure for predicting the duration of the approximately isothermal segments within the proposed drying regime as a function of the drying air parameters

NASA Astrophysics Data System (ADS)

Vasić, M.; Radojević, Z.

2017-08-01

One of the main disadvantages of the recently reported method, for setting up the drying regime based on the theory of moisture migration during drying, lies in a fact that it is based on a large number of isothermal experiments. In addition each isothermal experiment requires the use of different drying air parameters. The main goal of this paper was to find a way how to reduce the number of isothermal experiments without affecting the quality of the previously proposed calculation method. The first task was to define the lower and upper inputs as well as the output of the “black box” which will be used in the Box-Wilkinson’s orthogonal multi-factorial experimental design. Three inputs (drying air temperature, humidity and velocity) were used within the experimental design. The output parameter of the model represents the time interval between any two chosen characteristic points presented on the Deff - t. The second task was to calculate the output parameter for each planed experiments. The final output of the model is the equation which can predict the time interval between any two chosen characteristic points as a function of the drying air parameters. This equation is valid for any value of the drying air parameters which are within the defined area designated with lower and upper limiting values.

Modeling of Carbon Mortar Color Expression Using Artificial Neural Network.

PubMed

Jang, Hong-Seok; Kim, Ju-Hee; Shuli, Xing; So, Seung-Young

2018-09-01

Colored concrete uses pigments and white Portland cement (WPC) to perform decorative functions together with structural function. Pigments are used in permanent coloring of concrete with colors different from the natural color of the cement or the aggregates with mixing WPC. In this study, an artificial neural networks study was carried out to predict the color evaluation of black mortar using pigment and carbon black. A data set of a laboratory work, in which a total of 9 mortars were produced, was utilized in the Artificial Neural Networks (ANNs) study. The mortar mixture parameters were nine different pigment and carbon black ratios. Each mortar was measured at ten locations on the surface and averaged. Color can be evaluated by measurements of tristimulus values L* , a* and b* , represented in the chromatic space CIELAB. The L* value is a measure of luminosity (0 darkness), from completely opaque (0) to completely transparent (100); a* is a measure of redness (-a* greenness) and b* of yellowness (-b* blueness). ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of three input parameters that cover the pigment, carbon black and WPC and, an output parameter which is the color parameters of the black colored mortar. The results showed that ANN can be an alternative approach for the predicting the color parameters using mortar ingredients as input parameters.

Stochastic empirical loading and dilution model (SELDM) version 1.0.0

USGS Publications Warehouse

Granato, Gregory E.

2013-01-01

The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.

The effect of welding parameters on high-strength SMAW all-weld-metal. Part 1: AWS E11018-M

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

Vercesi, J.; Surian, E.

Three AWS A5.5-81 all-weld-metal test assemblies were welded with an E110180-M electrode from a standard production batch, varying the welding parameters in such a way as to obtain three energy inputs: high heat input and high interpass temperature (hot), medium heat input and medium interpass temperature (medium) and low heat input and low interpass temperature (cold). Mechanical properties and metallographic studies were performed in the as-welded condition, and it was found that only the tensile properties obtained with the test specimen made with the intermediate energy input satisfied the AWS E11018-M requirements. With the cold specimen, the maximal yield strengthmore » was exceeded, and with the hot one, neither the yield nor the tensile minimum strengths were achieved. The elongation and the impact properties were high enough to fulfill the minimal requirements, but the best Charpy-V notch values were obtained with the intermediate energy input. Metallographic studies showed that as the energy input increased the percentage of the columnar zones decreased, the grain size became larger, and in the as-welded zone, there was a little increment of both acicular ferrite and ferrite with second phase, with a consequent decrease of primary ferrite. These results showed that this type of alloy is very sensitive to the welding parameters and that very precise instructions must be given to secure the desired tensile properties in the all-weld-metal test specimens and under actual working conditions.« less

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

NASA Technical Reports Server (NTRS)

Morin, Cory; Monaghan, Andrew; Quattrochi, Dale; Crosson, William; Hayden, Mary; Ernst, Kacey

2015-01-01

Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US-Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Uncertainties in Galactic Chemical Evolution Models

DOE PAGES

Cote, Benoit; Ritter, Christian; Oshea, Brian W.; ...

2016-06-15

Here we use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number ofmore » SNe Ia per M ⊙ formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.« less

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

Cote, Benoit; Ritter, Christian; Oshea, Brian W.

Here we use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number ofmore » SNe Ia per M ⊙ formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.« less

Intelligent person identification system using stereo camera-based height and stride estimation

NASA Astrophysics Data System (ADS)

Ko, Jung-Hwan; Jang, Jae-Hun; Kim, Eun-Soo

2005-05-01

In this paper, a stereo camera-based intelligent person identification system is suggested. In the proposed method, face area of the moving target person is extracted from the left image of the input steros image pair by using a threshold value of YCbCr color model and by carrying out correlation between the face area segmented from this threshold value of YCbCr color model and the right input image, the location coordinates of the target face can be acquired, and then these values are used to control the pan/tilt system through the modified PID-based recursive controller. Also, by using the geometric parameters between the target face and the stereo camera system, the vertical distance between the target and stereo camera system can be calculated through a triangulation method. Using this calculated vertical distance and the angles of the pan and tilt, the target's real position data in the world space can be acquired and from them its height and stride values can be finally extracted. Some experiments with video images for 16 moving persons show that a person could be identified with these extracted height and stride parameters.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Using global sensitivity analysis of demographic models for ecological impact assessment.

PubMed

Aiello-Lammens, Matthew E; Akçakaya, H Resit

2017-02-01

Population viability analysis (PVA) is widely used to assess population-level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input-parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input-parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea-level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions. © 2016 Society for Conservation Biology.

LIMEPY: Lowered Isothermal Model Explorer in PYthon

NASA Astrophysics Data System (ADS)

Gieles, Mark; Zocchi, Alice

2017-10-01

LIMEPY solves distribution function (DF) based lowered isothermal models. It solves Poisson's equation used on input parameters and offers fast solutions for isotropic/anisotropic, single/multi-mass models, normalized DF values, density and velocity moments, projected properties, and generates discrete samples.

A modified PATH algorithm rapidly generates transition states comparable to those found by other well established algorithms

PubMed Central

Chandrasekaran, Srinivas Niranj; Das, Jhuma; Dokholyan, Nikolay V.; Carter, Charles W.

2016-01-01

PATH rapidly computes a path and a transition state between crystal structures by minimizing the Onsager-Machlup action. It requires input parameters whose range of values can generate different transition-state structures that cannot be uniquely compared with those generated by other methods. We outline modifications to estimate these input parameters to circumvent these difficulties and validate the PATH transition states by showing consistency between transition-states derived by different algorithms for unrelated protein systems. Although functional protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH. PMID:26958584

Nonstationary multivariate modeling of cerebral autoregulation during hypercapnia.

PubMed

Kostoglou, Kyriaki; Debert, Chantel T; Poulin, Marc J; Mitsis, Georgios D

2014-05-01

We examined the time-varying characteristics of cerebral autoregulation and hemodynamics during a step hypercapnic stimulus by using recursively estimated multivariate (two-input) models which quantify the dynamic effects of mean arterial blood pressure (ABP) and end-tidal CO2 tension (PETCO2) on middle cerebral artery blood flow velocity (CBFV). Beat-to-beat values of ABP and CBFV, as well as breath-to-breath values of PETCO2 during baseline and sustained euoxic hypercapnia were obtained in 8 female subjects. The multiple-input, single-output models used were based on the Laguerre expansion technique, and their parameters were updated using recursive least squares with multiple forgetting factors. The results reveal the presence of nonstationarities that confirm previously reported effects of hypercapnia on autoregulation, i.e. a decrease in the MABP phase lead, and suggest that the incorporation of PETCO2 as an additional model input yields less time-varying estimates of dynamic pressure autoregulation obtained from single-input (ABP-CBFV) models. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Reconstruction of an input function from a dynamic PET water image using multiple tissue curves

NASA Astrophysics Data System (ADS)

Kudomi, Nobuyuki; Maeda, Yukito; Yamamoto, Yuka; Nishiyama, Yoshihiro

2016-08-01

Quantification of cerebral blood flow (CBF) is important for the understanding of normal and pathologic brain physiology. When CBF is assessed using PET with {{\\text{H}}2} 15O or C15O2, its calculation requires an arterial input function, which generally requires invasive arterial blood sampling. The aim of the present study was to develop a new technique to reconstruct an image derived input function (IDIF) from a dynamic {{\\text{H}}2} 15O PET image as a completely non-invasive approach. Our technique consisted of using a formula to express the input using tissue curve with rate constant parameter. For multiple tissue curves extracted from the dynamic image, the rate constants were estimated so as to minimize the sum of the differences of the reproduced inputs expressed by the extracted tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects (n  =  29) and was compared to the blood sampling method. Simulation studies were performed to examine the magnitude of potential biases in CBF and to optimize the number of multiple tissue curves used for the input reconstruction. In the PET study, the estimated IDIFs were well reproduced against the measured ones. The difference between the calculated CBF values obtained using the two methods was small as around  <8% and the calculated CBF values showed a tight correlation (r  =  0.97). The simulation showed that errors associated with the assumed parameters were  <10%, and that the optimal number of tissue curves to be used was around 500. Our results demonstrate that IDIF can be reconstructed directly from tissue curves obtained through {{\\text{H}}2} 15O PET imaging. This suggests the possibility of using a completely non-invasive technique to assess CBF in patho-physiological studies.

Feasibility study of parallel optical correlation-decoding analysis of lightning

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

Descour, M.R.; Sweatt, W.C.; Elliott, G.R.

The optical correlator described in this report is intended to serve as an attention-focusing processor. The objective is to narrowly bracket the range of a parameter value that characterizes the correlator input. The input is a waveform collected by a satellite-borne receiver. In the correlator, this waveform is simultaneously correlated with an ensemble of ionosphere impulse-response functions, each corresponding to a different total-electron-count (TEC) value. We have found that correlation is an effective method of bracketing the range of TEC values likely to be represented by the input waveform. High accuracy in a computational sense is not required of themore » correlator. Binarization of the impulse-response functions and the input waveforms prior to correlation results in a lower correlation-peak-to-background-fluctuation (signal-to-noise) ratio than the peak that is obtained when all waveforms retain their grayscale values. The results presented in this report were obtained by means of an acousto-optic correlator previously developed at SNL as well as by simulation. An optical-processor architecture optimized for 1D correlation of long waveforms characteristic of this application is described. Discussions of correlator components, such as optics, acousto-optic cells, digital micromirror devices, laser diodes, and VCSELs are included.« less

Averages of b-hadron, c-hadron, and τ-lepton properties as of summer 2016

DOE PAGES

Amhis, Y.; Banerjee, Sw.; Ben-Haim, E.; ...

2017-12-21

Here, this article reports world averages of measurements of b-hadron, c-hadron, and τ-lepton properties obtained by the Heavy Flavor Averaging Group using results available through summer 2016. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters,more » $$C\\!P$$  violation parameters, parameters of semileptonic decays, and Cabbibo–Kobayashi–Maskawa matrix elements.« less

Averages of b-hadron, c-hadron, and τ-lepton properties as of summer 2016

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

Amhis, Y.; Banerjee, Sw.; Ben-Haim, E.

Here, this article reports world averages of measurements of b-hadron, c-hadron, and τ-lepton properties obtained by the Heavy Flavor Averaging Group using results available through summer 2016. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters,more » $$C\\!P$$  violation parameters, parameters of semileptonic decays, and Cabbibo–Kobayashi–Maskawa matrix elements.« less

Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield

NASA Astrophysics Data System (ADS)

Xiong, Wei; Skalský, Rastislav; Porter, Cheryl H.; Balkovič, Juraj; Jones, James W.; Yang, Di

2016-09-01

Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation.

Mapping an operator's perception of a parameter space

NASA Technical Reports Server (NTRS)

Pew, R. W.; Jagacinski, R. J.

1972-01-01

Operators monitored the output of two versions of the crossover model having a common random input. Their task was to make discrete, real-time adjustments of the parameters k and tau of one of the models to make its output time history converge to that of the other, fixed model. A plot was obtained of the direction of parameter change as a function of position in the (tau, k) parameter space relative to the nominal value. The plot has a great deal of structure and serves as one form of representation of the operator's perception of the parameter space.

Electron-impact excitation of He: Dependence of electron-photon coherence parameters on the principal quantum number

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

Hammond, P.; Khakoo, M.A.; McConkey, J.W.

1987-12-01

Measurements are presented representing a complete set of electron-photon polarization correlation parameters for the excitation of n /sup 1/P states of He at an incident energy of 80 eV and an electron scattering angle of 20/sup 0/. The data support the predictions of a recent theoretical paper that these parameters should exhibit little variation with n. However, disagreement in absolute values between experiment and theory indicates the need for additional theoretical input into the problem.

Variable frame rate transmission - A review of methodology and application to narrow-band LPC speech coding

NASA Astrophysics Data System (ADS)

Viswanathan, V. R.; Makhoul, J.; Schwartz, R. M.; Huggins, A. W. F.

1982-04-01

The variable frame rate (VFR) transmission methodology developed, implemented, and tested in the years 1973-1978 for efficiently transmitting linear predictive coding (LPC) vocoder parameters extracted from the input speech at a fixed frame rate is reviewed. With the VFR method, parameters are transmitted only when their values have changed sufficiently over the interval since their preceding transmission. Two distinct approaches to automatic implementation of the VFR method are discussed. The first bases the transmission decisions on comparisons between the parameter values of the present frame and the last transmitted frame. The second, which is based on a functional perceptual model of speech, compares the parameter values of all the frames that lie in the interval between the present frame and the last transmitted frame against a linear model of parameter variation over that interval. Also considered is the application of VFR transmission to the design of narrow-band LPC speech coders with average bit rates of 2000-2400 bts/s.

Optical sectioning microscopy using two-frame structured illumination and Hilbert-Huang data processing

NASA Astrophysics Data System (ADS)

Trusiak, M.; Patorski, K.; Tkaczyk, T.

2014-12-01

We propose a fast, simple and experimentally robust method for reconstructing background-rejected optically-sectioned microscopic images using two-shot structured illumination approach. Innovative data demodulation technique requires two grid-illumination images mutually phase shifted by π (half a grid period) but precise phase displacement value is not critical. Upon subtraction of the two frames the input pattern with increased grid modulation is computed. The proposed demodulation procedure comprises: (1) two-dimensional data processing based on the enhanced, fast empirical mode decomposition (EFEMD) method for the object spatial frequency selection (noise reduction and bias term removal), and (2) calculating high contrast optically-sectioned image using the two-dimensional spiral Hilbert transform (HS). The proposed algorithm effectiveness is compared with the results obtained for the same input data using conventional structured-illumination (SIM) and HiLo microscopy methods. The input data were collected for studying highly scattering tissue samples in reflectance mode. In comparison with the conventional three-frame SIM technique we need one frame less and no stringent requirement on the exact phase-shift between recorded frames is imposed. The HiLo algorithm outcome is strongly dependent on the set of parameters chosen manually by the operator (cut-off frequencies for low-pass and high-pass filtering and η parameter value for optically-sectioned image reconstruction) whereas the proposed method is parameter-free. Moreover very short processing time required to efficiently demodulate the input pattern predestines proposed method for real-time in-vivo studies. Current implementation completes full processing in 0.25s using medium class PC (Inter i7 2,1 GHz processor and 8 GB RAM). Simple modification employed to extract only first two BIMFs with fixed filter window size results in reducing the computing time to 0.11s (8 frames/s).

Fuzzy simulation in concurrent engineering

NASA Technical Reports Server (NTRS)

Kraslawski, A.; Nystrom, L.

1992-01-01

Concurrent engineering is becoming a very important practice in manufacturing. A problem in concurrent engineering is the uncertainty associated with the values of the input variables and operating conditions. The problem discussed in this paper concerns the simulation of processes where the raw materials and the operational parameters possess fuzzy characteristics. The processing of fuzzy input information is performed by the vertex method and the commercial simulation packages POLYMATH and GEMS. The examples are presented to illustrate the usefulness of the method in the simulation of chemical engineering processes.

User’s Manual for SEEK TALK Full Scale Engineering Development Life Cycle Cost (LCC) Model. Volume II. Model Equations and Model Operations.

DTIC Science & Technology

1981-04-01

LIFE CYCLE COST (LCC) LCC SENSITIVITY ANALYSIS LCC MODE , REPAIR LEVEL ANALYSIS (RLA) 20 ABSTRACT (Cnn tlnue on reverse side It necessary and Identify... level analysis capability. Next it provides values for Air Force input parameters and instructions for contractor inputs, general operating...Maintenance Manhour Requirements 39 5.1.4 Calculation of Repair Level Fractions 43 5.2 Cost Element Equations 47 5.2.1 Production Cost Element 47

Divvy Economies Based On (An Abstract) Temperature

NASA Astrophysics Data System (ADS)

Collins, Dennis G.

2004-04-01

The Leontief Input-Output economic system can provide a model for a one-parameter family of economic systems based on an abstract temperature T. In particular, given a normalized input-output matrix R and taking R= R(1), a family of economic systems R(1/T)=R(α) is developed that represents heating (T>1) and cooling (T<1) of the economy relative to T=1. .The economy for a given value of T represents the solution of a constrained maximum entropy problem.

Estimating historical atmospheric mercury concentrations from silver mining and their legacies in present-day surface soil in Potosí, Bolivia

NASA Astrophysics Data System (ADS)

Hagan, Nicole; Robins, Nicholas; Hsu-Kim, Heileen; Halabi, Susan; Morris, Mark; Woodall, George; Zhang, Tong; Bacon, Allan; Richter, Daniel De B.; Vandenberg, John

2011-12-01

Detailed Spanish records of mercury use and silver production during the colonial period in Potosí, Bolivia were evaluated to estimate atmospheric emissions of mercury from silver smelting. Mercury was used in the silver production process in Potosí and nearly 32,000 metric tons of mercury were released to the environment. AERMOD was used in combination with the estimated emissions to approximate historical air concentrations of mercury from colonial mining operations during 1715, a year of relatively low silver production. Source characteristics were selected from archival documents, colonial maps and images of silver smelters in Potosí and a base case of input parameters was selected. Input parameters were varied to understand the sensitivity of the model to each parameter. Modeled maximum 1-h concentrations were most sensitive to stack height and diameter, whereas an index of community exposure was relatively insensitive to uncertainty in input parameters. Modeled 1-h and long-term concentrations were compared to inhalation reference values for elemental mercury vapor. Estimated 1-h maximum concentrations within 500 m of the silver smelters consistently exceeded present-day occupational inhalation reference values. Additionally, the entire community was estimated to have been exposed to levels of mercury vapor that exceed present-day acute inhalation reference values for the general public. Estimated long-term maximum concentrations of mercury were predicted to substantially exceed the EPA Reference Concentration for areas within 600 m of the silver smelters. A concentration gradient predicted by AERMOD was used to select soil sampling locations along transects in Potosí. Total mercury in soils ranged from 0.105 to 155 mg kg-1, among the highest levels reported for surface soils in the scientific literature. The correlation between estimated air concentrations and measured soil concentrations will guide future research to determine the extent to which the current community of Potosí and vicinity is at risk of adverse health effects from historical mercury contamination.

Measurement of myocardial blood flow by cardiovascular magnetic resonance perfusion: comparison of distributed parameter and Fermi models with single and dual bolus.

PubMed

Papanastasiou, Giorgos; Williams, Michelle C; Kershaw, Lucy E; Dweck, Marc R; Alam, Shirjel; Mirsadraee, Saeed; Connell, Martin; Gray, Calum; MacGillivray, Tom; Newby, David E; Semple, Scott Ik

2015-02-17

Mathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow. Saturation of left ventricle signal during standard contrast administration can compromise the input function used when applying these models. This saturation effect is evident during application of standard Fermi models in single bolus perfusion data. Dual bolus injection protocols have been suggested to eliminate saturation but are much less practical in the clinical setting. The distributed parameter model can also be used for absolute quantification but has not been applied in patients with coronary artery disease. We assessed whether distributed parameter modeling might be less dependent on arterial input function saturation than Fermi modeling in healthy volunteers. We validated the accuracy of each model in detecting reduced myocardial blood flow in stenotic vessels versus gold-standard invasive methods. Eight healthy subjects were scanned using a dual bolus cardiac perfusion protocol at 3T. We performed both single and dual bolus analysis of these data using the distributed parameter and Fermi models. For the dual bolus analysis, a scaled pre-bolus arterial input function was used. In single bolus analysis, the arterial input function was extracted from the main bolus. We also performed analysis using both models of single bolus data obtained from five patients with coronary artery disease and findings were compared against independent invasive coronary angiography and fractional flow reserve. Statistical significance was defined as two-sided P value < 0.05. Fermi models overestimated myocardial blood flow in healthy volunteers due to arterial input function saturation in single bolus analysis compared to dual bolus analysis (P < 0.05). No difference was observed in these volunteers when applying distributed parameter-myocardial blood flow between single and dual bolus analysis. In patients, distributed parameter modeling was able to detect reduced myocardial blood flow at stress (<2.5 mL/min/mL of tissue) in all 12 stenotic vessels compared to only 9 for Fermi modeling. Comparison of single bolus versus dual bolus values suggests that distributed parameter modeling is less dependent on arterial input function saturation than Fermi modeling. Distributed parameter modeling showed excellent accuracy in detecting reduced myocardial blood flow in all stenotic vessels.

Sampling of Stochastic Input Parameters for Rockfall Calculations and for Structural Response Calculations Under Vibratory Ground Motion

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

M. Gross

2004-09-01

The purpose of this scientific analysis is to define the sampled values of stochastic (random) input parameters for (1) rockfall calculations in the lithophysal and nonlithophysal zones under vibratory ground motions, and (2) structural response calculations for the drip shield and waste package under vibratory ground motions. This analysis supplies: (1) Sampled values of ground motion time history and synthetic fracture pattern for analysis of rockfall in emplacement drifts in nonlithophysal rock (Section 6.3 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (2) Sampled values of ground motion time history and rock mechanical properties category for analysis of rockfall inmore » emplacement drifts in lithophysal rock (Section 6.4 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (3) Sampled values of ground motion time history and metal to metal and metal to rock friction coefficient for analysis of waste package and drip shield damage to vibratory motion in ''Structural Calculations of Waste Package Exposed to Vibratory Ground Motion'' (BSC 2004 [DIRS 167083]) and in ''Structural Calculations of Drip Shield Exposed to Vibratory Ground Motion'' (BSC 2003 [DIRS 163425]). The sampled values are indices representing the number of ground motion time histories, number of fracture patterns and rock mass properties categories. These indices are translated into actual values within the respective analysis and model reports or calculations. This report identifies the uncertain parameters and documents the sampled values for these parameters. The sampled values are determined by GoldSim V6.04.007 [DIRS 151202] calculations using appropriate distribution types and parameter ranges. No software development or model development was required for these calculations. The calculation of the sampled values allows parameter uncertainty to be incorporated into the rockfall and structural response calculations that support development of the seismic scenario for the Total System Performance Assessment for the License Application (TSPA-LA). The results from this scientific analysis also address project requirements related to parameter uncertainty, as specified in the acceptance criteria in ''Yucca Mountain Review Plan, Final Report'' (NRC 2003 [DIRS 163274]). This document was prepared under the direction of ''Technical Work Plan for: Regulatory Integration Modeling of Drift Degradation, Waste Package and Drip Shield Vibratory Motion and Seismic Consequences'' (BSC 2004 [DIRS 170528]) which directed the work identified in work package ARTM05. This document was prepared under procedure AP-SIII.9Q, ''Scientific Analyses''. There are no specific known limitations to this analysis.« less

Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions

NASA Astrophysics Data System (ADS)

Jung, J. Y.; Niemann, J. D.; Greimann, B. P.

2016-12-01

Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

Newman, Perry A.; Putko, Michele M.; Taylor, Arthur C., III

2004-01-01

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

Design and Implementation of RF Energy Harvesting System for Low-Power Electronic Devices

NASA Astrophysics Data System (ADS)

Uzun, Yunus

2016-08-01

Radio frequency (RF) energy harvester systems are a good alternative for energizing of low-power electronics devices. In this work, an RF energy harvester is presented to obtain energy from Global System for Mobile Communications (GSM) 900 MHz signals. The energy harvester, consisting of a two-stage Dickson voltage multiplier circuit and L-type impedance matching circuits, was designed, simulated, fabricated and tested experimentally in terms of its performance. Simulation and experimental works were carried out for various input power levels, load resistances and input frequencies. Both simulation and experimental works have been carried out for this frequency band. An efficiency of 45% is obtained from the system at 0 dBm input power level using the impedance matching circuit. This corresponds to the power of 450 μW and this value is sufficient for many low-power devices. The most important parameters affecting the efficiency of the RF energy harvester are the input power level, frequency band, impedance matching and voltage multiplier circuits, load resistance and the selection of diodes. RF energy harvester designs should be optimized in terms of these parameters.

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Uncertainty in predictions of oil spill trajectories in a coastal zone

NASA Astrophysics Data System (ADS)

Sebastião, P.; Guedes Soares, C.

2006-12-01

A method is introduced to determine the uncertainties in the predictions of oil spill trajectories using a classic oil spill model. The method considers the output of the oil spill model as a function of random variables, which are the input parameters, and calculates the standard deviation of the output results which provides a measure of the uncertainty of the model as a result of the uncertainties of the input parameters. In addition to a single trajectory that is calculated by the oil spill model using the mean values of the parameters, a band of trajectories can be defined when various simulations are done taking into account the uncertainties of the input parameters. This band of trajectories defines envelopes of the trajectories that are likely to be followed by the spill given the uncertainties of the input. The method was applied to an oil spill that occurred in 1989 near Sines in the southwestern coast of Portugal. This model represented well the distinction between a wind driven part that remained offshore, and a tide driven part that went ashore. For both parts, the method defined two trajectory envelopes, one calculated exclusively with the wind fields, and the other using wind and tidal currents. In both cases reasonable approximation to the observed results was obtained. The envelope of likely trajectories that is obtained with the uncertainty modelling proved to give a better interpretation of the trajectories that were simulated by the oil spill model.

Aerodynamic Parameter Estimation for the X-43A (Hyper-X) from Flight Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Derry, Stephen D.; Smith, Mark S.

2005-01-01

Aerodynamic parameters were estimated based on flight data from the third flight of the X-43A hypersonic research vehicle, also called Hyper-X. Maneuvers were flown using multiple orthogonal phase-optimized sweep inputs applied as simultaneous control surface perturbations at Mach 8, 7, 6, 5, 4, and 3 during the vehicle descent. Aerodynamic parameters, consisting of non-dimensional longitudinal and lateral stability and control derivatives, were estimated from flight data at each Mach number. Multi-step inputs at nearly the same flight conditions were also flown to assess the prediction capability of the identified models. Prediction errors were found to be comparable in magnitude to the modeling errors, which indicates accurate modeling. Aerodynamic parameter estimates were plotted as a function of Mach number, and compared with estimates from the pre-flight aerodynamic database, which was based on wind-tunnel tests and computational fluid dynamics. Agreement between flight estimates and values computed from the aerodynamic database was excellent overall.

Direct computation of stochastic flow in reservoirs with uncertain parameters

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

Dainton, M.P.; Nichols, N.K.; Goldwater, M.H.

1997-01-15

A direct method is presented for determining the uncertainty in reservoir pressure, flow, and net present value (NPV) using the time-dependent, one phase, two- or three-dimensional equations of flow through a porous medium. The uncertainty in the solution is modelled as a probability distribution function and is computed from given statistical data for input parameters such as permeability. The method generates an expansion for the mean of the pressure about a deterministic solution to the system equations using a perturbation to the mean of the input parameters. Hierarchical equations that define approximations to the mean solution at each point andmore » to the field convariance of the pressure are developed and solved numerically. The procedure is then used to find the statistics of the flow and the risked value of the field, defined by the NPV, for a given development scenario. This method involves only one (albeit complicated) solution of the equations and contrasts with the more usual Monte-Carlo approach where many such solutions are required. The procedure is applied easily to other physical systems modelled by linear or nonlinear partial differential equations with uncertain data. 14 refs., 14 figs., 3 tabs.« less

PAR -- Interface to the ADAM Parameter System

NASA Astrophysics Data System (ADS)

Currie, Malcolm J.; Chipperfield, Alan J.

PAR is a library of Fortran subroutines that provides convenient mechanisms for applications to exchange information with the outside world, through input-output channels called parameters. Parameters enable a user to control an application's behaviour. PAR supports numeric, character, and logical parameters, and is currently implemented only on top of the ADAM parameter system. The PAR library permits parameter values to be obtained, without or with a variety of constraints. Results may be put into parameters to be passed onto other applications. Other facilities include setting a prompt string, and suggested defaults. This document also introduces a preliminary C interface for the PAR library -- this may be subject to change in the light of experience.

Preliminary design study of a regenerative life support system information management and display system

NASA Technical Reports Server (NTRS)

Parker, C. D.; Tommerdahl, J. B.

1972-01-01

The instrumentation requirements for a regenerative life support systems were studied to provide the earliest possible indication of a malfunction that will permit degradation of the environment. Four categories of parameters were investigated: environmental parameters that directly and immediately influence the health and safety of the cabin crew; subsystems' inputs to the cabin that directly maintain the cabin environmental parameters; indications for maintenance or repair; and parameters useful as diagnostic indicators. A data averager concept is introduced which provides a moving average of parameter values that is not influenced by spurious changes, and is convenient for detecting parameter rates of change. A system is included to provide alarms at preselected parameter levels.

A computer program to trace seismic ray distribution in complex two-dimensional geological models

USGS Publications Warehouse

Yacoub, Nazieh K.; Scott, James H.

1970-01-01

A computer program has been developed to trace seismic rays and their amplitudes and energies through complex two-dimensional geological models, for which boundaries between elastic units are defined by a series of digitized X-, Y-coordinate values. Input data for the program includes problem identification, control parameters, model coordinates and elastic parameter for the elastic units. The program evaluates the partitioning of ray amplitude and energy at elastic boundaries, computes the total travel time, total travel distance and other parameters for rays arising at the earth's surface. Instructions are given for punching program control cards and data cards, and for arranging input card decks. An example of printer output for a simple problem is presented. The program is written in FORTRAN IV language. The listing of the program is shown in the Appendix, with an example output from a CDC-6600 computer.

Control and optimization system

DOEpatents

Xinsheng, Lou

2013-02-12

A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Removing Visual Bias in Filament Identification: A New Goodness-of-fit Measure

NASA Astrophysics Data System (ADS)

Green, C.-E.; Cunningham, M. R.; Dawson, J. R.; Jones, P. A.; Novak, G.; Fissel, L. M.

2017-05-01

Different combinations of input parameters to filament identification algorithms, such as disperse and filfinder, produce numerous different output skeletons. The skeletons are a one-pixel-wide representation of the filamentary structure in the original input image. However, these output skeletons may not necessarily be a good representation of that structure. Furthermore, a given skeleton may not be as good of a representation as another. Previously, there has been no mathematical “goodness-of-fit” measure to compare output skeletons to the input image. Thus far this has been assessed visually, introducing visual bias. We propose the application of the mean structural similarity index (MSSIM) as a mathematical goodness-of-fit measure. We describe the use of the MSSIM to find the output skeletons that are the most mathematically similar to the original input image (the optimum, or “best,” skeletons) for a given algorithm, and independently of the algorithm. This measure makes possible systematic parameter studies, aimed at finding the subset of input parameter values returning optimum skeletons. It can also be applied to the output of non-skeleton-based filament identification algorithms, such as the Hessian matrix method. The MSSIM removes the need to visually examine thousands of output skeletons, and eliminates the visual bias, subjectivity, and limited reproducibility inherent in that process, representing a major improvement upon existing techniques. Importantly, it also allows further automation in the post-processing of output skeletons, which is crucial in this era of “big data.”

Global SWOT Data Assimilation of River Hydrodynamic Model; the Twin Simulation Test of CaMa-Flood

NASA Astrophysics Data System (ADS)

Ikeshima, D.; Yamazaki, D.; Kanae, S.

2016-12-01

CaMa-Flood is a global scale model for simulating hydrodynamics in large scale rivers. It can simulate river hydrodynamics such as river discharge, flooded area, water depth and so on by inputting water runoff derived from land surface model. Recently many improvements at parameters or terrestrial data are under process to enhance the reproducibility of true natural phenomena. However, there are still some errors between nature and simulated result due to uncertainties in each model. SWOT (Surface water and Ocean Topography) is a satellite, which is going to be launched in 2021, can measure open water surface elevation. SWOT observed data can be used to calibrate hydrodynamics model at river flow forecasting and is expected to improve model's accuracy. Combining observation data into model to calibrate is called data assimilation. In this research, we developed data-assimilated river flow simulation system in global scale, using CaMa-Flood as river hydrodynamics model and simulated SWOT as observation data. Generally at data assimilation, calibrating "model value" with "observation value" makes "assimilated value". However, the observed data of SWOT satellite will not be available until its launch in 2021. Instead, we simulated the SWOT observed data using CaMa-Flood. Putting "pure input" into CaMa-Flood produce "true water storage". Extracting actual daily swath of SWOT from "true water storage" made simulated observation. For "model value", we made "disturbed water storage" by putting "noise disturbed input" to CaMa-Flood. Since both "model value" and "observation value" are made by same model, we named this twin simulation. At twin simulation, simulated observation of "true water storage" is combined with "disturbed water storage" to make "assimilated value". As the data assimilation method, we used ensemble Kalman filter. If "assimilated value" is closer to "true water storage" than "disturbed water storage", the data assimilation can be marked effective. Also by changing the input disturbance of "disturbed water storage", acceptable rate of uncertainty at the input may be discussed.

Flight Test Validation of Optimal Input Design and Comparison to Conventional Inputs

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1997-01-01

A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Angle of Attack Research Vehicle (HARV). Model parameter accuracies calculated from flight test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input form by the feedback control system, the optimal inputs increased estimated parameter accuracies compared to conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and practical utility of the optimal input design technique.

Averages of B-Hadron, C-Hadron, and tau-lepton properties as of early 2012

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

Amhis, Y.; et al.

2012-07-01

This article reports world averages of measurements of b-hadron, c-hadron, and tau-lepton properties obtained by the Heavy Flavor Averaging Group (HFAG) using results available through the end of 2011. In some cases results available in the early part of 2012 are included. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, CP violation parameters, parameters of semileptonic decays and CKM matrix elements.

Calibration by Hydrological Response Unit of a National Hydrologic Model to Improve Spatial Representation and Distribution of Parameters

NASA Astrophysics Data System (ADS)

Norton, P. A., II

2015-12-01

The U. S. Geological Survey is developing a National Hydrologic Model (NHM) to support consistent hydrologic modeling across the conterminous United States (CONUS). The Precipitation-Runoff Modeling System (PRMS) simulates daily hydrologic and energy processes in watersheds, and is used for the NHM application. For PRMS each watershed is divided into hydrologic response units (HRUs); by default each HRU is assumed to have a uniform hydrologic response. The Geospatial Fabric (GF) is a database containing initial parameter values for input to PRMS and was created for the NHM. The parameter values in the GF were derived from datasets that characterize the physical features of the entire CONUS. The NHM application is composed of more than 100,000 HRUs from the GF. Selected parameter values commonly are adjusted by basin in PRMS using an automated calibration process based on calibration targets, such as streamflow. Providing each HRU with distinct values that captures variability within the CONUS may improve simulation performance of the NHM. During calibration of the NHM by HRU, selected parameter values are adjusted for PRMS based on calibration targets, such as streamflow, snow water equivalent (SWE) and actual evapotranspiration (AET). Simulated SWE, AET, and runoff were compared to value ranges derived from multiple sources (e.g. the Snow Data Assimilation System, the Moderate Resolution Imaging Spectroradiometer (i.e. MODIS) Global Evapotranspiration Project, the Simplified Surface Energy Balance model, and the Monthly Water Balance Model). This provides each HRU with a distinct set of parameter values that captures the variability within the CONUS, leading to improved model performance. We present simulation results from the NHM after preliminary calibration, including the results of basin-level calibration for the NHM using: 1) default initial GF parameter values, and 2) parameter values calibrated by HRU.

Reconstruction of input functions from a dynamic PET image with sequential administration of 15O2 and [Formula: see text] for noninvasive and ultra-rapid measurement of CBF, OEF, and CMRO2.

PubMed

Kudomi, Nobuyuki; Maeda, Yukito; Yamamoto, Hiroyuki; Yamamoto, Yuka; Hatakeyama, Tetsuhiro; Nishiyama, Yoshihiro

2018-05-01

CBF, OEF, and CMRO 2 images can be quantitatively assessed using PET. Their image calculation requires arterial input functions, which require invasive procedure. The aim of the present study was to develop a non-invasive approach with image-derived input functions (IDIFs) using an image from an ultra-rapid O 2 and C 15 O 2 protocol. Our technique consists of using a formula to express the input using tissue curve with rate constants. For multiple tissue curves, the rate constants were estimated so as to minimize the differences of the inputs using the multiple tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects ( n = 24). The estimated IDIFs were well-reproduced against the measured ones. The difference in the calculated CBF, OEF, and CMRO 2 values by the two methods was small (<10%) against the invasive method, and the values showed tight correlations ( r = 0.97). The simulation showed errors associated with the assumed parameters were less than ∼10%. Our results demonstrate that IDIFs can be reconstructed from tissue curves, suggesting the possibility of using a non-invasive technique to assess CBF, OEF, and CMRO 2 .

Volatilisation and competing processes computed for a pesticide applied to plants in a wind tunnel system.

PubMed

Leistra, Minze; Wolters, André; van den Berg, Frederik

2008-06-01

Volatilisation of pesticides from crop canopies can be an important emission pathway. In addition to pesticide properties, competing processes in the canopy and environmental conditions play a part. A computation model is being developed to simulate the processes, but only some of the input data can be obtained directly from the literature. Three well-defined experiments on the volatilisation of radiolabelled parathion-methyl (as example compound) from plants in a wind tunnel system were simulated with the computation model. Missing parameter values were estimated by calibration against the experimental results. The resulting thickness of the air boundary layer, rate of plant penetation and rate of phototransformation were compared with a diversity of literature data. The sequence of importance of the canopy processes was: volatilisation > plant penetration > phototransformation. Computer simulation of wind tunnel experiments, with radiolabelled pesticide sprayed on plants, yields values for the rate coefficients of processes at the plant surface. As some input data for simulations are not required in the framework of registration procedures, attempts to estimate missing parameter values on the basis of divergent experimental results have to be continued. Copyright (c) 2008 Society of Chemical Industry.

Assessing the Internal Consistency of the Marine Carbon Dioxide System at High Latitudes: The Labrador Sea AR7W Line Study Case

NASA Astrophysics Data System (ADS)

Raimondi, L.; Azetsu-Scott, K.; Wallace, D.

2016-02-01

This work assesses the internal consistency of ocean carbon dioxide through the comparison of discrete measurements and calculated values of four analytical parameters of the inorganic carbon system: Total Alkalinity (TA), Dissolved Inorganic Carbon (DIC), pH and Partial Pressure of CO2 (pCO2). The study is based on 486 seawater samples analyzed for TA, DIC and pH and 86 samples for pCO2 collected during the 2014 Cruise along the AR7W line in Labrador Sea. The internal consistency has been assessed using all combinations of input parameters and eight sets of thermodynamic constants (K1, K2) in calculating each parameter through the CO2SYS software. Residuals of each parameter have been calculated as the differences between measured and calculated values (reported as ΔTA, ΔDIC, ΔpH and ΔpCO2). Although differences between the selected sets of constants were observed, the largest were obtained using different pairs of input parameters. As expected the couple pH-pCO2 produced to poorest results, suggesting that measurements of either TA or DIC are needed to define the carbonate system accurately and precisely. To identify signature of organic alkalinity we isolated the residuals in the bloom area. Therefore only ΔTA from surface waters (0-30 m) along the Greenland side of the basin were selected. The residuals showed that no measured value was higher than calculations and therefore we could not observe presence of organic bases in the shallower water column. The internal consistency in characteristic water masses of Labrador Sea (Denmark Strait Overflow Water, North East Atlantic Deep Water, Newly-ventilated Labrador Sea Water, Greenland and Labrador Shelf waters) will also be discussed.

Calibration Software for Use with Jurassicprok

NASA Technical Reports Server (NTRS)

Chapin, Elaine; Hensley, Scott; Siqueira, Paul

2004-01-01

The Jurassicprok Interferometric Calibration Software (also called "Calibration Processor" or simply "CP") estimates the calibration parameters of an airborne synthetic-aperture-radar (SAR) system, the raw measurement data of which are processed by the Jurassicprok software described in the preceding article. Calibration parameters estimated by CP include time delays, baseline offsets, phase screens, and radiometric offsets. CP examines raw radar-pulse data, single-look complex image data, and digital elevation map data. For each type of data, CP compares the actual values with values expected on the basis of ground-truth data. CP then converts the differences between the actual and expected values into updates for the calibration parameters in an interferometric calibration file (ICF) and a radiometric calibration file (RCF) for the particular SAR system. The updated ICF and RCF are used as inputs to both Jurassicprok and to the companion Motion Measurement Processor software (described in the following article) for use in generating calibrated digital elevation maps.

Dataset on the mean, standard deviation, broad-sense heritability and stability of wheat quality bred in three different ways and grown under organic and low-input conventional systems.

PubMed

Rakszegi, Marianna; Löschenberger, Franziska; Hiltbrunner, Jürg; Vida, Gyula; Mikó, Péter

2016-06-01

An assessment was previously made of the effects of organic and low-input field management systems on the physical, grain compositional and processing quality of wheat and on the performance of varieties developed using different breeding methods ("Comparison of quality parameters of wheat varieties with different breeding origin under organic and low-input conventional conditions" [1]). Here, accompanying data are provided on the performance and stability analysis of the genotypes using the coefficient of variation and the 'ranking' and 'which-won-where' plots of GGE biplot analysis for the most important quality traits. Broad-sense heritability was also evaluated and is given for the most important physical and quality properties of the seed in organic and low-input management systems, while mean values and standard deviation of the studied properties are presented separately for organic and low-input fields.

Development of a Standard Set of Software Indicators for Aeronautical Systems Center.

DTIC Science & Technology

1992-09-01

29:12). The composite models listed include COCOMO and the Software Productivity, Quality, and Reliability Model ( SPQR ) (29:12). The SPQR model was...determine the values of the 68 input parameters. Source provides no specifics. Indicator Name SPQR (SW Productivity, Qual, Reliability) Indicator Class

TIM Version 3.0 beta Technical Description and User Guide - Appendix A - User's Guidance for TIM v.3.0(beta)

EPA Pesticide Factsheets

Provides detailed guidance to the user on how to select input parameters for running the Terrestrial Investigation Model (TIM) and recommendations for default values that can be used when no chemical-specific or species-specific information are available.

Local identifiability and sensitivity analysis of neuromuscular blockade and depth of hypnosis models.

PubMed

Silva, M M; Lemos, J M; Coito, A; Costa, B A; Wigren, T; Mendonça, T

2014-01-01

This paper addresses the local identifiability and sensitivity properties of two classes of Wiener models for the neuromuscular blockade and depth of hypnosis, when drug dose profiles like the ones commonly administered in the clinical practice are used as model inputs. The local parameter identifiability was assessed based on the singular value decomposition of the normalized sensitivity matrix. For the given input signal excitation, the results show an over-parameterization of the standard pharmacokinetic/pharmacodynamic models. The same identifiability assessment was performed on recently proposed minimally parameterized parsimonious models for both the neuromuscular blockade and the depth of hypnosis. The results show that the majority of the model parameters are identifiable from the available input-output data. This indicates that any identification strategy based on the minimally parameterized parsimonious Wiener models for the neuromuscular blockade and for the depth of hypnosis is likely to be more successful than if standard models are used. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

NASA Technical Reports Server (NTRS)

Orme, John S.; Gilyard, Glenn B.

1992-01-01

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Investigation into the influence of laser energy input on selective laser melted thin-walled parts by response surface method

NASA Astrophysics Data System (ADS)

Liu, Yang; Zhang, Jian; Pang, Zhicong; Wu, Weihui

2018-04-01

Selective laser melting (SLM) provides a feasible way for manufacturing of complex thin-walled parts directly, however, the energy input during SLM process, namely derived from the laser power, scanning speed, layer thickness and scanning space, etc. has great influence on the thin wall's qualities. The aim of this work is to relate the thin wall's parameters (responses), namely track width, surface roughness and hardness to the process parameters considered in this research (laser power, scanning speed and layer thickness) and to find out the optimal manufacturing conditions. Design of experiment (DoE) was used by implementing composite central design to achieve better manufacturing qualities. Mathematical models derived from the statistical analysis were used to establish the relationships between the process parameters and the responses. Also, the effects of process parameters on each response were determined. Then, a numerical optimization was performed to find out the optimal process set at which the quality features are at their desired values. Based on this study, the relationship between process parameters and SLMed thin-walled structure was revealed and thus, the corresponding optimal process parameters can be used to manufactured thin-walled parts with high quality.

Effect of Different Solar Radiation Data Sources on the Variation of Techno-Economic Feasibility of PV Power System

NASA Astrophysics Data System (ADS)

Alghoul, M. A.; Ali, Amer; Kannanaikal, F. V.; Amin, N.; Aljaafar, A. A.; Kadhim, Mohammed; Sopian, K.

2017-11-01

The aim of this study is to evaluate the variation in techno-economic feasibility of PV power system under different data sources of solar radiation. HOMER simulation tool is used to predict the techno-economic feasibility parameters of PV power system in Baghdad city, Iraq located at (33.3128° N, 44.3615° E) as a case study. Four data sources of solar radiation, different annual capacity shortages percentage (0, 2.5, 5, and 7.5), and wide range of daily load profile (10-100 kWh/day) are implemented. The analyzed parameters of the techno-economic feasibility are COE (/kWh), PV array power capacity (kW), PV electrical production (kWh/year), No. of batteries and battery lifetime (year). The main results of the study revealed the followings: (1) solar radiation from different data sources caused observed to significant variation in the values of the techno-economic feasibility parameters; therefore, careful attention must be paid to ensure the use of an accurate solar input data; (2) Average solar radiation from different data sources can be recommended as a reasonable input data; (3) it is observed that as the size and of PV power system increases, the effect of different data sources of solar radiation increases and causes significant variation in the values of the techno-economic feasibility parameters.

Sensitivity Analysis of Cf-252 (sf) Neutron and Gamma Observables in CGMF

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

Carter, Austin Lewis; Talou, Patrick; Stetcu, Ionel

CGMF is a Monte Carlo code that simulates the decay of primary fission fragments by emission of neutrons and gamma rays, according to the Hauser-Feshbach equations. As the CGMF code was recently integrated into the MCNP6.2 transport code, great emphasis has been placed on providing optimal parameters to CGMF such that many different observables are accurately represented. Of these observables, the prompt neutron spectrum, prompt neutron multiplicity, prompt gamma spectrum, and prompt gamma multiplicity are crucial for accurate transport simulations of criticality and nonproliferation applications. This contribution to the ongoing efforts to improve CGMF presents a study of the sensitivitymore » of various neutron and gamma observables to several input parameters for Californium-252 spontaneous fission. Among the most influential parameters are those that affect the input yield distributions in fragment mass and total kinetic energy (TKE). A new scheme for representing Y(A,TKE) was implemented in CGMF using three fission modes, S1, S2 and SL. The sensitivity profiles were calculated for 17 total parameters, which show that the neutron multiplicity distribution is strongly affected by the TKE distribution of the fragments. The total excitation energy (TXE) of the fragments is shared according to a parameter RT, which is defined as the ratio of the light to heavy initial temperatures. The sensitivity profile of the neutron multiplicity shows a second order effect of RT on the mean neutron multiplicity. A final sensitivity profile was produced for the parameter alpha, which affects the spin of the fragments. Higher values of alpha lead to higher fragment spins, which inhibit the emission of neutrons. Understanding the sensitivity of the prompt neutron and gamma observables to the many CGMF input parameters provides a platform for the optimization of these parameters.« less

Evaluation of limited blood sampling population input approaches for kinetic quantification of [18F]fluorothymidine PET data.

PubMed

Contractor, Kaiyumars B; Kenny, Laura M; Coombes, Charles R; Turkheimer, Federico E; Aboagye, Eric O; Rosso, Lula

2012-03-24

Quantification of kinetic parameters of positron emission tomography (PET) imaging agents normally requires collecting arterial blood samples which is inconvenient for patients and difficult to implement in routine clinical practice. The aim of this study was to investigate whether a population-based input function (POP-IF) reliant on only a few individual discrete samples allows accurate estimates of tumour proliferation using [18F]fluorothymidine (FLT). Thirty-six historical FLT-PET data with concurrent arterial sampling were available for this study. A population average of baseline scans blood data was constructed using leave-one-out cross-validation for each scan and used in conjunction with individual blood samples. Three limited sampling protocols were investigated including, respectively, only seven (POP-IF7), five (POP-IF5) and three (POP-IF3) discrete samples of the historical dataset. Additionally, using the three-point protocol, we derived a POP-IF3M, the only input function which was not corrected for the fraction of radiolabelled metabolites present in blood. The kinetic parameter for net FLT retention at steady state, Ki, was derived using the modified Patlak plot and compared with the original full arterial set for validation. Small percentage differences in the area under the curve between all the POP-IFs and full arterial sampling IF was found over 60 min (4.2%-5.7%), while there were, as expected, larger differences in the peak position and peak height.A high correlation between Ki values calculated using the original arterial input function and all the population-derived IFs was observed (R2 = 0.85-0.98). The population-based input showed good intra-subject reproducibility of Ki values (R2 = 0.81-0.94) and good correlation (R2 = 0.60-0.85) with Ki-67. Input functions generated using these simplified protocols over scan duration of 60 min estimate net PET-FLT retention with reasonable accuracy.

Design optimum frac jobs using virtual intelligence techniques

NASA Astrophysics Data System (ADS)

Mohaghegh, Shahab; Popa, Andrei; Ameri, Sam

2000-10-01

Designing optimal frac jobs is a complex and time-consuming process. It usually involves the use of a two- or three-dimensional computer model. For the computer models to perform as intended, a wealth of input data is required. The input data includes wellbore configuration and reservoir characteristics such as porosity, permeability, stress and thickness profiles of the pay layers as well as the overburden layers. Among other essential information required for the design process is fracturing fluid type and volume, proppant type and volume, injection rate, proppant concentration and frac job schedule. Some of the parameters such as fluid and proppant types have discrete possible choices. Other parameters such as fluid and proppant volume, on the other hand, assume values from within a range of minimum and maximum values. A potential frac design for a particular pay zone is a combination of all of these parameters. Finding the optimum combination is not a trivial process. It usually requires an experienced engineer and a considerable amount of time to tune the parameters in order to achieve desirable outcome. This paper introduces a new methodology that integrates two virtual intelligence techniques, namely, artificial neural networks and genetic algorithms to automate and simplify the optimum frac job design process. This methodology requires little input from the engineer beyond the reservoir characterizations and wellbore configuration. The software tool that has been developed based on this methodology uses the reservoir characteristics and an optimization criteria indicated by the engineer, for example a certain propped frac length, and provides the detail of the optimum frac design that will result in the specified criteria. An ensemble of neural networks is trained to mimic the two- or three-dimensional frac simulator. Once successfully trained, these networks are capable of providing instantaneous results in response to any set of input parameters. These networks will be used as the fitness function for a genetic algorithm routine that will search for the best combination of the design parameters for the frac job. The genetic algorithm will search through the entire solution space and identify the optimal combination of parameters to be used in the design process. Considering the complexity of this task this methodology converges relatively fast, providing the engineer with several near-optimum scenarios for the frac job design. These scenarios, which can be achieved in just a minute or two, can be valuable initial points for the engineer to start his/her design job and save him/her hours of runs on the simulator.

Automated forward mechanical modeling of wrinkle ridges on Mars

NASA Astrophysics Data System (ADS)

Nahm, Amanda; Peterson, Samuel

2016-04-01

One of the main goals of the InSight mission to Mars is to understand the internal structure of Mars [1], in part through passive seismology. Understanding the shallow surface structure of the landing site is critical to the robust interpretation of recorded seismic signals. Faults, such as the wrinkle ridges abundant in the proposed landing site in Elysium Planitia, can be used to determine the subsurface structure of the regions they deform. Here, we test a new automated method for modeling of the topography of a wrinkle ridge (WR) in Elysium Planitia, allowing for faster and more robust determination of subsurface fault geometry for interpretation of the local subsurface structure. We perform forward mechanical modeling of fault-related topography [e.g., 2, 3], utilizing the modeling program Coulomb [4, 5] to model surface displacements surface induced by blind thrust faulting. Fault lengths are difficult to determine for WR; we initially assume a fault length of 30 km, but also test the effects of different fault lengths on model results. At present, we model the wrinkle ridge as a single blind thrust fault with a constant fault dip, though WR are likely to have more complicated fault geometry [e.g., 6-8]. Typically, the modeling is performed using the Coulomb GUI. This approach can be time consuming, requiring user inputs to change model parameters and to calculate the associated displacements for each model, which limits the number of models and parameter space that can be tested. To reduce active user computation time, we have developed a method in which the Coulomb GUI is bypassed. The general modeling procedure remains unchanged, and a set of input files is generated before modeling with ranges of pre-defined parameter values. The displacement calculations are divided into two suites. For Suite 1, a total of 3770 input files were generated in which the fault displacement (D), dip angle (δ), depth to upper fault tip (t), and depth to lower fault tip (B) were varied. A second set of input files was created (Suite 2) after the best-fit model from Suite 1 was determined, in which fault parameters were varied with a smaller range and incremental changes, resulting in a total of 28,080 input files. RMS values were calculated for each Coulomb model. RMS values for Suite 1 models were calculated over the entire profile and for a restricted x range; the latter shows a reduced RMS misfit by 1.2 m. The minimum RMS value for Suite 2 models decreases again by 0.2 m, resulting in an overall reduction of the RMS value of ~1.4 m (18%). Models with different fault lengths (15, 30, and 60 km) are visually indistinguishable. Values for δ, t, B, and RMS misfit are either the same or very similar for each best fit model. These results indicate that the subsurface structure can be reliably determined from forward mechanical modeling even with uncertainty in fault length. Future work will test this method with the more realistic WR fault geometry. References: [1] Banerdt et al. (2013), 44th LPSC, #1915. [2] Cohen (1999), Adv. Geophys., 41, 133-231. [3] Schultz and Lin (2001), JGR, 106, 16549-16566. [4] Lin and Stein (2004), JGR, 109, B02303, doi:10.1029/2003JB002607. [5] Toda et al. (2005), JGR, 103, 24543-24565. [6] Okubo and Schultz (2004), GSAB, 116, 597-605. [7] Watters (2004), Icarus, 171, 284-294. [8] Schultz (2000), JGR, 105, 12035-12052.

UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

USGS Publications Warehouse

Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen

2006-01-01

This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a weighted least-squares objective function is minimized with respect to the parameter values using a modified Gauss-Newton method or a double-dogleg technique. Sensitivities needed for the method can be read from files produced by process models that can calculate sensitivities, such as MODFLOW-2000, or can be calculated by UCODE_2005 using a more general, but less accurate, forward- or central-difference perturbation technique. Problems resulting from inaccurate sensitivities and solutions related to the perturbation techniques are discussed in the report. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; and (3) evaluating how well the model represents the simulated processes. Results from UCODE_2005 and codes RESIDUAL_ANALYSIS and RESIDUAL_ANALYSIS_ADV can be used to evaluate how accurately the model represents the processes it simulates. Results from LINEAR_UNCERTAINTY can be used to quantify the uncertainty of model simulated values if the model is sufficiently linear. Results from MODEL_LINEARITY and MODEL_LINEARITY_ADV can be used to evaluate model linearity and, thereby, the accuracy of the LINEAR_UNCERTAINTY results. UCODE_2005 can also be used to calculate nonlinear confidence and predictions intervals, which quantify the uncertainty of model simulated values when the model is not linear. CORFAC_PLUS can be used to produce factors that allow intervals to account for model intrinsic nonlinearity and small-scale variations in system characteristics that are not explicitly accounted for in the model or the observation weighting. The six post-processing programs are independent of UCODE_2005 and can use the results of other programs that produce the required data-exchange files. UCODE_2005 and the other six codes are intended for use on any computer operating system. The programs con

Effect of alkaline microwaving pretreatment on anaerobic digestion and biogas production of swine manure.

PubMed

Yu, Tao; Deng, Yihuan; Liu, Hongyu; Yang, Chunping; Wu, Bingwen; Zeng, Guangming; Lu, Li; Nishimura, Fumitake

2017-05-10

Microwave assisted with alkaline (MW-A) condition was applied in the pretreatment of swine manure, and the effect of the pretreatment on anaerobic treatment and biogas production was evaluated in this study. The two main microwaving (MW) parameters, microwaving power and reaction time, were optimized for the pretreatment. Response surface methodology (RSM) was used to investigate the effect of alkaline microwaving process for manure pretreatment at various values of pH and energy input. Results showed that the manure disintegration degree was maximized of 63.91% at energy input of 54 J/g and pH of 12.0, and variance analysis indicated that pH value played a more important role in the pretreatment than in energy input. Anaerobic digestion results demonstrated that MW-A pretreatment not only significantly increased cumulative biogas production, but also shortened the duration for a stable biogas production rate. Therefore, the alkaline microwaving pretreatment could become an alternative process for effective treatment of swine manure.

Non-input analysis for incomplete trapping irreversible tracer with PET.

PubMed

Ohya, Tomoyuki; Kikuchi, Tatsuya; Fukumura, Toshimitsu; Zhang, Ming-Rong; Irie, Toshiaki

2013-07-01

When using metabolic trapping type tracers, the tracers are not always trapped in the target tissue; i.e., some are completely trapped in the target, but others can be eliminated from the target tissue at a measurable rate. The tracers that can be eliminated are termed 'incomplete trapping irreversible tracers'. These incomplete trapping irreversible tracers may be clinically useful when the tracer β-value, the ratio of the tracer (metabolite) elimination rate to the tracer efflux rate, is under approximately 0.1. In this study, we propose a non-input analysis for incomplete trapping irreversible tracers based on the shape analysis (Shape), a non-input analysis used for irreversible tracers. A Monte Carlo simulation study based on experimental monkey data with two actual PET tracers (a complete trapping irreversible tracer [(11)C]MP4A and an incomplete trapping irreversible tracer [(18)F]FEP-4MA) was performed to examine the effects of the environmental error and the tracer elimination rate on the estimation of the k3-parameter (corresponds to metabolic rate) using Shape (original) and modified Shape (M-Shape) analysis. The simulation results were also compared with the experimental results obtained with the two PET tracers. When the tracer β-value was over 0.03, the M-Shape method was superior to the Shape method for the estimation of the k3-parameter. The simulation results were also in reasonable agreement with the experimental ones. M-Shape can be used as the non-input analysis of incomplete trapping irreversible tracers for PET study. Copyright © 2013 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2013-09-01

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

40 CFR 98.315 - Procedures for estimating missing data.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 21 2014-07-01 2014-07-01 false Procedures for estimating missing data... estimating missing data. For the petroleum coke input procedure in § 98.313(b), a complete record of all... substitute data value for the missing parameter shall be used in the calculations as specified in the...

40 CFR 98.315 - Procedures for estimating missing data.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 22 2012-07-01 2012-07-01 false Procedures for estimating missing data... estimating missing data. For the petroleum coke input procedure in § 98.313(b), a complete record of all... substitute data value for the missing parameter shall be used in the calculations as specified in the...

40 CFR 98.315 - Procedures for estimating missing data.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 22 2013-07-01 2013-07-01 false Procedures for estimating missing data... estimating missing data. For the petroleum coke input procedure in § 98.313(b), a complete record of all... substitute data value for the missing parameter shall be used in the calculations as specified in the...

40 CFR 98.315 - Procedures for estimating missing data.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 21 2011-07-01 2011-07-01 false Procedures for estimating missing data... estimating missing data. For the petroleum coke input procedure in § 98.313(b), a complete record of all... substitute data value for the missing parameter shall be used in the calculations as specified in the...

Generating nonlinear FM chirp radar signals by multiple integrations

DOEpatents

Doerry, Armin W [Albuquerque, NM

2011-02-01

A phase component of a nonlinear frequency modulated (NLFM) chirp radar pulse can be produced by performing digital integration operations over a time interval defined by the pulse width. Each digital integration operation includes applying to a respectively corresponding input parameter value a respectively corresponding number of instances of digital integration.

Calculation of the Poisson cumulative distribution function

NASA Technical Reports Server (NTRS)

Bowerman, Paul N.; Nolty, Robert G.; Scheuer, Ernest M.

1990-01-01

A method for calculating the Poisson cdf (cumulative distribution function) is presented. The method avoids computer underflow and overflow during the process. The computer program uses this technique to calculate the Poisson cdf for arbitrary inputs. An algorithm that determines the Poisson parameter required to yield a specified value of the cdf is presented.

The hydraulic capacity of deteriorating sewer systems.

PubMed

Pollert, J; Ugarelli, R; Saegrov, S; Schilling, W; Di Federico, V

2005-01-01

Sewer and wastewater systems suffer from insufficient capacity, construction flaws and pipe deterioration. Consequences are structural failures, local floods, surface erosion and pollution of receiving waters bodies. European cities spend in the order of five billion Euro per year for wastewater network rehabilitation. This amount is estimated to increase due to network ageing. The project CARE-S (Computer Aided RE-habilitation of Sewer Networks) deals with sewer and storm water networks. The final project goal is to develop integrated software, which provides the most cost-efficient system of maintenance, repair and rehabilitation of sewer networks. Decisions on investments in rehabilitation often have to be made with uncertain information about the structural condition and the hydraulic performance of a sewer system. Because of this, decision-making involves considerable risks. This paper presents the results of research focused on the study of hydraulic effects caused by failures due to temporal decline of sewer systems. Hydraulic simulations are usually carried out by running commercial models that apply, as input, default values of parameters that strongly influence results. Using CCTV inspections information as dataset to catalogue principal types of failures affecting pipes, a 3D model was used to evaluate their hydraulic consequences. The translation of failures effects in parameters values producing the same hydraulic conditions caused by failures was carried out through the comparison of laboratory experiences and 3D simulations results. Those parameters could be the input of 1D commercial models instead of the default values commonly inserted.

Study of eigenfrequencies with the help of Prony's method

NASA Astrophysics Data System (ADS)

Drobakhin, O. O.; Olevskyi, O. V.; Olevskyi, V. I.

2017-10-01

Eigenfrequencies can be crucial in the design of a construction. They define many parameters that determine limit parameters of the structure. Exceeding these values can lead to the structural failure of an object. It is especially important in the design of structures which support heavy equipment or are subjected to the forces of airflow. One of the most effective ways to acquire the frequencies' values is a computer-based numerical simulation. The existing methods do not allow to acquire the whole range of needed parameters. It is well known that Prony's method, is highly effective for the investigation of dynamic processes. Thus, it is rational to adapt Prony's method for such investigation. The Prony method has advantage in comparison with other numerical schemes because it provides the possibility to process not only the results of numerical simulation, but also real experimental data. The research was carried out for a computer model of a steel plate. The input data was obtained by using the Dassault Systems SolidWorks computer package with the Simulation add-on. We investigated the acquired input data with the help of Prony's method. The result of the numerical experiment shows that Prony's method can be used to investigate the mechanical eigenfrequencies with good accuracy. The output of Prony's method not only contains the information about values of frequencies themselves, but also contains data regarding the amplitudes, initial phases and decaying factors of any given mode of oscillation, which can also be used in engineering.

A new method to estimate average hourly global solar radiation on the horizontal surface

NASA Astrophysics Data System (ADS)

Pandey, Pramod K.; Soupir, Michelle L.

2012-10-01

A new model, Global Solar Radiation on Horizontal Surface (GSRHS), was developed to estimate the average hourly global solar radiation on the horizontal surfaces (Gh). The GSRHS model uses the transmission function (Tf,ij), which was developed to control hourly global solar radiation, for predicting solar radiation. The inputs of the model were: hour of day, day (Julian) of year, optimized parameter values, solar constant (H0), latitude, and longitude of the location of interest. The parameter values used in the model were optimized at a location (Albuquerque, NM), and these values were applied into the model for predicting average hourly global solar radiations at four different locations (Austin, TX; El Paso, TX; Desert Rock, NV; Seattle, WA) of the United States. The model performance was assessed using correlation coefficient (r), Mean Absolute Bias Error (MABE), Root Mean Square Error (RMSE), and coefficient of determinations (R2). The sensitivities of parameter to prediction were estimated. Results show that the model performed very well. The correlation coefficients (r) range from 0.96 to 0.99, while coefficients of determination (R2) range from 0.92 to 0.98. For daily and monthly prediction, error percentages (i.e. MABE and RMSE) were less than 20%. The approach we proposed here can be potentially useful for predicting average hourly global solar radiation on the horizontal surface for different locations, with the use of readily available data (i.e. latitude and longitude of the location) as inputs.

Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction

PubMed Central

Desikan, Radhika

2016-01-01

Cellular signal transduction usually involves activation cascades, the sequential activation of a series of proteins following the reception of an input signal. Here, we study the classic model of weakly activated cascades and obtain analytical solutions for a variety of inputs. We show that in the special but important case of optimal gain cascades (i.e. when the deactivation rates are identical) the downstream output of the cascade can be represented exactly as a lumped nonlinear module containing an incomplete gamma function with real parameters that depend on the rates and length of the cascade, as well as parameters of the input signal. The expressions obtained can be applied to the non-identical case when the deactivation rates are random to capture the variability in the cascade outputs. We also show that cascades can be rearranged so that blocks with similar rates can be lumped and represented through our nonlinear modules. Our results can be used both to represent cascades in computational models of differential equations and to fit data efficiently, by reducing the number of equations and parameters involved. In particular, the length of the cascade appears as a real-valued parameter and can thus be fitted in the same manner as Hill coefficients. Finally, we show how the obtained nonlinear modules can be used instead of delay differential equations to model delays in signal transduction. PMID:27581482

Data collection handbook to support modeling the impacts of radioactive material in soil

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

Yu, C.; Cheng, J.J.; Jones, L.G.

1993-04-01

A pathway analysis computer code called RESRAD has been developed for implementing US Department of Energy Residual Radioactive Material Guidelines. Hydrogeological, meteorological, geochemical, geometrical (size, area, depth), and material-related (soil, concrete) parameters are used in the RESRAD code. This handbook discusses parameter definitions, typical ranges, variations, measurement methodologies, and input screen locations. Although this handbook was developed primarily to support the application of RESRAD, the discussions and values are valid for other model applications.

A program and data base for evaluating SMMR algorithms

NASA Technical Reports Server (NTRS)

1979-01-01

A program (PARAM) is described which enables a user to compare the values of meteorological parameters derived from data obtained by the scanning multichannel microwave radiometer (SMMR) instrument on NIMBUS 7 with surface observations made over the ocean. The input to this program is a data base, also described, which contains the surface observations and coincident SMMR data. The evaluation of meteorological parameters using SMMR data is done by a user supplied subroutine. Instruments are given for executing the program and writing the subroutine.

Optimization of the transmission of observable expectation values and observable statistics in continuous-variable teleportation

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

Albano Farias, L.; Stephany, J.

2010-12-15

We analyze the statistics of observables in continuous-variable (CV) quantum teleportation in the formalism of the characteristic function. We derive expressions for average values of output-state observables, in particular, cumulants which are additive in terms of the input state and the resource of teleportation. Working with a general class of teleportation resources, the squeezed-bell-like states, which may be optimized in a free parameter for better teleportation performance, we discuss the relation between resources optimal for fidelity and those optimal for different observable averages. We obtain the values of the free parameter of the squeezed-bell-like states which optimize the central momentamore » and cumulants up to fourth order. For the cumulants the distortion between in and out states due to teleportation depends only on the resource. We obtain optimal parameters {Delta}{sub (2)}{sup opt} and {Delta}{sub (4)}{sup opt} for the second- and fourth-order cumulants, which do not depend on the squeezing of the resource. The second-order central momenta, which are equal to the second-order cumulants, and the photon number average are also optimized by the resource with {Delta}{sub (2)}{sup opt}. We show that the optimal fidelity resource, which has been found previously to depend on the characteristics of input, approaches for high squeezing to the resource that optimizes the second-order momenta. A similar behavior is obtained for the resource that optimizes the photon statistics, which is treated here using the sum of the squared differences in photon probabilities of input versus output states as the distortion measure. This is interpreted naturally to mean that the distortions associated with second-order momenta dominate the behavior of the output state for large squeezing of the resource. Optimal fidelity resources and optimal photon statistics resources are compared, and it is shown that for mixtures of Fock states both resources are equivalent.« less

AutoBayes Program Synthesis System Users Manual

NASA Technical Reports Server (NTRS)

Schumann, Johann; Jafari, Hamed; Pressburger, Tom; Denney, Ewen; Buntine, Wray; Fischer, Bernd

2008-01-01

Program synthesis is the systematic, automatic construction of efficient executable code from high-level declarative specifications. AutoBayes is a fully automatic program synthesis system for the statistical data analysis domain; in particular, it solves parameter estimation problems. It has seen many successful applications at NASA and is currently being used, for example, to analyze simulation results for Orion. The input to AutoBayes is a concise description of a data analysis problem composed of a parameterized statistical model and a goal that is a probability term involving parameters and input data. The output is optimized and fully documented C/C++ code computing the values for those parameters that maximize the probability term. AutoBayes can solve many subproblems symbolically rather than having to rely on numeric approximation algorithms, thus yielding effective, efficient, and compact code. Statistical analysis is faster and more reliable, because effort can be focused on model development and validation rather than manual development of solution algorithms and code.

An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory.

PubMed

Peck, Jay; Oluwole, Oluwayemisi O; Wong, Hsi-Wu; Miake-Lye, Richard C

2013-03-01

To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. An algorithm to estimate the cruise condition black carbon emission index for commercial aircraft engines was developed. Using the ICAO certification data, the algorithm can evaluate the black carbon emission at given cruise altitude and speed.

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

PubMed

Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

2015-09-15

The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicted carbonation of existing concrete building based on the Indonesian tropical micro-climate

NASA Astrophysics Data System (ADS)

Hilmy, M.; Prabowo, H.

2018-03-01

This paper is aimed to predict the carbonation progress based on the previous mathematical model. It shortly explains the nature of carbonation including the processes and effects. Environmental humidity and temperature of the existing concrete building are measured and compared to data from local Meteorological, Climatological, and Geophysical Agency. The data gained are expressed in the form of annual hygrothermal values which will use as the input parameter in carbonation model. The physical properties of the observed building such as its location, dimensions, and structural material used are quantified. These data then utilized as an important input parameter for carbonation coefficients. The relationships between relative humidity and the rate of carbonation established. The results can provide a basis for repair and maintenance of existing concrete buildings and the sake of service life analysis of them.

Numerical simulations of flares on M dwarf stars. I - Hydrodynamics and coronal X-ray emission

NASA Technical Reports Server (NTRS)

Cheng, Chung-Chieh; Pallavicini, Roberto

1991-01-01

Flare-loop models are utilized to simulate the time evolution and physical characteristics of stellar X-ray flares by varying the values of flare-energy input and loop parameters. The hydrodynamic evolution is studied in terms of changes in the parameters of the mass, energy, and momentum equations within an area bounded by the chromosphere and the corona. The zone supports a magnetically confined loop for which processes are described including the expansion of heated coronal gas, chromospheric evaporation, and plasma compression at loop footpoints. The intensities, time profiles, and average coronal temperatures of X-ray flares are derived from the simulations and compared to observational evidence. Because the amount of evaporated material does not vary linearly with flare-energy input, large loops are required to produce the energy measured from stellar flares.

Utility of coupling nonlinear optimization methods with numerical modeling software

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

Murphy, M.J.

1996-08-05

Results of using GLO (Global Local Optimizer), a general purpose nonlinear optimization software package for investigating multi-parameter problems in science and engineering is discussed. The package consists of the modular optimization control system (GLO), a graphical user interface (GLO-GUI), a pre-processor (GLO-PUT), a post-processor (GLO-GET), and nonlinear optimization software modules, GLOBAL & LOCAL. GLO is designed for controlling and easy coupling to any scientific software application. GLO runs the optimization module and scientific software application in an iterative loop. At each iteration, the optimization module defines new values for the set of parameters being optimized. GLO-PUT inserts the new parametermore » values into the input file of the scientific application. GLO runs the application with the new parameter values. GLO-GET determines the value of the objective function by extracting the results of the analysis and comparing to the desired result. GLO continues to run the scientific application over and over until it finds the ``best`` set of parameters by minimizing (or maximizing) the objective function. An example problem showing the optimization of material model is presented (Taylor cylinder impact test).« less

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film.

PubMed

Chang, C T; Zeng, F; Li, X J; Dong, W S; Lu, S H; Gao, S; Pan, F

2016-01-07

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

PubMed Central

Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.

2016-01-01

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP. PMID:26739613

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

NASA Astrophysics Data System (ADS)

Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.

2016-01-01

The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.

Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters

PubMed Central

Wang, Hai-yi; Su, Zi-hua; Xu, Xiao; Sun, Zhi-peng; Duan, Fei-xue; Song, Yuan-yuan; Li, Lu; Wang, Ying-wei; Ma, Xin; Guo, Ai-tao; Ma, Lin; Ye, Hui-yi

2016-01-01

Pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been increasingly used to evaluate the permeability of tumor vessel. Histogram metrics are a recognized promising method of quantitative MR imaging that has been recently introduced in analysis of DCE-MRI pharmacokinetic parameters in oncology due to tumor heterogeneity. In this study, 21 patients with renal cell carcinoma (RCC) underwent paired DCE-MRI studies on a 3.0 T MR system. Extended Tofts model and population-based arterial input function were used to calculate kinetic parameters of RCC tumors. Mean value and histogram metrics (Mode, Skewness and Kurtosis) of each pharmacokinetic parameter were generated automatically using ImageJ software. Intra- and inter-observer reproducibility and scan–rescan reproducibility were evaluated using intra-class correlation coefficients (ICCs) and coefficient of variation (CoV). Our results demonstrated that the histogram method (Mode, Skewness and Kurtosis) was not superior to the conventional Mean value method in reproducibility evaluation on DCE-MRI pharmacokinetic parameters (K trans & Ve) in renal cell carcinoma, especially for Skewness and Kurtosis which showed lower intra-, inter-observer and scan-rescan reproducibility than Mean value. Our findings suggest that additional studies are necessary before wide incorporation of histogram metrics in quantitative analysis of DCE-MRI pharmacokinetic parameters. PMID:27380733

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Direct system parameter identification of mechanical structures with application to modal analysis

NASA Technical Reports Server (NTRS)

Leuridan, J. M.; Brown, D. L.; Allemang, R. J.

1982-01-01

In this paper a method is described to estimate mechanical structure characteristics in terms of mass, stiffness and damping matrices using measured force input and response data. The estimated matrices can be used to calculate a consistent set of damped natural frequencies and damping values, mode shapes and modal scale factors for the structure. The proposed technique is attractive as an experimental modal analysis method since the estimation of the matrices does not require previous estimation of frequency responses and since the method can be used, without any additional complications, for multiple force input structure testing.

Image segmentation algorithm based on improved PCNN

NASA Astrophysics Data System (ADS)

Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

2017-11-01

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

Constant-Elasticity-of-Substitution Simulation

NASA Technical Reports Server (NTRS)

Reiter, G.

1986-01-01

Program simulates constant elasticity-of-substitution (CES) production function. CES function used by economic analysts to examine production costs as well as uncertainties in production. User provides such input parameters as price of labor, price of capital, and dispersion levels. CES minimizes expected cost to produce capital-uncertainty pair. By varying capital-value input, one obtains series of capital-uncertainty pairs. Capital-uncertainty pairs then used to generate several cost curves. CES program menu driven and features specific print menu for examining selected output curves. Program written in BASIC for interactive execution and implemented on IBM PC-series computer.

A comparison of airborne wake vortex detection measurements with values predicted from potential theory

NASA Technical Reports Server (NTRS)

Stewart, Eric C.

1991-01-01

An analysis of flight measurements made near a wake vortex was conducted to explore the feasibility of providing a pilot with useful wake avoidance information. The measurements were made with relatively low cost flow and motion sensors on a light airplane flying near the wake vortex of a turboprop airplane weighing approximately 90000 lbs. Algorithms were developed which removed the response of the airplane to control inputs from the total airplane response and produced parameters which were due solely to the flow field of the vortex. These parameters were compared with values predicted by potential theory. The results indicated that the presence of the vortex could be detected by a combination of parameters derived from the simple sensors. However, the location and strength of the vortex cannot be determined without additional and more accurate sensors.

An online air pollution forecasting system using neural networks.

PubMed

Kurt, Atakan; Gulbagci, Betul; Karaca, Ferhat; Alagha, Omar

2008-07-01

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.

Approach for Input Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Taylor, Arthur C., III; Newman, Perry A.; Green, Lawrence L.

2002-01-01

An implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for quasi 3-D Euler CFD code is presented. Given uncertainties in statistically independent, random, normally distributed input variables, first- and second-order statistical moment procedures are performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, these moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

NASA Technical Reports Server (NTRS)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

Modeling of the radiation belt megnetosphere in decisional timeframes

DOEpatents

Koller, Josef; Reeves, Geoffrey D; Friedel, Reiner H.W.

2013-04-23

Systems and methods for calculating L* in the magnetosphere with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, and/or spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models. The feedforward neural network can have a plurality of input-layer units, there being at least one input-layer unit for each physics-based model parameter, a plurality of hidden layer units and at least one output unit for the value of L*.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

PubMed

Herzallah, Randa

2015-03-01

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of spatial and temporal aggregation of input parameters on the assessment of irrigation scheme performance

NASA Astrophysics Data System (ADS)

Lorite, I. J.; Mateos, L.; Fereres, E.

2005-01-01

SummaryThe simulations of dynamic, spatially distributed non-linear models are impacted by the degree of spatial and temporal aggregation of their input parameters and variables. This paper deals with the impact of these aggregations on the assessment of irrigation scheme performance by simulating water use and crop yield. The analysis was carried out on a 7000 ha irrigation scheme located in Southern Spain. Four irrigation seasons differing in rainfall patterns were simulated (from 1996/1997 to 1999/2000) with the actual soil parameters and with hypothetical soil parameters representing wider ranges of soil variability. Three spatial aggregation levels were considered: (I) individual parcels (about 800), (II) command areas (83) and (III) the whole irrigation scheme. Equally, five temporal aggregation levels were defined: daily, weekly, monthly, quarterly and annually. The results showed little impact of spatial aggregation in the predictions of irrigation requirements and of crop yield for the scheme. The impact of aggregation was greater in rainy years, for deep-rooted crops (sunflower) and in scenarios with heterogeneous soils. The highest impact on irrigation requirement estimations was in the scenario of most heterogeneous soil and in 1999/2000, a year with frequent rainfall during the irrigation season: difference of 7% between aggregation levels I and III was found. Equally, it was found that temporal aggregation had only significant impact on irrigation requirements predictions for time steps longer than 4 months. In general, simulated annual irrigation requirements decreased as the time step increased. The impact was greater in rainy years (specially with abundant and concentrated rain events) and in crops which cycles coincide in part with the rainy season (garlic, winter cereals and olive). It is concluded that in this case, average, representative values for the main inputs of the model (crop, soil properties and sowing dates) can generate results within 1% of those obtained by providing spatially specific values for about 800 parcels.

TAIR- TRANSONIC AIRFOIL ANALYSIS COMPUTER CODE

NASA Technical Reports Server (NTRS)

Dougherty, F. C.

1994-01-01

The Transonic Airfoil analysis computer code, TAIR, was developed to employ a fast, fully implicit algorithm to solve the conservative full-potential equation for the steady transonic flow field about an arbitrary airfoil immersed in a subsonic free stream. The full-potential formulation is considered exact under the assumptions of irrotational, isentropic, and inviscid flow. These assumptions are valid for a wide range of practical transonic flows typical of modern aircraft cruise conditions. The primary features of TAIR include: a new fully implicit iteration scheme which is typically many times faster than classical successive line overrelaxation algorithms; a new, reliable artifical density spatial differencing scheme treating the conservative form of the full-potential equation; and a numerical mapping procedure capable of generating curvilinear, body-fitted finite-difference grids about arbitrary airfoil geometries. Three aspects emphasized during the development of the TAIR code were reliability, simplicity, and speed. The reliability of TAIR comes from two sources: the new algorithm employed and the implementation of effective convergence monitoring logic. TAIR achieves ease of use by employing a "default mode" that greatly simplifies code operation, especially by inexperienced users, and many useful options including: several airfoil-geometry input options, flexible user controls over program output, and a multiple solution capability. The speed of the TAIR code is attributed to the new algorithm and the manner in which it has been implemented. Input to the TAIR program consists of airfoil coordinates, aerodynamic and flow-field convergence parameters, and geometric and grid convergence parameters. The airfoil coordinates for many airfoil shapes can be generated in TAIR from just a few input parameters. Most of the other input parameters have default values which allow the user to run an analysis in the default mode by specifing only a few input parameters. Output from TAIR may include aerodynamic coefficients, the airfoil surface solution, convergence histories, and printer plots of Mach number and density contour maps. The TAIR program is written in FORTRAN IV for batch execution and has been implemented on a CDC 7600 computer with a central memory requirement of approximately 155K (octal) of 60 bit words. The TAIR program was developed in 1981.

Uncertainty quantification of Antarctic contribution to sea-level rise using the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model

NASA Astrophysics Data System (ADS)

Bulthuis, Kevin; Arnst, Maarten; Pattyn, Frank; Favier, Lionel

2017-04-01

Uncertainties in sea-level rise projections are mostly due to uncertainties in Antarctic ice-sheet predictions (IPCC AR5 report, 2013), because key parameters related to the current state of the Antarctic ice sheet (e.g. sub-ice-shelf melting) and future climate forcing are poorly constrained. Here, we propose to improve the predictions of Antarctic ice-sheet behaviour using new uncertainty quantification methods. As opposed to ensemble modelling (Bindschadler et al., 2013) which provides a rather limited view on input and output dispersion, new stochastic methods (Le Maître and Knio, 2010) can provide deeper insight into the impact of uncertainties on complex system behaviour. Such stochastic methods usually begin with deducing a probabilistic description of input parameter uncertainties from the available data. Then, the impact of these input parameter uncertainties on output quantities is assessed by estimating the probability distribution of the outputs by means of uncertainty propagation methods such as Monte Carlo methods or stochastic expansion methods. The use of such uncertainty propagation methods in glaciology may be computationally costly because of the high computational complexity of ice-sheet models. This challenge emphasises the importance of developing reliable and computationally efficient ice-sheet models such as the f.ETISh ice-sheet model (Pattyn, 2015), a new fast thermomechanical coupled ice sheet/ice shelf model capable of handling complex and critical processes such as the marine ice-sheet instability mechanism. Here, we apply these methods to investigate the role of uncertainties in sub-ice-shelf melting, calving rates and climate projections in assessing Antarctic contribution to sea-level rise for the next centuries using the f.ETISh model. We detail the methods and show results that provide nominal values and uncertainty bounds for future sea-level rise as a reflection of the impact of the input parameter uncertainties under consideration, as well as a ranking of the input parameter uncertainties in the order of the significance of their contribution to uncertainty in future sea-level rise. In addition, we discuss how limitations posed by the available information (poorly constrained data) pose challenges that motivate our current research.

Practical input optimization for aircraft parameter estimation experiments. Ph.D. Thesis, 1990

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1993-01-01

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

2D dynamic studies combined with the surface curvature analysis to predict Arias Intensity amplification

NASA Astrophysics Data System (ADS)

Torgoev, Almaz; Havenith, Hans-Balder

2016-07-01

A 2D elasto-dynamic modelling of the pure topographic seismic response is performed for six models with a total length of around 23.0 km. These models are reconstructed from the real topographic settings of the landslide-prone slopes situated in the Mailuu-Suu River Valley, Southern Kyrgyzstan. The main studied parameter is the Arias Intensity (Ia, m/sec), which is applied in the GIS-based Newmark method to regionally map the seismically-induced landslide susceptibility. This method maps the Ia values via empirical attenuation laws and our studies investigate a potential to include topographic input into them. Numerical studies analyse several signals with varying shape and changing central frequency values. All tests demonstrate that the spectral amplification patterns directly affect the amplification of the Ia values. These results let to link the 2D distribution of the topographically amplified Ia values with the parameter called as smoothed curvature. The amplification values for the low-frequency signals are better correlated with the curvature smoothed over larger spatial extent, while those values for the high-frequency signals are more linked to the curvature with smaller smoothing extent. The best predictions are provided by the curvature smoothed over the extent calculated according to Geli's law. The sample equations predicting the Ia amplification based on the smoothed curvature are presented for the sinusoid-shape input signals. These laws cannot be directly implemented in the regional Newmark method, as 3D amplification of the Ia values addresses more problem complexities which are not studied here. Nevertheless, our 2D results prepare the theoretical framework which can potentially be applied to the 3D domain and, therefore, represent a robust basis for these future research targets.

Off-line tracking of series parameters in distribution systems using AMI data

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

Williams, Tess L.; Sun, Yannan; Schneider, Kevin

2016-05-01

Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimationmore » procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.« less

An automatic scaling method for obtaining the trace and parameters from oblique ionogram based on hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

Song, Huan; Hu, Yaogai; Jiang, Chunhua; Zhou, Chen; Zhao, Zhengyu; Zou, Xianjian

2016-12-01

Scaling oblique ionogram plays an important role in obtaining ionospheric structure at the midpoint of oblique sounding path. The paper proposed an automatic scaling method to extract the trace and parameters of oblique ionogram based on hybrid genetic algorithm (HGA). The extracted 10 parameters come from F2 layer and Es layer, such as maximum observation frequency, critical frequency, and virtual height. The method adopts quasi-parabolic (QP) model to describe F2 layer's electron density profile that is used to synthesize trace. And it utilizes secant theorem, Martyn's equivalent path theorem, image processing technology, and echoes' characteristics to determine seven parameters' best fit values, and three parameter's initial values in QP model to set up their searching spaces which are the needed input data of HGA. Then HGA searches the three parameters' best fit values from their searching spaces based on the fitness between the synthesized trace and the real trace. In order to verify the performance of the method, 240 oblique ionograms are scaled and their results are compared with manual scaling results and the inversion results of the corresponding vertical ionograms. The comparison results show that the scaling results are accurate or at least adequate 60-90% of the time.

Methods for Combining Payload Parameter Variations with Input Environment

NASA Technical Reports Server (NTRS)

Merchant, D. H.; Straayer, J. W.

1975-01-01

Methods are presented for calculating design limit loads compatible with probabilistic structural design criteria. The approach is based on the concept that the desired limit load, defined as the largest load occuring in a mission, is a random variable having a specific probability distribution which may be determined from extreme-value theory. The design limit load, defined as a particular value of this random limit load, is the value conventionally used in structural design. Methods are presented for determining the limit load probability distributions from both time-domain and frequency-domain dynamic load simulations. Numerical demonstrations of the methods are also presented.

A new method of building footprints detection using airborne laser scanning data and multispectral image

NASA Astrophysics Data System (ADS)

Luo, Yiping; Jiang, Ting; Gao, Shengli; Wang, Xin

2010-10-01

It presents a new approach for detecting building footprints in a combination of registered aerial image with multispectral bands and airborne laser scanning data synchronously obtained by Leica-Geosystems ALS40 and Applanix DACS-301 on the same platform. A two-step method for building detection was presented consisting of selecting 'building' candidate points and then classifying candidate points. A digital surface model(DSM) derived from last pulse laser scanning data was first filtered and the laser points were classified into classes 'ground' and 'building or tree' based on mathematic morphological filter. Then, 'ground' points were resample into digital elevation model(DEM), and a Normalized DSM(nDSM) was generated from DEM and DSM. The candidate points were selected from 'building or tree' points by height value and area threshold in nDSM. The candidate points were further classified into building points and tree points by using the support vector machines(SVM) classification method. Two classification tests were carried out using features only from laser scanning data and associated features from two input data sources. The features included height, height finite difference, RGB bands value, and so on. The RGB value of points was acquired by matching laser scanning data and image using collinear equation. The features of training points were presented as input data for SVM classification method, and cross validation was used to select best classification parameters. The determinant function could be constructed by the classification parameters and the class of candidate points was determined by determinant function. The result showed that associated features from two input data sources were superior to features only from laser scanning data. The accuracy of more than 90% was achieved for buildings in first kind of features.

GNSS derived TEC data ingestion into IRI 2012

NASA Astrophysics Data System (ADS)

Migoya-Orué, Yenca; Nava, Bruno; Radicella, Sandro; Alazo-Cuartas, Katy

2015-04-01

Experimental vertical total electron content (VTEC) data given by Global Ionospheric Maps (GIM) has been ingested into the IRI version 2012, aiming to obtain grids of effective input parameter values that allow to minimize the difference between the experimental and modeled vertical TEC. Making use of the experience gained with the technique of model adaptation applied to NeQuick (Nava et al., 2005), it has been found possible to compute IRI world grids of effective ionosphere index parameters (IG). The IG grids thus obtained can be interpolated in space and time to calculate with IRI the 3D electron density at any location and also the TEC along any ground-to-satellite ray-path for a given epoch. In this study, the ingestion technique is presented and a posteriori validation, along with an assessment of the capability of the 'ingested' IRI to reproduce the ionosphere day-to-day foF2 variability during disturbed and quiet periods. The foF2 values retrieved are compared with data from about 20 worldwide ionosondes for selected periods of high (year 2000) and moderate to low solar activity (year 2006). It was found that the use of the ingestion scheme enhances the performance of the model when compared with its standard use based on solar activity drivers (R12 and F10.7), especially for high solar activity. As an example, the mean and standard deviation of the differences between experimental and reconstructed F2-peak values for April of year 2000 is 0.09 and 1.28 MHz for ingested IRI, compared to -0.81 and 1.27 MHz (IRI with R12 input) and -0.02 and 1.46 MHz (IRI with F10.7 input).

An alternate protocol to achieve stochastic and deterministic resonances

NASA Astrophysics Data System (ADS)

Tiwari, Ishant; Dave, Darshil; Phogat, Richa; Khera, Neev; Parmananda, P.

2017-10-01

Periodic and Aperiodic Stochastic Resonance (SR) and Deterministic Resonance (DR) are studied in this paper. To check for the ubiquitousness of the phenomena, two unrelated systems, namely, FitzHugh-Nagumo and a particle in a bistable potential well, are studied. Instead of the conventional scenario of noise amplitude (in the case of SR) or chaotic signal amplitude (in the case of DR) variation, a tunable system parameter ("a" in the case of FitzHugh-Nagumo model and the damping coefficient "j" in the bistable model) is regulated. The operating values of these parameters are defined as the "setpoint" of the system throughout the present work. Our results indicate that there exists an optimal value of the setpoint for which maximum information transfer between the input and the output signals takes place. This information transfer from the input sub-threshold signal to the output dynamics is quantified by the normalised cross-correlation coefficient ( | CCC | ). | CCC | as a function of the setpoint exhibits a unimodal variation which is characteristic of SR (or DR). Furthermore, | CCC | is computed for a grid of noise (or chaotic signal) amplitude and setpoint values. The heat map of | CCC | over this grid yields the presence of a resonance region in the noise-setpoint plane for which the maximum enhancement of the input sub-threshold signal is observed. This resonance region could be possibly used to explain how organisms maintain their signal detection efficacy with fluctuating amounts of noise present in their environment. Interestingly, the method of regulating the setpoint without changing the noise amplitude was not able to induce Coherence Resonance (CR). A possible, qualitative reasoning for this is provided.

Effect of pore water velocities and solute input methods on chloride transport in the undisturbed soil columns of Loess Plateau

NASA Astrophysics Data System (ADS)

Zhou, BeiBei; Wang, QuanJiu

2017-09-01

Studies on solute transport under different pore water velocity and solute input methods in undisturbed soil could play instructive roles for crop production. Based on the experiments in the laboratory, the effect of solute input methods with small pulse input and large pulse input, as well as four pore water velocities, on chloride transport in the undisturbed soil columns obtained from the Loess Plateau under controlled condition was studied. Chloride breakthrough curves (BTCs) were generated using the miscible displacement method under water-saturated, steady flow conditions. Using the 0.15 mol L-1 CaCl2 solution as a tracer, a small pulse (0.1 pore volumes) was first induced, and then, after all the solution was wash off, a large pulse (0.5 pore volumes) was conducted. The convection-dispersion equation (CDE) and the two-region model (T-R) were used to describe the BTCs, and their prediction accuracies and fitted parameters were compared as well. All the BTCs obtained for the different input methods and the four pore water velocities were all smooth. However, the shapes of the BTCs varied greatly; small pulse inputs resulted in more rapid attainment of peak values that appeared earlier with increases in pore water velocity, whereas large pulse inputs resulted in an opposite trend. Both models could fit the experimental data well, but the prediction accuracy of the T-R was better. The values of the dispersivity, λ, calculated from the dispersion coefficient obtained from the CDE were about one order of magnitude larger than those calculated from the dispersion coefficient given by the T-R, but the calculated Peclet number, Pe, was lower. The mobile-immobile partition coefficient, β, decreased, while the mass exchange coefficient increased with increases in pore water velocity.

Predictive control of thermal state of blast furnace

NASA Astrophysics Data System (ADS)

Barbasova, T. A.; Filimonova, A. A.

2018-05-01

The work describes the structure of the model for predictive control of the thermal state of a blast furnace. The proposed model contains the following input parameters: coke rate; theoretical combustion temperature, comprising: natural gas consumption, blasting temperature, humidity, oxygen, blast furnace cooling water; blast furnace gas utilization rate. The output parameter is the cast iron temperature. The results for determining the cast iron temperature were obtained following the identification using the Hammerstein-Wiener model. The result of solving the cast iron temperature stabilization problem was provided for the calculated values of process parameters of the target area of the respective blast furnace operation mode.

Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-04-01

This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO 4 3- , which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R 2  ≥ 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.

TU-FG-209-04: Testing of Digital Image Receptors Using AAPM TG-150’s Draft Recommendations - Investigating the Impact of Different Processing Parameters

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

Finley, C; Dave, J

Purpose: To evaluate implementation of AAPM TG-150’s draft recommendations via a parameter study for testing the performance of digital image receptors. Methods: Flat field images were acquired from 9 calibrated digital image receptors associated with 9 new portable digital radiography systems (Carestream Health, Inc.) based on the draft recommendations and manufacturer-specified calibration conditions (set of 4 images at input detector air kerma ranging from 1 to 25 µGy). Effects of exposure response function (linearized and logarithmic), ‘Presentation Intent Type’ (‘For Processing’ and ‘For Presentation’), detector orientation with respect to the anode-cathode axis (4 orientations; 900 rotations per iteration), different ROImore » sizes (5×5–40×40 mm{sup 2}) and elimination of varying dimensions of image border (0 mm i.e., without boundary elimination to 150 mm) on signal, noise, signal-to-noise ratio (SNR) and the associated nonuniformities were evaluated. Images were analyzed in Matlab and quantities were compared using ANOVA. Results: Signal, noise and SNR values averaged over 9 systems with default parameter values in draft recommendations were 4837.2±139.4, 19.7±0.9 and 246.4±10.1 (mean ± standard deviation), respectively (at input detector air kerma: 12.5 µGy). Signal, noise and SNR showed characteristic dependency on exposure response function and on ‘Presentation Intent Type’. These values were not affected by ROI size and detector orientation, but analysis showed that eliminating the edge pixels along the boundary was required for the noise parameter (coefficient of variation range for noise: 72%–106% and 3%–4% without and with boundary elimination; respectively). Local and global nonuniformities showed a similar dependence on the need for boundary elimination. Interestingly, computed non-uniformities showed agreement with manufacturer-reported values except for noise non-uniformities in two units; artifacts were seen in images from these two units highlighting the importance of independent evaluations. Conclusion: The effect of different parameters on performance characterization of digital image receptors was evaluated based on TG-150’s draft recommendations.« less

Evaluation of calibration efficacy under different levels of uncertainty

DOE PAGES

Heo, Yeonsook; Graziano, Diane J.; Guzowski, Leah; ...

2014-06-10

This study examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty.We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data withmore » differing levels of detail in building design, usage, and operation.« less

Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture

NASA Astrophysics Data System (ADS)

Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang

2016-12-01

In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.

A Predictor Analysis Framework for Surface Radiation Budget Reprocessing Using Design of Experiments

NASA Astrophysics Data System (ADS)

Quigley, Patricia Allison

Earth's Radiation Budget (ERB) is an accounting of all incoming energy from the sun and outgoing energy reflected and radiated to space by earth's surface and atmosphere. The National Aeronautics and Space Administration (NASA)/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project produces and archives long-term datasets representative of this energy exchange system on a global scale. The data are comprised of the longwave and shortwave radiative components of the system and is algorithmically derived from satellite and atmospheric assimilation products, and acquired atmospheric data. It is stored as 3-hourly, daily, monthly/3-hourly, and monthly averages of 1° x 1° grid cells. Input parameters used by the algorithms are a key source of variability in the resulting output data sets. Sensitivity studies have been conducted to estimate the effects this variability has on the output data sets using linear techniques. This entails varying one input parameter at a time while keeping all others constant or by increasing all input parameters by equal random percentages, in effect changing input values for every cell for every three hour period and for every day in each month. This equates to almost 11 million independent changes without ever taking into consideration the interactions or dependencies among the input parameters. A more comprehensive method is proposed here for the evaluating the shortwave algorithm to identify both the input parameters and parameter interactions that most significantly affect the output data. This research utilized designed experiments that systematically and simultaneously varied all of the input parameters of the shortwave algorithm. A D-Optimal design of experiments (DOE) was chosen to accommodate the 14 types of atmospheric properties computed by the algorithm and to reduce the number of trials required by a full factorial study from millions to 128. A modified version of the algorithm was made available for testing such that global calculations of the algorithm were tuned to accept information for a single temporal and spatial point and for one month of averaged data. The points were from each of four atmospherically distinct regions to include the Amazon Rainforest, Sahara Desert, Indian Ocean and Mt. Everest. The same design was used for all of the regions. Least squares multiple regression analysis of the results of the modified algorithm identified those parameters and parameter interactions that most significantly affected the output products. It was found that Cosine solar zenith angle was the strongest influence on the output data in all four regions. The interaction of Cosine Solar Zenith Angle and Cloud Fraction had the strongest influence on the output data in the Amazon, Sahara Desert and Mt. Everest Regions, while the interaction of Cloud Fraction and Cloudy Shortwave Radiance most significantly affected output data in the Indian Ocean region. Second order response models were built using the resulting regression coefficients. A Monte Carlo simulation of each model extended the probability distribution beyond the initial design trials to quantify variability in the modeled output data.

Display device for indicating the value of a parameter in a process plant

DOEpatents

Scarola, Kenneth; Jamison, David S.; Manazir, Richard M.; Rescorl, Robert L.; Harmon, Daryl L.

1993-01-01

An advanced control room complex for a nuclear power plant, including a discrete indicator and alarm system (72) which is nuclear qualified for rapid response to changes in plant parameters and a component control system (64) which together provide a discrete monitoring and control capability at a panel (14-22, 26, 28) in the control room (10). A separate data processing system (70), which need not be nuclear qualified, provides integrated and overview information to the control room and to each panel, through CRTs (84) and a large, overhead integrated process status overview board (24). The discrete indicator and alarm system (72) and the data processing system (70) receive inputs from common plant sensors and validate the sensor outputs to arrive at a representative value of the parameter for use by the operator during both normal and accident conditions, thereby avoiding the need for him to assimilate data from each sensor individually. The integrated process status board (24) is at the apex of an information hierarchy that extends through four levels and provides access at each panel to the full display hierarchy. The control room panels are preferably of a modular construction, permitting the definition of inputs and outputs, the man machine interface, and the plant specific algorithms, to proceed in parallel with the fabrication of the panels, the installation of the equipment and the generic testing thereof.

Wake Vortex Inverse Model User's Guide

NASA Technical Reports Server (NTRS)

Lai, David; Delisi, Donald

2008-01-01

NorthWest Research Associates (NWRA) has developed an inverse model for inverting landing aircraft vortex data. The data used for the inversion are the time evolution of the lateral transport position and vertical position of both the port and starboard vortices. The inverse model performs iterative forward model runs using various estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Forward model predictions of lateral transport and altitude are then compared with the observed data. Differences between the data and model predictions guide the choice of vortex parameter values, crosswind profile and circulation evolution in the next iteration. Iterations are performed until a user-defined criterion is satisfied. Currently, the inverse model is set to stop when the improvement in the rms deviation between the data and model predictions is less than 1 percent for two consecutive iterations. The forward model used in this inverse model is a modified version of the Shear-APA model. A detailed description of this forward model, the inverse model, and its validation are presented in a different report (Lai, Mellman, Robins, and Delisi, 2007). This document is a User's Guide for the Wake Vortex Inverse Model. Section 2 presents an overview of the inverse model program. Execution of the inverse model is described in Section 3. When executing the inverse model, a user is requested to provide the name of an input file which contains the inverse model parameters, the various datasets, and directories needed for the inversion. A detailed description of the list of parameters in the inversion input file is presented in Section 4. A user has an option to save the inversion results of each lidar track in a mat-file (a condensed data file in Matlab format). These saved mat-files can be used for post-inversion analysis. A description of the contents of the saved files is given in Section 5. An example of an inversion input file, with preferred parameters values, is given in Appendix A. An example of the plot generated at a normal completion of the inversion is shown in Appendix B.

Exemplifying the Effects of Parameterization Shortcomings in the Numerical Simulation of Geological Energy and Mass Storage

NASA Astrophysics Data System (ADS)

Dethlefsen, Frank; Tilmann Pfeiffer, Wolf; Schäfer, Dirk

2016-04-01

Numerical simulations of hydraulic, thermal, geomechanical, or geochemical (THMC-) processes in the subsurface have been conducted for decades. Often, such simulations are commenced by applying a parameter set that is as realistic as possible. Then, a base scenario is calibrated on field observations. Finally, scenario simulations can be performed, for instance to forecast the system behavior after varying input data. In the context of subsurface energy and mass storage, however, these model calibrations based on field data are often not available, as these storage actions have not been carried out so far. Consequently, the numerical models merely rely on the parameter set initially selected, and uncertainties as a consequence of a lack of parameter values or process understanding may not be perceivable, not mentioning quantifiable. Therefore, conducting THMC simulations in the context of energy and mass storage deserves a particular review of the model parameterization with its input data, and such a review so far hardly exists to the required extent. Variability or aleatory uncertainty exists for geoscientific parameter values in general, and parameters for that numerous data points are available, such as aquifer permeabilities, may be described statistically thereby exhibiting statistical uncertainty. In this case, sensitivity analyses for quantifying the uncertainty in the simulation resulting from varying this parameter can be conducted. There are other parameters, where the lack of data quantity and quality implies a fundamental changing of ongoing processes when such a parameter value is varied in numerical scenario simulations. As an example for such a scenario uncertainty, varying the capillary entry pressure as one of the multiphase flow parameters can either allow or completely inhibit the penetration of an aquitard by gas. As the last example, the uncertainty of cap-rock fault permeabilities and consequently potential leakage rates of stored gases into shallow compartments are regarded as recognized ignorance by the authors of this study, as no realistic approach exists to determine this parameter and values are best guesses only. In addition to these aleatory uncertainties, an equivalent classification is possible for rating epistemic uncertainties describing the degree of understanding processes such as the geochemical and hydraulic effects following potential gas intrusions from deeper reservoirs into shallow aquifers. As an outcome of this grouping of uncertainties, prediction errors of scenario simulations can be calculated by sensitivity analyses, if the uncertainties are identified as statistical. However, if scenario uncertainties exist or even recognized ignorance has to be attested to a parameter or a process in question, the outcomes of simulations mainly depend on the decision of the modeler by choosing parameter values or by interpreting the occurring of processes. In that case, the informative value of numerical simulations is limited by ambiguous simulation results, which cannot be refined without improving the geoscientific database through laboratory or field studies on a longer term basis, so that the effects of the subsurface use may be predicted realistically. This discussion, amended by a compilation of available geoscientific data to parameterize such simulations, will be presented in this study.

SIFT optimization and automation for matching images from multiple temporal sources

NASA Astrophysics Data System (ADS)

Castillo-Carrión, Sebastián; Guerrero-Ginel, José-Emilio

2017-05-01

Scale Invariant Feature Transformation (SIFT) was applied to extract tie-points from multiple source images. Although SIFT is reported to perform reliably under widely different radiometric and geometric conditions, using the default input parameters resulted in too few points being found. We found that the best solution was to focus on large features as these are more robust and not prone to scene changes over time, which constitutes a first approach to the automation of processes using mapping applications such as geometric correction, creation of orthophotos and 3D models generation. The optimization of five key SIFT parameters is proposed as a way of increasing the number of correct matches; the performance of SIFT is explored in different images and parameter values, finding optimization values which are corroborated using different validation imagery. The results show that the optimization model improves the performance of SIFT in correlating multitemporal images captured from different sources.

Users manual for updated computer code for axial-flow compressor conceptual design

NASA Technical Reports Server (NTRS)

Glassman, Arthur J.

1992-01-01

An existing computer code that determines the flow path for an axial-flow compressor either for a given number of stages or for a given overall pressure ratio was modified for use in air-breathing engine conceptual design studies. This code uses a rapid approximate design methodology that is based on isentropic simple radial equilibrium. Calculations are performed at constant-span-fraction locations from tip to hub. Energy addition per stage is controlled by specifying the maximum allowable values for several aerodynamic design parameters. New modeling was introduced to the code to overcome perceived limitations. Specific changes included variable rather than constant tip radius, flow path inclination added to the continuity equation, input of mass flow rate directly rather than indirectly as inlet axial velocity, solution for the exact value of overall pressure ratio rather than for any value that met or exceeded it, and internal computation of efficiency rather than the use of input values. The modified code was shown to be capable of computing efficiencies that are compatible with those of five multistage compressors and one fan that were tested experimentally. This report serves as a users manual for the revised code, Compressor Spanline Analysis (CSPAN). The modeling modifications, including two internal loss correlations, are presented. Program input and output are described. A sample case for a multistage compressor is included.

Investigation on Effect of Material Hardness in High Speed CNC End Milling Process.

PubMed

Dhandapani, N V; Thangarasu, V S; Sureshkannan, G

2015-01-01

This research paper analyzes the effects of material properties on surface roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface roughness characteristics of the precision machining part. The prediction method suggested is based on various experimental analysis of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The results show a basis for selection of parameters to get better results of surface roughness values as predicted by the case study results.

Investigation on Effect of Material Hardness in High Speed CNC End Milling Process

PubMed Central

Dhandapani, N. V.; Thangarasu, V. S.; Sureshkannan, G.

2015-01-01

This research paper analyzes the effects of material properties on surface roughness, material removal rate, and tool wear on high speed CNC end milling process with various ferrous and nonferrous materials. The challenge of material specific decision on the process parameters of spindle speed, feed rate, depth of cut, coolant flow rate, cutting tool material, and type of coating for the cutting tool for required quality and quantity of production is addressed. Generally, decision made by the operator on floor is based on suggested values of the tool manufacturer or by trial and error method. This paper describes effect of various parameters on the surface roughness characteristics of the precision machining part. The prediction method suggested is based on various experimental analysis of parameters in different compositions of input conditions which would benefit the industry on standardization of high speed CNC end milling processes. The results show a basis for selection of parameters to get better results of surface roughness values as predicted by the case study results. PMID:26881267

Application of a statistical emulator to fire emission modeling

Treesearch

Marwan Katurji; Jovanka Nikolic; Shiyuan Zhong; Scott Pratt; Lejiang Yu; Warren E. Heilman

2015-01-01

We have demonstrated the use of an advanced Gaussian-Process (GP) emulator to estimate wildland fire emissions over a wide range of fuel and atmospheric conditions. The Fire Emission Production Simulator, or FEPS, is used to produce an initial set of emissions data that correspond to some selected values in the domain of the input fuel and atmospheric parameters for...

Maximum likelihood identification and optimal input design for identifying aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Stepner, D. E.; Mehra, R. K.

1973-01-01

A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.

Environment Modeling Using Runtime Values for JPF-Android

NASA Technical Reports Server (NTRS)

van der Merwe, Heila; Tkachuk, Oksana; Nel, Seal; van der Merwe, Brink; Visser, Willem

2015-01-01

Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

GCAM 3.0 Agriculture and Land Use: Data Sources and Methods

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

Kyle, G. Page; Luckow, Patrick; Calvin, Katherine V.

This report presents the data processing methods used in the GCAM 3.0 agriculture and land use component, starting from all source data used, and detailing all calculations and assumptions made in generating the model inputs. The report starts with a brief introduction to modeling of agriculture and land use in GCAM 3.0, and then provides documentation of the data and methods used for generating the base-year dataset and future scenario parameters assumed in the model input files. Specifically, the report addresses primary commodity production, secondary (animal) commodity production, disposition of commodities, land allocation, land carbon contents, and land values.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Bayesian inference to identify parameters in viscoelasticity

NASA Astrophysics Data System (ADS)

Rappel, Hussein; Beex, Lars A. A.; Bordas, Stéphane P. A.

2017-08-01

This contribution discusses Bayesian inference (BI) as an approach to identify parameters in viscoelasticity. The aims are: (i) to show that the prior has a substantial influence for viscoelasticity, (ii) to show that this influence decreases for an increasing number of measurements and (iii) to show how different types of experiments influence the identified parameters and their uncertainties. The standard linear solid model is the material description of interest and a relaxation test, a constant strain-rate test and a creep test are the tensile experiments focused on. The experimental data are artificially created, allowing us to make a one-to-one comparison between the input parameters and the identified parameter values. Besides dealing with the aforementioned issues, we believe that this contribution forms a comprehensible start for those interested in applying BI in viscoelasticity.

Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

NASA Astrophysics Data System (ADS)

Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul

2018-03-01

This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02-0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.

Comparison of interplanetary CME arrival times and solar wind parameters based on the WSA-ENLIL model with three cone types and observations

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.-J.; Lee, Jae-Ok; Na, Hyeonock

2014-09-01

We have made a comparison between coronal mass ejection (CME)-associated shock propagations based on the Wang-Sheeley-Arge (WSA)-ENLIL model using three cone types and in situ observations. For this we use 28 full-halo CMEs, whose cone parameters are determined and their corresponding interplanetary shocks were observed at the Earth, from 2001 to 2002. We consider three different cone types (an asymmetric cone model, an ice cream cone model, and an elliptical cone model) to determine 3-D CME cone parameters (radial velocity, angular width, and source location), which are the input values of the WSA-ENLIL model. The mean absolute error of the CME-associated shock travel times for the WSA-ENLIL model using the ice-cream cone model is 9.9 h, which is about 1 h smaller than those of the other models. We compare the peak values and profiles of solar wind parameters (speed and density) with in situ observations. We find that the root-mean-square errors of solar wind peak speed and density for the ice cream and asymmetric cone model are about 190 km/s and 24/cm3, respectively. We estimate the cross correlations between the models and observations within the time lag of ± 2 days from the shock travel time. The correlation coefficients between the solar wind speeds from the WSA-ENLIL model using three cone types and in situ observations are approximately 0.7, which is larger than those of solar wind density (cc ˜0.6). Our preliminary investigations show that the ice cream cone model seems to be better than the other cone models in terms of the input parameters of the WSA-ENLIL model.

Direct statistical modeling and its implications for predictive mapping in mining exploration

NASA Astrophysics Data System (ADS)

Sterligov, Boris; Gumiaux, Charles; Barbanson, Luc; Chen, Yan; Cassard, Daniel; Cherkasov, Sergey; Zolotaya, Ludmila

2010-05-01

Recent advances in geosciences make more and more multidisciplinary data available for mining exploration. This allowed developing methodologies for computing forecast ore maps from the statistical combination of such different input parameters, all based on an inverse problem theory. Numerous statistical methods (e.g. algebraic method, weight of evidence, Siris method, etc) with varying degrees of complexity in their development and implementation, have been proposed and/or adapted for ore geology purposes. In literature, such approaches are often presented through applications on natural examples and the results obtained can present specificities due to local characteristics. Moreover, though crucial for statistical computations, "minimum requirements" needed for input parameters (number of minimum data points, spatial distribution of objects, etc) are often only poorly expressed. From these, problems often arise when one has to choose between one and the other method for her/his specific question. In this study, a direct statistical modeling approach is developed in order to i) evaluate the constraints on the input parameters and ii) test the validity of different existing inversion methods. The approach particularly focused on the analysis of spatial relationships between location of points and various objects (e.g. polygons and /or polylines) which is particularly well adapted to constrain the influence of intrusive bodies - such as a granite - and faults or ductile shear-zones on spatial location of ore deposits (point objects). The method is designed in a way to insure a-dimensionality with respect to scale. In this approach, both spatial distribution and topology of objects (polygons and polylines) can be parametrized by the user (e.g. density of objects, length, surface, orientation, clustering). Then, the distance of points with respect to a given type of objects (polygons or polylines) is given using a probability distribution. The location of points is computed assuming either independency or different grades of dependency between the two probability distributions. The results show that i)polygons surface mean value, polylines length mean value, the number of objects and their clustering are critical and ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used. In addition, this combined approach of direct and inverse modeling offers an opportunity to test the robustness of the inferred distribution point laws with respect to the quality of the input data set.

Incorporation of MRI-AIF Information For Improved Kinetic Modelling of Dynamic PET Data

NASA Astrophysics Data System (ADS)

Sari, Hasan; Erlandsson, Kjell; Thielemans, Kris; Atkinson, David; Ourselin, Sebastien; Arridge, Simon; Hutton, Brian F.

2015-06-01

In the analysis of dynamic PET data, compartmental kinetic analysis methods require an accurate knowledge of the arterial input function (AIF). Although arterial blood sampling is the gold standard of the methods used to measure the AIF, it is usually not preferred as it is an invasive method. An alternative method is the simultaneous estimation method (SIME), where physiological parameters and the AIF are estimated together, using information from different anatomical regions. Due to the large number of parameters to estimate in its optimisation, SIME is a computationally complex method and may sometimes fail to give accurate estimates. In this work, we try to improve SIME by utilising an input function derived from a simultaneously obtained DSC-MRI scan. With the assumption that the true value of one of the six parameter PET-AIF model can be derived from an MRI-AIF, the method is tested using simulated data. The results indicate that SIME can yield more robust results when the MRI information is included with a significant reduction in absolute bias of Ki estimates.

Reconstructing solar magnetic fields from historical observations. II. Testing the surface flux transport model

NASA Astrophysics Data System (ADS)

Virtanen, I. O. I.; Virtanen, I. I.; Pevtsov, A. A.; Yeates, A.; Mursula, K.

2017-07-01

Aims: We aim to use the surface flux transport model to simulate the long-term evolution of the photospheric magnetic field from historical observations. In this work we study the accuracy of the model and its sensitivity to uncertainties in its main parameters and the input data. Methods: We tested the model by running simulations with different values of meridional circulation and supergranular diffusion parameters, and studied how the flux distribution inside active regions and the initial magnetic field affected the simulation. We compared the results to assess how sensitive the simulation is to uncertainties in meridional circulation speed, supergranular diffusion, and input data. We also compared the simulated magnetic field with observations. Results: We find that there is generally good agreement between simulations and observations. Although the model is not capable of replicating fine details of the magnetic field, the long-term evolution of the polar field is very similar in simulations and observations. Simulations typically yield a smoother evolution of polar fields than observations, which often include artificial variations due to observational limitations. We also find that the simulated field is fairly insensitive to uncertainties in model parameters or the input data. Due to the decay term included in the model the effects of the uncertainties are somewhat minor or temporary, lasting typically one solar cycle.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

1997-01-01

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

Effect of Pin Length on Hook Size and Joint Properties in Friction Stir Lap Welding of 7B04 Aluminum Alloy

NASA Astrophysics Data System (ADS)

Wang, Min; Zhang, Huijie; Zhang, Jingbao; Zhang, Xiao; Yang, Lei

2014-05-01

Friction stir lap welding of 7B04 aluminum alloy was conducted in the present paper, and the effect of pin length on hook size and joint properties was investigated in detail. It is found that for each given set of process parameters, the size of hook defect on the advancing side shows an "M" type evolution trend as the pin length is increased. The affecting characteristics of pin length on joint properties are dependent on the heat input levels. When the heat input is low, the fracture strength is firstly increased to a peak value and then shows a decrease. When the heat input is relatively high, the evolution trend of fracture strength tends to exhibit a "W" type with increasing the pin length.

Dynamic control of remelting processes

DOEpatents

Bertram, Lee A.; Williamson, Rodney L.; Melgaard, David K.; Beaman, Joseph J.; Evans, David G.

2000-01-01

An apparatus and method of controlling a remelting process by providing measured process variable values to a process controller; estimating process variable values using a process model of a remelting process; and outputting estimated process variable values from the process controller. Feedback and feedforward control devices receive the estimated process variable values and adjust inputs to the remelting process. Electrode weight, electrode mass, electrode gap, process current, process voltage, electrode position, electrode temperature, electrode thermal boundary layer thickness, electrode velocity, electrode acceleration, slag temperature, melting efficiency, cooling water temperature, cooling water flow rate, crucible temperature profile, slag skin temperature, and/or drip short events are employed, as are parameters representing physical constraints of electroslag remelting or vacuum arc remelting, as applicable.

Improvement of shallow landslide prediction accuracy using soil parameterisation for a granite area in South Korea

NASA Astrophysics Data System (ADS)

Kim, M. S.; Onda, Y.; Kim, J. K.

2015-01-01

SHALSTAB model applied to shallow landslides induced by rainfall to evaluate soil properties related with the effect of soil depth for a granite area in Jinbu region, Republic of Korea. Soil depth measured by a knocking pole test and two soil parameters from direct shear test (a and b) as well as one soil parameters from a triaxial compression test (c) were collected to determine the input parameters for the model. Experimental soil data were used for the first simulation (Case I) and, soil data represented the effect of measured soil depth and average soil depth from soil data of Case I were used in the second (Case II) and third simulations (Case III), respectively. All simulations were analysed using receiver operating characteristic (ROC) analysis to determine the accuracy of prediction. ROC analysis results for first simulation showed the low ROC values under 0.75 may be due to the internal friction angle and particularly the cohesion value. Soil parameters calculated from a stochastic hydro-geomorphological model were applied to the SHALSTAB model. The accuracy of Case II and Case III using ROC analysis showed higher accuracy values rather than first simulation. Our results clearly demonstrate that the accuracy of shallow landslide prediction can be improved when soil parameters represented the effect of soil thickness.

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

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

Qin, Qing; Wang, Jiang; Yu, Haitao

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-spacemore » method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.« less

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

NASA Astrophysics Data System (ADS)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

2016-06-01

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

Colored noise and a stochastic fractional model for correlated inputs and adaptation in neuronal firing.

PubMed

Pirozzi, Enrica

2018-04-01

High variability in the neuronal response to stimulations and the adaptation phenomenon cannot be explained by the standard stochastic leaky integrate-and-fire model. The main reason is that the uncorrelated inputs involved in the model are not realistic. There exists some form of dependency between the inputs, and it can be interpreted as memory effects. In order to include these physiological features in the standard model, we reconsider it with time-dependent coefficients and correlated inputs. Due to its hard mathematical tractability, we perform simulations of it for a wide investigation of its output. A Gauss-Markov process is constructed for approximating its non-Markovian dynamics. The first passage time probability density of such a process can be numerically evaluated, and it can be used to fit the histograms of simulated firing times. Some estimates of the moments of firing times are also provided. The effect of the correlation time of the inputs on firing densities and on firing rates is shown. An exponential probability density of the first firing time is estimated for low values of input current and high values of correlation time. For comparison, a simulation-based investigation is also carried out for a fractional stochastic model that allows to preserve the memory of the time evolution of the neuronal membrane potential. In this case, the memory parameter that affects the firing activity is the fractional derivative order. In both models an adaptation level of spike frequency is attained, even if along different modalities. Comparisons and discussion of the obtained results are provided.

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Optimisation of Ferrochrome Addition Using Multi-Objective Evolutionary and Genetic Algorithms for Stainless Steel Making via AOD Converter

NASA Astrophysics Data System (ADS)

Behera, Kishore Kumar; Pal, Snehanshu

2018-03-01

This paper describes a new approach towards optimum utilisation of ferrochrome added during stainless steel making in AOD converter. The objective of optimisation is to enhance end blow chromium content of steel and reduce the ferrochrome addition during refining. By developing a thermodynamic based mathematical model, a study has been conducted to compute the optimum trade-off between ferrochrome addition and end blow chromium content of stainless steel using a predator prey genetic algorithm through training of 100 dataset considering different input and output variables such as oxygen, argon, nitrogen blowing rate, duration of blowing, initial bath temperature, chromium and carbon content, weight of ferrochrome added during refining. Optimisation is performed within constrained imposed on the input parameters whose values fall within certain ranges. The analysis of pareto fronts is observed to generate a set of feasible optimal solution between the two conflicting objectives that provides an effective guideline for better ferrochrome utilisation. It is found out that after a certain critical range, further addition of ferrochrome does not affect the chromium percentage of steel. Single variable response analysis is performed to study the variation and interaction of all individual input parameters on output variables.

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

NASA Astrophysics Data System (ADS)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

SU-G-IeP3-11: On the Utility of Pixel Variance to Characterize Noise for Image Receptors of Digital Radiography Systems

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

Finley, C; Dave, J

Purpose: To characterize noise for image receptors of digital radiography systems based on pixel variance. Methods: Nine calibrated digital image receptors associated with nine new portable digital radiography systems (Carestream Health, Inc., Rochester, NY) were used in this study. For each image receptor, thirteen images were acquired with RQA5 beam conditions for input detector air kerma ranging from 0 to 110 µGy, and linearized ‘For Processing’ images were extracted. Mean pixel value (MPV), standard deviation (SD) and relative noise (SD/MPV) were obtained from each image using ROI sizes varying from 2.5×2.5 to 20×20 mm{sup 2}. Variance (SD{sup 2}) was plottedmore » as a function of input detector air kerma and the coefficients of the quadratic fit were used to derive structured, quantum and electronic noise coefficients. Relative noise was also fitted as a function of input detector air kerma to identify noise sources. The fitting functions used least-squares approach. Results: The coefficient of variation values obtained using different ROI sizes was less than 1% for all the images. The structured, quantum and electronic coefficients obtained from the quadratic fit of variance (r>0.97) were 0.43±0.10, 3.95±0.27 and 2.89±0.74 (mean ± standard deviation), respectively, indicating that overall the quantum noise was the dominant noise source. However, for one system electronic noise coefficient (3.91) was greater than quantum noise coefficient (3.56) indicating electronic noise to be dominant. Using relative noise values, the power parameter of the fitting equation (|r|>0.93) showed a mean and standard deviation of 0.46±0.02. A 0.50 value for this power parameter indicates quantum noise to be the dominant noise source whereas values around 0.50 indicate presence of other noise sources. Conclusion: Characterizing noise from pixel variance assists in identifying contributions from various noise sources that, eventually, may affect image quality. This approach may be integrated during periodic quality assessments of digital image receptors.« less

CARE3MENU- A CARE III USER FRIENDLY INTERFACE

NASA Technical Reports Server (NTRS)

Pierce, J. L.

1994-01-01

CARE3MENU generates an input file for the CARE III program. CARE III is used for reliability prediction of complex, redundant, fault-tolerant systems including digital computers, aircraft, nuclear and chemical control systems. The CARE III input file often becomes complicated and is not easily formatted with a text editor. CARE3MENU provides an easy, interactive method of creating an input file by automatically formatting a set of user-supplied inputs for the CARE III system. CARE3MENU provides detailed on-line help for most of its screen formats. The reliability model input process is divided into sections using menu-driven screen displays. Each stage, or set of identical modules comprising the model, must be identified and described in terms of number of modules, minimum number of modules for stage operation, and critical fault threshold. The fault handling and fault occurence models are detailed in several screens by parameters such as transition rates, propagation and detection densities, Weibull or exponential characteristics, and model accuracy. The system fault tree and critical pairs fault tree screens are used to define the governing logic and to identify modules affected by component failures. Additional CARE3MENU screens prompt the user for output options and run time control values such as mission time and truncation values. There are fourteen major screens, many with default values and HELP options. The documentation includes: 1) a users guide with several examples of CARE III models, the dialog required to input them to CARE3MENU, and the output files created; and 2) a maintenance manual for assistance in changing the HELP files and modifying any of the menu formats or contents. CARE3MENU is written in FORTRAN 77 for interactive execution and has been implemented on a DEC VAX series computer operating under VMS. This program was developed in 1985.

Automatic dynamic range adjustment for ultrasound B-mode imaging.

PubMed

Lee, Yeonhwa; Kang, Jinbum; Yoo, Yangmo

2015-02-01

In medical ultrasound imaging, dynamic range (DR) is defined as the difference between the maximum and minimum values of the displayed signal to display and it is one of the most essential parameters that determine its image quality. Typically, DR is given with a fixed value and adjusted manually by operators, which leads to low clinical productivity and high user dependency. Furthermore, in 3D ultrasound imaging, DR values are unable to be adjusted during 3D data acquisition. A histogram matching method, which equalizes the histogram of an input image based on that from a reference image, can be applied to determine the DR value. However, it could be lead to an over contrasted image. In this paper, a new Automatic Dynamic Range Adjustment (ADRA) method is presented that adaptively adjusts the DR value by manipulating input images similar to a reference image. The proposed ADRA method uses the distance ratio between the log average and each extreme value of a reference image. To evaluate the performance of the ADRA method, the similarity between the reference and input images was measured by computing a correlation coefficient (CC). In in vivo experiments, the CC values were increased by applying the ADRA method from 0.6872 to 0.9870 and from 0.9274 to 0.9939 for kidney and liver data, respectively, compared to the fixed DR case. In addition, the proposed ADRA method showed to outperform the histogram matching method with in vivo liver and kidney data. When using 3D abdominal data with 70 frames, while the CC value from the ADRA method is slightly increased (i.e., 0.6%), the proposed method showed improved image quality in the c-plane compared to its fixed counterpart, which suffered from a shadow artifact. These results indicate that the proposed method can enhance image quality in 2D and 3D ultrasound B-mode imaging by improving the similarity between the reference and input images while eliminating unnecessary manual interaction by the user. Copyright © 2014 Elsevier B.V. All rights reserved.

Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

Simulating maize yield and bomass with spatial variability of soil field capacity

USGS Publications Warehouse

Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

2015-01-01

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

An economic evaluation of maxillary implant overdentures based on six vs. four implants.

PubMed

Listl, Stefan; Fischer, Leonhard; Giannakopoulos, Nikolaos Nikitas

2014-08-18

The purpose of the present study was to assess the value for money achieved by bar-retained implant overdentures based on six implants compared with four implants as treatment alternatives for the edentulous maxilla. A Markov decision tree model was constructed and populated with parameter estimates for implant and denture failure as well as patient-centred health outcomes as available from recent literature. The decision scenario was modelled within a ten year time horizon and relied on cost reimbursement regulations of the German health care system. The cost-effectiveness threshold was identified above which the six-implant solution is preferable over the four-implant solution. Uncertainties regarding input parameters were incorporated via one-way and probabilistic sensitivity analysis based on Monte-Carlo simulation. Within a base case scenario of average treatment complexity, the cost-effectiveness threshold was identified to be 17,564 € per year of denture satisfaction gained above of which the alternative with six implants is preferable over treatment including four implants. Sensitivity analysis yielded that, depending on the specification of model input parameters such as patients' denture satisfaction, the respective cost-effectiveness threshold varies substantially. The results of the present study suggest that bar-retained maxillary overdentures based on six implants provide better patient satisfaction than bar-retained overdentures based on four implants but are considerably more expensive. Final judgements about value for money require more comprehensive clinical evidence including patient-centred health outcomes.

An economic evaluation of maxillary implant overdentures based on six vs. four implants

PubMed Central

2014-01-01

Background The purpose of the present study was to assess the value for money achieved by bar-retained implant overdentures based on six implants compared with four implants as treatment alternatives for the edentulous maxilla. Methods A Markov decision tree model was constructed and populated with parameter estimates for implant and denture failure as well as patient-centred health outcomes as available from recent literature. The decision scenario was modelled within a ten year time horizon and relied on cost reimbursement regulations of the German health care system. The cost-effectiveness threshold was identified above which the six-implant solution is preferable over the four-implant solution. Uncertainties regarding input parameters were incorporated via one-way and probabilistic sensitivity analysis based on Monte-Carlo simulation. Results Within a base case scenario of average treatment complexity, the cost-effectiveness threshold was identified to be 17,564 € per year of denture satisfaction gained above of which the alternative with six implants is preferable over treatment including four implants. Sensitivity analysis yielded that, depending on the specification of model input parameters such as patients’ denture satisfaction, the respective cost-effectiveness threshold varies substantially. Conclusions The results of the present study suggest that bar-retained maxillary overdentures based on six implants provide better patient satisfaction than bar-retained overdentures based on four implants but are considerably more expensive. Final judgements about value for money require more comprehensive clinical evidence including patient-centred health outcomes. PMID:25135370

Electron transport in furfural: dependence of the electron ranges on the cross sections and the energy loss distribution functions

NASA Astrophysics Data System (ADS)

Ellis-Gibbings, L.; Krupa, K.; Colmenares, R.; Blanco, F.; Muńoz, A.; Mendes, M.; Ferreira da Silva, F.; Limá Vieira, P.; Jones, D. B.; Brunger, M. J.; García, G.

2016-09-01

Recent theoretical and experimental studies have provided a complete set of differential and integral electron scattering cross section data from furfural over a broad energy range. The energy loss distribution functions have been determined in this study by averaging electron energy loss spectra for different incident energies and scattering angles. All these data have been used as input parameters for an event by event Monte Carlo simulation procedure to obtain the electron energy deposition patterns and electron ranges in liquid furfural. The dependence of these results on the input cross sections is then analysed to determine the uncertainty of the simulated values.

Monitoring and modeling as a continuing learning process: the use of hydrological models in a general probabilistic framework.

NASA Astrophysics Data System (ADS)

Baroni, G.; Gräff, T.; Reinstorf, F.; Oswald, S. E.

2012-04-01

Nowadays uncertainty and sensitivity analysis are considered basic tools for the assessment of hydrological models and the evaluation of the most important sources of uncertainty. In this context, in the last decades several methods have been developed and applied in different hydrological conditions. However, in most of the cases, the studies have been done by investigating mainly the influence of the parameter uncertainty on the simulated outputs and few approaches tried to consider also other sources of uncertainty i.e. input and model structure. Moreover, several constrains arise when spatially distributed parameters are involved. To overcome these limitations a general probabilistic framework based on Monte Carlo simulations and the Sobol method has been proposed. In this study, the general probabilistic framework was applied at field scale using a 1D physical-based hydrological model (SWAP). Furthermore, the framework was extended at catchment scale in combination with a spatially distributed hydrological model (SHETRAN). The models are applied in two different experimental sites in Germany: a relatively flat cropped field close to Potsdam (Brandenburg) and a small mountainous catchment with agricultural land use (Schaefertal, Harz Mountains). For both cases, input and parameters are considered as major sources of uncertainty. Evaluation of the models was based on soil moisture detected at plot scale in different depths and, for the catchment site, also with daily discharge values. The study shows how the framework can take into account all the various sources of uncertainty i.e. input data, parameters (either in scalar or spatially distributed form) and model structures. The framework can be used in a loop in order to optimize further monitoring activities used to improve the performance of the model. In the particular applications, the results show how the sources of uncertainty are specific for each process considered. The influence of the input data as well as the presence of compensating errors become clear by the different processes simulated.

Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample

PubMed Central

Oakley, Jeremy E.; Brennan, Alan; Breeze, Penny

2015-01-01

Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method. PMID:25810269

NEWTONP - CUMULATIVE BINOMIAL PROGRAMS

NASA Technical Reports Server (NTRS)

Bowerman, P. N.

1994-01-01

The cumulative binomial program, NEWTONP, is one of a set of three programs which calculate cumulative binomial probability distributions for arbitrary inputs. The three programs, NEWTONP, CUMBIN (NPO-17555), and CROSSER (NPO-17557), can be used independently of one another. NEWTONP can be used by statisticians and users of statistical procedures, test planners, designers, and numerical analysts. The program has been used for reliability/availability calculations. NEWTONP calculates the probably p required to yield a given system reliability V for a k-out-of-n system. It can also be used to determine the Clopper-Pearson confidence limits (either one-sided or two-sided) for the parameter p of a Bernoulli distribution. NEWTONP can determine Bayesian probability limits for a proportion (if the beta prior has positive integer parameters). It can determine the percentiles of incomplete beta distributions with positive integer parameters. It can also determine the percentiles of F distributions and the midian plotting positions in probability plotting. NEWTONP is designed to work well with all integer values 0 < k <= n. To run the program, the user simply runs the executable version and inputs the information requested by the program. NEWTONP is not designed to weed out incorrect inputs, so the user must take care to make sure the inputs are correct. Once all input has been entered, the program calculates and lists the result. It also lists the number of iterations of Newton's method required to calculate the answer within the given error. The NEWTONP program is written in C. It was developed on an IBM AT with a numeric co-processor using Microsoft C 5.0. Because the source code is written using standard C structures and functions, it should compile correctly with most C compilers. The program format is interactive. It has been implemented under DOS 3.2 and has a memory requirement of 26K. NEWTONP was developed in 1988.

Comparison of the WSA-ENLIL model with three CME cone types

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.; Na, H.

2013-07-01

We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.Abstract (2,250 Maximum Characters): We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.

Theory of a Traveling Wave Feed for a Planar Slot Array Antenna

NASA Technical Reports Server (NTRS)

Rengarajan, Sembiam

2012-01-01

Planar arrays of waveguide-fed slots have been employed in many radar and remote sensing applications. Such arrays are designed in the standing wave configuration because of high efficiency. Traveling wave arrays can produce greater bandwidth at the expense of efficiency due to power loss in the load or loads. Traveling wave planar slot arrays may be designed with a long feed waveguide consisting of centered-inclined coupling slots. The feed waveguide is terminated in a matched load, and the element spacing in the feed waveguide is chosen to produce a beam squinted from the broadside. The traveling wave planar slot array consists of a long feed waveguide containing resonant-centered inclined coupling slots in the broad wall, coupling power into an array of stacked radiating waveguides orthogonal to it. The radiating waveguides consist of longitudinal offset radiating slots in a standing wave configuration. For the traveling wave feed of a planar slot array, one has to design the tilt angle and length of each coupling slot such that the amplitude and phase of excitation of each radiating waveguide are close to the desired values. The coupling slot spacing is chosen for an appropriate beam squint. Scattering matrix parameters of resonant coupling slots are used in the design process to produce appropriate excitations of radiating waveguides with constraints placed only on amplitudes. Since the radiating slots in each radiating waveguide are designed to produce a certain total admittance, the scattering (S) matrix of each coupling slot is reduced to a 2x2 matrix. Elements of each 2x2 S-matrix and the amount of coupling into the corresponding radiating waveguide are expressed in terms of the element S11. S matrices are converted into transmission (T) matrices, and the T matrices are multiplied to cascade the coupling slots and waveguide sections, starting from the load end and proceeding towards the source. While the use of non-resonant coupling slots may provide an additional degree of freedom in the design, resonant coupling slots simplify the design process. The amplitude of the wave going to the load is set at unity. The S11 parameter, r of the coupling slot closest to the load, is assigned an arbitrary value. A larger value of r will reduce the power dissipated in the load while increasing the reflection coefficient at the input port. It is now possible to obtain the excitation of the radiating waveguide closest to the load and the coefficients of the wave incident and reflected at the input port of this coupling slot. The next coupling slot parameter, r , is chosen to realize the excitation of that radiating waveguide. One continues this process moving towards the source, until all the coupling slot parameters r and hence the S11 parameter of the 4-port coupler, r, are known for each coupling slot. The goal is to produce the desired array aperture distribution in the feed direction. From an interpolation of the computed moment method data for the slot parameters, all the coupling slot tilt angles and lengths are obtained. From the excitations of the radiating waveguides computed from the coupling values, radiating slot parameters may be obtained so as to attain the desired total normalized slot admittances. This process yields the radiating slot parameters, offsets, and lengths. The design is repeated by choosing different values of r for the last coupling slot until the percentage of power dissipated in the load and the input reflection coefficient values are satisfactory. Numerical results computed for the radiation pattern, the tilt angles and lengths of coupling slots, and excitation phases of the radiating waveguides, are presented for an array with uniform amplitude excitation. The design process has been validated using computer simulations. This design procedure is valid for non-uniform amplitude excitations as well.

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Sources of Uncertainty in the Prediction of LAI / fPAR from MODIS

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Ganapol, Barry D.; Brass, James A. (Technical Monitor)

2002-01-01

To explicate the sources of uncertainty in the prediction of biophysical variables over space, consider the general equation: where z is a variable with values on some nominal, ordinal, interval or ratio scale; y is a vector of input variables; u is the spatial support of y and z ; x and u are the spatial locations of y and z , respectively; f is a model and B is the vector of the parameters of this model. Any y or z has a value and a spatial extent which is called its support. Viewed in this way, categories of uncertainty are from variable (e.g. measurement), parameter, positional. support and model (e.g. structural) sources. The prediction of Leaf Area Index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) are examples of z variables predicted using model(s) as a function of y variables and spatially constant parameters. The MOD15 algorithm is an example of f, called f(sub 1), with parameters including those defined by one of six biome types and solar and view angles. The Leaf Canopy Model (LCM)2, a nested model that combines leaf radiative transfer with a full canopy reflectance model through the phase function, is a simpler though similar radiative transfer approach to f(sub 1). In a previous study, MOD15 and LCM2 gave similar results for the broadleaf forest biome. Differences between these two models can be used to consider the structural uncertainty in prediction results. In an effort to quantify each of the five sources of uncertainty and rank their relative importance for the LAI/fPAR prediction problem, we used recent data for an EOS Core Validation Site in the broadleaf biome with coincident surface reflectance, vegetation index, fPAR and LAI products from the Moderate Resolution Imaging Spectrometer (MODIS). Uncertainty due to support on the input reflectance variable was characterized using Landsat ETM+ data. Input uncertainties were propagated through the LCM2 model and compared with published uncertainties from the MOD15 algorithm.

Robust decentralized controller for minimizing coupling effect in single inductor multiple output DC-DC converter operating in continuous conduction mode.

PubMed

Medeiros, Renan Landau Paiva de; Barra, Walter; Bessa, Iury Valente de; Chaves Filho, João Edgar; Ayres, Florindo Antonio de Cavalho; Neves, Cleonor Crescêncio das

2018-02-01

This paper describes a novel robust decentralized control design methodology for a single inductor multiple output (SIMO) DC-DC converter. Based on a nominal multiple input multiple output (MIMO) plant model and performance requirements, a pairing input-output analysis is performed to select the suitable input to control each output aiming to attenuate the loop coupling. Thus, the plant uncertainty limits are selected and expressed in interval form with parameter values of the plant model. A single inductor dual output (SIDO) DC-DC buck converter board is developed for experimental tests. The experimental results show that the proposed methodology can maintain a desirable performance even in the presence of parametric uncertainties. Furthermore, the performance indexes calculated from experimental data show that the proposed methodology outperforms classical MIMO control techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Sinusoidal input describing function for hysteresis followed by elementary backlash

NASA Technical Reports Server (NTRS)

Ringland, R. F.

1976-01-01

The author proposes a new sinusoidal input describing function which accounts for the serial combination of hysteresis followed by elementary backlash in a single nonlinear element. The output of the hysteresis element drives the elementary backlash element. Various analytical forms of the describing function are given, depending on the a/A ratio, where a is the half width of the hysteresis band or backlash gap, and A is the amplitude of the assumed input sinusoid, and on the value of the parameter representing the fraction of a attributed to the backlash characteristic. The negative inverse describing function is plotted on a gain-phase plot, and it is seen that a relatively small amount of backlash leads to domination of the backlash character in the describing function. The extent of the region of the gain-phase plane covered by the describing function is such as to guarantee some form of limit cycle behavior in most closed-loop systems.

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay.

PubMed

Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan

2015-11-01

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization of MR fluid Yield stress using Taguchi Method and Response Surface Methodology Techniques

NASA Astrophysics Data System (ADS)

Mangal, S. K.; Sharma, Vivek

2018-02-01

Magneto rheological fluids belong to a class of smart materials whose rheological characteristics such as yield stress, viscosity etc. changes in the presence of applied magnetic field. In this paper, optimization of MR fluid constituents is obtained with on-state yield stress as response parameter. For this, 18 samples of MR fluids are prepared using L-18 Orthogonal Array. These samples are experimentally tested on a developed & fabricated electromagnet setup. It has been found that the yield stress of MR fluid mainly depends on the volume fraction of the iron particles and type of carrier fluid used in it. The optimal combination of the input parameters for the fluid are found to be as Mineral oil with a volume percentage of 67%, iron powder of 300 mesh size with a volume percentage of 32%, oleic acid with a volume percentage of 0.5% and tetra-methyl-ammonium-hydroxide with a volume percentage of 0.7%. This optimal combination of input parameters has given the on-state yield stress as 48.197 kPa numerically. An experimental confirmation test on the optimized MR fluid sample has been then carried out and the response parameter thus obtained has found matching quite well (less than 1% error) with the numerically obtained values.

Teleportation of quantum resources and quantum Fisher information under Unruh effect

NASA Astrophysics Data System (ADS)

Jafarzadeh, M.; Rangani Jahromi, H.; Amniat-Talab, M.

2018-07-01

Considering a pair of Unruh-DeWitt detectors, when one of them is kept inertial and the other one is accelerated and coupled to a scalar field, we address the teleportation of a two-qubit entangled state ( |ψ _in> = {cos} θ /2 |10> +e^{iφ} {sin} θ /2 |01> ) through the quantum channel created by the above system and investigate how thermal noise induced by Unruh effect affects the quantum resources and quantum Fisher information (QFI) teleportation. Our results showed while the teleported quantum resources and QFI with respect to phase parameter φ( F_{ {out}}( φ ) ) reduce with increasing acceleration and effective coupling, QFI with respect to weight parameter θ ( F_{ {out}}( θ ) ) interestingly increases after a specified value of acceleration and effective coupling. We also find that the teleported quantum resources and the precision of estimating phase parameter φ can be improved by a more entangled input state and more entangled channel. Moreover, the precision of estimating weight parameter θ increases for a maximally entangled input state only in large acceleration regime, while it does not change considerably for both maximally and partially entangled states of the channel. The influence of Unruh effect on fidelity of teleportation is also investigated. We showed that for small effective coupling the average fidelity is always larger than 2/3.

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

PubMed Central

Commowick, Olivier; Akhondi-Asl, Alireza; Warfield, Simon K.

2012-01-01

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the- art algorithms. PMID:22562727

An advanced technique for the prediction of decelerator system dynamics.

NASA Technical Reports Server (NTRS)

Talay, T. A.; Morris, W. D.; Whitlock, C. H.

1973-01-01

An advanced two-body six-degree-of-freedom computer model employing an indeterminate structures approach has been developed for the parachute deployment process. The program determines both vehicular and decelerator responses to aerodynamic and physical property inputs. A better insight into the dynamic processes that occur during parachute deployment has been developed. The model is of value in sensitivity studies to isolate important parameters that affect the vehicular response.

Applying 3-PG, a simple process-based model designed to produce practical results, to data from loblolly pine experiments

Treesearch

Joe J. Landsberg; Kurt H. Johnsen; Timothy J. Albaugh; H. Lee Allen; Steven E. McKeand

2001-01-01

3-PG is a simple process-based model that requires few parameter values and only readily available input data. We tested the structure of the model by calibrating it against loblolly pine data from the control treatment of the SETRES experiment in Scotland County, NC, then altered the fertility rating to simulate the effects of fertilization. There was excellent...

Development of weight/sizing design synthesis computer program. Volume 3: User Manual

NASA Technical Reports Server (NTRS)

Garrison, J. M.

1973-01-01

The user manual for the weight/sizing design synthesis program is presented. The program is applied to an analysis of the basic weight relationships for the space shuttle which contribute significant portions of the inert weight. The relationships measure the parameters of load, geometry, material, and environment. A verbal description of the processes simulated, data input procedures, output data, and values present in the program is included.

Phase transition of Boolean networks with partially nested canalizing functions

NASA Astrophysics Data System (ADS)

Jansen, Kayse; Matache, Mihaela Teodora

2013-07-01

We generate the critical condition for the phase transition of a Boolean network governed by partially nested canalizing functions for which a fraction of the inputs are canalizing, while the remaining non-canalizing inputs obey a complementary threshold Boolean function. Past studies have considered the stability of fully or partially nested canalizing functions paired with random choices of the complementary function. In some of those studies conflicting results were found with regard to the presence of chaotic behavior. Moreover, those studies focus mostly on ergodic networks in which initial states are assumed equally likely. We relax that assumption and find the critical condition for the sensitivity of the network under a non-ergodic scenario. We use the proposed mathematical model to determine parameter values for which phase transitions from order to chaos occur. We generate Derrida plots to show that the mathematical model matches the actual network dynamics. The phase transition diagrams indicate that both order and chaos can occur, and that certain parameters induce a larger range of values leading to order versus chaos. The edge-of-chaos curves are identified analytically and numerically. It is shown that the depth of canalization does not cause major dynamical changes once certain thresholds are reached; these thresholds are fairly small in comparison to the connectivity of the nodes.

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

PubMed

Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

2009-10-01

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

Bayesian inference for OPC modeling

NASA Astrophysics Data System (ADS)

Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.

2016-03-01

The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.

Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks

NASA Astrophysics Data System (ADS)

Ogawa, K.; Fukushima, M.; Kubota, K.; Hisa, N.

1998-12-01

The aim of the study is to establish a computer-aided diagnostic system for diffuse liver diseases such as chronic active hepatitis (CAH) and liver cirrhosis (LC). The authors introduced an artificial neural network in the classification of these diseases. In this system the neural network was trained by feature parameters extracted from B-mode ultrasonic images of normal liver (NL), CAH and LC. For input data the authors used six parameters calculated by a region of interest (ROI) and a parameter calculated by five ROIs in each image. They were variance of pixel values, coefficient of variation, annular Fourier power spectrum, longitudinal Fourier power spectrum which were calculated for the ROI, and variation of the means of the five ROIs. In addition, the authors used two more parameters calculated from a co-occurrence matrix of pixel values in the ROI. The results showed that the neural network classifier was 83.8% in sensitivity for LC, 90.0% in sensitivity for CAH and 93.6% in specificity, and the system was considered to be helpful for clinical and educational use.

Etching Characteristics of VO2 Thin Films Using Inductively Coupled Cl2/Ar Plasma

NASA Astrophysics Data System (ADS)

Ham, Yong-Hyun; Efremov, Alexander; Min, Nam-Ki; Lee, Hyun Woo; Yun, Sun Jin; Kwon, Kwang-Ho

2009-08-01

A study on both etching characteristics and mechanism of VO2 thin films in the Cl2/Ar inductively coupled plasma was carried. The variable parameters were gas pressure (4-10 mTorr) and input power (400-700 W) at fixed bias power of 150 W and initial mixture composition of 25% Cl2 + 75% Ar. It was found that an increase in both gas pressure and input power results in increasing VO2 etch rate while the etch selectivity over photoresist keeps a near to constant values. Plasma diagnostics by Langmuir probes and zero-dimensional plasma model provided the data on plasma parameters, steady-state densities and fluxes of active species on the etched surface. The model-based analysis of the etch mechanism showed that, for the given ranges of operating conditions, the VO2 etch kinetics corresponds to the transitional regime of ion-assisted chemical reaction and is influenced by both neutral and ion fluxes with a higher sensitivity to the neutral flux.

Markets, Herding and Response to External Information.

PubMed

Carro, Adrián; Toral, Raúl; San Miguel, Maxi

2015-01-01

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany's leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information.

Open-source LCA tool for estimating greenhouse gas emissions from crude oil production using field characteristics.

PubMed

El-Houjeiri, Hassan M; Brandt, Adam R; Duffy, James E

2013-06-04

Existing transportation fuel cycle emissions models are either general and calculate nonspecific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing. We have developed the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, rigorous GHG assessments for use in scientific assessment, regulatory processes, and analysis of GHG mitigation options by producers. OPGEE uses petroleum engineering fundamentals to model emissions from oil and gas production operations. We introduce OPGEE and explain the methods and assumptions used in its construction. We run OPGEE on a small set of fictional oil fields and explore model sensitivity to selected input parameters. Results show that upstream emissions from petroleum production operations can vary from 3 gCO2/MJ to over 30 gCO2/MJ using realistic ranges of input parameters. Significant drivers of emissions variation are steam injection rates, water handling requirements, and rates of flaring of associated gas.

An automated algorithm for determining photometric redshifts of quasars

NASA Astrophysics Data System (ADS)

Wang, Dan; Zhang, Yanxia; Zhao, Yongheng

2010-07-01

We employ k-nearest neighbor algorithm (KNN) for photometric redshift measurement of quasars with the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). KNN is an instance learning algorithm where the result of new instance query is predicted based on the closest training samples. The regressor do not use any model to fit and only based on memory. Given a query quasar, we find the known quasars or (training points) closest to the query point, whose redshift value is simply assigned to be the average of the values of its k nearest neighbors. Three kinds of different colors (PSF, Model or Fiber) and spectral redshifts are used as input parameters, separatively. The combination of the three kinds of colors is also taken as input. The experimental results indicate that the best input pattern is PSF + Model + Fiber colors in all experiments. With this pattern, 59.24%, 77.34% and 84.68% of photometric redshifts are obtained within ▵z < 0.1, 0.2 and 0.3, respectively. If only using one kind of colors as input, the model colors achieve the best performance. However, when using two kinds of colors, the best result is achieved by PSF + Fiber colors. In addition, nearest neighbor method (k = 1) shows its superiority compared to KNN (k ≠ 1) for the given sample.

Multi-objective optimization in the development of oil and water repellent cellulose fabric based on response surface methodology and the desirability function

NASA Astrophysics Data System (ADS)

Ahmad, Naseer; Kamal, Shahid; Raza, Zulfiqar Ali; Hussain, Tanveer

2017-03-01

The present study investigated multi-response optimization of certain input parameters viz. concentrations of oil and water repellent finish (Oleophobol CP-C®), dimethylol dihydroxy ethylene urea based cross linking agent (Knittex FEL) and curing temperature on some mechanical, (i.e. tear and tensile strengths), functional (i.e., water contact angle ‘WCA’, oil contact angle ‘OCA’) and comfort (i.e. crease recovery angle ‘CRA’, air permeability ‘AP’, and stiffness) properties of an oleo-hydrophobic finished fabric under response surface methodology and the desirability function. The results have been examined using analysis of variance (ANOVA) and desirability function for the identification of optimum levels of input variables. The ANOVA was employed also to identify the percentage contribution of process factors. Under the optimized conditions, which were obtained with a total desirability value of 0.7769, the experimental values of Oleophobol CP-C® (O-CPC), Knittex FEL (K-FEL) and curing temperature (C-Temp) agreed closely with the predicted values. The optimized process parameters for maximum WCA (135°), OCA (129°), AP (290 m s-1), CRA (214°), tear (1492 gf) and tensile (764 N) strengths and minimum stiffness (3.2928 cm) were found to be: concentration of OCP-C as 44.44 g l-1, concentration of cross linker K-FEL as 32.07 g l-1 and C-Temp as 161.81 °C.

Local thermal sensation modeling-a review on the necessity and availability of local clothing properties and local metabolic heat production.

PubMed

Veselá, S; Kingma, B R M; Frijns, A J H

2017-03-01

Local thermal sensation modeling gained importance due to developments in personalized and locally applied heating and cooling systems in office environments. The accuracy of these models depends on skin temperature prediction by thermophysiological models, which in turn rely on accurate environmental and personal input data. Environmental parameters are measured or prescribed, but personal factors such as clothing properties and metabolic rates have to be estimated. Data for estimating the overall values of clothing properties and metabolic rates are available in several papers and standards. However, local values are more difficult to retrieve. For local clothing, this study revealed that full and consistent data sets are not available in the published literature for typical office clothing sets. Furthermore, the values for local heat production were not verified for characteristic office activities, but were adapted empirically. Further analyses showed that variations in input parameters can lead to local skin temperature differences (∆T skin,loc  = 0.4-4.4°C). These differences can affect the local sensation output, where ∆T skin,loc  = 1°C is approximately one step on a 9-point thermal sensation scale. In conclusion, future research should include a systematic study of local clothing properties and the development of feasible methods for measuring and validating local heat production. © 2016 The Authors. Indoor Air published by John Wiley & Sons Ltd.

Calibration of Watershed Lag Time Equation for Philippine Hydrology using RADARSAT Digital Elevation Models

NASA Astrophysics Data System (ADS)

Cipriano, F. R.; Lagmay, A. M. A.; Horritt, M.; Mendoza, J.; Sabio, G.; Punay, K. N.; Taniza, H. J.; Uichanco, C.

2015-12-01

Widespread flooding is a major problem in the Philippines. The country experiences heavy amount of rainfall throughout the year and several areas are prone to flood hazards because of its unique topography. Human casualties and destruction of infrastructure are just some of the damages caused by flooding and the Philippine government has undertaken various efforts to mitigate these hazards. One of the solutions was to create flood hazard maps of different floodplains and use them to predict the possible catastrophic results of different rain scenarios. To produce these maps with accurate output, different input parameters were needed and one of those is calculating hydrological components from topographical data. This paper presents how a calibrated lag time (TL) equation was obtained using measurable catchment parameters. Lag time is an essential input in flood mapping and is defined as the duration between the peak rainfall and peak discharge of the watershed. The lag time equation involves three measurable parameters, namely, watershed length (L), maximum potential retention (S) derived from the curve number, and watershed slope (Y), all of which were available from RADARSAT Digital Elevation Models (DEM). This approach was based on a similar method developed by CH2M Hill and Horritt for Taiwan, which has a similar set of meteorological and hydrological parameters with the Philippines. Rainfall data from fourteen water level sensors covering 67 storms from all the regions in the country were used to estimate the actual lag time. These sensors were chosen by using a screening process that considers the distance of the sensors from the sea, the availability of recorded data, and the catchment size. The actual lag time values were plotted against the values obtained from the Natural Resource Conservation Management handbook lag time equation. Regression analysis was used to obtain the final calibrated equation that would be used to calculate the lag time specifically for rivers in the Philippine setting. The calculated lag time values could then be used as a parameter for modeling different flood scenarios in the country.

NEUTRON STAR MASS–RADIUS CONSTRAINTS USING EVOLUTIONARY OPTIMIZATION

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

Stevens, A. L.; Morsink, S. M.; Fiege, J. D.

The equation of state of cold supra-nuclear-density matter, such as in neutron stars, is an open question in astrophysics. A promising method for constraining the neutron star equation of state is modeling pulse profiles of thermonuclear X-ray burst oscillations from hot spots on accreting neutron stars. The pulse profiles, constructed using spherical and oblate neutron star models, are comparable to what would be observed by a next-generation X-ray timing instrument like ASTROSAT , NICER , or a mission similar to LOFT . In this paper, we showcase the use of an evolutionary optimization algorithm to fit pulse profiles to determinemore » the best-fit masses and radii. By fitting synthetic data, we assess how well the optimization algorithm can recover the input parameters. Multiple Poisson realizations of the synthetic pulse profiles, constructed with 1.6 million counts and no background, were fitted with the Ferret algorithm to analyze both statistical and degeneracy-related uncertainty and to explore how the goodness of fit depends on the input parameters. For the regions of parameter space sampled by our tests, the best-determined parameter is the projected velocity of the spot along the observer’s line of sight, with an accuracy of ≤3% compared to the true value and with ≤5% statistical uncertainty. The next best determined are the mass and radius; for a neutron star with a spin frequency of 600 Hz, the best-fit mass and radius are accurate to ≤5%, with respective uncertainties of ≤7% and ≤10%. The accuracy and precision depend on the observer inclination and spot colatitude, with values of ∼1% achievable in mass and radius if both the inclination and colatitude are ≳60°.« less

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

NASA Astrophysics Data System (ADS)

Mankin, Romi; Paekivi, Sander

2018-01-01

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent αc≈0.402 , which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Solar Energy Monitor In Space (SEMIS)

NASA Technical Reports Server (NTRS)

Thekaekara, M. P.

1974-01-01

Measurements made at high altitudes from aircraft have resulted in the establishment of standard values of the solar constant and extraterrestrial solar spectral irradiance. These standard values and other solar spectral curves are described. The problem of possible variations of the solar constant and solar spectrum and their influence on the earth-atmosphere system and weather related phenomena is examined. It is shown that the solar energy input parameters should be determined with considerably greater accuracy and precision than has been possible. An instrument package designed as a compact, low weight solar energy monitor in space (SEMIS) is described.

MWRRET Value-Added Product: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer (MWR) Datasets

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

KL Gaustad; DD Turner

2007-09-30

This report provides a short description of the Atmospheric Radiation Measurement (ARM) microwave radiometer (MWR) RETrievel (MWRRET) Value-Added Product (VAP) algorithm. This algorithm utilizes complimentary physical and statistical retrieval methods and applies brightness temperature offsets to reduce spurious liquid water path (LWP) bias in clear skies resulting in significantly improved precipitable water vapor (PWV) and LWP retrievals. We present a general overview of the technique, input parameters, output products, and describe data quality checks. A more complete discussion of the theory and results is given in Turner et al. (2007b).

Adaptation of an urban land surface model to a tropical suburban area: Offline evaluation, sensitivity analysis, and optimization of TEB/ISBA (SURFEX)

NASA Astrophysics Data System (ADS)

Harshan, Suraj

The main objective of the present thesis is the improvement of the TEB/ISBA (SURFEX) urban land surface model (ULSM) through comprehensive evaluation, sensitivity analysis, and optimization experiments using energy balance and radiative and air temperature data observed during 11 months at a tropical sub-urban site in Singapore. Overall the performance of the model is satisfactory, with a small underestimation of net radiation and an overestimation of sensible heat flux. Weaknesses in predicting the latent heat flux are apparent with smaller model values during daytime and the model also significantly underpredicts both the daytime peak and nighttime storage heat. Surface temperatures of all facets are generally overpredicted. Significant variation exists in the model behaviour between dry and wet seasons. The vegetation parametrization used in the model is inadequate to represent the moisture dynamics, producing unrealistically low latent heat fluxes during a particularly dry period. The comprehensive evaluation of the USLM shows the need for accurate estimation of input parameter values for present site. Since obtaining many of these parameters through empirical methods is not feasible, the present study employed a two step approach aimed at providing information about the most sensitive parameters and an optimized parameter set from model calibration. Two well established sensitivity analysis methods (global: Sobol and local: Morris) and a state-of-the-art multiobjective evolutionary algorithm (Borg) were employed for sensitivity analysis and parameter estimation. Experiments were carried out for three different weather periods. The analysis indicates that roof related parameters are the most important ones in controlling the behaviour of the sensible heat flux and net radiation flux, with roof and road albedo as the most influential parameters. Soil moisture initialization parameters are important in controlling the latent heat flux. The built (town) fraction has a significant influence on all fluxes considered. Comparison between the Sobol and Morris methods shows similar sensitivities, indicating the robustness of the present analysis and that the Morris method can be employed as a computationally cheaper alternative of Sobol's method. Optimization as well as the sensitivity experiments for the three periods (dry, wet and mixed), show a noticeable difference in parameter sensitivity and parameter convergence, indicating inadequacies in model formulation. Existence of a significant proportion of less sensitive parameters might be indicating an over-parametrized model. Borg MOEA showed great promise in optimizing the input parameters set. The optimized model modified using the site specific values for thermal roughness length parametrization shows an improvement in the performances of outgoing longwave radiation flux, overall surface temperature, heat storage flux and sensible heat flux.

Computer simulation of storm runoff for three watersheds in Albuquerque, New Mexico

USGS Publications Warehouse

Knutilla, R.L.; Veenhuis, J.E.

1994-01-01

Rainfall-runoff data from three watersheds were selected for calibration and verification of the U.S. Geological Survey's Distributed Routing Rainfall-Runoff Model. The watersheds chosen are residentially developed. The conceptually based model uses an optimization process that adjusts selected parameters to achieve the best fit between measured and simulated runoff volumes and peak discharges. Three of these optimization parameters represent soil-moisture conditions, three represent infiltration, and one accounts for effective impervious area. Each watershed modeled was divided into overland-flow segments and channel segments. The overland-flow segments were further subdivided to reflect pervious and impervious areas. Each overland-flow and channel segment was assigned representative values of area, slope, percentage of imperviousness, and roughness coefficients. Rainfall-runoff data for each watershed were separated into two sets for use in calibration and verification. For model calibration, seven input parameters were optimized to attain a best fit of the data. For model verification, parameter values were set using values from model calibration. The standard error of estimate for calibration of runoff volumes ranged from 19 to 34 percent, and for peak discharge calibration ranged from 27 to 44 percent. The standard error of estimate for verification of runoff volumes ranged from 26 to 31 percent, and for peak discharge verification ranged from 31 to 43 percent.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Branch, Oliver; Attinger, Sabine; Thober, Stephan

2016-09-01

Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard-coded parameters in the model code of the Noah land surface model with multiple process options (Noah-MP). We performed a Sobol' global sensitivity analysis of Noah-MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol' indexes above 1%). The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah-MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Parameter identification for nonlinear aerodynamic systems

NASA Technical Reports Server (NTRS)

Pearson, Allan E.

1990-01-01

Parameter identification for nonlinear aerodynamic systems is examined. It is presumed that the underlying model can be arranged into an input/output (I/O) differential operator equation of a generic form. The algorithm estimation is especially efficient since the equation error can be integrated exactly given any I/O pair to obtain an algebraic function of the parameters. The algorithm for parameter identification was extended to the order determination problem for linear differential system. The degeneracy in a least squares estimate caused by feedback was addressed. A method of frequency analysis for determining the transfer function G(j omega) from transient I/O data was formulated using complex valued Fourier based modulating functions in contrast with the trigonometric modulating functions for the parameter estimation problem. A simulation result of applying the algorithm is given under noise-free conditions for a system with a low pass transfer function.

Optimization of Operating Parameters for Minimum Mechanical Specific Energy in Drilling

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

Hamrick, Todd

2011-01-01

Efficiency in drilling is measured by Mechanical Specific Energy (MSE). MSE is the measure of the amount of energy input required to remove a unit volume of rock, expressed in units of energy input divided by volume removed. It can be expressed mathematically in terms of controllable parameters; Weight on Bit, Torque, Rate of Penetration, and RPM. It is well documented that minimizing MSE by optimizing controllable factors results in maximum Rate of Penetration. Current methods for computing MSE make it possible to minimize MSE in the field only through a trial-and-error process. This work makes it possible to computemore » the optimum drilling parameters that result in minimum MSE. The parameters that have been traditionally used to compute MSE are interdependent. Mathematical relationships between the parameters were established, and the conventional MSE equation was rewritten in terms of a single parameter, Weight on Bit, establishing a form that can be minimized mathematically. Once the optimum Weight on Bit was determined, the interdependent relationship that Weight on Bit has with Torque and Penetration per Revolution was used to determine optimum values for those parameters for a given drilling situation. The improved method was validated through laboratory experimentation and analysis of published data. Two rock types were subjected to four treatments each, and drilled in a controlled laboratory environment. The method was applied in each case, and the optimum parameters for minimum MSE were computed. The method demonstrated an accurate means to determine optimum drilling parameters of Weight on Bit, Torque, and Penetration per Revolution. A unique application of micro-cracking is also presented, which demonstrates that rock failure ahead of the bit is related to axial force more than to rotation speed.« less

Development and weighting of a life cycle assessment screening model

NASA Astrophysics Data System (ADS)

Bates, Wayne E.; O'Shaughnessy, James; Johnson, Sharon A.; Sisson, Richard

2004-02-01

Nearly all life cycle assessment tools available today are high priced, comprehensive and quantitative models requiring a significant amount of data collection and data input. In addition, most of the available software packages require a great deal of training time to learn how to operate the model software. Even after this time investment, results are not guaranteed because of the number of estimations and assumptions often necessary to run the model. As a result, product development, design teams and environmental specialists need a simplified tool that will allow for the qualitative evaluation and "screening" of various design options. This paper presents the development and design of a generic, qualitative life cycle screening model and demonstrates its applicability and ease of use. The model uses qualitative environmental, health and safety factors, based on site or product-specific issues, to sensitize the overall results for a given set of conditions. The paper also evaluates the impact of different population input ranking values on model output. The final analysis is based on site or product-specific variables. The user can then evaluate various design changes and the apparent impact or improvement on the environment, health and safety, compliance cost and overall corporate liability. Major input parameters can be varied, and factors such as materials use, pollution prevention, waste minimization, worker safety, product life, environmental impacts, return of investment, and recycle are evaluated. The flexibility of the model format will be discussed in order to demonstrate the applicability and usefulness within nearly any industry sector. Finally, an example using audience input value scores will be compared to other population input results.

Application of modern radiative transfer tools to model laboratory quartz emissivity

NASA Astrophysics Data System (ADS)

Pitman, Karly M.; Wolff, Michael J.; Clayton, Geoffrey C.

2005-08-01

Planetary remote sensing of regolith surfaces requires use of theoretical models for interpretation of constituent grain physical properties. In this work, we review and critically evaluate past efforts to strengthen numerical radiative transfer (RT) models with comparison to a trusted set of nadir incidence laboratory quartz emissivity spectra. By first establishing a baseline statistical metric to rate successful model-laboratory emissivity spectral fits, we assess the efficacy of hybrid computational solutions (Mie theory + numerically exact RT algorithm) to calculate theoretical emissivity values for micron-sized α-quartz particles in the thermal infrared (2000-200 cm-1) wave number range. We show that Mie theory, a widely used but poor approximation to irregular grain shape, fails to produce the single scattering albedo and asymmetry parameter needed to arrive at the desired laboratory emissivity values. Through simple numerical experiments, we show that corrections to single scattering albedo and asymmetry parameter values generated via Mie theory become more necessary with increasing grain size. We directly compare the performance of diffraction subtraction and static structure factor corrections to the single scattering albedo, asymmetry parameter, and emissivity for dense packing of grains. Through these sensitivity studies, we provide evidence that, assuming RT methods work well given sufficiently well-quantified inputs, assumptions about the scatterer itself constitute the most crucial aspect of modeling emissivity values.

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm.

PubMed

Tan, Xia; Ji, Zhong; Zhang, Yadan

2018-04-25

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

Prediction of compressibility parameters of the soils using artificial neural network.

PubMed

Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

2016-01-01

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

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

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

QCD Coupling from a Nonperturbative Determination of the Three-Flavor Λ Parameter

DOE PAGES

Bruno, Mattia; Brida, Mattia Dalla; Fritzsch, Patrick; ...

2017-09-08

We present a lattice determination of the Λ parameter in three-flavor QCD and the strong coupling at the Z pole mass. Computing the nonperturbative running of the coupling in the range from 0.2 to 70 GeV, and using experimental input values for the masses and decay constants of the pion and the kaon, we obtain Λ(3)MS=341(12) MeV. The nonperturbative running up to very high energies guarantees that systematic effects associated with perturbation theory are well under control. Using the four-loop prediction for Λ(5)MS/Λ(3)MS yields α(5)MS(mZ)=0.11852(84).

A general method for the layout of ailerons and elevators of gliders and motorplanes

NASA Technical Reports Server (NTRS)

Hiller, M. H.

1979-01-01

A method is described which allows the layout of the spatial driving mechanism of the aileron for a glider or a motorplane to be performed in a systematic manner. In particular, a prescribed input-output behavior of the mechanism can be realized by variation of individual parameters of the spatial four-bar mechanisms which constitute the entire driving mechanism. By means of a sensitivity analysis, a systematic choice of parameters is possible. At the same time the forces acting in the mechanism can be limited by imposing maximum values of the forces as secondary conditions during the variation process.

An adaptive optimal control for smart structures based on the subspace tracking identification technique

NASA Astrophysics Data System (ADS)

Ripamonti, Francesco; Resta, Ferruccio; Borroni, Massimo; Cazzulani, Gabriele

2014-04-01

A new method for the real-time identification of mechanical system modal parameters is used in order to design different adaptive control logics aiming to reduce the vibrations in a carbon fiber plate smart structure. It is instrumented with three piezoelectric actuators, three accelerometers and three strain gauges. The real-time identification is based on a recursive subspace tracking algorithm whose outputs are elaborated by an ARMA model. A statistical approach is finally applied to choose the modal parameter correct values. These are given in input to model-based control logics such as a gain scheduling and an adaptive LQR control.

UCODE, a computer code for universal inverse modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1999-01-01

This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation and Fortran90, which efficiently performs numerical calculations.

Monte-Carlo Method Application for Precising Meteor Velocity from TV Observations

NASA Astrophysics Data System (ADS)

Kozak, P.

2014-12-01

Monte-Carlo method (method of statistical trials) as an application for meteor observations processing was developed in author's Ph.D. thesis in 2005 and first used in his works in 2008. The idea of using the method consists in that if we generate random values of input data - equatorial coordinates of the meteor head in a sequence of TV frames - in accordance with their statistical distributions we get a possibility to plot the probability density distributions for all its kinematical parameters, and to obtain their mean values and dispersions. At that the theoretical possibility appears to precise the most important parameter - geocentric velocity of a meteor - which has the highest influence onto precision of meteor heliocentric orbit elements calculation. In classical approach the velocity vector was calculated in two stages: first we calculate the vector direction as a vector multiplication of vectors of poles of meteor trajectory big circles, calculated from two observational points. Then we calculated the absolute value of velocity independently from each observational point selecting any of them from some reasons as a final parameter. In the given method we propose to obtain a statistical distribution of velocity absolute value as an intersection of two distributions corresponding to velocity values obtained from different points. We suppose that such an approach has to substantially increase the precision of meteor velocity calculation and remove any subjective inaccuracies.

Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

USGS Publications Warehouse

Raia, S.; Alvioli, M.; Rossi, M.; Baum, R.L.; Godt, J.W.; Guzzetti, F.

2013-01-01

Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Prediction of Layer Thickness in Molten Borax Bath with Genetic Evolutionary Programming

NASA Astrophysics Data System (ADS)

Taylan, Fatih

2011-04-01

In this study, the vanadium carbide coating in molten borax bath process is modeled by evolutionary genetic programming (GEP) with bath composition (borax percentage, ferro vanadium (Fe-V) percentage, boric acid percentage), bath temperature, immersion time, and layer thickness data. Five inputs and one output data exist in the model. The percentage of borax, Fe-V, and boric acid, temperature, and immersion time parameters are used as input data and the layer thickness value is used as output data. For selected bath components, immersion time, and temperature variables, the layer thicknesses are derived from the mathematical expression. The results of the mathematical expressions are compared to that of experimental data; it is determined that the derived mathematical expression has an accuracy of 89%.

Hole-assisted fiber based fiber fuse terminator supporting 22 W input

NASA Astrophysics Data System (ADS)

Tsujikawa, Kyozo; Kurokawa, Kenji; Hanzawa, Nobutomo; Nozoe, Saki; Matsui, Takashi; Nakajima, Kazuhide

2018-05-01

We investigated the air hole structure in hole-assisted fiber (HAF) with the aim of terminating fiber fuse propagation. We focused on two structural parameters c/MFD and S1/S2, which are related respectively to the position and area of the air holes, and mapped their appropriate values for terminating fiber fuse propagation. Here, MFD is the mode field diameter, c is the diameter of an inscribed circle linking the air holes, S1 is the total area of the air holes, and S2 is the area of a circumscribed circle linking the air holes. On the basis of these results, we successfully realized a compact fiber fuse terminator consisting of a 1.35 mm-long HAF, which can terminate fiber fuse propagation even with a 22 W input. In addition, we observed fiber fuse termination using a high-speed camera. We additionally confirmed that the HAF-based fiber fuse terminator is effective under various input power conditions. The penetration length of the optical discharge in the HAF was only less than 300 μm when the input power was from 2 to 22 W.

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

PubMed

Aumentado-Armstrong, Tristan; Metzen, Michael G; Sproule, Michael K J; Chacron, Maurice J

2015-10-01

Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

Observations of the directional distribution of the wind energy input function over swell waves

NASA Astrophysics Data System (ADS)

Shabani, Behnam; Babanin, Alex V.; Baldock, Tom E.

2016-02-01

Field measurements of wind stress over shallow water swell traveling in different directions relative to the wind are presented. The directional distribution of the measured stresses is used to confirm the previously proposed but unverified directional distribution of the wind energy input function. The observed wind energy input function is found to follow a much narrower distribution (β∝cos⁡3.6θ) than the Plant (1982) cosine distribution. The observation of negative stress angles at large wind-wave angles, however, indicates that the onset of negative wind shearing occurs at about θ≈ 50°, and supports the use of the Snyder et al. (1981) directional distribution. Taking into account the reverse momentum transfer from swell to the wind, Snyder's proposed parameterization is found to perform exceptionally well in explaining the observed narrow directional distribution of the wind energy input function, and predicting the wind drag coefficients. The empirical coefficient (ɛ) in Snyder's parameterization is hypothesised to be a function of the wave shape parameter, with ɛ value increasing as the wave shape changes between sinusoidal, sawtooth, and sharp-crested shoaling waves.

PEBBED Uncertainty and Sensitivity Analysis of the CRP-5 PBMR DLOFC Transient Benchmark with the SUSA Code

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

Gerhard Strydom

2011-01-01

The need for a defendable and systematic uncertainty and sensitivity approach that conforms to the Code Scaling, Applicability, and Uncertainty (CSAU) process, and that could be used for a wide variety of software codes, was defined in 2008. The GRS (Gesellschaft für Anlagen und Reaktorsicherheit) company of Germany has developed one type of CSAU approach that is particularly well suited for legacy coupled core analysis codes, and a trial version of their commercial software product SUSA (Software for Uncertainty and Sensitivity Analyses) was acquired on May 12, 2010. This report summarized the results of the initial investigations performed with SUSA,more » utilizing a typical High Temperature Reactor benchmark (the IAEA CRP-5 PBMR 400MW Exercise 2) and the PEBBED-THERMIX suite of codes. The following steps were performed as part of the uncertainty and sensitivity analysis: 1. Eight PEBBED-THERMIX model input parameters were selected for inclusion in the uncertainty study: the total reactor power, inlet gas temperature, decay heat, and the specific heat capability and thermal conductivity of the fuel, pebble bed and reflector graphite. 2. The input parameters variations and probability density functions were specified, and a total of 800 PEBBED-THERMIX model calculations were performed, divided into 4 sets of 100 and 2 sets of 200 Steady State and Depressurized Loss of Forced Cooling (DLOFC) transient calculations each. 3. The steady state and DLOFC maximum fuel temperature, as well as the daily pebble fuel load rate data, were supplied to SUSA as model output parameters of interest. The 6 data sets were statistically analyzed to determine the 5% and 95% percentile values for each of the 3 output parameters with a 95% confidence level, and typical statistical indictors were also generated (e.g. Kendall, Pearson and Spearman coefficients). 4. A SUSA sensitivity study was performed to obtain correlation data between the input and output parameters, and to identify the primary contributors to the output data uncertainties. It was found that the uncertainties in the decay heat, pebble bed and reflector thermal conductivities were responsible for the bulk of the propagated uncertainty in the DLOFC maximum fuel temperature. It was also determined that the two standard deviation (2s) uncertainty on the maximum fuel temperature was between ±58oC (3.6%) and ±76oC (4.7%) on a mean value of 1604 oC. These values mostly depended on the selection of the distributions types, and not on the number of model calculations above the required Wilks criteria (a (95%,95%) statement would usually require 93 model runs).« less

Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification

NASA Astrophysics Data System (ADS)

Bates, Matthew E.; Keisler, Jeffrey M.; Zussblatt, Niels P.; Plourde, Kenton J.; Wender, Ben A.; Linkov, Igor

2016-02-01

Risk research for nanomaterials is currently prioritized by means of expert workshops and other deliberative processes. However, analytical techniques that quantify and compare alternative research investments are increasingly recommended. Here, we apply value of information and portfolio decision analysis—methods commonly applied in financial and operations management—to prioritize risk research for multiwalled carbon nanotubes and nanoparticulate silver and titanium dioxide. We modify the widely accepted CB Nanotool hazard evaluation framework, which combines nano- and bulk-material properties into a hazard score, to operate probabilistically with uncertain inputs. Literature is reviewed to develop uncertain estimates for each input parameter, and a Monte Carlo simulation is applied to assess how different research strategies can improve hazard classification. The relative cost of each research experiment is elicited from experts, which enables identification of efficient research portfolios—combinations of experiments that lead to the greatest improvement in hazard classification at the lowest cost. Nanoparticle shape, diameter, solubility and surface reactivity were most frequently identified within efficient portfolios in our results.

Intelligent Flow Friction Estimation.

PubMed

Brkić, Dejan; Ćojbašić, Žarko

2016-01-01

Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (λ). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe (ε/D) were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness (ε/D) ranging between 5000 and 10(8) and between 10(-7) and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation.

Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification.

PubMed

Bates, Matthew E; Keisler, Jeffrey M; Zussblatt, Niels P; Plourde, Kenton J; Wender, Ben A; Linkov, Igor

2016-02-01

Risk research for nanomaterials is currently prioritized by means of expert workshops and other deliberative processes. However, analytical techniques that quantify and compare alternative research investments are increasingly recommended. Here, we apply value of information and portfolio decision analysis-methods commonly applied in financial and operations management-to prioritize risk research for multiwalled carbon nanotubes and nanoparticulate silver and titanium dioxide. We modify the widely accepted CB Nanotool hazard evaluation framework, which combines nano- and bulk-material properties into a hazard score, to operate probabilistically with uncertain inputs. Literature is reviewed to develop uncertain estimates for each input parameter, and a Monte Carlo simulation is applied to assess how different research strategies can improve hazard classification. The relative cost of each research experiment is elicited from experts, which enables identification of efficient research portfolios-combinations of experiments that lead to the greatest improvement in hazard classification at the lowest cost. Nanoparticle shape, diameter, solubility and surface reactivity were most frequently identified within efficient portfolios in our results.

Investigation of a Macromechanical Approach to Analyzing Triaxially-Braided Polymer Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Blinzler, Brina J.; Binienda, Wieslaw K.

2010-01-01

A macro level finite element-based model has been developed to simulate the mechanical and impact response of triaxially-braided polymer matrix composites. In the analytical model, the triaxial braid architecture is simulated by using four parallel shell elements, each of which is modeled as a laminated composite. The commercial transient dynamic finite element code LS-DYNA is used to conduct the simulations, and a continuum damage mechanics model internal to LS-DYNA is used as the material constitutive model. The material stiffness and strength values required for the constitutive model are determined based on coupon level tests on the braided composite. Simulations of quasi-static coupon tests of a representative braided composite are conducted. Varying the strength values that are input to the material model is found to have a significant influence on the effective material response predicted by the finite element analysis, sometimes in ways that at first glance appear non-intuitive. A parametric study involving the input strength parameters provides guidance on how the analysis model can be improved.

Using model order tests to determine sensory inputs in a motion study

NASA Technical Reports Server (NTRS)

Repperger, D. W.; Junker, A. M.

1977-01-01

In the study of motion effects on tracking performance, a problem of interest is the determination of what sensory inputs a human uses in controlling his tracking task. In the approach presented here a simple canonical model (FID or a proportional, integral, derivative structure) is used to model the human's input-output time series. A study of significant changes in reduction of the output error loss functional is conducted as different permutations of parameters are considered. Since this canonical model includes parameters which are related to inputs to the human (such as the error signal, its derivatives and integration), the study of model order is equivalent to the study of which sensory inputs are being used by the tracker. The parameters are obtained which have the greatest effect on reducing the loss function significantly. In this manner the identification procedure converts the problem of testing for model order into the problem of determining sensory inputs.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.

Certification Testing Methodology for Composite Structure. Volume 2. Methodology Development

DTIC Science & Technology

1986-10-01

parameter, sample size and fa- tigue test duration. The required input are 1. Residual strength Weibull shape parameter ( ALPR ) 2. Fatigue life Weibull shape...INPUT STRENGTH ALPHA’) READ(*,*) ALPR ALPRI = 1.O/ ALPR WRITE(*, 2) 2 FORMAT( 2X, ’PLEASE INPUT LIFE ALPHA’) READ(*,*) ALPL ALPLI - 1.0/ALPL WRITE(*, 3...3 FORMAT(2X,’PLEASE INPUT SAMPLE SIZE’) READ(*,*) N AN - N WRITE(*,4) 4 FORMAT(2X,’PLEASE INPUT TEST DURATION’) READ(*,*) T RALP - ALPL/ ALPR ARGR - 1

User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.

Quantifying uncertainty in NDSHA estimates due to earthquake catalogue

NASA Astrophysics Data System (ADS)

Magrin, Andrea; Peresan, Antonella; Vaccari, Franco; Panza, Giuliano

2014-05-01

The procedure for the neo-deterministic seismic zoning, NDSHA, is based on the calculation of synthetic seismograms by the modal summation technique. This approach makes use of information about the space distribution of large magnitude earthquakes, which can be defined based on seismic history and seismotectonics, as well as incorporating information from a wide set of geological and geophysical data (e.g., morphostructural features and ongoing deformation processes identified by earth observations). Hence the method does not make use of attenuation models (GMPE), which may be unable to account for the complexity of the product between seismic source tensor and medium Green function and are often poorly constrained by the available observations. NDSHA defines the hazard from the envelope of the values of ground motion parameters determined considering a wide set of scenario earthquakes; accordingly, the simplest outcome of this method is a map where the maximum of a given seismic parameter is associated to each site. In NDSHA uncertainties are not statistically treated as in PSHA, where aleatory uncertainty is traditionally handled with probability density functions (e.g., for magnitude and distance random variables) and epistemic uncertainty is considered by applying logic trees that allow the use of alternative models and alternative parameter values of each model, but the treatment of uncertainties is performed by sensitivity analyses for key modelling parameters. To fix the uncertainty related to a particular input parameter is an important component of the procedure. The input parameters must account for the uncertainty in the prediction of fault radiation and in the use of Green functions for a given medium. A key parameter is the magnitude of sources used in the simulation that is based on catalogue informations, seismogenic zones and seismogenic nodes. Because the largest part of the existing catalogues is based on macroseismic intensity, a rough estimate of ground motion error can therefore be the factor of 2, intrinsic in MCS scale. We tested this hypothesis by the analysis of uncertainty in ground motion maps due to the catalogue random errors in magnitude and localization.

Potential for noninvasive assessment of lung inhomogeneity using highly precise, highly time-resolved measurements of gas exchange

PubMed Central

Mountain, James E.; Santer, Peter; O’Neill, David P.; Smith, Nicholas M. J.; Ciaffoni, Luca; Couper, John H.; Ritchie, Grant A. D.; Hancock, Gus; Whiteley, Jonathan P.

2018-01-01

Inhomogeneity in the lung impairs gas exchange and can be an early marker of lung disease. We hypothesized that highly precise measurements of gas exchange contain sufficient information to quantify many aspects of the inhomogeneity noninvasively. Our aim was to explore whether one parameterization of lung inhomogeneity could both fit such data and provide reliable parameter estimates. A mathematical model of gas exchange in an inhomogeneous lung was developed, containing inhomogeneity parameters for compliance, vascular conductance, and dead space, all relative to lung volume. Inputs were respiratory flow, cardiac output, and the inspiratory and pulmonary arterial gas compositions. Outputs were expiratory and pulmonary venous gas compositions. All values were specified every 10 ms. Some parameters were set to physiologically plausible values. To estimate the remaining unknown parameters and inputs, the model was embedded within a nonlinear estimation routine to minimize the deviations between model and data for CO2, O2, and N2 flows during expiration. Three groups, each of six individuals, were studied: young (20–30 yr); old (70–80 yr); and patients with mild to moderate chronic obstructive pulmonary disease (COPD). Each participant undertook a 15-min measurement protocol six times. For all parameters reflecting inhomogeneity, highly significant differences were found between the three participant groups (P < 0.001, ANOVA). Intraclass correlation coefficients were 0.96, 0.99, and 0.94 for the parameters reflecting inhomogeneity in deadspace, compliance, and vascular conductance, respectively. We conclude that, for the particular participants selected, highly repeatable estimates for parameters reflecting inhomogeneity could be obtained from noninvasive measurements of respiratory gas exchange. NEW & NOTEWORTHY This study describes a new method, based on highly precise measures of gas exchange, that quantifies three distributions that are intrinsic to the lung. These distributions represent three fundamentally different types of inhomogeneity that together give rise to ventilation-perfusion mismatch and result in impaired gas exchange. The measurement technique has potentially broad clinical applicability because it is simple for both patient and operator, it does not involve ionizing radiation, and it is completely noninvasive. PMID:29074714

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Performance of Solar Proxy Options of IRI-Plas Model for Equinox Seasons

NASA Astrophysics Data System (ADS)

Sezen, Umut; Gulyaeva, Tamara L.; Arikan, Feza

2018-02-01

International Reference Ionosphere (IRI) is the most acclaimed climatic model of the ionosphere. Since 2009, the range of the IRI model has been extended to the Global Positioning System (GPS) orbital height of 20,000 km in the plasmasphere. The new model, which is called IRI extended to Plasmasphere (IRI-Plas), can input not only the ionosonde foF2 and hmF2 but also the GPS-total electron content (TEC). IRI-Plas has been provided at www.ionolab.org, where online computation of ionospheric parameters is accomplished through a user-friendly interface. The solar proxies that are available in IRI-Plas can be listed as sunspot number (SSN1), SSN2, F10.7, global electron content (GEC), TEC, IG, Mg II, Lyman-α, and GEC_RZ. In this study, ionosonde foF2 data are compared with IRI-Plas foF2 values with the Consultative Committee International Radio (CCIR) and International Union of Radio Science (URSI) model choices for each solar proxy, with or without the GPS-TEC input for the equinox months of October 2011 and March 2015. It has been observed that the best fitting model choices in Root Mean Square (RMS) and Normalized RMS (NRMS) sense are the Jet Propulsion Laboratory global ionospheric maps-TEC input with Lyman-α solar proxy option for both months. The input of TEC definitely lowers the difference between the model and ionosonde foF2 values. The IG and Mg II solar proxies produce similar model foF2 values, and they usually are the second and third best fits to the ionosonde foF2 for the midlatitude ionosphere. In high-latitude regions, Jet Propulsion Laboratory global ionospheric map-TEC inputs to IRI-Plas with Lyman-α, GEC_RZ, and TEC solar proxies are the best choices. In equatorial region, the best fitting solar proxies are IG, Lyman-α, and Mg II.

First-spike latency in Hodgkin's three classes of neurons.

PubMed

Wang, Hengtong; Chen, Yueling; Chen, Yong

2013-07-07

We study the first-spike latency (FSL) in Hodgkin's three classes of neurons with the Morris-Lecar neuron model. It is found that all the three classes of neurons can encode an external stimulus into FSLs. With DC inputs, the FSLs of all of the neurons decrease with input intensity. With input current decreased to the threshold, class 1 neurons show an arbitrary long FSL whereas class 2 and 3 neurons exhibit the short-limit FSLs. When the input current is sinusoidal, the amplitude, frequency and initial phase can be encoded by all the three classes of neurons. The FSLs of all of the neurons decrease with the input amplitude and frequency. When the input frequency is too high, all of the neurons respond with infinite FSLs. When the initial phase increases, the FSL decreases and then jumps to a maximal value and finally decreases linearly. With changes in the input parameters, the FSLs of the class 1 and 2 neurons exhibit similar properties. However, the FSL of the class 3 neurons became slightly longer and only produces responses for a narrow range of initial phase if input frequencies are low. Moreover, our results also show that the FSL and firing rate responses are mutually independent processes and that neurons can encode an external stimulus into different FSLs and firing rates simultaneously. This finding is consistent with the current theory of dual or multiple complementary coding mechanisms. Copyright © 2013 Elsevier Ltd. All rights reserved.

System and method to determine electric motor efficiency nonintrusively

DOEpatents

Lu, Bin [Kenosha, WI; Habetler, Thomas G [Snellville, GA; Harley, Ronald G [Lawrenceville, GA

2011-08-30

A system and method for nonintrusively determining electric motor efficiency includes a processor programed to, while the motor is in operation, determine a plurality of stator input currents, electrical input data, a rotor speed, a value of stator resistance, and an efficiency of the motor based on the determined rotor speed, the value of stator resistance, the plurality of stator input currents, and the electrical input data. The determination of the rotor speed is based on one of the input power and the plurality of stator input currents. The determination of the value of the stator resistance is based on at least one of a horsepower rating and a combination of the plurality of stator input currents and the electrical input data. The electrical input data includes at least one of an input power and a plurality of stator input voltages.

A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network.

PubMed

Savitha, R; Suresh, S; Sundararajan, N

2012-08-01

This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-valued neural network called "Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN)". McFCRN has two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Relaxation Network (FCRN) with a fully complex-valued Gaussian like activation function (sech) in the hidden layer and an exponential activation function in the output layer forms the cognitive component. The meta-cognitive component contains a self-regulatory learning mechanism which controls the learning ability of FCRN by deciding what-to-learn, when-to-learn and how-to-learn from a sequence of training data. The input parameters of cognitive components are chosen randomly and the output parameters are estimated by minimizing a logarithmic error function. The problem of explicit minimization of magnitude and phase errors in the logarithmic error function is converted to system of linear equations and output parameters of FCRN are computed analytically. McFCRN starts with zero hidden neuron and builds the number of neurons required to approximate the target function. The meta-cognitive component selects the best learning strategy for FCRN to acquire the knowledge from training data and also adapts the learning strategies to implement best human learning components. Performance studies on a function approximation and real-valued classification problems show that proposed McFCRN performs better than the existing results reported in the literature. Copyright © 2012 Elsevier Ltd. All rights reserved.

Parameterisation of Biome BGC to assess forest ecosystems in Africa

NASA Astrophysics Data System (ADS)

Gautam, Sishir; Pietsch, Stephan A.

2010-05-01

African forest ecosystems are an important environmental and economic resource. Several studies show that tropical forests are critical to society as economic, environmental and societal resources. Tropical forests are carbon dense and thus play a key role in climate change mitigation. Unfortunately, the response of tropical forests to environmental change is largely unknown owing to insufficient spatially extensive observations. Developing regions like Africa where records of forest management for long periods are unavailable the process-based ecosystem simulation model - BIOME BGC could be a suitable tool to explain forest ecosystem dynamics. This ecosystem simulation model uses descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns within vegetation functional types, or biomes. Undocumented parameters for larger-resolution simulations are currently the major limitations to regional modelling in African forest ecosystems. This study was conducted to document input parameters for BIOME-BGC for major natural tropical forests in the Congo basin. Based on available literature and field measurements updated values for turnover and mortality, allometry, carbon to nitrogen ratios, allocation of plant material to labile, cellulose, and lignin pools, tree morphology and other relevant factors were assigned. Daily climate input data for the model applications were generated using the statistical weather generator MarkSim. The forest was inventoried at various sites and soil samples of corresponding stands across Gabon were collected. Carbon and nitrogen in the collected soil samples were determined from soil analysis. The observed tree volume, soil carbon and soil nitrogen were then compared with the simulated model outputs to evaluate the model performance. Furthermore, the simulation using Congo Basin specific parameters and generalised BIOME BGC parameters for tropical evergreen broadleaved tree species were also executed and the simulated results compared. Once the model was optimised for forests in the Congo basin it was validated against observed tree volume, soil carbon and soil nitrogen from a set of independent plots.

Dynamic optimization and adaptive controller design

NASA Astrophysics Data System (ADS)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Aeroelastic Modeling of X-56A Stiff-Wing Configuration Flight Test Data

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Boucher, Matthew J.

2017-01-01

Aeroelastic stability and control derivatives for the X-56A Multi-Utility Technology Testbed (MUTT), in the stiff-wing configuration, were estimated from flight test data using the output-error method. Practical aspects of the analysis are discussed. The orthogonal phase-optimized multisine inputs provided excellent data information for aeroelastic modeling. Consistent parameter estimates were determined using output error in both the frequency and time domains. The frequency domain analysis converged faster and was less sensitive to starting values for the model parameters, which was useful for determining the aeroelastic model structure and obtaining starting values for the time domain analysis. Including a modal description of the structure from a finite element model reduced the complexity of the estimation problem and improved the modeling results. Effects of reducing the model order on the short period stability and control derivatives were investigated.

Influence of phonon-phonon coupling on superconducting state in honeycomb-type crystal lattice

NASA Astrophysics Data System (ADS)

Drzazga, E. A.; Szczȩśniak, R.; Domagalska, I. A.

2018-01-01

We have taken into account the superconducting state inducing in the crystal lattice of the honeycomb-type. In the framework of the Eliashberg theory, we have determined the thermodynamic properties of the system. The phonon spectral function, which is the input parameter in the Eliashberg equations, has been calculated by using the thermodynamic Green functions. We have considered the model of the coupled Einstein oscillators with frequency ω0 = 100 meV. We have shown that the increasing inter-phonon coupling constant (f) causes the rapid growth of the critical temperature ([TC]max = 36.2 K) just below the maximum value of f equal to 0.25ω0. Simultaneously, the order parameter and the thermodynamic critical field take the values increasingly distant from the predictions of the BCS theory, which results from the strong-coupling and the retardation effects.

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

PubMed

Tuckwell, Henry C

2006-01-01

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.

Groebner Basis Solutions to Satellite Trajectory Control by Pole Placement

NASA Astrophysics Data System (ADS)

Kukelova, Z.; Krsek, P.; Smutny, V.; Pajdla, T.

2013-09-01

Satellites play an important role, e.g., in telecommunication, navigation and weather monitoring. Controlling their trajectories is an important problem. In [1], an approach to the pole placement for the synthesis of a linear controller has been presented. It leads to solving five polynomial equations in nine unknown elements of the state space matrices of a compensator. This is an underconstrained system and therefore four of the unknown elements need to be considered as free parameters and set to some prior values to obtain a system of five equations in five unknowns. In [1], this system was solved for one chosen set of free parameters with the help of Dixon resultants. In this work, we study and present Groebner basis solutions to this problem of computation of a dynamic compensator for the satellite for different combinations of input free parameters. We show that the Groebner basis method for solving systems of polynomial equations leads to very simple solutions for all combinations of free parameters. These solutions require to perform only the Gauss-Jordan elimination of a small matrix and computation of roots of a single variable polynomial. The maximum degree of this polynomial is not greater than six in general but for most combinations of the input free parameters its degree is even lower. [1] B. Palancz. Application of Dixon resultant to satellite trajectory control by pole placement. Journal of Symbolic Computation, Volume 50, March 2013, Pages 79-99, Elsevier.

Cost-effectiveness models for chronic obstructive pulmonary disease: cross-model comparison of hypothetical treatment scenarios.

PubMed

Hoogendoorn, Martine; Feenstra, Talitha L; Asukai, Yumi; Borg, Sixten; Hansen, Ryan N; Jansson, Sven-Arne; Samyshkin, Yevgeniy; Wacker, Margarethe; Briggs, Andrew H; Lloyd, Adam; Sullivan, Sean D; Rutten-van Mölken, Maureen P M H

2014-07-01

To compare different chronic obstructive pulmonary disease (COPD) cost-effectiveness models with respect to structure and input parameters and to cross-validate the models by running the same hypothetical treatment scenarios. COPD modeling groups simulated four hypothetical interventions with their model and compared the results with a reference scenario of no intervention. The four interventions modeled assumed 1) 20% reduction in decline in lung function, 2) 25% reduction in exacerbation frequency, 3) 10% reduction in all-cause mortality, and 4) all these effects combined. The interventions were simulated for a 5-year and lifetime horizon with standardization, if possible, for sex, age, COPD severity, smoking status, exacerbation frequencies, mortality due to other causes, utilities, costs, and discount rates. Furthermore, uncertainty around the outcomes of intervention four was compared. Seven out of nine contacted COPD modeling groups agreed to participate. The 5-year incremental cost-effectiveness ratios (ICERs) for the most comprehensive intervention, intervention four, was €17,000/quality-adjusted life-year (QALY) for two models, €25,000 to €28,000/QALY for three models, and €47,000/QALY for the remaining two models. Differences in the ICERs could mainly be explained by differences in input values for disease progression, exacerbation-related mortality, and all-cause mortality, with high input values resulting in low ICERs and vice versa. Lifetime results were mainly affected by the input values for mortality. The probability of intervention four to be cost-effective at a willingness-to-pay value of €50,000/QALY was 90% to 100% for five models and about 70% and 50% for the other two models, respectively. Mortality was the most important factor determining the differences in cost-effectiveness outcomes between models. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Two statistics for evaluating parameter identifiability and error reduction

USGS Publications Warehouse

Doherty, John; Hunt, Randall J.

2009-01-01

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu

2016-01-01

The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Li, Yong; Conte, Joel P.

2017-09-01

A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.

A Computational approach in optimizing process parameters of GTAW for SA 106 Grade B steel pipes using Response surface methodology

NASA Astrophysics Data System (ADS)

Sumesh, A.; Sai Ramnadh, L. V.; Manish, P.; Harnath, V.; Lakshman, V.

2016-09-01

Welding is one of the most common metal joining techniques used in industry for decades. As in the global manufacturing scenario the products should be more cost effective. Therefore the selection of right process with optimal parameters will help the industry in minimizing their cost of production. SA 106 Grade B steel has a wide application in Automobile chassis structure, Boiler tubes and pressure vessels industries. Employing central composite design the process parameters for Gas Tungsten Arc Welding was optimized. The input parameters chosen were weld current, peak current and frequency. The joint tensile strength was the response considered in this study. Analysis of variance was performed to determine the statistical significance of the parameters and a Regression analysis was performed to determine the effect of input parameters over the response. From the experiment the maximum tensile strength obtained was 95 KN reported for a weld current of 95 Amp, frequency of 50 Hz and peak current of 100 Amp. With an aim of maximizing the joint strength using Response optimizer a target value of 100 KN is selected and regression models were optimized. The output results are achievable with a Weld current of 62.6148 Amp, Frequency of 23.1821 Hz, and Peak current of 65.9104 Amp. Using Die penetration test the weld joints were also classified in to 2 categories as good weld and weld with defect. This will also help in getting a defect free joint when welding is performed using GTAW process.

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

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

Adams, Brian M.; Coleman, Kayla; Gilkey, Lindsay N.

Sandia’s Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a physics-based computational model. This can lend efficiency and rigor to manual parameter perturbation studies already being conducted by analysts. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, riskmore » analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. Dakota algorithms enrich complex science and engineering models, enabling an analyst to answer crucial questions of - Sensitivity: Which are the most important input factors or parameters entering the simulation, and how do they influence key outputs?; Uncertainty: What is the uncertainty or variability in simulation output, given uncertainties in input parameters? How safe, reliable, robust, or variable is my system? (Quantification of margins and uncertainty, QMU); Optimization: What parameter values yield the best performing design or operating condition, given constraints? Calibration: What models and/or parameters best match experimental data? In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers.« less

Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control.

PubMed

Romani, Santina; Cevoli, Chiara; Fabbri, Angelo; Alessandrini, Laura; Dalla Rosa, Marco

2012-09-01

An electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability. Actually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization. © 2012 Institute of Food Technologists®

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China.

PubMed

Wen, Xiaohu; Fang, Jing; Diao, Meina; Zhang, Chuanqi

2013-05-01

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl(-)), calcium (Ca(2+)), total alkalinity, total hardness, nitrate nitrogen (NO3-N), and ammonical nitrogen (NH4-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO3-N, NH4-N, and Ca(2+). Cl(-) was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.

PubMed

Torres, Rita; Pereira, Elisa; Vasconcelos, Vítor; Teles, Luís Oliva

2011-06-01

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

Efficient stochastic approaches for sensitivity studies of an Eulerian large-scale air pollution model

NASA Astrophysics Data System (ADS)

Dimov, I.; Georgieva, R.; Todorov, V.; Ostromsky, Tz.

2017-10-01

Reliability of large-scale mathematical models is an important issue when such models are used to support decision makers. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is crucial for improving the reliability of mathematical models. A comprehensive experimental study of Monte Carlo algorithms based on Sobol sequences for multidimensional numerical integration has been done. A comparison with Latin hypercube sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The concentration values have been generated by the Unified Danish Eulerian Model. The sensitivity study has been done for the areas of several European cities with different geographical locations. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing small by value sensitivity indices. It is a crucial element since even small indices may be important to be estimated in order to achieve a more accurate distribution of inputs influence and a more reliable interpretation of the mathematical model results.

Quantification of tumor perfusion using dynamic contrast-enhanced ultrasound: impact of mathematical modeling

NASA Astrophysics Data System (ADS)

Doury, Maxime; Dizeux, Alexandre; de Cesare, Alain; Lucidarme, Olivier; Pellot-Barakat, Claire; Bridal, S. Lori; Frouin, Frédérique

2017-02-01

Dynamic contrast-enhanced ultrasound has been proposed to monitor tumor therapy, as a complement to volume measurements. To assess the variability of perfusion parameters in ideal conditions, four consecutive test-retest studies were acquired in a mouse tumor model, using controlled injections. The impact of mathematical modeling on parameter variability was then investigated. Coefficients of variation (CV) of tissue blood volume (BV) and tissue blood flow (BF) based-parameters were estimated inside 32 sub-regions of the tumors, comparing the log-normal (LN) model with a one-compartment model fed by an arterial input function (AIF) and improved by the introduction of a time delay parameter. Relative perfusion parameters were also estimated by normalization of the LN parameters and normalization of the one-compartment parameters estimated with the AIF, using a reference tissue (RT) region. A direct estimation (rRTd) of relative parameters, based on the one-compartment model without using the AIF, was also obtained by using the kinetics inside the RT region. Results of test-retest studies show that absolute regional parameters have high CV, whatever the approach, with median values of about 30% for BV, and 40% for BF. The positive impact of normalization was established, showing a coherent estimation of relative parameters, with reduced CV (about 20% for BV and 30% for BF using the rRTd approach). These values were significantly lower (p  <  0.05) than the CV of absolute parameters. The rRTd approach provided the smallest CV and should be preferred for estimating relative perfusion parameters.

Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope

NASA Astrophysics Data System (ADS)

Aiello, S.; Akrame, S. E.; Ameli, F.; Anassontzis, E. G.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anton, G.; Ardid, M.; Aublin, J.; Avgitas, T.; Baars, M.; Bagatelas, C.; Barbarino, G.; Baret, B.; Barrios-Martí, J.; Belias, A.; Berbee, E.; van den Berg, A.; Bertin, V.; Biagi, S.; Biagioni, A.; Biernoth, C.; Bormuth, R.; Boumaaza, J.; Bourret, S.; Bouwhuis, M.; Bozza, C.; Brânzaş, H.; Briukhanova, N.; Bruijn, R.; Brunner, J.; Buis, E.; Buompane, R.; Busto, J.; Calvo, D.; Capone, A.; Caramete, L.; Celli, S.; Chabab, M.; Cherubini, S.; Chiarella, V.; Chiarusi, T.; Circella, M.; Cocimano, R.; Coelho, J. A. B.; Coleiro, A.; Colomer Molla, M.; Coniglione, R.; Coyle, P.; Creusot, A.; Cuttone, G.; D'Onofrio, A.; Dallier, R.; De Sio, C.; Di Palma, I.; Díaz, A. F.; Distefano, C.; Domi, A.; Donà, R.; Donzaud, C.; Dornic, D.; Dörr, M.; Durocher, M.; Eberl, T.; van Eijk, D.; El Bojaddaini, I.; Elsaesser, D.; Enzenhöfer, A.; Ferrara, G.; Fusco, L. A.; Gal, T.; Garufi, F.; Gauchery, S.; Geißelsöder, S.; Gialanella, L.; Giorgio, E.; Giuliante, A.; Gozzini, S. R.; Ruiz, R. Gracia; Graf, K.; Grasso, D.; Grégoire, T.; Grella, G.; Hallmann, S.; van Haren, H.; Heid, T.; Heijboer, A.; Hekalo, A.; Hernández-Rey, J. J.; Hofestädt, J.; Illuminati, G.; James, C. W.; Jongen, M.; Jongewaard, B.; de Jong, M.; de Jong, P.; Kadler, M.; Kalaczyński, P.; Kalekin, O.; Katz, U. F.; Chowdhury, N. R. Khan; Kieft, G.; Kießling, D.; Koffeman, E. N.; Kooijman, P.; Kouchner, A.; Kreter, M.; Kulikovskiy, V.; Lahmann, R.; Le Breton, R.; Leone, F.; Leonora, E.; Levi, G.; Lincetto, M.; Lonardo, A.; Longhitano, F.; Lotze, M.; Loucatos, S.; Maggi, G.; Mańczak, J.; Mannheim, K.; Margiotta, A.; Marinelli, A.; Markou, C.; Martin, L.; Martínez-Mora, J. A.; Martini, A.; Marzaioli, F.; Mele, R.; Melis, K. W.; Migliozzi, P.; Migneco, E.; Mijakowski, P.; Miranda, L. S.; Mollo, C. M.; Morganti, M.; Moser, M.; Moussa, A.; Muller, R.; Musumeci, M.; Nauta, L.; Navas, S.; Nicolau, C. A.; Nielsen, C.; Organokov, M.; Orlando, A.; Panagopoulos, V.; Papalashvili, G.; Papaleo, R.; Păvălaş, G. E.; Pellegrini, G.; Pellegrino, C.; Pérez Romero, J.; Perrin-Terrin, M.; Piattelli, P.; Pikounis, K.; Pisanti, O.; Poirè, C.; Polydefki, G.; Poma, G. E.; Popa, V.; Post, M.; Pradier, T.; Pühlhofer, G.; Pulvirenti, S.; Quinn, L.; Raffaelli, F.; Randazzo, N.; Razzaque, S.; Real, D.; Resvanis, L.; Reubelt, J.; Riccobene, G.; Richer, M.; Rovelli, A.; Salvadori, I.; Samtleben, D. F. E.; Sánchez Losa, A.; Sanguineti, M.; Santangelo, A.; Sapienza, P.; Schermer, B.; Sciacca, V.; Seneca, J.; Sgura, I.; Shanidze, R.; Sharma, A.; Simeone, F.; Sinopoulou, A.; Spisso, B.; Spurio, M.; Stavropoulos, D.; Steijger, J.; Stellacci, S. M.; Strandberg, B.; Stransky, D.; Stüven, T.; Taiuti, M.; Tatone, F.; Tayalati, Y.; Tenllado, E.; Thakore, T.; Timmer, P.; Trovato, A.; Tsagkli, S.; Tzamariudaki, E.; Tzanetatos, D.; Valieri, C.; Vallage, B.; Van Elewyck, V.; Versari, F.; Viola, S.; Vivolo, D.; Volkert, M.; de Waardt, L.; Wilms, J.; de Wolf, E.; Zaborov, D.; Zornoza, J. D.; Zúñiga, J.

2018-05-01

The Hamamatsu R12199-02 3-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum efficiency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.

Evaluation of a Physiologically Based Pharmacokinetic (PBPK) Model Used to Develop Health Protective Levels for Trichloroethylene

DTIC Science & Technology

2017-02-28

is the response variable resulting from an input parameter value of θ+. EPA (2011) used a variation of this equation to calculate their...EPA (2011) used both individual and grouped data. While individual data are preferred as it maintains any intra-individual variation , it is often...grouped data, the user would, ideally, account for the loss of intra-individual variation in some manner; however, there is not a commonly accepted

MWRRET Value-Added Product: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer (MWR) Datasets May 2009

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

Gaustad, KL; Turner, DD

2009-05-30

This report provides a short description of the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) microwave radiometer (MWR) RETrievel (MWRRET) value-added product (VAP) algorithm. This algorithm utilizes a complementary physical retrieval method and applies brightness temperature offsets to reduce spurious liquid water path (LWP) bias in clear skies resulting in significantly improved precipitable water vapor (PWV) and LWP retrievals. We present a general overview of the technique, input parameters, output products, and describe data quality checks. A more complete discussion of the theory and results is given in Turner et al. (2007b).

MWRRET Value-Added Product: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer (MWR) Data Sets (Revision 2)

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

Gaustad, KL; Turner, DD; McFarlane, SA

2011-07-25

This report provides a short description of the Atmospheric Radiation Measurement (ARM) Climate Research Facility microwave radiometer (MWR) Retrieval (MWRRET) value-added product (VAP) algorithm. This algorithm utilizes a complementary physical retrieval method and applies brightness temperature offsets to reduce spurious liquid water path (LWP) bias in clear skies resulting in significantly improved precipitable water vapor (PWV) and LWP retrievals. We present a general overview of the technique, input parameters, output products, and describe data quality checks. A more complete discussion of the theory and results is given in Turner et al. (2007b).

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Comprehensive optimization of friction stir weld parameters of lap joint AA1100 plates using artificial neural networks and modified NSGA-II

NASA Astrophysics Data System (ADS)

Khalkhali, Abolfazl; Ebrahimi-Nejad, Salman; Geran Malek, Nima

2018-06-01

Friction stir welding (FSW) process overcomes many difficulties arising in conventional fusion welding processes of aluminum alloys. The current paper presents a comprehensive investigation on the effects of rotational speed, traverse speed, tool tilt angle and tool pin profile on the longitudinal force, axial force, maximum temperature, tensile strength, percent elongation, grain size, micro-hardness of welded zone and welded zone thickness of AA1100 aluminum alloy sheets. Design of experiments (DOE) was applied using the Taguchi approach and subsequently, effects of the input parameter on process outputs were investigated using analysis of variance (ANOVA). A perceptron neural network model was developed to find a correlation between the inputs and outputs. Multi-objective optimization using modified NSGA-II was implemented followed by NIP and TOPSIS approaches to propose optimum points for each of the square, pentagon, hexagon, and circular pin profiles. Results indicate that the optimization process can reach horizontal and vertical forces as low as 1452 N and 2913 N, respectively and a grain size as low as 2 μm. This results in hardness values of up to 57.2 and tensile strength, elongation and joint thickness of 2126 N, 5.9% and 3.7 mm, respectively. The maximum operating temperature can also reach a sufficiently high value of 374 °C to provide adequate material flow.

Stimulus dependence of local field potential spectra: experiment versus theory.

PubMed

Barbieri, Francesca; Mazzoni, Alberto; Logothetis, Nikos K; Panzeri, Stefano; Brunel, Nicolas

2014-10-29

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings. Copyright © 2014 the authors 0270-6474/14/3414589-17$15.00/0.

Global Nitrous Oxide Emissions from Agricultural Soils: Magnitude and Uncertainties Associated with Input Data and Model Parameters

NASA Astrophysics Data System (ADS)

Xu, R.; Tian, H.; Pan, S.; Yang, J.; Lu, C.; Zhang, B.

2016-12-01

Human activities have caused significant perturbations of the nitrogen (N) cycle, resulting in about 21% increase of atmospheric N2O concentration since the pre-industrial era. This large increase is mainly caused by intensive agricultural activities including the application of nitrogen fertilizer and the expansion of leguminous crops. Substantial efforts have been made to quantify the global and regional N2O emission from agricultural soils in the last several decades using a wide variety of approaches, such as ground-based observation, atmospheric inversion, and process-based model. However, large uncertainties exist in those estimates as well as methods themselves. In this study, we used a coupled biogeochemical model (DLEM) to estimate magnitude, spatial, and temporal patterns of N2O emissions from global croplands in the past five decades (1961-2012). To estimate uncertainties associated with input data and model parameters, we have implemented a number of simulation experiments with DLEM, accounting for key parameter values that affect calculation of N2O fluxes (i.e., maximum nitrification and denitrification rates, N fixation rate, and the adsorption coefficient for soil ammonium and nitrate), different sets of input data including climate, land management practices (i.e., nitrogen fertilizer types, application rates and timings, with/without irrigation), N deposition, and land use and land cover change. This work provides a robust estimate of global N2O emissions from agricultural soils as well as identifies key gaps and limitations in the existing model and data that need to be investigated in the future.

Cognitive diagnosis modelling incorporating item response times.

PubMed

Zhan, Peida; Jiao, Hong; Liao, Dandan

2018-05-01

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters. © 2017 The British Psychological Society.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

NASA Astrophysics Data System (ADS)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

Economic design of control charts considering process shift distributions

NASA Astrophysics Data System (ADS)

Vommi, Vijayababu; Kasarapu, Rukmini V.

2014-09-01

Process shift is an important input parameter in the economic design of control charts. Earlier control chart designs considered constant shifts to occur in the mean of the process for a given assignable cause. This assumption has been criticized by many researchers since it may not be realistic to produce a constant shift whenever an assignable cause occurs. To overcome this difficulty, in the present work, a distribution for the shift parameter has been considered instead of a single value for a given assignable cause. Duncan's economic design model for chart has been extended to incorporate the distribution for the process shift parameter. It is proposed to minimize total expected loss-cost to obtain the control chart parameters. Further, three types of process shifts namely, positively skewed, uniform and negatively skewed distributions are considered and the situations where it is appropriate to use the suggested methodology are recommended.

An adaptive tracking observer for failure-detection systems

NASA Technical Reports Server (NTRS)

Sidar, M.

1982-01-01

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.

A Simple Model of Cirrus Horizontal Inhomogeneity and Cloud Fraction

NASA Technical Reports Server (NTRS)

Smith, Samantha A.; DelGenio, Anthony D.

1998-01-01

A simple model of horizontal inhomogeneity and cloud fraction in cirrus clouds has been formulated on the basis that all internal horizontal inhomogeneity in the ice mixing ratio is due to variations in the cloud depth, which are assumed to be Gaussian. The use of such a model was justified by the observed relationship between the normalized variability of the ice water mixing ratio (and extinction) and the normalized variability of cloud depth. Using radar cloud depth data as input, the model reproduced well the in-cloud ice water mixing ratio histograms obtained from horizontal runs during the FIRE2 cirrus campaign. For totally overcast cases the histograms were almost Gaussian, but changed as cloud fraction decreased to exponential distributions which peaked at the lowest nonzero ice value for cloud fractions below 90%. Cloud fractions predicted by the model were always within 28% of the observed value. The predicted average ice water mixing ratios were within 34% of the observed values. This model could be used in a GCM to produce the ice mixing ratio probability distribution function and to estimate cloud fraction. It only requires basic meteorological parameters, the depth of the saturated layer and the standard deviation of cloud depth as input.

Development and validation of the European Cluster Assimilation Techniques run libraries

NASA Astrophysics Data System (ADS)

Facskó, G.; Gordeev, E.; Palmroth, M.; Honkonen, I.; Janhunen, P.; Sergeev, V.; Kauristie, K.; Milan, S.

2012-04-01

The European Commission funded the European Cluster Assimilation Techniques (ECLAT) project as a collaboration of five leader European universities and research institutes. A main contribution of the Finnish Meteorological Institute (FMI) is to provide a wide range global MHD runs with the Grand Unified Magnetosphere Ionosphere Coupling simulation (GUMICS). The runs are divided in two categories: Synthetic runs investigating the extent of solar wind drivers that can influence magnetospheric dynamics, as well as dynamic runs using measured solar wind data as input. Here we consider the first set of runs with synthetic solar wind input. The solar wind density, velocity and the interplanetary magnetic field had different magnitudes and orientations; furthermore two F10.7 flux values were selected for solar radiation minimum and maximum values. The solar wind parameter values were constant such that a constant stable solution was archived. All configurations were run several times with three different (-15°, 0°, +15°) tilt angles in the GSE X-Z plane. The result of the 192 simulations named so called "synthetic run library" were visualized and uploaded to the homepage of the FMI after validation. Here we present details of these runs.

National assessment of geologic carbon dioxide storage resources: data

USGS Publications Warehouse

,

2013-01-01

In 2012, the U.S. Geological Survey (USGS) completed the national assessment of geologic carbon dioxide storage resources. Its data and results are reported in three publications: the assessment data publication (this report), the assessment results publication (U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013a, USGS Circular 1386), and the assessment summary publication (U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013b, USGS Fact Sheet 2013–3020). This data publication supports the results publication and contains (1) individual storage assessment unit (SAU) input data forms with all input parameters and details on the allocation of the SAU surface land area by State and general land-ownership category; (2) figures representing the distribution of all storage classes for each SAU; (3) a table containing most input data and assessment result values for each SAU; and (4) a pairwise correlation matrix specifying geological and methodological dependencies between SAUs that are needed for aggregation of results.

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Newman, Perry A.; Taylor, Arthur C., III; Green, Lawrence L.

2001-01-01

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Optimal nonlinear codes for the perception of natural colours.

PubMed

von der Twer, T; MacLeod, D I

2001-08-01

We discuss how visual nonlinearity can be optimized for the precise representation of environmental inputs. Such optimization leads to neural signals with a compressively nonlinear input-output function the gradient of which is matched to the cube root of the probability density function (PDF) of the environmental input values (and not to the PDF directly as in histogram equalization). Comparisons between theory and psychophysical and electrophysiological data are roughly consistent with the idea that parvocellular (P) cells are optimized for precision representation of colour: their contrast-response functions span a range appropriately matched to the environmental distribution of natural colours along each dimension of colour space. Thus P cell codes for colour may have been selected to minimize error in the perceptual estimation of stimulus parameters for natural colours. But magnocellular (M) cells have a much stronger than expected saturating nonlinearity; this supports the view that the function of M cells is mainly to detect boundaries rather than to specify contrast or lightness.

The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations

NASA Technical Reports Server (NTRS)

Glasser, M. E.; Rundel, R. D.

1978-01-01

A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.

A method for approximating acoustic-field-amplitude uncertainty caused by environmental uncertainties.

PubMed

James, Kevin R; Dowling, David R

2008-09-01

In underwater acoustics, the accuracy of computational field predictions is commonly limited by uncertainty in environmental parameters. An approximate technique for determining the probability density function (PDF) of computed field amplitude, A, from known environmental uncertainties is presented here. The technique can be applied to several, N, uncertain parameters simultaneously, requires N+1 field calculations, and can be used with any acoustic field model. The technique implicitly assumes independent input parameters and is based on finding the optimum spatial shift between field calculations completed at two different values of each uncertain parameter. This shift information is used to convert uncertain-environmental-parameter distributions into PDF(A). The technique's accuracy is good when the shifted fields match well. Its accuracy is evaluated in range-independent underwater sound channels via an L(1) error-norm defined between approximate and numerically converged results for PDF(A). In 50-m- and 100-m-deep sound channels with 0.5% uncertainty in depth (N=1) at frequencies between 100 and 800 Hz, and for ranges from 1 to 8 km, 95% of the approximate field-amplitude distributions generated L(1) values less than 0.52 using only two field calculations. Obtaining comparable accuracy from traditional methods requires of order 10 field calculations and up to 10(N) when N>1.

Comparison between different sets of suspension parameters and introduction of new modified skyhook control strategy incorporating varying road condition

NASA Astrophysics Data System (ADS)

Abul Kashem, Saad Bin; Ektesabi, Mehran; Nagarajah, Romesh

2012-07-01

This study examines the uncertainties in modelling a quarter car suspension system caused by the effect of different sets of suspension parameters of a corresponding mathematical model. To overcome this problem, 11 sets of identified parameters of a suspension system have been compared, taken from the most recent published work. From this investigation, a set of parameters were chosen which showed a better performance than others in respect of peak amplitude and settling time. These chosen parameters were then used to investigate the performance of a new modified continuous skyhook control strategy with adaptive gain that dictates the vehicle's semi-active suspension system. The proposed system first captures the road profile input over a certain period. Then it calculates the best possible value of the skyhook gain (SG) for the subsequent process. Meanwhile the system is controlled according to the new modified skyhook control law using an initial or previous value of the SG. In this study, the proposed suspension system is compared with passive and other recently reported skyhook controlled semi-active suspension systems. Its performances have been evaluated in terms of ride comfort and road handling performance. The model has been validated in accordance with the international standards of admissible acceleration levels ISO2631 and human vibration perception.

Neural network models for biological waste-gas treatment systems.

PubMed

Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

2011-12-15

This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression coefficient values (R(2)) for the test data set. The results obtained from this modelling work can be useful for obtaining important relationships between different bioreactor parameters and for estimating their safe operating regimes. Copyright © 2011. Published by Elsevier B.V.

iTOUGH2 Universal Optimization Using the PEST Protocol

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

Finsterle, S.A.

2010-07-01

iTOUGH2 (http://www-esd.lbl.gov/iTOUGH2) is a computer program for parameter estimation, sensitivity analysis, and uncertainty propagation analysis [Finsterle, 2007a, b, c]. iTOUGH2 contains a number of local and global minimization algorithms for automatic calibration of a model against measured data, or for the solution of other, more general optimization problems (see, for example, Finsterle [2005]). A detailed residual and estimation uncertainty analysis is conducted to assess the inversion results. Moreover, iTOUGH2 can be used to perform a formal sensitivity analysis, or to conduct Monte Carlo simulations for the examination for prediction uncertainties. iTOUGH2's capabilities are continually enhanced. As the name implies, iTOUGH2more » is developed for use in conjunction with the TOUGH2 forward simulator for nonisothermal multiphase flow in porous and fractured media [Pruess, 1991]. However, iTOUGH2 provides FORTRAN interfaces for the estimation of user-specified parameters (see subroutine USERPAR) based on user-specified observations (see subroutine USEROBS). These user interfaces can be invoked to add new parameter or observation types to the standard set provided in iTOUGH2. They can also be linked to non-TOUGH2 models, i.e., iTOUGH2 can be used as a universal optimization code, similar to other model-independent, nonlinear parameter estimation packages such as PEST [Doherty, 2008] or UCODE [Poeter and Hill, 1998]. However, to make iTOUGH2's optimization capabilities available for use with an external code, the user is required to write some FORTRAN code that provides the link between the iTOUGH2 parameter vector and the input parameters of the external code, and between the output variables of the external code and the iTOUGH2 observation vector. While allowing for maximum flexibility, the coding requirement of this approach limits its applicability to those users with FORTRAN coding knowledge. To make iTOUGH2 capabilities accessible to many application models, the PEST protocol [Doherty, 2007] has been implemented into iTOUGH2. This protocol enables communication between the application (which can be a single 'black-box' executable or a script or batch file that calls multiple codes) and iTOUGH2. The concept requires that for the application model: (1) Input is provided on one or more ASCII text input files; (2) Output is returned to one or more ASCII text output files; (3) The model is run using a system command (executable or script/batch file); and (4) The model runs to completion without any user intervention. For each forward run invoked by iTOUGH2, select parameters cited within the application model input files are then overwritten with values provided by iTOUGH2, and select variables cited within the output files are extracted and returned to iTOUGH2. It should be noted that the core of iTOUGH2, i.e., its optimization routines and related analysis tools, remains unchanged; it is only the communication format between input parameters, the application model, and output variables that are borrowed from PEST. The interface routines have been provided by Doherty [2007]. The iTOUGH2-PEST architecture is shown in Figure 1. This manual contains installation instructions for the iTOUGH2-PEST module, and describes the PEST protocol as well as the input formats needed in iTOUGH2. Examples are provided that demonstrate the use of model-independent optimization and analysis using iTOUGH2.« less

A global sensitivity analysis of crop virtual water content

NASA Astrophysics Data System (ADS)

Tamea, S.; Tuninetti, M.; D'Odorico, P.; Laio, F.; Ridolfi, L.

2015-12-01

The concepts of virtual water and water footprint are becoming widely used in the scientific literature and they are proving their usefulness in a number of multidisciplinary contexts. With such growing interest a measure of data reliability (and uncertainty) is becoming pressing but, as of today, assessments of data sensitivity to model parameters, performed at the global scale, are not known. This contribution aims at filling this gap. Starting point of this study is the evaluation of the green and blue virtual water content (VWC) of four staple crops (i.e. wheat, rice, maize, and soybean) at a global high resolution scale. In each grid cell, the crop VWC is given by the ratio between the total crop evapotranspiration over the growing season and the crop actual yield, where evapotranspiration is determined with a detailed daily soil water balance and actual yield is estimated using country-based data, adjusted to account for spatial variability. The model provides estimates of the VWC at a 5x5 arc minutes and it improves on previous works by using the newest available data and including multi-cropping practices in the evaluation. The model is then used as the basis for a sensitivity analysis, in order to evaluate the role of model parameters in affecting the VWC and to understand how uncertainties in input data propagate and impact the VWC accounting. In each cell, small changes are exerted to one parameter at a time, and a sensitivity index is determined as the ratio between the relative change of VWC and the relative change of the input parameter with respect to its reference value. At the global scale, VWC is found to be most sensitive to the planting date, with a positive (direct) or negative (inverse) sensitivity index depending on the typical season of crop planting date. VWC is also markedly dependent on the length of the growing period, with an increase in length always producing an increase of VWC, but with higher spatial variability for rice than for other crops. The sensitivity to the reference evapotranspiration is highly variable with the considered crop and ranges from positive values (for soybean), to negative values (for rice and maize) and near-zero values for wheat. This variability reflects the different yield response factors of crops, which expresses their tolerance to water stress.

Optimizing pulsed Nd:YAG laser beam welding process parameters to attain maximum ultimate tensile strength for thin AISI316L sheet using response surface methodology and simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Torabi, Amir; Kolahan, Farhad

2018-07-01

Pulsed laser welding is a powerful technique especially suitable for joining thin sheet metals. In this study, based on experimental data, pulsed laser welding of thin AISI316L austenitic stainless steel sheet has been modeled and optimized. The experimental data required for modeling are gathered as per Central Composite Design matrix in Response Surface Methodology (RSM) with full replication of 31 runs. Ultimate Tensile Strength (UTS) is considered as the main quality measure in laser welding. Furthermore, the important process parameters including peak power, pulse duration, pulse frequency and welding speed are selected as input process parameters. The relation between input parameters and the output response is established via full quadratic response surface regression with confidence level of 95%. The adequacy of the regression model was verified using Analysis of Variance technique results. The main effects of each factor and the interactions effects with other factors were analyzed graphically in contour and surface plot. Next, to maximum joint UTS, the best combinations of parameters levels were specified using RSM. Moreover, the mathematical model is implanted into a Simulated Annealing (SA) optimization algorithm to determine the optimal values of process parameters. The results obtained by both SA and RSM optimization techniques are in good agreement. The optimal parameters settings for peak power of 1800 W, pulse duration of 4.5 ms, frequency of 4.2 Hz and welding speed of 0.5 mm/s would result in a welded joint with 96% of the base metal UTS. Computational results clearly demonstrate that the proposed modeling and optimization procedures perform quite well for pulsed laser welding process.

Heat and mass transport during microwave heating of mashed potato in domestic oven--model development, validation, and sensitivity analysis.

PubMed

Chen, Jiajia; Pitchai, Krishnamoorthy; Birla, Sohan; Negahban, Mehrdad; Jones, David; Subbiah, Jeyamkondan

2014-10-01

A 3-dimensional finite-element model coupling electromagnetics and heat and mass transfer was developed to understand the interactions between the microwaves and fresh mashed potato in a 500 mL tray. The model was validated by performing heating of mashed potato from 25 °C on a rotating turntable in a microwave oven, rated at 1200 W, for 3 min. The simulated spatial temperature profiles on the top and bottom layer of the mashed potato showed similar hot and cold spots when compared to the thermal images acquired by an infrared camera. Transient temperature profiles at 6 locations collected by fiber-optic sensors showed good agreement with predicted results, with the root mean square error ranging from 1.6 to 11.7 °C. The predicted total moisture loss matched well with the observed result. Several input parameters, such as the evaporation rate constant, the intrinsic permeability of water and gas, and the diffusion coefficient of water and gas, are not readily available for mashed potato, and they cannot be easily measured experimentally. Reported values for raw potato were used as baseline values. A sensitivity analysis of these input parameters on the temperature profiles and the total moisture loss was evaluated by changing the baseline values to their 10% and 1000%. The sensitivity analysis showed that the gas diffusion coefficient, intrinsic water permeability, and the evaporation rate constant greatly influenced the predicted temperature and total moisture loss, while the intrinsic gas permeability and the water diffusion coefficient had little influence. This model can be used by the food product developers to understand microwave heating of food products spatially and temporally. This tool will allow food product developers to design food package systems that would heat more uniformly in various microwave ovens. The sensitivity analysis of this study will help us determine the most significant parameters that need to be measured accurately for reliable model prediction. © 2014 Institute of Food Technologists®

CalFitter: a web server for analysis of protein thermal denaturation data.

PubMed

Mazurenko, Stanislav; Stourac, Jan; Kunka, Antonin; Nedeljkovic, Sava; Bednar, David; Prokop, Zbynek; Damborsky, Jiri

2018-05-14

Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

Markets, Herding and Response to External Information

PubMed Central

Carro, Adrián; Toral, Raúl; San Miguel, Maxi

2015-01-01

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany’s leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information. PMID:26204451

Exploiting active subspaces to quantify uncertainty in the numerical simulation of the HyShot II scramjet

NASA Astrophysics Data System (ADS)

Constantine, P. G.; Emory, M.; Larsson, J.; Iaccarino, G.

2015-12-01

We present a computational analysis of the reactive flow in a hypersonic scramjet engine with focus on effects of uncertainties in the operating conditions. We employ a novel methodology based on active subspaces to characterize the effects of the input uncertainty on the scramjet performance. The active subspace identifies one-dimensional structure in the map from simulation inputs to quantity of interest that allows us to reparameterize the operating conditions; instead of seven physical parameters, we can use a single derived active variable. This dimension reduction enables otherwise infeasible uncertainty quantification, considering the simulation cost of roughly 9500 CPU-hours per run. For two values of the fuel injection rate, we use a total of 68 simulations to (i) identify the parameters that contribute the most to the variation in the output quantity of interest, (ii) estimate upper and lower bounds on the quantity of interest, (iii) classify sets of operating conditions as safe or unsafe corresponding to a threshold on the output quantity of interest, and (iv) estimate a cumulative distribution function for the quantity of interest.

Stochastic resonance in an array of integrate-and-fire neurons with threshold

NASA Astrophysics Data System (ADS)

Zhou, Bingchang; Qi, Qianqian

2018-06-01

We investigate the phenomenon of stochastic resonance (SR) in parallel integrate-and-fire neuronal arrays with threshold driven by additive noise or signal-dependent noise (SDN) and a noisy input signal. SR occurs in this system. Whether the system is subject to the additive noise or SDN, the input noise η(t) weakens the performance of SR but the array size N and signal parameter I1 promote the performance of SR. Signal parameter I0 promotes the performance of SR for the additive noise, but the peak values of the output signal-to-noise ratio (SNRout) first decrease, then increase as I0 increases for the SDN. Moreover, when N tends to infinity, for the SDN, the curve of SNRout first increases and then decreases, however, for the additive noise, the curve of SNRout increases to reach a plain. By comparing system performance with the additive noise to one with SDN, we also find that the information transmission of a periodic signal with SDN is significantly better than one with the additive noise in limited array size N.

EQ3NR, a computer program for geochemical aqueous speciation-solubility calculations: Theoretical manual, user`s guide, and related documentation (Version 7.0); Part 3

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

Wolery, T.J.

1992-09-14

EQ3NR is an aqueous solution speciation-solubility modeling code. It is part of the EQ3/6 software package for geochemical modeling. It computes the thermodynamic state of an aqueous solution by determining the distribution of chemical species, including simple ions, ion pairs, and complexes, using standard state thermodynamic data and various equations which describe the thermodynamic activity coefficients of these species. The input to the code describes the aqueous solution in terms of analytical data, including total (analytical) concentrations of dissolved components and such other parameters as the pH, pHCl, Eh, pe, and oxygen fugacity. The input may also include a desiredmore » electrical balancing adjustment and various constraints which impose equilibrium with special pure minerals, solid solution end-member components (of specified mole fractions), and gases (of specified fugacities). The code evaluates the degree of disequilibrium in terms of the saturation index (SI = 1og Q/K) and the thermodynamic affinity (A = {minus}2.303 RT log Q/K) for various reactions, such as mineral dissolution or oxidation-reduction in the aqueous solution itself. Individual values of Eh, pe, oxygen fugacity, and Ah (redox affinity) are computed for aqueous redox couples. Equilibrium fugacities are computed for gas species. The code is highly flexible in dealing with various parameters as either model inputs or outputs. The user can specify modification or substitution of equilibrium constants at run time by using options on the input file.« less

Distributed approximating functional fit of the H{sub 3} {ital ab initio} potential-energy data of Liu and Siegbahn

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

Frishman, A.; Hoffman, D.K.; Kouri, D.J.

1997-07-01

We report a distributed approximating functional (DAF) fit of the {ital ab initio} potential-energy data of Liu [J. Chem. Phys. {bold 58}, 1925 (1973)] and Siegbahn and Liu [{ital ibid}. {bold 68}, 2457 (1978)]. The DAF-fit procedure is based on a variational principle, and is systematic and general. Only two adjustable parameters occur in the DAF leading to a fit which is both accurate (to the level inherent in the input data; RMS error of 0.2765 kcal/mol) and smooth ({open_quotes}well-tempered,{close_quotes} in DAF terminology). In addition, the LSTH surface of Truhlar and Horowitz based on this same data [J. Chem. Phys.more » {bold 68}, 2466 (1978)] is itself approximated using only the values of the LSTH surface on the same grid coordinate points as the {ital ab initio} data, and the same DAF parameters. The purpose of this exercise is to demonstrate that the DAF delivers a well-tempered approximation to a known function that closely mimics the true potential-energy surface. As is to be expected, since there is only roundoff error present in the LSTH input data, even more significant figures of fitting accuracy are obtained. The RMS error of the DAF fit, of the LSTH surface at the input points, is 0.0274 kcal/mol, and a smooth fit, accurate to better than 1cm{sup {minus}1}, can be obtained using more than 287 input data points. {copyright} {ital 1997 American Institute of Physics.}« less

Canonical multi-valued input Reed-Muller trees and forms

NASA Technical Reports Server (NTRS)

Perkowski, M. A.; Johnson, P. D.

1991-01-01

There is recently an increased interest in logic synthesis using EXOR gates. The paper introduces the fundamental concept of Orthogonal Expansion, which generalizes the ring form of the Shannon expansion to the logic with multiple-valued (mv) inputs. Based on this concept we are able to define a family of canonical tree circuits. Such circuits can be considered for binary and multiple-valued input cases. They can be multi-level (trees and DAG's) or flattened to two-level AND-EXOR circuits. Input decoders similar to those used in Sum of Products (SOP) PLA's are used in realizations of multiple-valued input functions. In the case of the binary logic the family of flattened AND-EXOR circuits includes several forms discussed by Davio and Green. For the case of the logic with multiple-valued inputs, the family of the flattened mv AND-EXOR circuits includes three expansions known from literature and two new expansions.

Nonlinear system analysis in bipolar integrated circuits

NASA Astrophysics Data System (ADS)

Fang, T. F.; Whalen, J. J.

1980-01-01

Since analog bipolar integrated circuits (IC's) have become important components in modern communication systems, the study of the Radio Frequency Interference (RFI) effects in bipolar IC amplifiers is an important subject for electromagnetic compatibility (EMC) engineering. The investigation has focused on using the nonlinear circuit analysis program (NCAP) to predict RF demodulation effects in broadband bipolar IC amplifiers. The audio frequency (AF) voltage at the IC amplifier output terminal caused by an amplitude modulated (AM) RF signal at the IC amplifier input terminal was calculated and compared to measured values. Two broadband IC amplifiers were investigated: (1) a cascode circuit using a CA3026 dual differential pair; (2) a unity gain voltage follower circuit using a micro A741 operational amplifier (op amp). Before using NCAP for RFI analysis, the model parameters for each bipolar junction transistor (BJT) in the integrated circuit were determined. Probe measurement techniques, manufacturer's data, and other researcher's data were used to obtain the required NCAP BJT model parameter values. An important contribution included in this effort is a complete set of NCAP BJT model parameters for most of the transistor types used in linear IC's.

Surface tension and contact angles: Molecular origins and associated microstructure

NASA Technical Reports Server (NTRS)

Davis, H. T.

1982-01-01

Gradient theory converts the molecular theory of inhomogeneous fluid into nonlinear boundary value problems for density and stress distributions in fluid interfaces, contact line regions, nuclei and microdroplets, and other fluid microstructures. The relationship between the basic patterns of fluid phase behavior and the occurrence and stability of fluid microstructures was clearly established by the theory. All the inputs of the theory have molecular expressions which are computable from simple models. On another level, the theory becomes a phenomenological framework in which the equation of state of homogeneous fluid and sets of influence parameters of inhomogeneous fluids are the inputs and the structures, stress tensions and contact angles of menisci are the outputs. These outputs, which find applications in the science and technology of drops and bubbles, are discussed.

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. PMID:24688412

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

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

Kamp, F.; Brueningk, S.C.; Wilkens, J.J.

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g.more » RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

Hubert: Software for efficient analysis of in-situ nuclear forward scattering experiments

NASA Astrophysics Data System (ADS)

Vrba, Vlastimil; Procházka, Vít; Smrčka, David; Miglierini, Marcel

2016-10-01

Combination of short data acquisition time and local investigation of a solid state through hyperfine parameters makes nuclear forward scattering (NFS) a unique experimental technique for investigation of fast processes. However, the total number of acquired NFS time spectra may be very high. Therefore an efficient way of the data evaluation is needed. In this paper we report the development of Hubert software package as a response to the rapidly developing field of in-situ NFS experiments. Hubert offers several useful features for data files processing and could significantly shorten the evaluation time by using a simple connection between the neighboring time spectra through their input and output parameter values.

pp interaction at very high energies in cosmic ray experiments

NASA Astrophysics Data System (ADS)

Kendi Kohara, A.; Ferreira, Erasmo; Kodama, Takeshi

2014-11-01

An analysis of p-air cross section data from extensive air shower measurements is presented, based on an analytical representation of the pp scattering amplitudes that describes with high precision all available accelerator data at ISR, SPS and LHC energies. The theoretical basis of the representation, together with the very smooth energy dependence of parameters controlled by unitarity and dispersion relations, permits reliable extrapolation to high energy cosmic ray (CR) and asymptotic energy ranges. Calculations of σ p-airprod based on Glauber formalism are made using the input values of the quantities σ , ρ , BI and BR at high energies, with attention given to the independence of the slope parameters, with {{B}R}\

ptchg: A FORTRAN program for point-charge calculations of electric field gradients (EFGs)

NASA Astrophysics Data System (ADS)

Spearing, Dane R.

1994-05-01

ptchg, a FORTRAN program, has been developed to calculate electric field gradients (EFG) around an atomic site in crystalline solids using the point-charge direct-lattice summation method. It uses output from the crystal structure generation program Atoms as its input. As an application of ptchg, a point-charge calculation of the EFG quadrupolar parameters around the oxygen site in SiO 2 cristobalite is demonstrated. Although point-charge calculations of electric field gradients generally are limited to ionic compounds, the computed quadrupolar parameters around the oxygen site in SiO 2 cristobalite, a highly covalent material, are in good agreement with the experimentally determined values from nuclear magnetic resonance (NMR) spectroscopy.

MPS Solidification Model. Volume 2: Operating guide and software documentation for the unsteady model

NASA Technical Reports Server (NTRS)

Maples, A. L.

1981-01-01

The operation of solidification Model 2 is described and documentation of the software associated with the model is provided. Model 2 calculates the macrosegregation in a rectangular ingot of a binary alloy as a result of unsteady horizontal axisymmetric bidirectional solidification. The solidification program allows interactive modification of calculation parameters as well as selection of graphical and tabular output. In batch mode, parameter values are input in card image form and output consists of printed tables of solidification functions. The operational aspects of Model 2 that differ substantially from Model 1 are described. The global flow diagrams and data structures of Model 2 are included. The primary program documentation is the code itself.

Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

PubMed

Celik, Turgay; Tjahjadi, Tardi

2012-01-01

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

PubMed Central

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-01-01

Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results. PMID:17714598

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

PubMed

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-08-23

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer.

PubMed

Chen, Xiaoliang; Xu, Yanyan; Duan, Jianghui; Li, Chuandong; Sun, Hongliang; Wang, Wu

2017-07-01

To investigate the potential relationship between perfusion parameters from first-pass dual-input perfusion computed tomography (DI-PCT) and iodine uptake levels estimated from dual-energy CT (DE-CT).The pre-experimental part of this study included a dynamic DE-CT protocol in 15 patients to evaluate peak arterial enhancement of lung cancer based on time-attenuation curves, and the scan time of DE-CT was determined. In the prospective part of the study, 28 lung cancer patients underwent whole-volume perfusion CT and single-source DE-CT using 320-row CT. Pulmonary flow (PF, mL/min/100 mL), aortic flow (AF, mL/min/100 mL), and a perfusion index (PI = PF/[PF + AF]) were automatically generated by in-house commercial software using the dual-input maximum slope method for DI-PCT. For the dual-energy CT data, iodine uptake was estimated by the difference (λ) and the slope (λHU). λ was defined as the difference of CT values between 40 and 70 KeV monochromatic images in lung lesions. λHU was calculated by the following equation: λHU = |λ/(70 - 40)|. The DI-PCT and DE-CT parameters were analyzed by Pearson/Spearman correlation analysis, respectively.All subjects were pathologically proved as lung cancer patients (including 16 squamous cell carcinoma, 8 adenocarcinoma, and 4 small cell lung cancer) by surgery or CT-guided biopsy. Interobserver reproducibility in DI-PCT (PF, AF, PI) and DE-CT (λ, λHU) were relatively good to excellent (intraclass correlation coefficient [ICC]Inter = 0.8726-0.9255, ICCInter = 0.8179-0.8842; ICCInter = 0.8881-0.9177, ICCInter = 0.9820-0.9970, ICCInter = 0.9780-0.9971, respectively). Correlation coefficient between λ and AF, and PF were as follows: 0.589 (P < .01) and 0.383 (P < .05). Correlation coefficient between λHU and AF, and PF were as follows: 0.564 (P < .01) and 0.388 (P < .05).Both the single-source DE-CT and dual-input CT perfusion analysis method can be applied to assess blood supply of lung cancer patients. Preliminary results demonstrated that the iodine uptake relevant parameters derived from DE-CT significantly correlated with perfusion parameters derived from DI-PCT.

Aircraft Hydraulic Systems Dynamic Analysis Component Data Handbook

DTIC Science & Technology

1980-04-01

82 13. QUINCKE TUBE ...................................... 85 14. 11EAT EXCHANGER ............. ................... 90...Input Parameters ....... ........... .7 61 )uincke Tube Input Parameters with Hole Locat ions 87 62 "rototype Quincke Tube Data ........... 89 6 3 Fo-,:ed...Elasticity (Line 3) PSI 1.6E7 FIGURE 58 HSFR INPUT DATA FOR PULSCO TYPE ACOUSTIC FILTER 84 13. QUINCKE TUBE A means to dampen acoustic noise at resonance

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Nonstationary Extreme Value Analysis in a Changing Climate: A Software Package

NASA Astrophysics Data System (ADS)

Cheng, L.; AghaKouchak, A.; Gilleland, E.

2013-12-01

Numerous studies show that climatic extremes have increased substantially in the second half of the 20th century. For this reason, analysis of extremes under a nonstationary assumption has received a great deal of attention. This paper presents a software package developed for estimation of return levels, return periods, and risks of climatic extremes in a changing climate. This MATLAB software package offers tools for analysis of climate extremes under both stationary and non-stationary assumptions. The Nonstationary Extreme Value Analysis (hereafter, NEVA) provides an efficient and generalized framework for analyzing extremes using Bayesian inference. NEVA estimates the extreme value parameters using a Differential Evolution Markov Chain (DE-MC) which utilizes the genetic algorithm Differential Evolution (DE) for global optimization over the real parameter space with the Markov Chain Monte Carlo (MCMC) approach and has the advantage of simplicity, speed of calculation and convergence over conventional MCMC. NEVA also offers the confidence interval and uncertainty bounds of estimated return levels based on the sampled parameters. NEVA integrates extreme value design concepts, data analysis tools, optimization and visualization, explicitly designed to facilitate analysis extremes in geosciences. The generalized input and output files of this software package make it attractive for users from across different fields. Both stationary and nonstationary components of the package are validated for a number of case studies using empirical return levels. The results show that NEVA reliably describes extremes and their return levels.

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

Suggestions for CAP-TSD mesh and time-step input parameters

NASA Technical Reports Server (NTRS)

Bland, Samuel R.

1991-01-01

Suggestions for some of the input parameters used in the CAP-TSD (Computational Aeroelasticity Program-Transonic Small Disturbance) computer code are presented. These parameters include those associated with the mesh design and time step. The guidelines are based principally on experience with a one-dimensional model problem used to study wave propagation in the vertical direction.

Unsteady hovering wake parameters identified from dynamic model tests, part 1

NASA Technical Reports Server (NTRS)

Hohenemser, K. H.; Crews, S. T.

1977-01-01

The development of a 4-bladed model rotor is reported that can be excited with a simple eccentric mechanism in progressing and regressing modes with either harmonic or transient inputs. Parameter identification methods were applied to the problem of extracting parameters for linear perturbation models, including rotor dynamic inflow effects, from the measured blade flapping responses to transient pitch stirring excitations. These perturbation models were then used to predict blade flapping response to other pitch stirring transient inputs, and rotor wake and blade flapping responses to harmonic inputs. The viability and utility of using parameter identification methods for extracting the perturbation models from transients are demonstrated through these combined analytical and experimental studies.

Land and Water Use Characteristics and Human Health Input Parameters for use in Environmental Dosimetry and Risk Assessments at the Savannah River Site. 2016 Update

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

Jannik, G. Tim; Hartman, Larry; Stagich, Brooke

Operations at the Savannah River Site (SRS) result in releases of small amounts of radioactive materials to the atmosphere and to the Savannah River. For regulatory compliance purposes, potential offsite radiological doses are estimated annually using computer models that follow U.S. Nuclear Regulatory Commission (NRC) regulatory guides. Within the regulatory guides, default values are provided for many of the dose model parameters, but the use of applicant site-specific values is encouraged. Detailed surveys of land-use and water-use parameters were conducted in 1991 and 2010. They are being updated in this report. These parameters include local characteristics of meat, milk andmore » vegetable production; river recreational activities; and meat, milk and vegetable consumption rates, as well as other human usage parameters required in the SRS dosimetry models. In addition, the preferred elemental bioaccumulation factors and transfer factors (to be used in human health exposure calculations at SRS) are documented. The intent of this report is to establish a standardized source for these parameters that is up to date with existing data, and that is maintained via review of future-issued national references (to evaluate the need for changes as new information is released). These reviews will continue to be added to this document by revision.« less

Land and Water Use Characteristics and Human Health Input Parameters for use in Environmental Dosimetry and Risk Assessments at the Savannah River Site 2017 Update

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

Jannik, T.; Stagich, B.

Operations at the Savannah River Site (SRS) result in releases of relatively small amounts of radioactive materials to the atmosphere and to the Savannah River. For regulatory compliance purposes, potential offsite radiological doses are estimated annually using computer models that follow U.S. Nuclear Regulatory Commission (NRC) regulatory guides. Within the regulatory guides, default values are provided for many of the dose model parameters, but the use of site-specific values is encouraged. Detailed surveys of land-use and water-use parameters were conducted in 1991, 2008, 2010, and 2016 and are being concurred with or updated in this report. These parameters include localmore » characteristics of meat, milk, and vegetable production; river recreational activities; and meat, milk, and vegetable consumption rates, as well as other human usage parameters required in the SRS dosimetry models. In addition, the preferred elemental bioaccumulation factors and transfer factors (to be used in human health exposure calculations at SRS) are documented. The intent of this report is to establish a standardized source for these parameters that is up to date with existing data, and that is maintained via review of future-issued national references (to evaluate the need for changes as new information is released). These reviews will continue to be added to this document by revision.« less

Data Driven Ionospheric Modeling in Relation to Space Weather: Percent Cloud Coverage

NASA Astrophysics Data System (ADS)

Tulunay, Y.; Senalp, E. T.; Tulunay, E.

2009-04-01

Since 1990, a small group at METU has been developing data driven models in order to forecast some critical system parameters related with the near-Earth space processes. The background on the subject supports new achievements, which contributed the COST 724 activities, which will contribute to the new ES0803 activities. This work mentions one of the outstanding contributions, namely forecasting of meteorological parameters by considering the probable influence of cosmic rays (CR) and sunspot numbers (SSN). The data-driven method is generic and applicable to many Near-Earth Space processes including ionospheric/plasmaspheric interactions. It is believed that the EURIPOS initiative would be useful in supplying wide range reliable data to the models developed. Quantification of physical mechanisms, which causally link Space Weather to the Earth's Weather, has been a challenging task. In this basis, the percent cloud coverage (%CC) and cloud top temperatures (CTT) were forecast one month ahead of time between geographic coordinates of (22.5˚N; 57.5˚N); and (7.5˚W; 47.5˚E) at 96 grid locations and covering the years of 1983 to 2000 using the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M) [Tulunay, 2008]. The Near Earth Space variability at several different time scales arises from a number of separate factors and the physics of the variations cannot be modeled due to the lack of current information about the parameters of several natural processes. CR are shielded by the magnetosphere to a certain extent, but they can modulate the low level cloud cover. METU-FNN-M was developed, trained and applied for forecasting the %CC and CTT, by considering the history of those meteorological variables; Cloud Optical Depth (COD); the Ionization (I) value that is formulized and computed by using CR data and CTT; SSN; temporal variables; and defuzified cloudiness. The temporal and spatial variables and the cut off rigidity are used to compute the defuzified cloudiness. The forecast %CC and CTT values at uniformly spaced grids over the region of interest are used for mapping by Bezier surfaces. The major advantage of the fuzzy model is that it uses its inputs and the expert knowledge in coordination. Long-term cloud analysis was performed on a region having differences in terms of atmospheric activity, in order to show the generalization capability. Global and local parameters of the process were considered. Both CR Flux and SSN reflect the influence of Space Weather on general planetary situation; but other parameters in the inputs of the model reflect local situation. Error and correlation analysis on the forecast and observed parameters were performed. The correlations between the forecast and observed parameters are very promising. The model contributes to the dependence of the cloud formation process on CR Fluxes. The one-month in advance forecast values of the model can also be used as inputs to other models, which forecast some other local or global parameters in order to further test the hypothesis on possible link(s) between Space Weather and the Earth's Weather. The model based, theoretical and numerical works mentioned are promising and have potential for future research and developments. References Tulunay Y., E.T. Şenalp, Ş. Öz, L.I. Dorman, E. Tulunay, S.S. Menteş and M.E. Akcan (2008), A Fuzzy Neural Network Model to Forecast the Percent Cloud Coverage and Cloud Top Temperature Maps, Ann. Geophys., 26(12), 3945-3954, 2008.

The use of Meteonorm weather generator for climate change studies

NASA Astrophysics Data System (ADS)

Remund, J.; Müller, S. C.; Schilter, C.; Rihm, B.

2010-09-01

The global climatological database Meteonorm (www.meteonorm.com) is widely used as meteorological input for simulation of solar applications and buildings. It's a combination of a climate database, a spatial interpolation tool and a stochastic weather generator. Like this typical years with hourly or minute time resolution can be calculated for any site. The input of Meteonorm for global radiation is the Global Energy Balance Archive (GEBA, http://proto-geba.ethz.ch). All other meteorological parameters are taken from databases of WMO and NCDC (periods 1961-90 and 1996-2005). The stochastic generation of global radiation is based on a Markov chain model for daily values and an autoregressive model for hourly and minute values (Aguiar and Collares-Pereira, 1988 and 1992). The generation of temperature is based on global radiation and measured distribution of daily temperature values of approx. 5000 sites. Meteonorm generates also additional parameters like precipitation, wind speed or radiation parameters like diffuse and direct normal irradiance. Meteonorm can also be used for climate change studies. Instead of climate values, the results of IPCC AR4 results are used as input. From all 18 public models an average has been made at a resolution of 1°. The anomalies of the parameters temperature, precipitation and global radiation and the three scenarios B1, A1B and A2 have been included. With the combination of Meteonorm's current database 1961-90, the interpolation algorithms and the stochastic generation typical years can be calculated for any site, for different scenarios and for any period between 2010 and 2200. From the analysis of variations of year to year and month to month variations of temperature, precipitation and global radiation of the past ten years as well of climate model forecasts (from project prudence, http://prudence.dmi.dk) a simple autoregressive model has been formed which is used to generate realistic monthly time series of future periods. Meteonorm can therefore be used as a relatively simple method to enhance the spatial and temporal resolution instead of using complicated and time consuming downscaling methods based on regional climate models. The combination of Meteonorm, gridded historical (based on work of Luterbach et al.) and IPCC results has been used for studies of vegetation simulation between 1660 and 2600 (publication of first version based on IS92a scenario and limited time period 1950 - 2100: http://www.pbl.nl/images/H5_Part2_van%20CCE_opmaak%28def%29_tcm61-46625.pdf). It's also applicable for other adaptation studies for e.g. road surfaces or building simulation. In Meteonorm 6.1 one scenario (IS92a) and one climate model has been included (Hadley CM3). In the new Meteonorm 7 (coming spring 2011) the model averages of the three above mentioned scenarios of the IPCC AR4 will be included.

Rendering of HDR content on LDR displays: an objective approach

NASA Astrophysics Data System (ADS)

Krasula, Lukáš; Narwaria, Manish; Fliegel, Karel; Le Callet, Patrick

2015-09-01

Dynamic range compression (or tone mapping) of HDR content is an essential step towards rendering it on traditional LDR displays in a meaningful way. This is however non-trivial and one of the reasons is that tone mapping operators (TMOs) usually need content-specific parameters to achieve the said goal. While subjective TMO parameter adjustment is the most accurate, it may not be easily deployable in many practical applications. Its subjective nature can also influence the comparison of different operators. Thus, there is a need for objective TMO parameter selection to automate the rendering process. To that end, we investigate into a new objective method for TMO parameters optimization. Our method is based on quantification of contrast reversal and naturalness. As an important advantage, it does not require any prior knowledge about the input HDR image and works independently on the used TMO. Experimental results using a variety of HDR images and several popular TMOs demonstrate the value of our method in comparison to default TMO parameter settings.

Reliability Estimating Procedures for Electric and Thermochemical Propulsion Systems. Volume 2

DTIC Science & Technology

1977-02-01

final form. For some components, the parameters are calculated from design factors (e.g., design life) that must be input when requested. Each component...Components Components are regarded as statis- tically identical if they are drawn from the same production lot because the initial and sub- sequent...table yields b 0.0023 The - factors are obtained from Tables 2.2.4-1 through 2.2.4-5: Factor Value rE Space, flight 1 JANTXV quality 0.5 7A Small signal

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

Kato, Go

We consider the situation where s replicas of a qubit with an unknown state and its orthogonal k replicas are given as an input, and we try to make c clones of the qubit with the unknown state. As a function of s, k, and c, we obtain the optimal fidelity between the qubit with an unknown state and the clone by explicitly giving a completely positive trace-preserving (CPTP) map that represents a cloning machine. We discuss dependency of the fidelity on the values of the parameters s, k, and c.

Platform Routing and Data Fusion Technologies for Cooperative ISR - fmCortex (Trademark)

DTIC Science & Technology

2008-06-13

first “fuzzify” the “ crisp ” or real-values of pertinent parameters and, using membership functions, represent a degree to which the effects of that...IAVO Research and Scientific STTR Final Report: FA9550-07-C-0085 Scenario Range Aspect TTLE ! R ! T ! A Input: Crisp Data Membership...for vulnerable resource rv!V. 0 Dm 2 Number of time slices for which continuous task m must be performed. 0 nmmin 3 Minimum number of resources

Computer program for flat sector thrust bearing performance

NASA Technical Reports Server (NTRS)

Presler, A. F.; Etsion, I.

1977-01-01

A versatile computer program is presented which achieves a rapid, numerical solution of the Reynolds equation for a flat sector thrust pad bearing with either compressible or liquid lubricants. Program input includes a range in values of the geometric and operating parameters of the sector bearing. Performance characteristics are obtained from the calculated bearing pressure distribution. These are the load capacity, center of pressure coordinates, frictional energy dissipation, and flow rates of liquid lubricant across the bearing edges. Two sample problems are described.

Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard

PubMed Central

Tang, Liansheng Larry; Yuan, Ao; Collins, John; Che, Xuan; Chan, Leighton

2017-01-01

The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input “data.” It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. The key difference between the proposed method and the method of Tang and Zhou lies in the response variable. The response variable in the latter is transformed empirical ROC curves at different thresholds. It takes on many values for continuous test results, but few values for ordinal test results. The limited number of values for the response variable makes it impractical for ordinal data. However, the response variable in the proposed method takes on many more distinct values so that the method yields valid estimates for ordinal data. Extensive simulation studies are conducted to investigate and compare the finite sample performance of the proposed method with an existing method, and the method is then used to analyze 2 real cancer diagnostic example as an illustration. PMID:28469385

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: a simulation study.

PubMed

Padhi, Radhakant; Bhardhwaj, Jayender R

2009-06-01

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Examining the Feasibility and Utility of Estimating Partial Expected Value of Perfect Information (via a Nonparametric Approach) as Part of the Reimbursement Decision-Making Process in Ireland: Application to Drugs for Cancer.

PubMed

McCullagh, Laura; Schmitz, Susanne; Barry, Michael; Walsh, Cathal

2017-11-01

In Ireland, all new drugs for which reimbursement by the healthcare payer is sought undergo a health technology assessment by the National Centre for Pharmacoeconomics. The National Centre for Pharmacoeconomics estimate expected value of perfect information but not partial expected value of perfect information (owing to computational expense associated with typical methodologies). The objective of this study was to examine the feasibility and utility of estimating partial expected value of perfect information via a computationally efficient, non-parametric regression approach. This was a retrospective analysis of evaluations on drugs for cancer that had been submitted to the National Centre for Pharmacoeconomics (January 2010 to December 2014 inclusive). Drugs were excluded if cost effective at the submitted price. Drugs were excluded if concerns existed regarding the validity of the applicants' submission or if cost-effectiveness model functionality did not allow required modifications to be made. For each included drug (n = 14), value of information was estimated at the final reimbursement price, at a threshold equivalent to the incremental cost-effectiveness ratio at that price. The expected value of perfect information was estimated from probabilistic analysis. Partial expected value of perfect information was estimated via a non-parametric approach. Input parameters with a population value at least €1 million were identified as potential targets for research. All partial estimates were determined within minutes. Thirty parameters (across nine models) each had a value of at least €1 million. These were categorised. Collectively, survival analysis parameters were valued at €19.32 million, health state utility parameters at €15.81 million and parameters associated with the cost of treating adverse effects at €6.64 million. Those associated with drug acquisition costs and with the cost of care were valued at €6.51 million and €5.71 million, respectively. This research demonstrates that the estimation of partial expected value of perfect information via this computationally inexpensive approach could be considered feasible as part of the health technology assessment process for reimbursement purposes within the Irish healthcare system. It might be a useful tool in prioritising future research to decrease decision uncertainty.

The super-GUT CMSSM revisited

DOE PAGES

Ellis, John; Evans, Jason L.; Mustafayev, Azar; ...

2016-10-28

Here, we revisit minimal supersymmetric SU(5) grand unification (GUT) models in which the soft supersymmetry-breaking parameters of the minimal supersymmetric Standard Model (MSSM) are universal at some input scale, M in, above the supersymmetric gauge-coupling unification scale, M GUT. As in the constrained MSSM (CMSSM), we assume that the scalar masses and gaugino masses have common values, m 0 and m 1/2, respectively, at M in, as do the trilinear soft supersymmetry-breaking parameters A 0. Going beyond previous studies of such a super-GUT CMSSM scenario, we explore the constraints imposed by the lower limit on the proton lifetime and themore » LHC measurement of the Higgs mass, m h. We find regions of m 0, m 1/2 A 0 and the parameters of the SU(5) superpotential that are compatible with these and other phenomenological constraints such as the density of cold dark matter, which we assume to be provided by the lightest neutralino. Typically, these allowed regions appear for m 0 and m 1/2 in the multi-TeV region, for suitable values of the unknown SU(5) GUT-scale phases and superpotential couplings, and with the ratio of supersymmetric Higgs vacuum expectation values tan β≲6.« less

Assessment of the Effects of Entrainment and Wind Shear on Nuclear Cloud Rise Modeling

NASA Astrophysics Data System (ADS)

Zalewski, Daniel; Jodoin, Vincent

2001-04-01

Accurate modeling of nuclear cloud rise is critical in hazard prediction following a nuclear detonation. This thesis recommends improvements to the model currently used by DOD. It considers a single-term versus a three-term entrainment equation, the value of the entrainment and eddy viscous drag parameters, as well as the effect of wind shear in the cloud rise following a nuclear detonation. It examines departures from the 1979 version of the Department of Defense Land Fallout Interpretive Code (DELFIC) with the current code used in the Hazard Prediction and Assessment Capability (HPAC) code version 3.2. The recommendation for a single-term entrainment equation, with constant value parameters, without wind shear corrections, and without cloud oscillations is based on both a statistical analysis using 67 U.S. nuclear atmospheric test shots and the physical representation of the modeling. The statistical analysis optimized the parameter values of interest for four cases: the three-term entrainment equation with wind shear and without wind shear as well as the single-term entrainment equation with and without wind shear. The thesis then examines the effect of cloud oscillations as a significant departure in the code. Modifications to user input atmospheric tables are identified as a potential problem in the calculation of stabilized cloud dimensions in HPAC.

Simulation-based sensitivity analysis for non-ignorably missing data.

PubMed

Yin, Peng; Shi, Jian Q

2017-01-01

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness, where full-likelihood method cannot be adopted. It analyses how sensitively the conclusions (output) may depend on assumptions or parameters (input) about missing data, i.e. missing data mechanism. We call models with the problem of uncertainty sensitivity models. To make conventional sensitivity analysis more useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models and make evidence based analysis. We propose a novel approach in this paper on attempting to investigate the possibility of each missing data mechanism model assumption, by comparing the simulated datasets from various MNAR models with the observed data non-parametrically, using the K-nearest-neighbour distances. Some asymptotic theory has also been provided. A key step of this method is to plug in a plausibility evaluation system towards each sensitivity parameter, to select plausible values and reject unlikely values, instead of considering all proposed values of sensitivity parameters as in the conventional sensitivity analysis method. The method is generic and has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and mean estimation with non-ignorable missing data.

Natural background levels and threshold values for groundwater in fluvial Pleistocene and Tertiary marine aquifers in Flanders, Belgium

NASA Astrophysics Data System (ADS)

Coetsiers, Marleen; Blaser, Petra; Martens, Kristine; Walraevens, Kristine

2009-05-01

Aquifers from the same typology can have strongly different groundwater chemistry. Deducing the groundwater quality of less well-characterized aquifers from well-documented aquifers belonging to the same typology should be done with great reserve, and can only be considered as a preliminary approach. In the EU’s 6th FP BRIDGE project “Background cRiteria for the IDentification of Groundwater thrEsholds”, a methodology for the derivation of threshold values (TV) for groundwater bodies is proposed. This methodology is tested on four aquifers in Flanders of the sand and gravel typology. The methodology works well for all but the Ledo-Paniselian aquifer, where the subdivision into a fresh and saline part is disproved, as a gradual natural transition from fresh to saline conditions in the aquifer is observed. The 90 percentile is proposed as natural background level (NBL) for the unconfined Pleistocene deposits, ascribing the outliers to possible influence of pollution. For the Tertiary aquifers, high values for different parameters have a natural origin and the 97.7 percentile is preferred as NBL. The methodology leads to high TVs for parameters presenting low NBL, when compared to the standard used as a reference. This would allow for substantial anthropogenic inputs of these parameters.

Sensitivity analysis and nonlinearity assessment of steam cracking furnace process

NASA Astrophysics Data System (ADS)

Rosli, M. N.; Sudibyo, Aziz, N.

2017-11-01

In this paper, sensitivity analysis and nonlinearity assessment of cracking furnace process are presented. For the sensitivity analysis, the fractional factorial design method is employed as a method to analyze the effect of input parameters, which consist of four manipulated variables and two disturbance variables, to the output variables and to identify the interaction between each parameter. The result of the factorial design method is used as a screening method to reduce the number of parameters, and subsequently, reducing the complexity of the model. It shows that out of six input parameters, four parameters are significant. After the screening is completed, step test is performed on the significant input parameters to assess the degree of nonlinearity of the system. The result shows that the system is highly nonlinear with respect to changes in an air-to-fuel ratio (AFR) and feed composition.

NLSCIDNT user's guide maximum likehood parameter identification computer program with nonlinear rotorcraft model

NASA Technical Reports Server (NTRS)

1979-01-01

A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.

The selection criteria elements of X-ray optics system

NASA Astrophysics Data System (ADS)

Plotnikova, I. V.; Chicherina, N. V.; Bays, S. S.; Bildanov, R. G.; Stary, O.

2018-01-01

At the design of new modifications of x-ray tomography there are difficulties in the right choice of elements of X-ray optical system. Now this problem is solved by practical consideration, selection of values of the corresponding parameters - tension on an x-ray tube taking into account the thickness and type of the studied material. For reduction of time and labor input of design it is necessary to create the criteria of the choice, to determine key parameters and characteristics of elements. In the article two main elements of X-ray optical system - an x-ray tube and the detector of x-ray radiation - are considered. Criteria of the choice of elements, their key characteristics, the main dependences of parameters, quality indicators and also recommendations according to the choice of elements of x-ray systems are received.

Evaluation of Uncertainty in Constituent Input Parameters for Modeling the Fate of IMX 101 Components

DTIC Science & Technology

2017-05-01

ER D C/ EL T R- 17 -7 Environmental Security Technology Certification Program (ESTCP) Evaluation of Uncertainty in Constituent Input...Environmental Security Technology Certification Program (ESTCP) ERDC/EL TR-17-7 May 2017 Evaluation of Uncertainty in Constituent Input Parameters...Environmental Evaluation and Characterization Sys- tem (TREECS™) was applied to a groundwater site and a surface water site to evaluate the sensitivity

Piloted Parameter Identification Flight Test Maneuvers for Closed Loop Modeling of the F-18 High Alpha Research Vehicle (HARV)

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1996-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for closed loop parameter identification purposes, specifically for longitudinal and lateral linear model parameter estimation at 5, 20, 30, 45, and 60 degrees angle of attack, using the NASA 1A control law. Each maneuver is to be realized by the pilot applying square wave inputs to specific pilot station controls. Maneuver descriptions and complete specifications of the time/amplitude points defining each input are included, along with plots of the input time histories.

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

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

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

DOE PAGES

Rosenthal, William Steven; Tartakovsky, Alex; Huang, Zhenyu

2017-10-31

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integratemore » them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.« less

Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

NASA Astrophysics Data System (ADS)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Huth, N.; Marin, F.; Martiné, J.-F.

2014-01-01

Agro-Land Surface Models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, a particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of Agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS' phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte-Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugar cane cultivation in Australia and Brazil. Ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting climate-mediated different sensitivities of modeled sugar cane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

NASA Astrophysics Data System (ADS)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Caubel, A.; Huth, N.; Marin, F.; Martiné, J.-F.

2014-06-01

Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

Systems and methods for compensating for electrical converter nonlinearities

DOEpatents

Perisic, Milun; Ransom, Ray M.; Kajouke, Lateef A.

2013-06-18

Systems and methods are provided for delivering energy from an input interface to an output interface. An electrical system includes an input interface, an output interface, an energy conversion module coupled between the input interface and the output interface, and a control module. The control module determines a duty cycle control value for operating the energy conversion module to produce a desired voltage at the output interface. The control module determines an input power error at the input interface and adjusts the duty cycle control value in a manner that is influenced by the input power error, resulting in a compensated duty cycle control value. The control module operates switching elements of the energy conversion module to deliver energy to the output interface with a duty cycle that is influenced by the compensated duty cycle control value.

INDES User's guide multistep input design with nonlinear rotorcraft modeling

NASA Technical Reports Server (NTRS)

1979-01-01

The INDES computer program, a multistep input design program used as part of a data processing technique for rotorcraft systems identification, is described. Flight test inputs base on INDES improve the accuracy of parameter estimates. The input design algorithm, program input, and program output are presented.

Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?

PubMed Central

Valente, Giordano; Pitto, Lorenzo; Testi, Debora; Seth, Ajay; Delp, Scott L.; Stagni, Rita; Viceconti, Marco; Taddei, Fulvia

2014-01-01

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur. PMID:25390896

Model Sensitivity and Use of the Comparative Finite Element Method in Mammalian Jaw Mechanics: Mandible Performance in the Gray Wolf

PubMed Central

Tseng, Zhijie Jack; Mcnitt-Gray, Jill L.; Flashner, Henryk; Wang, Xiaoming; Enciso, Reyes

2011-01-01

Finite Element Analysis (FEA) is a powerful tool gaining use in studies of biological form and function. This method is particularly conducive to studies of extinct and fossilized organisms, as models can be assigned properties that approximate living tissues. In disciplines where model validation is difficult or impossible, the choice of model parameters and their effects on the results become increasingly important, especially in comparing outputs to infer function. To evaluate the extent to which performance measures are affected by initial model input, we tested the sensitivity of bite force, strain energy, and stress to changes in seven parameters that are required in testing craniodental function with FEA. Simulations were performed on FE models of a Gray Wolf (Canis lupus) mandible. Results showed that unilateral bite force outputs are least affected by the relative ratios of the balancing and working muscles, but only ratios above 0.5 provided balancing-working side joint reaction force relationships that are consistent with experimental data. The constraints modeled at the bite point had the greatest effect on bite force output, but the most appropriate constraint may depend on the study question. Strain energy is least affected by variation in bite point constraint, but larger variations in strain energy values are observed in models with different number of tetrahedral elements, masticatory muscle ratios and muscle subgroups present, and number of material properties. These findings indicate that performance measures are differentially affected by variation in initial model parameters. In the absence of validated input values, FE models can nevertheless provide robust comparisons if these parameters are standardized within a given study to minimize variation that arise during the model-building process. Sensitivity tests incorporated into the study design not only aid in the interpretation of simulation results, but can also provide additional insights on form and function. PMID:21559475

Demonstration of the reproducibility of free-breathing diffusion-weighted MRI and dynamic contrast enhanced MRI in children with solid tumours: a pilot study.

PubMed

Miyazaki, Keiko; Jerome, Neil P; Collins, David J; Orton, Matthew R; d'Arcy, James A; Wallace, Toni; Moreno, Lucas; Pearson, Andrew D J; Marshall, Lynley V; Carceller, Fernando; Leach, Martin O; Zacharoulis, Stergios; Koh, Dow-Mu

2015-09-01

The objectives are to examine the reproducibility of functional MR imaging in children with solid tumours using quantitative parameters derived from diffusion-weighted (DW-) and dynamic contrast enhanced (DCE-) MRI. Patients under 16-years-of age with confirmed diagnosis of solid tumours (n = 17) underwent free-breathing DW-MRI and DCE-MRI on a 1.5 T system, repeated 24 hours later. DW-MRI (6 b-values, 0-1000 sec/mm(2)) enabled monoexponential apparent diffusion coefficient estimation using all (ADC0-1000) and only ≥100 sec/mm(2) (ADC100-1000) b-values. DCE-MRI was used to derive the transfer constant (K(trans)), the efflux constant (kep), the extracellular extravascular volume (ve), and the plasma fraction (vp), using a study cohort arterial input function (AIF) and the extended Tofts model. Initial area under the gadolinium enhancement curve and pre-contrast T1 were also calculated. Percentage coefficients of variation (CV) of all parameters were calculated. The most reproducible cohort parameters were ADC100-1000 (CV = 3.26%), pre-contrast T1 (CV = 6.21%), and K(trans) (CV = 15.23%). The ADC100-1000 was more reproducible than ADC0-1000, especially extracranially (CV = 2.40% vs. 2.78%). The AIF (n = 9) derived from this paediatric population exhibited sharper and earlier first-pass and recirculation peaks compared with the literature's adult population average. Free-breathing functional imaging protocols including DW-MRI and DCE-MRI are well-tolerated in children aged 6 - 15 with good to moderate measurement reproducibility. • Diffusion MRI protocol is feasible and well-tolerated in a paediatric oncology population. • DCE-MRI for pharmacokinetic evaluation is feasible and well tolerated in a paediatric oncology population. • Paediatric arterial input function (AIF) shows systematic differences from the adult population-average AIF. • Variation of quantitative parameters from paired functional MRI measurements were within 20%.

Comparative Analysis of Membership Function on Mamdani Fuzzy Inference System for Decision Making

NASA Astrophysics Data System (ADS)

harliana, Putri; Rahim, Robbi

2017-12-01

Membership function is a curve that shows mapping the input data points into the value or degree of membership which has an interval between 0 and 1. One way to get membership value is through a function approach. There are some membership functions can be used on mamdani fuzzy inference system. They are triangular, trapezoid, singleton, sigmoid, Gaussian, etc. In this paper only discuss three membership functions, are triangular, trapezoid and Gaussian. These three membership functions will be compared to see the difference in parameter values and results obtained. For case study in this paper is admission of students at popular school. There are three variable can be used, they are students’ report, IQ score and parents’ income. Which will then be created if-then rules.

Incorporating uncertainty in RADTRAN 6.0 input files.

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

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine ismore » required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.« less

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

PubMed Central

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement. PMID:26799483

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

PubMed

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

Sampling, characterisation and processing of solid recovered fuel production from municipal solid waste: An Italian plant case study.

PubMed

Ranieri, Ezio; Ionescu, Gabriela; Fedele, Arcangela; Palmieri, Eleonora; Ranieri, Ada Cristina; Campanaro, Vincenzo

2017-08-01

This article presents the classification of solid recovered fuel from the Massafra municipal solid waste treatment plant in Southern Italy in compliancy with the EN 15359 standard. In order to ensure the reproducibility of this study, the characterisation methods of waste input and output flow, the mechanical biological treatment line scheme and its main parameters for each stage of the processing chain are presented in details, together with the research results in terms of mass balance and derived fuel properties. Under this study, only 31% of refused municipal solid waste input stream from mechanical biological line was recovered as solid recovered fuel with a net heating value (NC=HV) average of 15.77 MJ kg -1 ; chlorine content average of 0.06% on a dry basis; median of mercury <0.0064 mg MJ -1 and 80th percentile <0.0068 mg MJ -1 . The solid recovered fuel produced meets the European Union standard requirements and can be classified with the class code: Net heating value (3); chlorine (1); mercury (1).

Assessment of land influence on a high-latitude marine coastal system: Tierra del Fuego, southernmost Argentina.

PubMed

Amin, Oscar; Comoglio, Laura; Spetter, Carla; Duarte, Claudia; Asteasuain, Raúl; Freije, Rubén Hugo; Marcovecchio, Jorge

2011-04-01

The study deals with the determination of physico-chemical parameters, inorganic nutrients, particulate organic matter, and photosynthetic pigments on a monthly basis during an annual cycle from nine sampling sites of the coastal zone of a high-latitude ecosystem (Tierra del Fuego, Argentina). Nitrites and phosphates concentrations were similar to other systems of the south Atlantic coast (median, 0.30 and 1.02 μM, respectively), while nitrates were higher in all sampling periods (median, 45.37 μM), and silicates were significantly smaller (median, 7.76 μM). Chlorophyll a and phaeopigments have shown median values of 0.38 and 0.85 mg m(-3), respectively, while saturated values of dissolved oxygen were recorded throughout the study. The analysis reflected that nutrient enrichment seems to be linked to an anthropogenic source, the presence of peatlands areas, and a sink of Nothofagus pumilio woods. The area could be characterized in three zones related to (1) high urban influence, (2) natural inputs of freshwater, and (3) mixed inputs coming from moderate urban impacts.

A probabilistic approach to the drag-based model

NASA Astrophysics Data System (ADS)

Napoletano, Gianluca; Forte, Roberta; Moro, Dario Del; Pietropaolo, Ermanno; Giovannelli, Luca; Berrilli, Francesco

2018-02-01

The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.

Intelligent Flow Friction Estimation

PubMed Central

Brkić, Dejan; Ćojbašić, Žarko

2016-01-01

Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor. However, the Colebrook equation is implicit with respect to the friction factor (λ). In the present study, a noniterative approach using Artificial Neural Network (ANN) was developed to calculate the friction factor. To configure the ANN model, the input parameters of the Reynolds Number (Re) and the relative roughness of pipe (ε/D) were transformed to logarithmic scales. The 90,000 sets of data were fed to the ANN model involving three layers: input, hidden, and output layers with, 2, 50, and 1 neurons, respectively. This configuration was capable of predicting the values of friction factor in the Colebrook equation for any given values of the Reynolds number (Re) and the relative roughness (ε/D) ranging between 5000 and 108 and between 10−7 and 0.1, respectively. The proposed ANN demonstrates the relative error up to 0.07% which had the high accuracy compared with the vast majority of the precise explicit approximations of the Colebrook equation. PMID:27127498

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

Pulse mode of operation of a spherical piezoceramic transducer filled with liquid and having a correcting electric circuit.

PubMed

Konovalov, S I; Kuz'menko, A G

2010-12-01

By means of a computational method, the possibility of radiating a short acoustic pulse by a transducer in the form of a piezoceramic sphere internally filled with liquid is investigated. An electric inductive-resistive circuit is connected to the electric input of the transducer. Solution is obtained based on scheme-analogs theory for piezoceramic transducers, and spectral Fourier transform theory. The values of parameters of the system, providing minimal durations of radiated signals, are determined. Computation was carried out for different values of relative thicknesses of the transducer wall. The estimates of durations and amplitudes of the acoustic signals radiated into the external medium are obtained.

Methods for combining payload parameter variations with input environment. [calculating design limit loads compatible with probabilistic structural design criteria

NASA Technical Reports Server (NTRS)

Merchant, D. H.

1976-01-01

Methods are presented for calculating design limit loads compatible with probabilistic structural design criteria. The approach is based on the concept that the desired limit load, defined as the largest load occurring in a mission, is a random variable having a specific probability distribution which may be determined from extreme-value theory. The design limit load, defined as a particular of this random limit load, is the value conventionally used in structural design. Methods are presented for determining the limit load probability distributions from both time-domain and frequency-domain dynamic load simulations. Numerical demonstrations of the method are also presented.

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

PubMed Central

Lomnitz, Jason G.; Savageau, Michael A.

2016-01-01

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits. PMID:27462346

On the physical properties of volcanic rock masses

NASA Astrophysics Data System (ADS)

Heap, M. J.; Villeneuve, M.; Ball, J. L.; Got, J. L.

2017-12-01

The physical properties (e.g., elastic properties, porosity, permeability, cohesion, strength, amongst others) of volcanic rocks are crucial input parameters for modelling volcanic processes. These parameters, however, are often poorly constrained and there is an apparent disconnect between modellers and those who measure/determine rock and rock mass properties. Although it is well known that laboratory measurements are scale dependent, experimentalists, field volcanologists, and modellers should work together to provide the most appropriate model input parameters. Our pluridisciplinary approach consists of (1) discussing with modellers to better understand their needs, (2) using experimental know-how to build an extensive database of volcanic rock properties, and (3) using geotechnical and field-based volcanological know-how to address scaling issues. For instance, increasing the lengthscale of interest from the laboratory-scale to the volcano-scale will reduce the elastic modulus and strength and increase permeability, but to what extent? How variable are the physical properties of volcanic rocks, and is it appropriate to assume constant, isotropic, and/or homogeneous values for volcanoes? How do alteration, depth, and temperature influence rock physical and mechanical properties? Is rock type important, or do rock properties such as porosity exert a greater control on such parameters? How do we upscale these laboratory-measured properties to rock mass properties using the "fracturedness" of a volcano or volcanic outcrop, and how do we quantify fracturedness? We hope to discuss and, where possible, address some of these issues through active discussion between two (or more) scientific communities.

Individual phase constitutive properties of a TRIP-assisted QP980 steel from a combined synchrotron X-ray diffraction and crystal plasticity approach

DOE PAGES

Hu, Xiao Hua; Sun, X.; Hector, Jr., L. G.; ...

2017-04-21

Here, microstructure-based constitutive models for multiphase steels require accurate constitutive properties of the individual phases for component forming and performance simulations. We address this requirement with a combined experimental/theoretical methodology which determines the critical resolved shear stresses and hardening parameters of the constituent phases in QP980, a TRIP assisted steel subject to a two-step quenching and partitioning heat treatment. High energy X-Ray diffraction (HEXRD) from a synchrotron source provided the average lattice strains of the ferrite, martensite, and austenite phases from the measured volume during in situ tensile deformation. The HEXRD data was then input to a computationally efficient, elastic-plasticmore » self-consistent (EPSC) crystal plasticity model which estimated the constitutive parameters of different slip systems for the three phases via a trial-and-error approach. The EPSC-estimated parameters are then input to a finite element crystal plasticity (CPFE) model representing the QP980 tensile sample. The predicted lattice strains and global stress versus strain curves are found to be 8% lower that the EPSC model predicted values and from the HEXRD measurements, respectively. This discrepancy, which is attributed to the stiff secant assumption in the EPSC formulation, is resolved with a second step in which CPFE is used to iteratively refine the EPSC-estimated parameters. Remarkably close agreement is obtained between the theoretically-predicted and experimentally derived flow curve for the QP980 material.« less

Individual phase constitutive properties of a TRIP-assisted QP980 steel from a combined synchrotron X-ray diffraction and crystal plasticity approach

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

Hu, X. H.; Sun, X.; Hector, L. G.

2017-06-01

Microstructure-based constitutive models for multiphase steels require accurate constitutive properties of the individual phases for component forming and performance simulations. We address this requirement with a combined experimental/theoretical methodology which determines the critical resolved shear stresses and hardening parameters of the constituent phases in QP980, a TRIP assisted steel subject to a two-step quenching and partitioning heat treatment. High energy X-Ray diffraction (HEXRD) from a synchrotron source provided the average lattice strains of the ferrite, martensite, and austenite phases from the measured volume during in situ tensile deformation. The HEXRD data was then input to a computationally efficient, elastic-plastic self-consistentmore » (EPSC) crystal plasticity model which estimated the constitutive parameters of different slip systems for the three phases via a trial-and-error approach. The EPSC-estimated parameters are then input to a finite element crystal plasticity (CPFE) model representing the QP980 tensile sample. The predicted lattice strains and global stress versus strain curves are found to be 8% lower that the EPSC model predicted values and from the HEXRD measurements, respectively. This discrepancy, which is attributed to the stiff secant assumption in the EPSC formulation, is resolved with a second step in which CPFE is used to iteratively refine the EPSC-estimated parameters. Remarkably close agreement is obtained between the theoretically-predicted and experimentally derived flow curve for the QP980 material.« less

Determination of solar wind energy input during different form of geomagnetic disturbances.

NASA Astrophysics Data System (ADS)

Dahal, S.; Adhikari, B.; Narayan, C.; Shapkota, N.

2017-12-01

A quantitative study on solar wind energy input during different form of geomagnetic disturbances as well as during quite period was performed. To enable a quantitative analysis, we estimate Akasofu parameter which plays an important role to understand the relationships between ionosphere-magnetosphere and solar wind energy input. For comparative purpose, the total energy budget of Non storm HILDCAA event (19th to 24th April 2003), Storm preceding HILDCAA event (14th to 19th May 2005), Geomagnetic sub-storm (12nd to 16th November 2003), Geomagnetic super sub-storm (12nd to 16th November 2003) and a Quiet period (18th to 21st July 2006) were also analyzed. Among these events the highest total energy budget was found during the occurrence of storm preceding HILDCAA. This is due to significant geomagnetic field perturbation as displayed on the value of interplanetary parameters. The principal cause of geomagnetic disturbance is the magnetic reconnection, which establishes an electrodynamic coupling between the solar plasma and the magnetosphere. Although there is distinct perturbation on SYM-H index for all events but the values are different. The highest pick value of SYM-H index ( -300nT) was found for the storm preceding HILDCAA.This results suggest that the effects of HILDCAAs, displayed on the value of the SYM-H index, depends on the amount of the energy injected into the ring current. In a complementary way, fluctuation pattern of Temperature, IMF magnitude, Bx component, By component, and AE index are also studied and the possible physical interpretations for the statistical results obtained during each events were discussed. We shall report the characteristics of Bz component during each events by the implementation of discrete wavelet transform (DWT) and cross correlation analysis. We did cross-correlation between solar wind energy and Bz component of IMF and found a negative correlation between them during the main phase of geomagnetic disturbances. These results help to understand the coupling process between solar wind and magnetosphere-ionosphere system. By DWT analysis we found distinct singularity in solar wind energy signal during the period when Bz component is highly perturbed. This result indicates that there are impulsive energy injections superposed to the smooth background process.

Comparison of Two Global Sensitivity Analysis Methods for Hydrologic Modeling over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Hameed, M.; Demirel, M. C.; Moradkhani, H.

2015-12-01

Global Sensitivity Analysis (GSA) approach helps identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. In this study, the effects of the Sacramento Soil Moisture Accounting (SAC-SMA) model parameters, forcing data, and initial conditions are analysed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. Four factors are considered and evaluated by using the two sensitivity analysis methods: the simulation length, parameter range, model initial conditions, and the reliability of the global sensitivity analysis methods. The reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. Another conclusion of this study is that the smaller parameter or initial condition ranges, the more consistency and coherence between the sensitivity analysis methods results.

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

A Universal Shape of Empirical Mass Formula for all Leptons and Quarks

NASA Astrophysics Data System (ADS)

Królikowski, W.

2006-09-01

A specific {ITALIC universal} shape of empirical mass formula is proposed for all leptons nu 1, nu 2 , nu 3 and e-, mu -, tau - as well as all quarks u, c, t and d, s, b of three generations, parametrized by three free constants mu , \\varepsilon , xi assuming four different triplets of values. Four such triplets of parameter values are determined or estimated from the present data. Mass spectra in the four cases are related to each other by {ITALIC shifting} the triplet of parameters mu , \\varepsilon , xi . For charged leptons xi simeq 0 (but probably xi not =0). If for them xi is put to be exactly 0, then mtau = 1776.80 MeV is {ITALIC predicted} after the input of experimental me and mmu (the central value of experimental mtau = 1776.99+ 0.29-0.26 MeV corresponds to xi =1.8times 10-3not =0). For neutrinos 1/xi simeq 0 (but 1/xi not =0 in the case of normal hierarchy m2nu 1 ll m2nu 2 ll m2nu 3). If for neutrinos 1/xi is conjectured to be exactly 0, then (mnu 1, mnu 2, mnu 3) sim (1.5, 1.2, 5.1)times 10-2 eV are {ITALIC predicted} after the input of experimental estimates |m2nu 2 - m2nu 1| sim 8.0times 10-5mskip thickmuskip {eV}2 and |m2nu 3 - m2nu 2| sim 2.4times 10-3mskip thickmuskip {eV}2. Thus, the mass ordering of neutrino states 1 and 2 is then {ITALIC inverted}, while the position of state 3 is {ITALIC normal}.

The Pedestrian Detection Method Using an Extension Background Subtraction about the Driving Safety Support Systems

NASA Astrophysics Data System (ADS)

Muranaka, Noriaki; Date, Kei; Tokumaru, Masataka; Imanishi, Shigeru

In recent years, the traffic accident occurs frequently with explosion of traffic density. Therefore, we think that the safe and comfortable transportation system to defend the pedestrian who is the traffic weak is necessary. First, we detect and recognize the pedestrian (the crossing person) by the image processing. Next, we inform all the drivers of the right or left turn that the pedestrian exists by the sound and the image and so on. By prompting a driver to do safe driving in this way, the accident to the pedestrian can decrease. In this paper, we are using a background subtraction method for the movement detection of the movement object. In the background subtraction method, the update method in the background was important, and as for the conventional way, the threshold values of the subtraction processing and background update were identical. That is, the mixing rate of the input image and the background image of the background update was a fixation value, and the fine tuning which corresponded to the environment change of the weather was difficult. Therefore, we propose the update method of the background image that the estimated mistake is difficult to be amplified. We experiment and examines in the comparison about five cases of sunshine, cloudy, evening, rain, sunlight change, except night. This technique can set separately the threshold values of the subtraction processing and background update processing which suited the environmental condition of the weather and so on. Therefore, the fine tuning becomes possible freely in the mixing rate of the input image and the background image of the background update. Because the setting of the parameter which suited an environmental condition becomes important to minimize mistaking percentage, we examine about the setting of a parameter.

Robustness analysis of an air heating plant and control law by using polynomial chaos

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

Colón, Diego; Ferreira, Murillo A. S.; Bueno, Átila M.

2014-12-10

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputsmore » (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.« less

Peak Dose Assessment for Proposed DOE-PPPO Authorized Limits

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

Maldonado, Delis

2012-06-01

The Oak Ridge Institute for Science and Education (ORISE), a U.S. Department of Energy (DOE) prime contractor, was contracted by the DOE Portsmouth/Paducah Project Office (DOE-PPPO) to conduct a peak dose assessment in support of the Authorized Limits Request for Solid Waste Disposal at Landfill C-746-U at the Paducah Gaseous Diffusion Plant (DOE-PPPO 2011a). The peak doses were calculated based on the DOE-PPPO Proposed Single Radionuclides Soil Guidelines and the DOE-PPPO Proposed Authorized Limits (AL) Volumetric Concentrations available in DOE-PPPO 2011a. This work is provided as an appendix to the Dose Modeling Evaluations and Technical Support Document for the Authorizedmore » Limits Request for the C-746-U Landfill at the Paducah Gaseous Diffusion Plant, Paducah, Kentucky (ORISE 2012). The receptors evaluated in ORISE 2012 were selected by the DOE-PPPO for the additional peak dose evaluations. These receptors included a Landfill Worker, Trespasser, Resident Farmer (onsite), Resident Gardener, Recreational User, Outdoor Worker and an Offsite Resident Farmer. The RESRAD (Version 6.5) and RESRAD-OFFSITE (Version 2.5) computer codes were used for the peak dose assessments. Deterministic peak dose assessments were performed for all the receptors and a probabilistic dose assessment was performed only for the Offsite Resident Farmer at the request of the DOE-PPPO. In a deterministic analysis, a single input value results in a single output value. In other words, a deterministic analysis uses single parameter values for every variable in the code. By contrast, a probabilistic approach assigns parameter ranges to certain variables, and the code randomly selects the values for each variable from the parameter range each time it calculates the dose (NRC 2006). The receptor scenarios, computer codes and parameter input files were previously used in ORISE 2012. A few modifications were made to the parameter input files as appropriate for this effort. Some of these changes included increasing the time horizon beyond 1,050 years (yr), and using the radionuclide concentrations provided by the DOE-PPPO as inputs into the codes. The deterministic peak doses were evaluated within time horizons of 70 yr (for the Landfill Worker and Trespasser), 1,050 yr, 10,000 yr and 100,000 yr (for the Resident Farmer [onsite], Resident Gardener, Recreational User, Outdoor Worker and Offsite Resident Farmer) at the request of the DOE-PPPO. The time horizons of 10,000 yr and 100,000 yr were used at the request of the DOE-PPPO for informational purposes only. The probabilistic peak of the mean dose assessment was performed for the Offsite Resident Farmer using Technetium-99 (Tc-99) and a time horizon of 1,050 yr. The results of the deterministic analyses indicate that among all receptors and time horizons evaluated, the highest projected dose, 2,700 mrem/yr, occurred for the Resident Farmer (onsite) at 12,773 yr. The exposure pathways contributing to the peak dose are ingestion of plants, external gamma, and ingestion of milk, meat and soil. However, this receptor is considered an implausible receptor. The only receptors considered plausible are the Landfill Worker, Recreational User, Outdoor Worker and the Offsite Resident Farmer. The maximum projected dose among the plausible receptors is 220 mrem/yr for the Outdoor Worker and it occurs at 19,045 yr. The exposure pathways contributing to the dose for this receptor are external gamma and soil ingestion. The results of the probabilistic peak of the mean dose analysis for the Offsite Resident Farmer indicate that the average (arithmetic mean) of the peak of the mean doses for this receptor is 0.98 mrem/yr and it occurs at 1,050 yr. This dose corresponds to Tc-99 within the time horizon of 1,050 yr.« less

Rapid Debris Analysis Project Task 3 Final Report - Sensitivity of Fallout to Source Parameters, Near-Detonation Environment Material Properties, Topography, and Meteorology

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

Goldstein, Peter

2014-01-24

This report describes the sensitivity of predicted nuclear fallout to a variety of model input parameters, including yield, height of burst, particle and activity size distribution parameters, wind speed, wind direction, topography, and precipitation. We investigate sensitivity over a wide but plausible range of model input parameters. In addition, we investigate a specific example with a relatively narrow range to illustrate the potential for evaluating uncertainties in predictions when there are more precise constraints on model parameters.

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2018-01-01

Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ˜ 2 to 3 g C m-2 yr-1 (with uncertain parameters) to ˜ 45 g C m-2 yr-1 (C3 grass) and ˜ 75 g C m-2 yr-1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ˜ 4.0-13.5 g C m-2 yr-1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

The dynamics of integrate-and-fire: mean versus variance modulations and dependence on baseline parameters.

PubMed

Pressley, Joanna; Troyer, Todd W

2011-05-01

The leaky integrate-and-fire (LIF) is the simplest neuron model that captures the essential properties of neuronal signaling. Yet common intuitions are inadequate to explain basic properties of LIF responses to sinusoidal modulations of the input. Here we examine responses to low and moderate frequency modulations of both the mean and variance of the input current and quantify how these responses depend on baseline parameters. Across parameters, responses to modulations in the mean current are low pass, approaching zero in the limit of high frequencies. For very low baseline firing rates, the response cutoff frequency matches that expected from membrane integration. However, the cutoff shows a rapid, supralinear increase with firing rate, with a steeper increase in the case of lower noise. For modulations of the input variance, the gain at high frequency remains finite. Here, we show that the low-frequency responses depend strongly on baseline parameters and derive an analytic condition specifying the parameters at which responses switch from being dominated by low versus high frequencies. Additionally, we show that the resonant responses for variance modulations have properties not expected for common oscillatory resonances: they peak at frequencies higher than the baseline firing rate and persist when oscillatory spiking is disrupted by high noise. Finally, the responses to mean and variance modulations are shown to have a complementary dependence on baseline parameters at higher frequencies, resulting in responses to modulations of Poisson input rates that are independent of baseline input statistics.

Generalized compliant motion primitive

NASA Technical Reports Server (NTRS)

Backes, Paul G. (Inventor)

1994-01-01

This invention relates to a general primitive for controlling a telerobot with a set of input parameters. The primitive includes a trajectory generator; a teleoperation sensor; a joint limit generator; a force setpoint generator; a dither function generator, which produces telerobot motion inputs in a common coordinate frame for simultaneous combination in sensor summers. Virtual return spring motion input is provided by a restoration spring subsystem. The novel features of this invention include use of a single general motion primitive at a remote site to permit the shared and supervisory control of the robot manipulator to perform tasks via a remotely transferred input parameter set.

Effects of salinity and nitrogen supply on the quality and health-related compounds of strawberry fruits (Fragaria × ananassa cv. Primoris).

PubMed

Cardeñosa, Vanessa; Medrano, Evangelina; Lorenzo, Pilar; Sánchez-Guerrero, Maria Cruz; Cuevas, Francisco; Pradas, Inmaculada; Moreno-Rojas, José M

2015-11-01

Different nitrogen inputs and/or development under adverse water conditions (water stress/low quality and/or high salinity/electrical conductivity), such as those prevailing in Almeria (Mediterranean coast, south-east Spain), may affect overall fruit and vegetable quality. This study evaluated the influence of salinity and nitrogen reduction in hydroponic nutrient solution on strawberry fruit quality and nutritional compounds (Fragaria × ananassa Duch., cv. Primoris). Strawberries obtained under salinity treatments recorded the highest values for soluble solids content (SSC; all samplings); fruit taste was thus enhanced. Additionally, salinity improved fruit nutritional value, with higher contents of antioxidants compounds (first sampling). During first and second samplings, strawberries grown under N reduction and non-saline conditions showed higher values for firmness compared to fruits developed under other treatments. Regarding health-related compounds, few differences were found except for total polyphenols concentration and antioxidant activity for the first sampling, where strawberries grown under saline treatments obtained the highest values for both parameters. The use of low-quality waters, such as those found in Almeria (salinity, N9S and N5S) and low nitrogen inputs (N5, avoid environmental impact) for strawberry cultivation does not exert a negative impact on overall quality. Positive differences could be found in SSC, firmness and health-related compounds when compared against the control treatment (N9). © 2014 Society of Chemical Industry.

Uncertainty analyses of the calibrated parameter values of a water quality model

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.; Lindenschmidt, K.-E.

2003-04-01

For river basin management water quality models are increasingly used for the analysis and evaluation of different management measures. However substantial uncertainties exist in parameter values depending on the available calibration data. In this paper an uncertainty analysis for a water quality model is presented, which considers the impact of available model calibration data and the variance of input variables. The investigation was conducted based on four extensive flowtime related longitudinal surveys in the River Elbe in the years 1996 to 1999 with varying discharges and seasonal conditions. For the model calculations the deterministic model QSIM of the BfG (Germany) was used. QSIM is a one dimensional water quality model and uses standard algorithms for hydrodynamics and phytoplankton dynamics in running waters, e.g. Michaelis Menten/Monod kinetics, which are used in a wide range of models. The multi-objective calibration of the model was carried out with the nonlinear parameter estimator PEST. The results show that for individual flow time related measuring surveys very good agreements between model calculation and measured values can be obtained. If these parameters are applied to deviating boundary conditions, substantial errors in model calculation can occur. These uncertainties can be decreased with an increased calibration database. More reliable model parameters can be identified, which supply reasonable results for broader boundary conditions. The extension of the application of the parameter set on a wider range of water quality conditions leads to a slight reduction of the model precision for the specific water quality situation. Moreover the investigations show that highly variable water quality variables like the algal biomass always allow a smaller forecast accuracy than variables with lower coefficients of variation like e.g. nitrate.

California Fault Parameters for the National Seismic Hazard Maps and Working Group on California Earthquake Probabilities 2007

USGS Publications Warehouse

Wills, Chris J.; Weldon, Ray J.; Bryant, W.A.

2008-01-01

This report describes development of fault parameters for the 2007 update of the National Seismic Hazard Maps and the Working Group on California Earthquake Probabilities (WGCEP, 2007). These reference parameters are contained within a database intended to be a source of values for use by scientists interested in producing either seismic hazard or deformation models to better understand the current seismic hazards in California. These parameters include descriptions of the geometry and rates of movements of faults throughout the state. These values are intended to provide a starting point for development of more sophisticated deformation models which include known rates of movement on faults as well as geodetic measurements of crustal movement and the rates of movements of the tectonic plates. The values will be used in developing the next generation of the time-independent National Seismic Hazard Maps, and the time-dependant seismic hazard calculations being developed for the WGCEP. Due to the multiple uses of this information, development of these parameters has been coordinated between USGS, CGS and SCEC. SCEC provided the database development and editing tools, in consultation with USGS, Golden. This database has been implemented in Oracle and supports electronic access (e.g., for on-the-fly access). A GUI-based application has also been developed to aid in populating the database. Both the continually updated 'living' version of this database, as well as any locked-down official releases (e.g., used in a published model for calculating earthquake probabilities or seismic shaking hazards) are part of the USGS Quaternary Fault and Fold Database http://earthquake.usgs.gov/regional/qfaults/ . CGS has been primarily responsible for updating and editing of the fault parameters, with extensive input from USGS and SCEC scientists.

Adaptive control of a quadrotor aerial vehicle with input constraints and uncertain parameters

NASA Astrophysics Data System (ADS)

Tran, Trong-Toan; Ge, Shuzhi Sam; He, Wei

2018-05-01

In this paper, we address the problem of adaptive bounded control for the trajectory tracking of a Quadrotor Aerial Vehicle (QAV) while the input saturations and uncertain parameters with the known bounds are simultaneously taken into account. First, to deal with the underactuated property of the QAV model, we decouple and construct the QAV model as a cascaded structure which consists of two fully actuated subsystems. Second, to handle the input constraints and uncertain parameters, we use a combination of the smooth saturation function and smooth projection operator in the control design. Third, to ensure the stability of the overall system of the QAV, we develop the technique for the cascaded system in the presence of both the input constraints and uncertain parameters. Finally, the region of stability of the closed-loop system is constructed explicitly, and our design ensures the asymptotic convergence of the tracking errors to the origin. The simulation results are provided to illustrate the effectiveness of the proposed method.

Real­-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

NASA Astrophysics Data System (ADS)

Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

2014-12-01

Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions. Real-time ensemble modeling of CME propagation is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL+cone model available at the Community Coordinated Modeling Center (CCMC). To estimate the effect of uncertainties in determining CME input parameters on arrival time predictions, a distribution of n (routinely n=48) CME input parameter sets are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest, including a probability distribution of CME arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). We present the results of ensemble simulations for a total of 38 CME events in 2013-2014. For 28 of the ensemble runs containing hits, the observed CME arrival was within the range of ensemble arrival time predictions for 14 runs (half). The average arrival time prediction was computed for each of the 28 ensembles predicting hits and using the actual arrival time, an average absolute error of 10.0 hours (RMSE=11.4 hours) was found for all 28 ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling sysem was used to complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME. The parameter sensitivity study suggests future directions for the system, such as running ensembles using various magnetogram inputs to the WSA model.

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.

[Tasseled cap triangle (TCT)-leaf area index (LAI)model of rice fields based on PROSAIL model and its application].

PubMed

Li, Ya Ni; Lu, Lei; Liu, Yong

2017-12-01

The tasseled cap triangle (TCT)-leaf area index (LAI) isoline is a model that reflects the distribution of LAI isoline in the spectral space constituted by reflectance of red and near-infrared (NIR) bands, and the LAI retrieval model developed on the basis of this is more accurate than the commonly used statistical relationship models. This study used ground-based measurements of the rice field, validated the applicability of PROSAIL model in simulating canopy reflectance of rice field, and calibrated the input parameters of the model. The ranges of values of PROSAIL input parameters for simulating rice canopy reflectance were determined. Based on this, the TCT-LAI isoline model of rice field was established, and a look-up table (LUT) required in remote sensing retrieval of LAI was developed. Then, the LUT was used for Landsat 8 and WorldView 3 data to retrieve LAI of rice field, respectively. The results showed that the LAI retrieved using the LUT developed from TCT-LAI isoline model had a good linear relationship with the measured LAI R 2 =0.76, RMSE=0.47. Compared with the LAI retrieved from Landsat 8, LAI values retrieved from WorldView 3 va-ried with wider range, and data distribution was more scattered. Resampling the Landsat 8 and WorldView 3 reflectance data to 1 km to retrieve LAI, the result of MODIS LAI product was significantly underestimated compared to that of retrieved LAI.

Concentration distribution and assessment of several heavy metals in sediments of west-four Pearl River Estuary

NASA Astrophysics Data System (ADS)

Wang, Shanshan; Cao, Zhimin; Lan, Dongzhao; Zheng, Zhichang; Li, Guihai

2008-09-01

Grain size parameters, trace metals (Co, Cu, Ni, Pb, Cr, Zn, Ba, Zr and Sr) and total organic matter (TOM) of 38 surficial sediments and a sediment core of west-four Pearl River Estuary region were analyzed. The spacial distribution and the transportation procession of the chemical element in surficial sediments were studied mainly. Multivariate statistics are used to analyses the interrelationship of metal elements, TOM and the grain size parameters. The results demonstrated that terrigenous sediment taken by the rivers are main sources of the trace metal elements and TOM, and the lithology of parent material is a dominating factor controlling the trace metal composition in the surficial sediment. In addition, the hydrodynamic condition and landform are the dominating factors controlling the large-scale distribution, while the anthropogenic input in the coastal area alters the regional distribution of heavy metal elements Co, Cu, Ni, Pb, Cr and Zn. The enrichment factor (EF) analysis was used for the differentiation of the metal source between anthropogenic and naturally occurring, and for the assessment of the anthropogenic influence, the deeper layer content of heavy metals were calculated as the background values and Zr was chosen as the reference element for Co, Cu, Ni, Pb, Cr and Zn. The result indicate prevalent enrichment of Co, Cu, Ni, Pb and Cr, and the contamination of Pb is most obvious, further more, the peculiar high EF value sites of Zn and Pb probably suggest point source input.

Artificial neural networks in knee injury risk evaluation among professional football players

NASA Astrophysics Data System (ADS)

Martyna, Michałowska; Tomasz, Walczak; Krzysztof, Grabski Jakub; Monika, Grygorowicz

2018-01-01

Lower limb injury risk assessment was proposed, based on isokinetic examination that is a part of standard athlete's biomechanical evaluation performed mainly twice a year. Information about non-contact knee injury (or lack of the injury) sustained within twelve months after isokinetic test, confirmed in USG were verified. Three the most common types of football injuries were taken into consideration: anterior cruciate ligament (ACL) rupture, hamstring and quadriceps muscles injuries. 22 parameters, obtained from isokinetic tests were divided into 4 groups and used as input parameters of five feedforward artificial neural networks (ANNs). The 5th group consisted of all considered parameters. The networks were trained with the use of Levenberg-Marquardt backpropagation algorithm to return value close to 1 for the sets of parameters corresponding injury event and close to 0 for parameters with no injury recorded within 6 - 12 months after isokinetic test. Results of this study shows that ANN might be useful tools, which simplify process of simultaneous interpretation of many numerical parameters, but the most important factor that significantly influence the results is database used for ANN training.

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.

Synthesis of feedback systems with large plant ignorance for prescribed time domain tolerances

NASA Technical Reports Server (NTRS)

Horowitz, I. M.; Sidi, M.

1971-01-01

There is given a minimum-phase plant transfer function, with prescribed bounds on its parameter values. The plant is imbedded in a two-degree-of freedom feedback system, which is to be designed such that the system time response to a deterministic input lies within specified boundaries. Subject to the above, the design should be such as to minimize the effect of sensor noise at the input to the plant. This report presents a design procedure for this purpose, based on frequency response concepts. The time-domain tolerances are translated into equivalent frequency response tolerances. The latter lead to bounds on the loop transmission function in the form of continuous curves on the Nichols chart. The properties of the loop transmission function which satisfy these bounds with minimum effect of sensor noise, are derived.

Mathematical modelling of methanogenic reactor start-up: Importance of volatile fatty acids degrading population.

PubMed

Jabłoński, Sławomir J; Łukaszewicz, Marcin

2014-12-01

Development of balanced community of microorganisms is one of the obligatory for stable anaerobic digestion. Application of mathematical models might be helpful in development of reliable procedures during the process start-up period. Yet, the accuracy of forecast depends on the quality of input and parameters. In this study, the specific anaerobic activity (SAA) tests were applied in order to estimate microbial community structure. Obtained data was applied as input conditions for mathematical model of anaerobic digestion. The initial values of variables describing the amount of acetate and propionate utilizing microorganisms could be calculated on the basis of SAA results. The modelling based on those optimized variables could successfully reproduce the behavior of a real system during the continuous fermentation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

A sensitivity analysis for a thermomechanical model of the Antarctic ice sheet and ice shelves

NASA Astrophysics Data System (ADS)

Baratelli, F.; Castellani, G.; Vassena, C.; Giudici, M.

2012-04-01

The outcomes of an ice sheet model depend on a number of parameters and physical quantities which are often estimated with large uncertainty, because of lack of sufficient experimental measurements in such remote environments. Therefore, the efforts to improve the accuracy of the predictions of ice sheet models by including more physical processes and interactions with atmosphere, hydrosphere and lithosphere can be affected by the inaccuracy of the fundamental input data. A sensitivity analysis can help to understand which are the input data that most affect the different predictions of the model. In this context, a finite difference thermomechanical ice sheet model based on the Shallow-Ice Approximation (SIA) and on the Shallow-Shelf Approximation (SSA) has been developed and applied for the simulation of the evolution of the Antarctic ice sheet and ice shelves for the last 200 000 years. The sensitivity analysis of the model outcomes (e.g., the volume of the ice sheet and of the ice shelves, the basal melt rate of the ice sheet, the mean velocity of the Ross and Ronne-Filchner ice shelves, the wet area at the base of the ice sheet) with respect to the model parameters (e.g., the basal sliding coefficient, the geothermal heat flux, the present-day surface accumulation and temperature, the mean ice shelves viscosity, the melt rate at the base of the ice shelves) has been performed by computing three synthetic numerical indices: two local sensitivity indices and a global sensitivity index. Local sensitivity indices imply a linearization of the model and neglect both non-linear and joint effects of the parameters. The global variance-based sensitivity index, instead, takes into account the complete variability of the input parameters but is usually conducted with a Monte Carlo approach which is computationally very demanding for non-linear complex models. Therefore, the global sensitivity index has been computed using a development of the model outputs in a neighborhood of the reference parameter values with a second-order approximation. The comparison of the three sensitivity indices proved that the approximation of the non-linear model with a second-order expansion is sufficient to show some differences between the local and the global indices. As a general result, the sensitivity analysis showed that most of the model outcomes are mainly sensitive to the present-day surface temperature and accumulation, which, in principle, can be measured more easily (e.g., with remote sensing techniques) than the other input parameters considered. On the other hand, the parameters to which the model resulted less sensitive are the basal sliding coefficient and the mean ice shelves viscosity.

Assessment of Areal Recharge to the Spokane Valley-Rathdrum Prairie Aquifer, Spokane County, Washington, and Bonner and Kootenai Counties, Idaho

USGS Publications Warehouse

Bartolino, James R.

2007-01-01

A numerical flow model of the Spokane Valley-Rathdrum Prairie aquifer currently (2007) being developed requires the input of values for areally-distributed recharge, a parameter that is often the most uncertain component of water budgets and ground-water flow models because it is virtually impossible to measure over large areas. Data from six active weather stations in and near the study area were used in four recharge-calculation techniques or approaches; the Langbein method, in which recharge is estimated on the basis of empirical data from other basins; a method developed by the U.S. Department of Agriculture (USDA), in which crop consumptive use and effective precipitation are first calculated and then subtracted from actual precipitation to yield an estimate of recharge; an approach developed as part of the Eastern Snake Plain Aquifer Model (ESPAM) Enhancement Project in which recharge is calculated on the basis of precipitation-recharge relations from other basins; and an approach in which reference evapotranspiration is calculated by the Food and Agriculture Organization (FAO) Penman-Monteith equation, crop consumptive use is determined (using a single or dual coefficient approach), and recharge is calculated. Annual recharge calculated by the Langbein method for the six weather stations was 4 percent of annual mean precipitation, yielding the lowest values of the methods discussed in this report, however, the Langbein method can be only applied to annual time periods. Mean monthly recharge calculated by the USDA method ranged from 53 to 73 percent of mean monthly precipitation. Mean annual recharge ranged from 64 to 69 percent of mean annual precipitation. Separate mean monthly recharge calculations were made with the ESPAM method using initial input parameters to represent thin-soil, thick-soil, and lava-rock conditions. The lava-rock parameters yielded the highest recharge values and the thick-soil parameters the lowest. For thin-soil parameters, calculated monthly recharge ranged from 10 to 29 percent of mean monthly precipitation and annual recharge ranged from 16 to 23 percent of mean annual precipitation. For thick-soil parameters, calculated monthly recharge ranged from 1 to 5 percent of mean monthly precipitation and mean annual recharge ranged from 2 to 4 percent of mean annual precipitation. For lava-rock parameters, calculated mean monthly recharge ranged from 37 to 57 percent of mean monthly precipitation and mean annual recharge ranged from 45 to 52 percent of mean annual precipitation. Single-coefficient (crop coefficient) FAO Penman-Monteith mean monthly recharge values were calculated for Spokane Weather Service Office (WSO) Airport, the only station for which the necessary meteorological data were available. Grass-referenced values of mean monthly recharge ranged from 0 to 81 percent of mean monthly precipitation and mean annual recharge was 21 percent of mean annual precipitation; alfalfa-referenced values of mean monthly recharge ranged from 0 to 85 percent of mean monthly precipitation and mean annual recharge was 24 percent of mean annual precipitation. Single-coefficient FAO Penman-Monteith calculations yielded a mean monthly recharge of zero during the eight warmest and driest months of the year (March-October). In order to refine the mean monthly recharge estimates, dual-coefficient (basal crop and soil evaporation coefficients) FAO Penman-Monteith dual-crop evapotranspiration and deep-percolation calculations were applied to daily values from the Spokane WSO Airport for January 1990 through December 2005. The resultant monthly totals display a temporal variability that is absent from the mean monthly values and demonstrate that the daily amount and timing of precipitation dramatically affect calculated recharge. The dual-coefficient FAO Penman-Monteith calculations were made for the remaining five stations using wind-speed values for Spokane WSO Airport and other assumptions regarding

Anchorage in Orthodontics: Three-dimensional Scanner Input.

PubMed

Nabbout, Fidele; Baron, Pascal

2018-01-01

The aim of this article is to re-evaluate anchorage coefficient values in orthodontics and their influence in the treatment decision through the usage of three-dimensional (3D) scanner. A sample of 80 patients was analyzed with the 3D scanner using the C2000 and Cepha 3DT softwares (CIRAD Montpellier, France). Tooth anatomy parameters (linear measurements, root, and crown volumes) were then calculated to determine new anchorage coefficients based on root volume. Data were collected and statistically evaluated with the StatView software (version 5.0). The anchorage coefficient values found in this study are compared to those established in previous studies. These new values affect and modify our approach in orthodontic treatment from the standpoint of anchorage. The use of new anchorage coefficient values has significant clinical implications in conventional and in microimplants-assisted orthodontic mechanics through the selection and delivery of the optimal force system (magnitude and moment) for an adequate biological response.

RIPL - Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

NASA Astrophysics Data System (ADS)

Capote, R.; Herman, M.; Obložinský, P.; Young, P. G.; Goriely, S.; Belgya, T.; Ignatyuk, A. V.; Koning, A. J.; Hilaire, S.; Plujko, V. A.; Avrigeanu, M.; Bersillon, O.; Chadwick, M. B.; Fukahori, T.; Ge, Zhigang; Han, Yinlu; Kailas, S.; Kopecky, J.; Maslov, V. M.; Reffo, G.; Sin, M.; Soukhovitskii, E. Sh.; Talou, P.

2009-12-01

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released in January 2009, and is available on the Web through http://www-nds.iaea.org/RIPL-3/. This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and γ-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from 51V to 239Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.

RIPL - Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

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

Capote, R.; Herman, M.; Oblozinsky, P.

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released inmore » January 2009, and is available on the Web through (http://www-nds.iaea.org/RIPL-3/). This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and {gamma}-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from {sup 51}V to {sup 239}Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.« less

RIPL-Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations

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

Capote, R.; Herman, M.; Capote,R.

We describe the physics and data included in the Reference Input Parameter Library, which is devoted to input parameters needed in calculations of nuclear reactions and nuclear data evaluations. Advanced modelling codes require substantial numerical input, therefore the International Atomic Energy Agency (IAEA) has worked extensively since 1993 on a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). A final RIPL coordinated research project (RIPL-3) was brought to a successful conclusion in December 2008, after 15 years of challenging work carried out through three consecutive IAEA projects. The RIPL-3 library was released inmore » January 2009, and is available on the Web through http://www-nds.iaea.org/RIPL-3/. This work and the resulting database are extremely important to theoreticians involved in the development and use of nuclear reaction modelling (ALICE, EMPIRE, GNASH, UNF, TALYS) both for theoretical research and nuclear data evaluations. The numerical data and computer codes included in RIPL-3 are arranged in seven segments: MASSES contains ground-state properties of nuclei for about 9000 nuclei, including three theoretical predictions of masses and the evaluated experimental masses of Audi et al. (2003). DISCRETE LEVELS contains 117 datasets (one for each element) with all known level schemes, electromagnetic and {gamma}-ray decay probabilities available from ENSDF in October 2007. NEUTRON RESONANCES contains average resonance parameters prepared on the basis of the evaluations performed by Ignatyuk and Mughabghab. OPTICAL MODEL contains 495 sets of phenomenological optical model parameters defined in a wide energy range. When there are insufficient experimental data, the evaluator has to resort to either global parameterizations or microscopic approaches. Radial density distributions to be used as input for microscopic calculations are stored in the MASSES segment. LEVEL DENSITIES contains phenomenological parameterizations based on the modified Fermi gas and superfluid models and microscopic calculations which are based on a realistic microscopic single-particle level scheme. Partial level densities formulae are also recommended. All tabulated total level densities are consistent with both the recommended average neutron resonance parameters and discrete levels. GAMMA contains parameters that quantify giant resonances, experimental gamma-ray strength functions and methods for calculating gamma emission in statistical model codes. The experimental GDR parameters are represented by Lorentzian fits to the photo-absorption cross sections for 102 nuclides ranging from {sup 51}V to {sup 239}Pu. FISSION includes global prescriptions for fission barriers and nuclear level densities at fission saddle points based on microscopic HFB calculations constrained by experimental fission cross sections.« less

Flight Control of Biomimetic Air Vehicles Using Vibrational Control and Averaging

NASA Astrophysics Data System (ADS)

Tahmasian, Sevak; Woolsey, Craig A.

2017-08-01

A combination of vibrational inputs and state feedback is applied to control the flight of a biomimetic air vehicle. First, a control strategy is developed for longitudinal flight, using a quasi-steady aerodynamic model and neglecting wing inertial effects. Vertical and forward motion is controlled by modulating the wings' stroke and feather angles, respectively. Stabilizing control parameter values are determined using the time-averaged dynamic model. Simulations of a system resembling a hawkmoth show that the proposed controller can overcome modeling error associated with the wing inertia and small parameter uncertainties when following a prescribed trajectory. After introducing the approach through an application to longitudinal flight, the control strategy is extended to address flight in three-dimensional space.

Gain and saturation energy measurements in low pressure longitudinally excited N 2-lasers

NASA Astrophysics Data System (ADS)

Ghoreyshi, S.; Rahimian, K.; Hariri, Akbar

2004-08-01

A flat-plate Blumlein circuit has been used for operating a low pressure longitudinally excited oscillator-amplifier N 2-laser at 14 kV input voltage (LE-LE type). For investigating the effect of the excitation length on the laser performances, various amplifiers made of glass tubes of different lengths ranging from 15.5 to 35 cm with 4 mm inner bore diameters have been used. The measurements have been carried out for the laser parameters: small signal gain, and saturation energy density; and the laser beam divergence. Details of our measurements are presented. The results of our measurements have also been compared with the reported values of laser parameters in TE-TEA and LE N 2-laser configurations.

QCD Coupling from a Nonperturbative Determination of the Three-Flavor Λ Parameter.

PubMed

Bruno, Mattia; Brida, Mattia Dalla; Fritzsch, Patrick; Korzec, Tomasz; Ramos, Alberto; Schaefer, Stefan; Simma, Hubert; Sint, Stefan; Sommer, Rainer

2017-09-08

We present a lattice determination of the Λ parameter in three-flavor QCD and the strong coupling at the Z pole mass. Computing the nonperturbative running of the coupling in the range from 0.2 to 70 GeV, and using experimental input values for the masses and decay constants of the pion and the kaon, we obtain Λ_{MS[over ¯]}^{(3)}=341(12)  MeV. The nonperturbative running up to very high energies guarantees that systematic effects associated with perturbation theory are well under control. Using the four-loop prediction for Λ_{MS[over ¯]}^{(5)}/Λ_{MS[over ¯]}^{(3)} yields α_{MS[over ¯]}^{(5)}(m_{Z})=0.11852(84).

Verification and Validation of Requirements on the CEV Parachute Assembly System Using Design of Experiments

NASA Technical Reports Server (NTRS)

Schulte, Peter Z.; Moore, James W.

2011-01-01

The Crew Exploration Vehicle Parachute Assembly System (CPAS) project conducts computer simulations to verify that flight performance requirements on parachute loads and terminal rate of descent are met. Design of Experiments (DoE) provides a systematic method for variation of simulation input parameters. When implemented and interpreted correctly, a DoE study of parachute simulation tools indicates values and combinations of parameters that may cause requirement limits to be violated. This paper describes one implementation of DoE that is currently being developed by CPAS, explains how DoE results can be interpreted, and presents the results of several preliminary studies. The potential uses of DoE to validate parachute simulation models and verify requirements are also explored.

Micro-shimmy of towed structures in experimentally uncharted unstable parameter domain

NASA Astrophysics Data System (ADS)

Takács, Dénes; Stépán, Gábor

2012-11-01

In this paper, the lateral instability of towed structures (trailers, caravans and articulated buses) is investigated with special attention to the small amplitude lateral vibration that leads to a higher energy consumption in certain parameter domains. A low degree-of-freedom mechanical model of a shimmying towed tyre is used that describes the dynamics of the tyre-ground contact patch by the time delayed differential equation. Stability charts are calculated and the theoretically predicted linear unstable islands of small amplitude shimmy motions are validated by laboratory experiments. A tyre is towed by a relatively long caster, and its temperature and the input current of the conveyor belt are measured in order to show the increased value of the rolling resistance.

Precise determination of anthropometric dimensions by means of image processing methods for estimating human body segment parameter values.

PubMed

Baca, A

1996-04-01

A method has been developed for the precise determination of anthropometric dimensions from the video images of four different body configurations. High precision is achieved by incorporating techniques for finding the location of object boundaries with sub-pixel accuracy, the implementation of calibration algorithms, and by taking into account the varying distances of the body segments from the recording camera. The system allows automatic segment boundary identification from the video image, if the boundaries are marked on the subject by black ribbons. In connection with the mathematical finite-mass-element segment model of Hatze, body segment parameters (volumes, masses, the three principal moments of inertia, the three local coordinates of the segmental mass centers etc.) can be computed by using the anthropometric data determined videometrically as input data. Compared to other, recently published video-based systems for the estimation of the inertial properties of body segments, the present algorithms reduce errors originating from optical distortions, inaccurate edge-detection procedures, and user-specified upper and lower segment boundaries or threshold levels for the edge-detection. The video-based estimation of human body segment parameters is especially useful in situations where ease of application and rapid availability of comparatively precise parameter values are of importance.

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement

PubMed Central

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-01-01

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro–meso–scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy–enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy–enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates. PMID:28869520

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement.

PubMed

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-09-03

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro-meso-scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy-enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy-enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2011-12-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

NASA Astrophysics Data System (ADS)

Qian, Y.; Yang, B.; Lin, G.; Leung, R.; Zhang, Y.

2012-04-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. The latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2012-03-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Multi-objective thermodynamic optimisation of supercritical CO2 Brayton cycles integrated with solar central receivers

NASA Astrophysics Data System (ADS)

Vasquez Padilla, Ricardo; Soo Too, Yen Chean; Benito, Regano; McNaughton, Robbie; Stein, Wes

2018-01-01

In this paper, optimisation of the supercritical CO? Brayton cycles integrated with a solar receiver, which provides heat input to the cycle, was performed. Four S-CO? Brayton cycle configurations were analysed and optimum operating conditions were obtained by using a multi-objective thermodynamic optimisation. Four different sets, each including two objective parameters, were considered individually. The individual multi-objective optimisation was performed by using Non-dominated Sorting Genetic Algorithm. The effect of reheating, solar receiver pressure drop and cycle parameters on the overall exergy and cycle thermal efficiency was analysed. The results showed that, for all configurations, the overall exergy efficiency of the solarised systems achieved at maximum value between 700°C and 750°C and the optimum value is adversely affected by the solar receiver pressure drop. In addition, the optimum cycle high pressure was in the range of 24.2-25.9 MPa, depending on the configurations and reheat condition.

View-limiting shrouds for insolation radiometers

NASA Technical Reports Server (NTRS)

Dennison, E. W.; Trentelman, G. F.

1985-01-01

Insolation radiometers (normal incidence pyrheliometers) are used to measure the solar radiation incident on solar concentrators for calibrating thermal power generation measurements. The measured insolation value is dependent on the atmospheric transparency, solar elevation angle, circumsolar radiation, and radiometer field of view. The radiant energy entering the thermal receiver is dependent on the same factors. The insolation value and the receiver input will be proportional if the concentrator and the radiometer have similar fields of view. This report describes one practical method for matching the field of view of a radiometer to that of a solar concentrator. The concentrator field of view can be calculated by optical ray tracing methods and the field of view of a radiometer with a simple shroud can be calculated by using geometric equations. The parameters for the shroud can be adjusted to provide an acceptable match between the respective fields of view. Concentrator fields of view have been calculated for a family of paraboloidal concentrators and receiver apertures. The corresponding shroud parameters have also been determined.

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm.

PubMed

Peng, Jiansheng; Meng, Fanmei; Ai, Yuncan

2013-06-01

The artificial neural network (ANN) and genetic algorithm (GA) were combined to optimize the fermentation process for enhancing production of marine bacteriocin 1701 in a 5-L-stirred-tank. Fermentation time, pH value, dissolved oxygen level, temperature and turbidity were used to construct a "5-10-1" ANN topology to identify the nonlinear relationship between fermentation parameters and the antibiotic effects (shown as in inhibition diameters) of bacteriocin 1701. The predicted values by the trained ANN model were coincided with the observed ones (the coefficient of R(2) was greater than 0.95). As the fermentation time was brought in as one of the ANN input nodes, fermentation parameters could be optimized by stages through GA, and an optimal fermentation process control trajectory was created. The production of marine bacteriocin 1701 was significantly improved by 26% under the guidance of fermentation control trajectory that was optimized by using of combined ANN-GA method. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Design of Experiments Approach Defining the Relationships Between Processing and Microstructure for Ti-6Al-4V

NASA Technical Reports Server (NTRS)

Wallace, Terryl A.; Bey, Kim S.; Taminger, Karen M. B.; Hafley, Robert A.

2004-01-01

A study was conducted to evaluate the relative significance of input parameters on Ti- 6Al-4V deposits produced by an electron beam free form fabrication process under development at the NASA Langley Research Center. Five input parameters where chosen (beam voltage, beam current, translation speed, wire feed rate, and beam focus), and a design of experiments (DOE) approach was used to develop a set of 16 experiments to evaluate the relative importance of these parameters on the resulting deposits. Both single-bead and multi-bead stacks were fabricated using 16 combinations, and the resulting heights and widths of the stack deposits were measured. The resulting microstructures were also characterized to determine the impact of these parameters on the size of the melt pool and heat affected zone. The relative importance of each input parameter on the height and width of the multi-bead stacks will be discussed. .

Incorporating rainfall uncertainty in a SWAT model: the river Zenne basin (Belgium) case study

NASA Astrophysics Data System (ADS)

Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2013-04-01

The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique. Subsequently, we only considered the most sensitive parameters for parameter optimization and UA. To explicitly account for the stream flow uncertainty, we assumed that the stream flow measurement error increases linearly with the stream flow value. To assess the uncertainty and infer posterior distributions of the parameters, we used a Markov Chain Monte Carlo (MCMC) sampler - differential evolution adaptive metropolis (DREAM) that uses sampling from an archive of past states to generate candidate points in each individual chain. It is shown that the marginal posterior distributions of the rainfall multipliers vary widely between individual events, as a consequence of rainfall measurement errors and the spatial variability of the rain. Only few of the rainfall events are well defined. The marginal posterior distributions of the SWAT model parameter values are well defined and identified by DREAM, within their prior ranges. The posterior distributions of output uncertainty parameter values also show that the stream flow data is highly uncertain. The approach of using rainfall multipliers to treat rainfall uncertainty for a complex model has an impact on the model parameter marginal posterior distributions and on the model results Corresponding author: Tel.: +32 (0)2629 3027; fax: +32(0)2629 3022. E-mail: otolessa@vub.ac.be

19 CFR 351.407 - Calculation of constructed value and cost of production.

Code of Federal Regulations, 2014 CFR

2014-04-01

... production. (See section 773(f) of the Act.) (b) Determination of value under the major input rule. For purposes of section 773(f)(3) of the Act, the Secretary normally will determine the value of a major input... to the affiliated person for the major input; (2) The amount usually reflected in sales of the major...

19 CFR 351.407 - Calculation of constructed value and cost of production.

Code of Federal Regulations, 2011 CFR

2011-04-01

... production. (See section 773(f) of the Act.) (b) Determination of value under the major input rule. For purposes of section 773(f)(3) of the Act, the Secretary normally will determine the value of a major input... to the affiliated person for the major input; (2) The amount usually reflected in sales of the major...

19 CFR 351.407 - Calculation of constructed value and cost of production.

Code of Federal Regulations, 2012 CFR

2012-04-01

... production. (See section 773(f) of the Act.) (b) Determination of value under the major input rule. For purposes of section 773(f)(3) of the Act, the Secretary normally will determine the value of a major input... to the affiliated person for the major input; (2) The amount usually reflected in sales of the major...

19 CFR 351.407 - Calculation of constructed value and cost of production.

Code of Federal Regulations, 2013 CFR

2013-04-01

... production. (See section 773(f) of the Act.) (b) Determination of value under the major input rule. For purposes of section 773(f)(3) of the Act, the Secretary normally will determine the value of a major input... to the affiliated person for the major input; (2) The amount usually reflected in sales of the major...

19 CFR 351.407 - Calculation of constructed value and cost of production.

Code of Federal Regulations, 2010 CFR

2010-04-01

... production. (See section 773(f) of the Act.) (b) Determination of value under the major input rule. For purposes of section 773(f)(3) of the Act, the Secretary normally will determine the value of a major input... to the affiliated person for the major input; (2) The amount usually reflected in sales of the major...