Effects of model schematisation, geometry and parameter values on urban flood modelling.
Vojinovic, Z; Seyoum, S D; Mwalwaka, J M; Price, R K
2011-01-01
One-dimensional (1D) hydrodynamic models have been used as a standard industry practice for urban flood modelling work for many years. More recently, however, model formulations have included a 1D representation of the main channels and a 2D representation of the floodplains. Since the physical process of describing exchanges of flows with the floodplains can be represented in different ways, the predictive capability of different modelling approaches can also vary. The present paper explores effects of some of the issues that concern urban flood modelling work. Impacts from applying different model schematisation, geometry and parameter values were investigated. The study has mainly focussed on exploring how different Digital Terrain Model (DTM) resolution, presence of different features on DTM such as roads and building structures and different friction coefficients affect the simulation results. Practical implications of these issues are analysed and illustrated in a case study from St Maarten, N.A. The results from this study aim to provide users of numerical models with information that can be used in the analyses of flooding processes in urban areas.
New Values of Cross-Talk Parameters for Twisted Pair Model
Directory of Open Access Journals (Sweden)
Milos Kozak
2010-01-01
Full Text Available Near-end Crosstalk (NEXT and Far-end Crosstalk (FEXT of unshielded twisted pair (UTP cable are the main factors limiting the information capacity in data transmission. Crosstalk depends mostly on the frequency. Frequency dependent transfer functions and crosstalk attenuation may be obtained by measurement, but for the analytical description of the transmission channel's parameters is useful to define functions modelling the crosstalk. The paper describes the measuring facility, presents the measured waveforms and the values of model parameters.
Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases
Energy Technology Data Exchange (ETDEWEB)
Snyder, Sandra F.; Arimescu, Carmen; Napier, Bruce A.; Hay, Tristan R.
2012-11-01
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 models are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.
von Thienen, Wolfhard; Metzler, Dirk; Witte, Volker
2015-05-07
The emergence of self-organizing behavior in ants has been modeled in various theoretical approaches in the past decades. One model explains experimental observations in which Argentine ants (Linepithema humile) selected the shorter of two alternative paths from their nest to a food source (shortest path experiments). This model serves as an important example for the emergence of collective behavior and self-organization in biological systems. In addition, it inspired the development of computer algorithms for optimization problems called ant colony optimization (ACO). In the model, a choice function describing how ants react to different pheromone concentrations is fundamental. However, the parameters of the choice function were not deduced experimentally but freely adapted so that the model fitted the observations of the shortest path experiments. Thus, important knowledge was lacking about crucial model assumptions. A recent study on the Argentine ant provided this information by measuring the response of the ants to varying pheromone concentrations. In said study, the above mentioned choice function was fitted to the experimental data and its parameters were deduced. In addition, a psychometric function was fitted to the data and its parameters deduced. Based on these findings, it is possible to test the shortest path model by applying realistic parameter values. Here we present the results of such tests using Monte Carlo simulations of shortest path experiments with Argentine ants. We compare the choice function and the psychometric function, both with parameter values deduced from the above-mentioned experiments. Our results show that by applying the psychometric function, the shortest path experiments can be explained satisfactorily by the model. The study represents the first example of how psychophysical theory can be used to understand and model collective foraging behavior of ants based on trail pheromones. These findings may be important for other
Recommended Parameter Values for INEEL Subsurface Disposal Area Source Release Modeling
Energy Technology Data Exchange (ETDEWEB)
Riley, Robert G.; Lopresti, Charles A.
2004-06-23
The purpose of this report is to summarize 1) associated information and values for key release model parameters (i.e., best estimate, minimum and maximum) obtained where possible from published experimental data, 2) a structure for selection of sensitivity tests cases that can be used to identify test cases, and 3) recommended test cases for selected contaminants of potential concern to assess remedy effectiveness against a no-treatment base case.
The value of stream level observations to constrain low-parameter hydrologic models
Seibert, J.; Vis, M.; Pool, S.
2014-12-01
While conceptual runoff models with a low number of model parameters are useful tools to capture the hydrological catchment functioning, these models usually rely on model calibration, which makes their use in ungauged basins challenging. One approach might be to take at least a few measurements. Recent studies demonstrated that few streamflow measurements, representing data that could be measured with limited efforts in an ungauged basin, might be helpful to constrain runoff models for simulations in ungauged basins. While in these previous studies we assumed that few streamflow measurements were taken, obviously it would also be reasonable to measure stream levels. Several approaches could be used in practice for such stream level observations: water level loggers have become less expensive and easier to install; stream levels will in the near future be increasingly available from satellite remote sensing resulting in evenly space time series; community-based approaches (e.g., crowdhydrology.org), finally, can offer level observations at irregular time intervals. Here we present a study where a runoff model (the HBV model) was calibrated for 600+ gauged basins in the US assuming that only a subset of the data was available. We pretended that only stream level observations at different time intervals, representing the temporal resolution of the different observation approaches mentioned before, were available. The model, which was calibrated based on these data subsets, was then evaluated on the full observed streamflow record. Our results indicate that streamlevel data alone already can provide surprisingly good model simulation results in humid catchments, whereas in arid catchments some form of quantitative information (streamflow observation or regional average value) is needed to obtain good results. These results are encouraging for hydrological observations in data scarce regions as level observations are much easier to obtain than streamflow observations
Median statistics cosmological parameter values
Energy Technology Data Exchange (ETDEWEB)
Crandall, Sara, E-mail: sara1990@ksu.edu; Ratra, Bharat, E-mail: ratra@phys.ksu.edu
2014-05-01
We present median statistics central values and ranges for 12 cosmological parameters, using 582 measurements (published during 1990–2010) collected by [9]. On comparing to the recent Planck Collaboration [1] estimates of 11 of these parameters, we find good consistency in ten cases.
Sensitivity of precipitation to parameter values in the community atmosphere model version 5
Energy Technology Data Exchange (ETDEWEB)
Johannesson, Gardar; Lucas, Donald; Qian, Yun; Swiler, Laura Painton; Wildey, Timothy Michael
2014-03-01
One objective of the Climate Science for a Sustainable Energy Future (CSSEF) program is to develop the capability to thoroughly test and understand the uncertainties in the overall climate model and its components as they are being developed. The focus on uncertainties involves sensitivity analysis: the capability to determine which input parameters have a major influence on the output responses of interest. This report presents some initial sensitivity analysis results performed by Lawrence Livermore National Laboratory (LNNL), Sandia National Laboratories (SNL), and Pacific Northwest National Laboratory (PNNL). In the 2011-2012 timeframe, these laboratories worked in collaboration to perform sensitivity analyses of a set of CAM5, 2° runs, where the response metrics of interest were precipitation metrics. The three labs performed their sensitivity analysis (SA) studies separately and then compared results. Overall, the results were quite consistent with each other although the methods used were different. This exercise provided a robustness check of the global sensitivity analysis metrics and identified some strongly influential parameters.
Parameter values for epidemiological models of foot-and-mouth disease in swine
Directory of Open Access Journals (Sweden)
Amy C Kinsley
2016-06-01
Full Text Available In the event of a foot-and-mouth disease (FMD incursion, response strategies are required to control, contain and eradicate the pathogen as efficiently as possible. Infectious disease simulation models are widely used tools that mimic disease dispersion in a population and that can be useful in the design and support of prevention and mitigation activities. However, there are often gaps in evidence-based research to supply models with quantities that are necessary to accurately reflect the system of interest. The objective of this study was to quantify values associated with the duration of the stages of FMD infection (latent period, subclinical period, incubation period, and duration of infection, probability of transmission (within-herd and between-herd via spatial spread, and diagnosis of a vesicular disease within a herd using a meta-analysis of the peer-reviewed literature and expert opinion. The latent period ranged from 1 to 7 days and incubation period ranged from 1 to 9 day; both were influenced by strain. In contrast, the subclinical period ranged from 0 to 6 days and was influenced by sampling method only. The duration of infection ranged from 1 to 10 days. The probability of spatial spread between an infected and fully susceptible swine farm was estimated as greatest within 5 km of the infected farm, highlighting the importance of possible long-range transmission through the movement of infected animals. Lastly, while most swine practitioners are confident in their ability to detect a vesicular disease in an average sized swine herd, a small proportion expect that up to half of the herd would need to show clinical signs before detection via passive surveillance would occur. The results of this study will be useful in within- and between-herd simulation models to develop efficient response strategies in the event an FMD in swine populations of disease-free countries or regions.
Energy Technology Data Exchange (ETDEWEB)
Karlsson, Sara; Bergstroem, Ulla [Studsvik Eco and Safety AB, Nykoeping (Sweden)
2002-05-01
In this report the element and nuclide specific parameter values used in the biospheric models of the safety assessments SR 97 and SAFE are presented. The references used are presented and where necessary the process of estimation of data is described. The parameters treated in this report are distribution coefficients in soil, organic soil and suspended matter in freshwater and brackish water, root uptake factors for pasturage, cereals, root crops and vegetables, bioaccumulation factors for freshwater fish, brackish water fish, freshwater invertebrates and marine water plants, transfer coefficients for transfer to milk and meat, translocation factors and dose coefficients for external exposure, ingestion (age-dependent values) and inhalation (age-dependent values). The radionuclides treated are those which could be of interest in the two safety assessments. Physical data such as half-lives and type of decay are also presented.
Directory of Open Access Journals (Sweden)
L. M. Pradhan
2012-01-01
Full Text Available This paper deals with the development of an inventory model for Weibull deteriorating items with constant demand when delay in payments is allowed to the retailer to settle the account against the purchases made. Shortages are not allowed and the salvage value is associated with the deteriorated units. In this paper, we consider two cases; those are for the case payment within the permissible time and for payment after the expiry of permissible time with interest. Numerical examples are provided to illustrate our results. Sensitivity analysis are carried out to analyze the effect of changes in the optimal solution with respect to change in one parameter at a time.
Directory of Open Access Journals (Sweden)
A. Valade
2014-01-01
Full Text Available 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
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
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
Directory of Open Access Journals (Sweden)
Ingo W Nader
Full Text Available Parameters of the two-parameter logistic model are generally estimated via the expectation-maximization algorithm, which improves initial values for all parameters iteratively until convergence is reached. Effects of initial values are rarely discussed in item response theory (IRT, but initial values were recently found to affect item parameters when estimating the latent distribution with full non-parametric maximum likelihood. However, this method is rarely used in practice. Hence, the present study investigated effects of initial values on item parameter bias and on recovery of item characteristic curves in BILOG-MG 3, a widely used IRT software package. Results showed notable effects of initial values on item parameters. For tighter convergence criteria, effects of initial values decreased, but item parameter bias increased, and the recovery of the latent distribution worsened. For practical application, it is advised to use the BILOG default convergence criterion with appropriate initial values when estimating the latent distribution from data.
Matteucci, M.; S. Mignani, Prof.; Veldkamp, Bernard P.
2012-01-01
In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candi
The strong prognostic value of KELIM, a model-based parameter from CA 125 kinetics in ovarian cancer
DEFF Research Database (Denmark)
You, Benoit; Colomban, Olivier; Heywood, Mark;
2013-01-01
Unexpected results were recently reported about the poor surrogacy of Gynecologic Cancer Intergroup (GCIG) defined CA-125 response in recurrent ovarian cancer (ROC) patients. Mathematical modeling may help describe CA-125 decline dynamically and discriminate prognostic kinetic parameters....
Drost, F.C.; van den Akker, R.; Werker, B.J.M.
2007-01-01
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on the no
Drost, F.C.; van den Akker, R.; Werker, B.J.M.
2008-01-01
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on the no
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)
Institute of Scientific and Technical Information of China (English)
李爱民; 刘杉; 吕安民
2011-01-01
logistic模型是一种重要的非线性预测模型,但如何确定饱和值L-直未得到很好解决.在学习以往方法的基础上,提出了一种新方法,即利用SPSS软件对logistic模型进行L值的选优计算,以模型计算值与样本值的标准离差是否趋于稳定作为值选取的标准,并以郑州市建成区非农人口的预测为例,说明了该方法在人口预测logistic模型中的具体应用.%Logistic model was the important non-linear model of time series forecasting. But how to ascertain the parameter saturated value of logistic model had been not well solved till now. A kind of new method was put forward on the base of the former methods. Some Logistic models were primarily made up with different parameter by SPSS, then whether the standard deviation between the forecast value with the real value became steady or not was the criteria of ascertaining the parameter finally. Prediction of nonagricultural population of built-up area in Zhengzhou city was taken as an example. It explained application value of this method.
Hannay, C.; Neale, R. B.; Rothstein, M.
2016-12-01
Projections of future climate change are inherently uncertain and regional details are heavily dependent on coupled climate model formulations. Bernstein and Neelin (2016) show that projections of future climate using the Community Earth System Model (CESM) can vary significantly depending on the (reasonable) value used for important but uncertain model parameters. This includes a wide variation in the tropical precipitation response due to perturbations of parameters inherent to the formulation of deep convection parameterization. The question is therefore which model formulation should be trusted most? Since true validation is, of course, not possible at present day, guidance has to be provided by other proxies. Using a simple metric that climate models that performing best in a standard present-day (AMIP-type) configuration should be trusted most for future climate projections is unsatisfactory here, as only a small tuning effort is required to produce simulations equally skillful to the unperturbed model configurations. Here we employ an alternative approach for "trusting" the future climate projections. It is based on using CESM for a series of CAPT-type hindcast simulations, mirroring the limited perturbed parameter ensemble approach of Bernstein and Neelin (2016). Simulation sets are run for the YOTC period of 2009-2010 using CAM5 at 1 degree resolution. In this talk we will show the regional variations of climate change signals in the hydrological cycle in response to deep convection dependent parameter sets (e.g., entrainment, timescale) and contrast them with the equivalent hindcast experiments using the same parameter set. With this analysis we are able to provide guidance as to which parameter value selections result in the highest skill in the hindcasts and how that corresponds with the equivalent CESM future climate change signals.
Energy Technology Data Exchange (ETDEWEB)
Tofts, Paul S. [Brighton and Sussex Medical School, Falmer, Sussex (United Kingdom); UCL Institute of Neurology, London (United Kingdom); Cutajar, Marica [Brighton and Sussex Medical School, Falmer, Sussex (United Kingdom); UCL Institute of Child Health, London (United Kingdom); Mendichovszky, Iosif A. [University of Manchester, Imaging Science and Biomedical Engineering, Manchester (United Kingdom); Peters, A.M. [Brighton and Sussex Medical School, Falmer, Sussex (United Kingdom); Gordon, Isky [UCL Institute of Child Health, London (United Kingdom)
2012-06-15
To model the uptake phase of T{sub 1}-weighted DCE-MRI data in normal kidneys and to demonstrate that the fitted physiological parameters correlate with published normal values. The model incorporates delay and broadening of the arterial vascular peak as it appears in the capillary bed, two distinct compartments for renal intravascular and extravascular Gd tracer, and uses a small-vessel haematocrit value of 24%. Four physiological parameters can be estimated: regional filtration K{sup trans} (ml min {sup -1} [ml tissue ]{sup -1}), perfusion F (ml min {sup -1} [100 ml tissue ]{sup -1}), blood volume v{sub b} (%) and mean residence time MRT (s). From these are found the filtration fraction (FF; %) and total GFR (ml min {sup -1}). Fifteen healthy volunteers were imaged twice using oblique coronal slices every 2.5 s to determine the reproducibility. Using parenchymal ROIs, group mean values for renal biomarkers all agreed with published values: K{sup trans}: 0.25; F: 219; v{sub b}: 34; MRT: 5.5; FF: 15; GFR: 115. Nominally cortical ROIs consistently underestimated total filtration (by {proportional_to} 50%). Reproducibility was 7-18%. Sensitivity analysis showed that these fitted parameters are most vulnerable to errors in the fixed parameters kidney T{sub 1}, flip angle, haematocrit and relaxivity. These renal biomarkers can potentially measure renal physiology in diagnosis and treatment. circle Dynamic contrast-enhanced magnetic resonance imaging can measure renal function. circle Filtration and perfusion values in healthy volunteers agree with published normal values. circle Precision measured in healthy volunteers is between 7 and 15%. (orig.)
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. The lumped-parameter model development have been reported by (Wolf 1991b; Wolf 1991a; Wolf and Paronesso 1991; Wolf and Paronesso 19...
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
Response model parameter linking
Barrett, Michelle Derbenwick
2015-01-01
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of equating observed scores on different test forms. This thesis argues, however, that the use of item response models does not require
Janssen, M; Verhulst, A; Valdramidis, V; Devlieghere, F; Van Impe, J F; Geeraerd, A H
2008-11-30
Organic acids (e.g., lactic acid, acetic acid and citric acid) are popular preservatives. In this study, the Listeria innocua inactivation is investigated under dynamic conditions of pH and undissociated lactic acid ([LaH]). A combined primary (Weibull-type) and secondary model developed for the L. innocua inactivation under static conditions [Janssen, M., Geeraerd, A.H., Cappuyns, A., Garcia-Gonzalez, L., Schockaert, G., Van Houteghem, N., Vereecken, K.M., Debevere, J., Devlieghere, F., Van Impe, J.F., 2007. Individual and combined effects of pH and lactic acid concentration on L. innocua inactivation: development of a predictive model and assessment of experimental variability. Applied and Environmental Microbiology 73(5), 1601-1611] was applied to predict the microbial inactivation under dynamic conditions. Because of its non-autonomous character, two approaches were proposed for the application of the Weibull-type model to dynamic conditions. The results quantitatively indicated that the L. innocua cell population was able to develop an induced acid stress resistance under dynamic conditions of pH and [LaH]. From a modeling point of view, it needs to be stressed that (i) inactivation model equations and associated parameter values, derived under static conditions, may not be suitable for use as such under dynamic conditions, and (ii) non-autonomous dynamic models reveal additional technical intricacies in comparison with autonomous models.
Distributed Parameter Modelling Applications
DEFF Research Database (Denmark)
2011-01-01
Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...
Estimation of Parameters of the Beta-Extreme Value Distribution
Directory of Open Access Journals (Sweden)
Zafar Iqbal
2008-09-01
Full Text Available In this research paper The Beta Extreme Value Type (III distribution which is developed by Zafar and Aleem (2007 is considered and parameters are estimated by using moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m’ & ‘n’ are real and moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m��� & ‘n’ are integers and then a Comparison between rth moments about origin when parameters are ‘m’ & ‘n’ are real and when parameters are ‘m’ & ‘n’ are integers. At the end second method, method of Maximum Likelihood is used to estimate the unknown parameters of the Beta Extreme Value Type (III distribution.
Application of lumped-parameter models
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil. Subsequently, the assembly of the dynamic stiffness matrix for the foundation is considered, and the solution for obtaining the steady state response, when using lumped-parameter models is given. (au)
Reference values for feeding parameters of isopods (Porcellioscaber, Isopoda, Crustacea).
Drobne, Damjana; Drobne, Samo
2014-01-01
The advantage of using terrestrial isopods in toxicity studies is that a battery of parameters can be tested at different levels of biological complexity. Feeding parameters for example link organism level response to potential ecological consequences but a problem with using feeding parameters in toxicity tests with terrestrial isopods is their high variability. The aim of our study was to set benchmark values for feeding parameters for isopod Porcellioscaber (Isopoda, Crustacea) in laboratory-controlled experiments. In the work presented here, the daily feeding rate of the central 50% of the control population of Porcellioscaber and a correlation between feeding rate and isopod weight were set. Values outside these ranges need additional evaluation to increase the relevance of test outcomes. We suggest using benchmark values for feeding parameters as well as the coefficient of variation (a) to identify animals with altered feeding parameters with respect to controls, and (b) to assess the data quality in each experiment.
Photovoltaic module parameters acquisition model
Cibira, Gabriel; Koščová, Marcela
2014-09-01
This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I-V and P-V characteristics for PV module based on equivalent electrical circuit. Then, limited I-V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.
Application of lumped-parameter models
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...
Statefinder parameters in two dark energy models
Panotopoulos, Grigoris
2007-01-01
The statefinder parameters ($r,s$) in two dark energy models are studied. In the first, we discuss in four-dimensional General Relativity a two fluid model, in which dark energy and dark matter are allowed to interact with each other. In the second model, we consider the DGP brane model generalized by taking a possible energy exchange between the brane and the bulk into account. We determine the values of the statefinder parameters that correspond to the unique attractor of the system at hand. Furthermore, we produce plots in which we show $s,r$ as functions of red-shift, and the ($s-r$) plane for each model.
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Diagnostic value of hematological parameters in patients with osteoarthritis
Serdar Hira; Cuneyt Tamam
2017-01-01
Purpose: Complete blood count parameters have been investigated as a predictor of inflammatory process in many diseases, but their role in osteoarthritis is unclear. The aim of present study was to investigate the diagnostic value of routine hematological parameters on osteoarthritis and to explore their clinical significance. Material and Methods: The study included 118 patients with osteoarthritis and 145 healthy individuals. Medical records, Erythrocyte sedimentation rate (ESR), C-reac...
A Binomial Integer-Valued ARCH Model.
Ristić, Miroslav M; Weiß, Christian H; Janjić, Ana D
2016-11-01
We present an integer-valued ARCH model which can be used for modeling time series of counts with under-, equi-, or overdispersion. The introduced model has a conditional binomial distribution, and it is shown to be strictly stationary and ergodic. The unknown parameters are estimated by three methods: conditional maximum likelihood, conditional least squares and maximum likelihood type penalty function estimation. The asymptotic distributions of the estimators are derived. A real application of the novel model to epidemic surveillance is briefly discussed. Finally, a generalization of the introduced model is considered by introducing an integer-valued GARCH model.
Correlating substituent parameter values to electron transport properties of molecules
Vedova-Brook, Natalie; Matsunaga, Nikita; Sohlberg, Karl
2004-03-01
There are a vast number of organic compounds that could be considered for use in molecular electronics. Because of this, the need for efficient and economical screening tools has emerged. We demonstrate that the substituent parameter values ( σ), commonly found in advanced organic chemistry textbooks, correlate strongly with features of the charge migration process, establishing them as useful indicators of electronic properties. Specifically, we report that ab initio derived electronic charge transfer values for 16 different substituted aromatic molecules for molecular junctions correlate to the σ values with a correlation coefficient squared ( R2) of 0.863.
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
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 or 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.
An alternative approach to absolute-value test for the parameters of ...
African Journals Online (AJOL)
An alternative approach to absolute-value test for the parameters of multiple linear regression models. ... Global Journal of Mathematical Sciences. Journal Home · ABOUT · Advanced ... Open Access DOWNLOAD FULL TEXT Subscription or ...
[Measurement and analysis of anatomical parameter values in tree shrews].
Li, Bo; Zhang, Rong-Ping; Li, Jin-Tao; He, Bao-Li; Zhen, Hong; Wang, Li-Mei; Jiao, Jian-Lin
2013-04-01
Anatomical parameter values in tree shrews are major biological characteristic indicators in laboratory animals. Body size, bones and mammilla, organ weights, coefficient intestinal canal and other anatomical data were measured and analyzed in laboratory domesticated tree shrews (7 to 9 months of age). Measurement of 31 anatomical parameters showed that body height, width of the right ear, ileum and colon had significant differences between males and females (P<0.05). Highly significant differences were also found in body slanting length, chest depth, torso length, left and right forelimb length, right hind limb length, left and right ear length, left ear width, keel bone length, left and right tibia length, duodenum and jejunum (P<0.01). With body length as the dependent variable, and tail length, torso length, right and left forelimb length, and left and right hind limb length as independent variables for stepwise regression analysis, the regression equation for body length = 13.90 + tail length × 0.16. The results of 37 organs weights between female and male tree shrews showed very significant differences (P<0.01) for weight of heart, lungs, spleen, left and right kidney, bladder, left and right hippocampus, left submandibular gland, and left and right thyroid gland, as well as significant (P<0.05) differences in the small intestine, right submandibular gland, and left adrenal gland. The coefficient of heart, lung, stomach, bladder, small and large intestine, brain, right hippocampus, and left adrenal gland showed highly significant differences (P<0.01), while differences in the right kidney, left hippocampus, left submandibular gland, right adrenal gland, and left and right thyroid gland were significant (P<0.05). With animal weight as the dependent variable and indicators of heart, lung, liver, spleen, left and right kidney and brain as independent variables for stepwise regression analysis, the regression equation showed that weight = 62.73 + left kidney
PV panel model based on datasheet values
DEFF Research Database (Denmark)
Sera, Dezso; Teodorescu, Remus; Rodriguez, Pedro
2007-01-01
This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell....../panel in Standard Test Conditions (STC) are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence....... Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested....
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Roe, Byron
2013-01-01
The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em
2017-02-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.
Levy, J; Sun, K; Findlay, R H; Farruggia, F T; Porter, J; Mumy, K L; Tomaras, J; Tomaras, A
2007-01-05
Bacterial transport through cores of intact, glacial-outwash aquifer sediment was investigated with the overall goal of better understanding bacterial transport and developing a predictive capability based on the sediment characteristics. Variability was great among the cores. Normalized maximum bacterial-effluent concentrations ranged from 5.4x10(-7) to 0.36 and effluent recovery ranged from 2.9x10(-4) to 59%. Bacterial breakthrough was generally rapid with a sharp peak occurring nearly twice as early as the bromide peak. Bacterial breakthrough exhibited a long tail of relatively constant concentration averaging three orders of magnitude less than the peak concentration for up to 32 pore volumes. The tails were consistent with non-equilibrium detachment, corroborated by the results of flow interruption experiments. Bacterial breakthrough was accurately simulated with a transport model incorporating advection, dispersion and first-order non-equilibrium attachment/detachment. Relationships among bacterial transport and sediment characteristics were explored with multiple regression analyses. These analyses indicated that for these cores and experimental conditions, easily-measurable sediment characteristics--median grain size, degree of sorting, organic-matter content and hydraulic conductivity--accounted for 66%, 61% and 89% of the core-to-core variability in the bacterial effective porosity, dispersivity and attachment-rate coefficient, respectively. In addition, the bacterial effective porosity, median grain size and organic-matter content accounted for 76% of the inter-core variability in the detachment-rate coefficient. The resulting regression equations allow prediction of bacterial transport based on sediment characteristics and are a possible alternative to using colloid-filtration theory. Colloid-filtration theory, used without the benefit of running bacterial transport experiments, did not as accurately replicate the observed variability in the attachment
Directory of Open Access Journals (Sweden)
Mladenović Zorica
2006-01-01
Full Text Available In this paper different aspects of value-at-risk estimation are considered. Daily returns of CISCO, INTEL and NASDAQ stock indices are analyzed for period: September 1996 - September 2006. Methods that incorporate time varying variability and heavy tails of the empirical distributions of returns are implemented. The main finding of the paper is that standard econometric methods underestimate the value-at-risk parameter if heavy tails of the empirical distribution are not explicitly taken into account. .
DEVELOPMENT OF VADOSE-ZONE HYDRAULIC PARAMETER VALUES
Energy Technology Data Exchange (ETDEWEB)
ROGERS PM
2008-01-21
Several approaches have been developed to establish a relation between the soil-moisture retention curve and readily available soil properties. Those relationships are referred to as pedotransfer functions. Described in this paper are the rationale, approach, and corroboration for use of a nonparametric pedotransfer function for the estimation of soil hydraulic-parameter values at the yucca Mountain area in Nevada for simulations of net infiltration. This approach, shown to be applicable for use at Yucca Mountain, is also applicable for use at the Hanford Site where the underlying data were collected.
Consistent Stochastic Modelling of Meteocean Design Parameters
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Sterndorff, M. J.
2000-01-01
Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...... velocity, and water level is presented. The stochastic model includes statistical uncertainty and dependency between the four stochastic variables. Further, a new stochastic model for annual maximum directional significant wave heights is presented. The model includes dependency between the maximum wave...... height from neighboring directional sectors. Numerical examples are presented where the models are calibrated using the Maximum Likelihood method to data from the central part of the North Sea. The calibration of the directional distributions is made such that the stochastic model for the omnidirectional...
Models for setting ATM parameter values
DEFF Research Database (Denmark)
Blaabjerg, Søren; Gravey, A.; Romæuf, L.
1996-01-01
In ATM networks, a user should negotiate at connection set-up a traffic contract which includes traffic characteristics and requested QoS. The traffic characteristics currently considered are the Peak Cell Rate, the Sustainable Cell Rate, the Intrinsic Burst Tolerance and the Cell Delay Variation...... to Network Interface (UNI) and at subsequent Inter Carrier Interfaces (ICIs), by algorithmic rules based on the Generic Cell Rate Algorithm (GCRA) formalism. Conformance rules are implemented by policing mechanisms that control the traffic submitted by the user and discard excess traffic. It is therefore...
Brand Value - Proposed Model Danrise
Directory of Open Access Journals (Sweden)
Daniel Nascimento Pereira da Silva
2011-12-01
Full Text Available Brands have taken dominance in the strategies of enterprises once they are able to generate feelings, sensations and emotions in their clients. These values, value for the enterprises and for the brands themselves, are not measurable. A strong brand configures itself as the highest representative of an enterprise and the brand is regarded as an asset of the enterprise. The evolution of a brand, as an intangible and strategic asset, becomes more vitally important for the enterprises, as a way of maximizing the results. This need, whether of the market or the enterprises, justifies the direction of the research for this vector – the value of the brand. A main objective of the research is to present a new model of brand evaluation. This model is supported by a tangible and intangible aspects and the intangible aspect, evaluates the knowledge and capacity of their managers and workers to build a brand with value through the correct ordering of the priorities of the dimensions of the proposed model. The model was tested on the brand ‗Blue Rise.‘
Multifractal Value at Risk model
Lee, Hojin; Song, Jae Wook; Chang, Woojin
2016-06-01
In this paper new Value at Risk (VaR) model is proposed and investigated. We consider the multifractal property of financial time series and develop a multifractal Value at Risk (MFVaR). MFVaR introduced in this paper is analytically tractable and not based on simulation. Empirical study showed that MFVaR can provide the more stable and accurate forecasting performance in volatile financial markets where large loss can be incurred. This implies that our multifractal VaR works well for the risk measurement of extreme credit events.
Sun, T T; Liu, W H; Zhang, Y Q; Li, L H; Wang, R; Ye, Y Y
2017-08-01
Objective: To explore the differential between the value of dynamic contrast-enhanced MRI quantitative pharmacokinetic parameters and relative pharmacokinetic quantitative parameters in breast lesions. Methods: Retrospective analysis of 255 patients(262 breast lesions) who was obtained by clinical palpation , ultrasound or full-field digital mammography , and then all lessions were pathologically confirmed in Zhongda Hospital, Southeast University from May 2012 to May 2016. A 3.0 T MRI scanner was used to obtain the quantitative MR pharmacokinetic parameters: volume transfer constant (K(trans)), exchange rate constant (k(ep))and extravascular extracellular volume fraction (V(e)). And measured the quantitative pharmacokinetic parameters of normal glands tissues which on the same side of the same level of the lesions; and then calculated the value of relative pharmacokinetic parameters: rK(rans)、rk(ep) and rV(e).To explore the diagnostic value of two pharmacokinetic parameters in differential diagnosis of benign and malignant breast lesions using receiver operating curves and model of logistic regression. Results: (1)There were significant differences between benign lesions and malignant lesions in K(trans) and k(ep) (t=15.489, 15.022, respectively, P0.05). The areas under the ROC curve(AUC)of K(trans), k(ep) and V(e) between malignant and benign lesions were 0.933, 0.948 and 0.387, the sensitivity of K(trans), k(ep) and V(e) were 77.1%, 85.0%, 51.0% , and the specificity of K(trans), k(ep) and V(e) were 96.3%, 93.6%, 60.8% for the differential diagnosis of breast lesions if taken the maximum Youden's index as cut-off. (2)There were significant differences between benign lesions and malignant lesions in rK(trans), rk(ep) and rV(e) (t=14.177, 11.726, 2.477, respectively, Pquantitative pharmacokinetic parameters and the prediction probability of relative quantitative pharmacokinetic parameters(Z=0.867, P=0.195). Conclusion: There was no significant difference between
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Eleven evaluating parameters for rice core collection were assessed based on genotypic values and molecular marker information. Monte Carlo simulation combined with mixed linear model was used to eliminate the interference from environment in order to draw more reliable results. The coincidence rate of range (CR) was the optimal parameter. Mean Simpson index (MD), mean Shannon-Weaver index of genetic diversity (M1) and mean polymorphism information content (MPIC) were important evaluating parameters. The variable rate of coefficient of variation (VR) could act as an important reference parameter for evaluating the variation degree of core collection. Percentage of polymorphic loci (p) could be used as a determination parameter for the size of core collection. Mean difference percentage (MD) was a determination parameter for the reliability judgment of core collection. The effective evaluating parameters for core collection selected in the research could be used as criteria for sampling percentage in different plant germplasm populations.
Achieving Value in Primary Care: The Primary Care Value Model.
Rollow, William; Cucchiara, Peter
2016-03-01
The patient-centered medical home (PCMH) model provides a compelling vision for primary care transformation, but studies of its impact have used insufficiently patient-centered metrics with inconsistent results. We propose a framework for defining patient-centered value and a new model for value-based primary care transformation: the primary care value model (PCVM). We advocate for use of patient-centered value when measuring the impact of primary care transformation, recognition, and performance-based payment; for financial support and research and development to better define primary care value-creating activities and their implementation; and for use of the model to support primary care organizations in transformation.
Transfer parameter values in temperate forest ecosystems: a review.
Calmon, Philippe; Thiry, Yves; Zibold, Gregor; Rantavaara, Aino; Fesenko, Sergei
2009-09-01
Compared to agricultural lands, forests are complex ecosystems as they can involve diverse plant species associations, several vegetative strata (overstorey, shrubs, herbaceous and other annual plant layer) and multi-layered soil profiles (forest floor, hemi-organic and mineral layers). A high degree of variability is thus generally observed in radionuclide transfers and redistribution patterns in contaminated forests. In the long term, the soil compartment represents the major reservoir of radionuclides which can give rise to long-term plant and hence food contamination. For practical reasons, the contamination of various specific forest products has commonly been quantified using the aggregated transfer factor (T(ag) in m(2)kg(-1)) which integrates various environmental parameters including soil and plant type, root distribution as well as nature and vertical distribution of the deposits. Long lasting availability of some radionuclides was shown to be the source of much higher transfer in forest ecosystems than in agricultural lands. This study aimed at reviewing the most relevant quantitative information on radionuclide transfers to forest biota including trees, understorey vegetation, mushrooms, berries and game animals. For both radiocaesium and radiostrontium in trees, the order of magnitude of mean T(ag) values was 10(-3)m(2)kg(-1) (dry weight). Tree foliage was usually 2-12 times more contaminated than trunk wood. Maximum contamination of tree components with radiocaesium was associated with (semi-)hydromorphic areas with thick humus layers. The transfer of radionuclides to mushrooms and berries is high, in comparison with foodstuffs grown in agricultural systems. Concerning caesium uptake by mushrooms, the transfer is characterized by a very large variability of T(ag), from 10(-3) to 10(1)m(2)kg(-1) (dry weight). For berries, typical values are around 0.01-0.1 m(2)kg(-1) (dry weight). Transfer of radioactive caesium to game animals and reindeer and the
Sensitivity of a Shallow-Water Model to Parameters
Kazantsev, Eugene
2011-01-01
An adjoint based technique is applied to a shallow water model in order to estimate the influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients Coriolis parameter and the amplitude of the wind stress tension are considered. Their influence is analyzed from three points of view: 1. flexibility of the model with respect to a parameter that is related to the lowest value of the cost function that can be obtained in the data assimilation experiment that controls this parameter; 2. possibility to improve the model by the parameter's control, i.e. whether the solution with the optimal parameter remains close to observations after the end of control; 3. sensitivity of the model solution to the parameter in a classical sense. That implies the analysis of the sensitivity estimates and their comparison with each other and with the local Lyapunov exponents that characterize the sensitivity of the mode...
PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL
Institute of Scientific and Technical Information of China (English)
钱炜祺; 蔡金狮
2001-01-01
A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.
Modelling tourists arrival using time varying parameter
Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.
2017-06-01
The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.
Sensitivity of NTCP parameter values against a change of dose calculation algorithm
DEFF Research Database (Denmark)
Brink, Carsten; Berg, Martin; Nielsen, Morten
2007-01-01
with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis with those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations...
Three-parameter discontinuous distributions for hydrological samples with zero values
Weglarczyk, Stanislaw; Strupczewski, Witold G.; Singh, Vijay P.
2005-10-01
A consistent approach to the frequency analysis of hydrologic data in arid and semiarid regions, i.e. the data series containing several zero values (e.g. monthly precipitation in dry seasons, annual peak flow discharges, etc.), requires using discontinuous probability distribution functions. Such an approach has received relatively limited attention. Along the lines of physically based models, the extensions of the Muskingum-based models to three parameter forms are considered. Using 44 peak flow series from the USGS data bank, the fitting ability of four three-parameter models was investigated: (1) the Dirac delta combined with Gamma distribution; (2) the Dirac delta combined with two-parameter generalized Pareto distribution; (3) the Dirac delta combined with two-parameter Weibull (DWe) distribution; (4) the kinematic diffusion with one additional parameter that controls the probability of the zero event (KD3). The goodness of fit of the models was assessed and compared both by evaluation of discrepancies between the results of both estimation methods (i.e. the method of moments (MOM) and the maximum likelihood method (MLM)) and using the log of likelihood function as a criterion. In most cases, the DWe distribution with MLM-estimated parameters showed the best fit of all the three-parameter models.
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
Reference physiological parameters for pharmacodynamic modeling of liver cancer
Energy Technology Data Exchange (ETDEWEB)
Travis, C.C.; Arms, A.D.
1988-01-01
This document presents a compilation of measured values for physiological parameters used in pharamacodynamic modeling of liver cancer. The physiological parameters include body weight, liver weight, the liver weight/body weight ratio, and number of hepatocytes. Reference values for use in risk assessment are given for each of the physiological parameters based on analyses of valid measurements taken from the literature and other reliable sources. The proposed reference values for rodents include sex-specific measurements for the B6C3F{sub 1}, mice and Fishcer 344/N, Sprague-Dawley, and Wistar rats. Reference values are also provided for humans. 102 refs., 65 tabs.
Parameter estimation and error analysis in environmental modeling and computation
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
Transfer function modeling of damping mechanisms in distributed parameter models
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Lubis, A. S.; Muis, Z. A.; Pasaribu, M. I.
2017-03-01
The strength and durability of pavement construction is highly dependent on the properties and subgrade bearing capacity. This then led to the idea of the selection methods to estimate the density of the soil with the proper implementation of the system, fast and economical. This study aims to estimate the compaction parameter value namely the maximum dry unit weight (γd max) and optimum moisture content (wopt) of the soil properties value that stabilized with Portland Cement. Tests conducted in the laboratory of soil mechanics to determine the index properties (fines and liquid limit) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) between 0-15% then mixed with Portland Cement (PC) with variations of 2%, 4%, 6%, 8% and 10%, each 10 samples. The results showed that the maximum dry unit weight (γd max) and wopt has a significant relationship with percent fines, liquid limit and the percentation of cement. Equation for the estimated maximum dry unit weight (γd max) = 1.782 - 0.011*LL + 0,000*F + 0.006*PS with R2 = 0.915 and the estimated optimum moisture content (wopt) = 3.441 + 0.594*LL + 0,025*F + 0,024*PS with R2 = 0.726.
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Designing of discrete mechatronic vibrating systems with negative value parameters
Buchacz, Andrzej; Gałęziowski, Damian
2016-10-01
In the paper, the known problem of vibration control, authors expanded for designing of mechatronic discrete systems that contains single or multiply piezoelectric elements connected to external electric networks. Main focus has been given for investigations in relation to damping performance and parameters study, in case of potential practical application. By different configurations of considered mechatronic discrete branched structures with two degrees of freedom, key negative parameters have been identified and investigated in case of vibration control effectiveness. Results have been presented in graphical form of amplitudes and dynamical flexibility functions.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
Estimating parameters for generalized mass action models with connectivity information
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2009-05-01
Full Text Available Abstract Background Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. Results In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters. Conclusion The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out
Energy Technology Data Exchange (ETDEWEB)
Djuric-Stefanovic, A., E-mail: avstefan@eunet.rs [Faculty of Medicine, University of Belgrade, Belgrade (Serbia); Unit of Digestive Radiology (First University Surgical Clinic), Center of Radiology and MR, Clinical Center of Serbia, Belgrade (Serbia); Saranovic, Dj., E-mail: crvzve4@gmail.com [Faculty of Medicine, University of Belgrade, Belgrade (Serbia); Unit of Digestive Radiology (First University Surgical Clinic), Center of Radiology and MR, Clinical Center of Serbia, Belgrade (Serbia); Sobic-Saranovic, D., E-mail: dsobic2@gmail.com [Faculty of Medicine, University of Belgrade, Belgrade (Serbia); Center of Nuclear Medicine, Clinical Center of Serbia, Belgrade (Serbia); Masulovic, D., E-mail: draganmasulovic@yahoo.com [Faculty of Medicine, University of Belgrade, Belgrade (Serbia); Unit of Digestive Radiology (First University Surgical Clinic), Center of Radiology and MR, Clinical Center of Serbia, Belgrade (Serbia); Artiko, V., E-mail: veraart@beotel.rs [Faculty of Medicine, University of Belgrade, Belgrade (Serbia); Center of Nuclear Medicine, Clinical Center of Serbia, Belgrade (Serbia)
2015-03-15
Purpose: Standardized perfusion value (SPV) is a universal indicator of tissue perfusion, normalized to the whole-body perfusion, which was proposed to simplify, unify and allow the interchangeability among the perfusion measurements and comparison between the tumor perfusion and metabolism. The aims of our study were to assess the standardized perfusion value (SPV) of the esophageal carcinoma, and its correlation with quantitative CT perfusion measurements: blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface area product (PS) of the same tumor volume samples, which were obtained by deconvolution-based CT perfusion analysis. Methods: Forty CT perfusion studies of the esophageal cancer were analyzed, using the commercial deconvolution-based CT perfusion software (Perfusion 3.0, GE Healthcare). The SPV of the esophageal tumor and neighboring skeletal muscle were correlated with the corresponding mean tumor and muscle quantitative CT perfusion parameter values, using Spearman's rank correlation coefficient (r{sub S}). Results: Median SPV of the esophageal carcinoma (7.1; range: 2.8–13.4) significantly differed from the SPV of the skeletal muscle (median: 1.0; range: 0.4–2.4), (Z = −5.511, p < 0.001). The cut-off value of the SPV of 2.5 enabled discrimination of esophageal cancer from the skeletal muscle with sensitivity and specificity of 100%. SPV of the esophageal carcinoma significantly correlated with corresponding tumor BF (r{sub S} = 0.484, p = 0.002), BV (r{sub S} = 0.637, p < 0.001) and PS (r{sub S} = 0.432, p = 0.005), and SPV of the skeletal muscle significantly correlated with corresponding muscle BF (r{sub S} = 0.573, p < 0.001), BV (r{sub S} = 0.849, p < 0.001) and PS (r{sub S} = 0.761, p < 0.001). Conclusions: We presented a database of the SPV for the esophageal cancer and proved that SPV of the esophageal neoplasm significantly differs from the SPV of the skeletal muscle, which represented a sample of healthy
Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds
Directory of Open Access Journals (Sweden)
Indrajeet Chaubey
2010-11-01
Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto; AJB van Boxtel
2012-01-01
Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally pro...
Parameter counting in models with global symmetries
Energy Technology Data Exchange (ETDEWEB)
Berger, Joshua [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: jb454@cornell.edu; Grossman, Yuval [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: yuvalg@lepp.cornell.edu
2009-05-18
We present rules for determining the number of physical parameters in models with exact flavor symmetries. In such models the total number of parameters (physical and unphysical) needed to described a matrix is less than in a model without the symmetries. Several toy examples are studied in order to demonstrate the rules. The use of global symmetries in studying the minimally supersymmetric standard model (MSSM) is examined.
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...
Cosmological models with constant deceleration parameter
Energy Technology Data Exchange (ETDEWEB)
Berman, M.S.; de Mello Gomide, F.
1988-02-01
Berman presented elsewhere a law of variation for Hubble's parameter that yields constant deceleration parameter models of the universe. By analyzing Einstein, Pryce-Hoyle and Brans-Dicke cosmologies, we derive here the necessary relations in each model, considering a perfect fluid.
Researches Regarding Microbiological Parameters Values of Telemea Cheese
Directory of Open Access Journals (Sweden)
Andra Suler
2010-10-01
Full Text Available The main objectives of this paper were microbiological parameters which characterized the Telemea cheese for each season, assessment of technologies and thus assortment defects as well as projection of hygienic solution for obtaining qualitative products according to actual standards. We studied 5 units of Telemea cheese processing replaced in different area. For obtaining concrete results we used STAS methodologies and analyze procedure was based on observation, mathematical estimation and experiments (in lab and processing units.
What's the Value of VAM (Value-Added Modeling)?
Scherrer, Jimmy
2012-01-01
The use of value-added modeling (VAM) in school accountability is expanding, but deciding how to embrace VAM is difficult. Various experts say it's too unreliable, causes more harm than good, and has a big margin for error. Others assert VAM is imperfect but useful, and provides valuable feedback. A closer look at the models, and their use,…
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Solution of Seventh Order Boundary Value Problems by Variation of Parameters Method
Directory of Open Access Journals (Sweden)
Muzammal Iftikhar
2013-01-01
Full Text Available The induction motor behavior is represented by a fifth order differential equation model. Addition of a torque correction factor to this model accurately reproduces the transient torques and instantaneous real and reactive power flows of the full seventh order differential equation model. The aim of this study is to solve the seventh order boundary value problems and the variation of parameters method is used for this purpose. The approximate solutions of the problems are obtained in terms of rapidly convergent series. Two numerical examples have been given to illustrate the efficiency and implementation of the method.
Trait Characteristics of Diffusion Model Parameters
Directory of Open Access Journals (Sweden)
Anna-Lena Schubert
2016-07-01
Full Text Available Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Directory of Open Access Journals (Sweden)
Hadiyanto Hadiyanto
2012-05-01
Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels. Abstrak PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan
Generalized Extreme Value distribution parameters as dynamical indicators of Stability
Faranda, Davide; Turchetti, Giorgio; Vaienti, Sandro
2011-01-01
We introduce a new dynamical indicator of stability based on the Extreme Value statistics showing that it provides an insight on the local stability properties of dynamical systems. The indicator perform faster than other based on the iteration of the tangent map since it requires only the evolution of the original systems and, in the chaotic regions, gives further information about the information dimension of the attractor. A numerical validation of the method is presented through the analysis of the motions in a Standard map.
Moisture buffer value: A comprehensive analysis of essential parameters
DEFF Research Database (Denmark)
Peuhkuri, Ruut Hannele; Rode, Carsten; Hansen, Kurt Kielsgaard
2006-01-01
for determination of the moisture buffer value of building materials. Special focus is given to the significance of e.g. the equilibrium state, the step size in the RH and whether one is studying absorption or desorption steps. In addition, the paper summarizes shortly the experience until now of studying...... the moisture buffer phenomenon. In the experiments the material samples were exposed to a sudden change in the RH of the ambient air which were either consecutive absorption and desorption steps or periodically varying cyclic steps....
Energy Technology Data Exchange (ETDEWEB)
Ducoste, J.; Brauer, R.
1999-07-01
Analysis of a computational fluid dynamics (CFD) model for a water treatment plant clearwell was done. Model parameters were analyzed to determine their influence on the effluent-residence time distribution (RTD) function. The study revealed that several model parameters could have significant impact on the shape of the RTD function and consequently raise the level of uncertainty on accurate predictions of clearwell hydraulics. The study also revealed that although the modeler could select a distribution of values for some of the model parameters, most of these values can be ruled out by requiring the difference between the calculated and theoretical hydraulic retention time to within 5% of the theoretical value.
Interpolation of groundwater quality parameters with some values below the detection limit
Directory of Open Access Journals (Sweden)
A. Bárdossy
2011-05-01
Full Text Available For many environmental variables, measurements cannot deliver exact observation values as their concentration is below the sensibility of the measuring device (detection limit. These observations provide useful information but cannot be treated in the same manner as the other measurements. In this paper a methodology for the spatial interpolation of these values is described. The method is based on spatial copulas. Here two copula models – the Gaussian and a non-Gaussian v-copula are used. First a mixed maximum likelihood approach is used to estimate the marginal distributions of the parameters. After removal of the marginal distributions the next step is the maximum likelihood estimation of the parameters of the spatial dependence including values below the detection limit into account. Interpolation using copulas yields full conditional distributions for the unobserved sites and can be used to estimate confidence intervals, and provides a good basis for spatial simulation. The methodology is demonstrated on three different groundwater quality parameters, i.e. arsenic, chloride and deethylatrazin, measured at more than 2000 locations in South-West Germany. The chloride values are artificially censored at different levels in order to evaluate the procedures on a complete dataset. Interpolation results are evaluated using a cross validation approach. The method is compared with ordinary kriging and indicator kriging. The uncertainty measures of the different approaches are also compared.
Interpolation of groundwater quality parameters with some values below the detection limit
Directory of Open Access Journals (Sweden)
A. Bárdossy
2011-09-01
Full Text Available For many environmental variables, measurements cannot deliver exact observation values as their concentration is below the sensitivity of the measuring device (detection limit. These observations provide useful information but cannot be treated in the same manner as the other measurements. In this paper a methodology for the spatial interpolation of these values is described. The method is based on spatial copulas. Here two copula models – the Gaussian and a non-Gaussian v-copula are used. First a mixed maximum likelihood approach is used to estimate the marginal distributions of the parameters. After removal of the marginal distributions the next step is the maximum likelihood estimation of the parameters of the spatial dependence including taking those values below the detection limit into account. Interpolation using copulas yields full conditional distributions for the unobserved sites and can be used to estimate confidence intervals, and provides a good basis for spatial simulation. The methodology is demonstrated on three different groundwater quality parameters, i.e. arsenic, chloride and deethylatrazin, measured at more than 2000 locations in South-West Germany. The chloride values are artificially censored at different levels in order to evaluate the procedures on a complete dataset by progressive decimation. Interpolation results are evaluated using a cross validation approach. The method is compared with ordinary kriging and indicator kriging. The uncertainty measures of the different approaches are also compared.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Parameter Symmetry of the Interacting Boson Model
Shirokov, A M; Smirnov, Yu F; Shirokov, Andrey M.; Smirnov, Yu. F.
1998-01-01
We discuss the symmetry of the parameter space of the interacting boson model (IBM). It is shown that for any set of the IBM Hamiltonian parameters (with the only exception of the U(5) dynamical symmetry limit) one can always find another set that generates the equivalent spectrum. We discuss the origin of the symmetry and its relevance for physical applications.
Gap Model for Dual Customer Values
Institute of Scientific and Technical Information of China (English)
HOU Lun; TANG Xiaowo
2008-01-01
The customer value, the key problem in customer relationship management (CRM), was studied to construct a gap model for dual customer values. A basic description of customer values is given, and then the gaps between products and services in different periods for the customers and companies are analyzed based on the product or service life-cycle. The main factors that influence the perceived customer value were analyzed to define the "recognized value gap" and a gap model for the dual customer values was constructed to supply companies with a tool to analyze existing customer value gaps and improve customer relationship management.
Wind Farm Decentralized Dynamic Modeling With Parameters
DEFF Research Database (Denmark)
Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran;
2010-01-01
Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...
Setting Parameters for Biological Models With ANIMO
Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran
2014-01-01
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions
Delineating Parameter Unidentifiabilities in Complex Models
Raman, Dhruva V; Papachristodoulou, Antonis
2016-01-01
Scientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over which model predictions are identical, or nearly so. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, and the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast timescale subsystems, as well as the regimes in which such approximations are valid. We base our algorithm on a novel quantification of regional parametric sensitivity: multiscale sloppiness. Traditional...
Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling
Directory of Open Access Journals (Sweden)
Engin Nas
2012-01-01
Full Text Available In this study, steel AISI 1050 is subjected to process of face milling in CNC milling machine and such parameters as cutting speed, feed rate, cutting tip, depth of cut influencing the surface roughness are investigated experimentally. Four different experiments are conducted by creating different combinations for parameters. In conducted experiments, cutting tools, which are coated by PVD method used in forcing steel and spheroidal graphite cast iron are used. Surface roughness values, which are obtained by using specified parameters with cutting tools, are measured and correlation between measured surface roughness values and parameters is modeled mathematically by using curve fitting algorithm. Mathematical models are evaluated according to coefficients of determination (R2 and the most ideal one is suggested for theoretical works. Mathematical models, which are proposed for each experiment, are estipulated.
Directory of Open Access Journals (Sweden)
Yu. V. Zelenko
2016-06-01
Full Text Available Purpose.The success of the traffic on the railways of Ukraine depends on the number and the operational fleet of electric locomotives. Today, the locomotive depot exploit physically and morally outdated locomotives that have low reliability. Modernization of electric locomotives is not economically justified. The aim of this study is to improve the safety of the traction rolling stock by the frequency analysis of dynamical systems, which allows conducting the calculation of the natural (of resonant frequencies of the design and related forms of vibrations.Methodology.The study was conducted by methods of analytical mechanics and mathematical modeling of operating loads of freight locomotive when driving at different speeds on the straight and curved track sections. The theoretical value of the work is the technique of choice of constructive schemes and rational parameters of perspective electric locomotive taking into account the electric inertia ratios and stiffness coefficients of Lagrange second-order equations.Findings. The problems of theoretical research and the development of a mathematical model of the spatial electric vibrations are solved. The theoretical studies of the effect of inertia ratios and stiffness coefficients on the dynamic values and the parameter values of electric locomotive undercarriages are presented.Originality.The set of developed regulations and obtained results is a practical solution to selecting rational parameters of bogies of the freight mainline locomotive for railways of Ukraine. A concept of choice of constructive scheme and rational parameters of perspective locomotive is formulated. It is developed the method of calculation of spatial electric locomotive oscillations to determine its dynamic performance. The software complex for processing the data of experimental studies of dynamic parameters of electric locomotive and comparing the results of the theoretical calculations with the data of full
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
The Transform between the space of observed values and the space of possible values of the parameter
Bityukov, S; Smirnova, V; Taperechkina, V
2013-01-01
In ref [math.ST/0411462] the notion of statistically dual distributions is introduced. The reconstruction of confidence density [AIP Conference Proceedings 803 (2005) 398] for the location parameter for several pairs of statistically dual distributions (Poisson and Gamma, normal and normal, Cauchy and Cauchy, Laplace and Laplace) in the case of single observation of the random variable is a unique. It allows to introduce the Transform between the space of observed values and the space of possible values of the parameter.
Energy Technology Data Exchange (ETDEWEB)
Dyvorne, Hadrien, E-mail: hadrien.dyvorne@mountsinai.org [Department of Radiology, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY 10029 (United States); Jajamovich, Guido, E-mail: guido.jajamovich@mountsinai.org [Department of Radiology, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY 10029 (United States); Kakite, Suguru, E-mail: sugkaki@med.tottori-u.ac.jp [Department of Radiology, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY 10029 (United States); Kuehn, Bernd, E-mail: bernd.kuehn@siemens.com [Siemens AG, Healthcare Sector, Erlangen (Germany); Taouli, Bachir, E-mail: bachir.taouli@mountsinai.org [Department of Radiology, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY 10029 (United States)
2014-12-15
Highlights: • We assess the precision and reproducibility of liver IVIM diffusion parameters. • Liver IVIM DWI can be performed with 4 b-values with good parameter precision. • Liver IVIM DWI can be performed with 4 b-values with good parameter reproducibility. - Abstract: Purpose: To increase diffusion sampling efficiency in intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) of the liver by reducing the number of diffusion weightings (b-values). Materials and methods: In this IRB approved HIPAA compliant prospective study, 53 subjects (M/F 38/15, mean age 52 ± 13 y) underwent IVIM DWI at 1.5 T using 16 b-values (0–800 s/mm{sup 2}), with 14 subjects having repeat exams to assess IVIM parameter reproducibility. A biexponential diffusion model was used to quantify IVIM hepatic parameters (PF: perfusion fraction, D: true diffusion and D*: pseudo diffusion). All possible subsets of the 16 b-values were probed, with number of b values ranging from 4 to 15, and corresponding parameters were quantified for each subset. For each b-value subset, global parameter estimation error was computed against the parameters obtained with all 16 b-values and the subsets providing the lowest error were selected. Interscan estimation error was also evaluated between repeat exams to assess reproducibility of the IVIM technique in the liver. The optimal b-values distribution was selected such that the number of b-values was minimal while keeping parameter estimation error below interscan reproducibility error. Results: As the number of b-values decreased, the estimation error increased for all parameters, reflecting decreased precision of IVIM metrics. Using an optimal set of 4 b-values (0, 15, 150 and 800 s/mm{sup 2}), the errors were 6.5, 22.8 and 66.1% for D, PF and D* respectively. These values lie within the range of test–retest reproducibility for the corresponding parameters, with errors of 12.0, 32.3 and 193.8% for D, PF and D* respectively. Conclusion
Towards predictive food process models: A protocol for parameter estimation.
Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E
2016-05-31
Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.
Integer Valued Autoregressive Models for Tipping Bucket Rainfall Measurements
DEFF Research Database (Denmark)
Thyregod, Peter; Carstensen, Niels Jacob; Madsen, Henrik
1999-01-01
A new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge is proposed. The considered models belong to the class of integer valued autoregressive processes. The models take the autocorelation and discrete nature of the data into account. A first order, a second order...... and a threshold model are presented together with methods to estimate the parameters of each model. The models are demonstrated to provide a good description of dt from actual rain events requiring only two to four parameters....
Value Modeling for Enterprise Resilience
Energy Technology Data Exchange (ETDEWEB)
Henderson, Dale L.; Lancaster, Mary J.
2015-10-20
Abstract. The idea that resilience is a tangible, measureable, and desirable system attribute has grown rapidly over the last decade beyond is origins in explaining ecological, physiological, psychological, and social systems. Operational enterprise resilience requires two types of measurement. First, the system must monitor various operational conditions in order to respond to disruptions. These measurements are part of one or more observation, orientation, decision, and action (OODA) loops The OODA control processes that implement a resilience strategy use these measurements to provide robustness, rapid recovery and reconstitution. In order to assess the effectiveness of the resilience strategy, a different class of measurements is necessary. This second type consists of measurements about how well the OODA processes cover critical enterprise functions and the hazards to which the enterprise is exposed. They allow assessment of how well enterprise management processes anticipate, mitigate, and adapt to a changing environment and the degree to which the system is fault tolerant. This paper nominates a theoretical framework, in the form of definitions, a model, and a syntax, that accounts for this important distinction, and in so doing provides a mechanism for bridging resilience management process models and the many proposed cyber-defense metric enumerations.
da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; Picanço, Marcelo Coutinho
2017-02-01
A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using open-field Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed "sensitive". Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive.
Delineating parameter unidentifiabilities in complex models
Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis
2017-03-01
Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.
Models and parameters for environmental radiological assessments
Energy Technology Data Exchange (ETDEWEB)
Miller, C W [ed.
1984-01-01
This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)
Institute of Scientific and Technical Information of China (English)
Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN
2005-01-01
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
Moolenaar, H.E.; Selten, F.M.
2004-01-01
Climate models contain numerous parameters for which the numeric values are uncertain. In the context of climate simulation and prediction, a relevant question is what range of climate outcomes is possible given the range of parameter uncertainties. Which parameter perturbation changes the climate i
Universally sloppy parameter sensitivities in systems biology models.
Directory of Open Access Journals (Sweden)
Ryan N Gutenkunst
2007-10-01
Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
MODELING A VALUE CHAIN IN PUBLIC SECTOR
Directory of Open Access Journals (Sweden)
Daiva Rapcevičienė
2014-08-01
Full Text Available Purpose – Over the past three decades comprehensive insights were made in order to design and manage the value chain. A lot of scholars discuss differences between private sector value chain – creation profit for the business and public sector value chain, the approach that public sector creates value through the services that it provides. However, there is a lack of a common understanding of what public sector value chain is in general. This paper reviews the literature on how the private value chain was transformed into public value chain and reviews a determination and architecture of a value chain in public sector which gives a structural approach to greater picture of how all structure works. It reviews an approach that the value chain for the public sector shows how the public sector organizes itself to ensure it is of value to the citizens. Design/methodology/approach – descriptive method, analysis of scientific literature. Findings – The public sector value chain is an adaptation of the private sector value chain. The difference between the two is that the customer is the focus of the public sector context, versus the profit focus in the private sector context. There are significant similarities between the two chain models. Each of the chain models are founded on a series of core components. For the public sector context, the core components are people, service and trust. Research limitations/implications – this paper based on presenting value chain for both private and public sectors and giving deeper knowledge for public sector value chain model. Practical implications – comprehension of general value chain model concept and public sector value chain model helps to see multiple connections throughout the entire process: from the beginning to the end. The paper presents the theoretical framework for further study of the value chain model for waste management creation. Originality/Value – The paper reveals the systematic
Predictability of extreme values in geophysical models
Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.
2012-01-01
Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model
Appropriate model selection methods for nonstationary generalized extreme value models
Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng
2017-04-01
Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.
Hyperbolic value addition and general models of animal choice.
Mazur, J E
2001-01-01
Three mathematical models of choice--the contextual-choice model (R. Grace, 1994), delay-reduction theory (N. Squires & E. Fantino, 1971), and a new model called the hyperbolic value-added model--were compared in their ability to predict the results from a wide variety of experiments with animal subjects. When supplied with 2 or 3 free parameters, all 3 models made fairly accurate predictions for a large set of experiments that used concurrent-chain procedures. One advantage of the hyperbolic value-added model is that it is derived from a simpler model that makes accurate predictions for many experiments using discrete-trial adjusting-delay procedures. Some results favor the hyperbolic value-added model and delay-reduction theory over the contextual-choice model, but more data are needed from choice situations for which the models make distinctly different predictions.
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2004-09-10
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. A. Wasiolek
2003-06-27
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699
Estimation of Model Parameters for Steerable Needles
Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451
Estimation of Model Parameters for Steerable Needles.
Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.
Solar Model Parameters and Direct Measurements of Solar Neutrino Fluxes
Bandyopadhyay, A; Goswami, S; Petcov, S T; Bandyopadhyay, Abhijit; Choubey, Sandhya; Goswami, Srubabati
2006-01-01
We explore a novel possibility of determining the solar model parameters, which serve as input in the calculations of the solar neutrino fluxes, by exploiting the data from direct measurements of the fluxes. More specifically, we use the rather precise value of the $^8B$ neutrino flux, $\\phi_B$ obtained from the global analysis of the solar neutrino and KamLAND data, to derive constraints on each of the solar model parameters on which $\\phi_B$ depends. We also use more precise values of $^7Be$ and $pp$ fluxes as can be obtained from future prospective data and discuss whether such measurements can help in reducing the uncertainties of one or more input parameters of the Standard Solar Model.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Modeling Business Strategy: A Consumer Value Perspective
Svee, Eric-Oluf; Giannoulis, Constantinos; Zdravkovic, Jelena
2011-01-01
Part 3: Business Modeling; International audience; Business strategy lays out the plan of an enterprise to achieve its vision by providing value to its customers. Typically, business strategy focuses on economic value and its relevant exchanges with customers and does not directly address consumer values. However, consumer values drive customers’ choices and decisions to use a product or service, and therefore should have a direct impact on business strategy. This paper explores whether and h...
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
the parameters, including the noise terms. The parameter estimation method is a maximum likelihood method (ML) where the likelihood function is evaluated using a Kalman filter technique. The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Ragulina, Galina; Reitan, Trond
2016-04-01
Assessing the probability of extreme precipitation events is of great importance in civil planning. This requires understanding of how return values change with different return periods, which is essentially described by the Generalized Extreme Value distribution's shape parameter. Some works in the field have suggested a constant shape parameter, while our analysis indicates a non-universal value. We first re-analyse an older precipitation dataset (169 stations) extended by Norwegian data (71 stations). We show that while each set seems to have a constant shape parameter, it differs between the two datasets, indicating regional differences. For a more comprehensive analysis of spatial effects, we examine a global dataset (1495 stations). We provide shape parameter maps for two models. We find clear evidence for the shape parameter being dependent on elevation while the effect of latitude remains uncertain. Our results confirm an explanation in terms of dominating precipitation systems based on a proxy derived from the Köppen-Geiger climate classification.
Analysis of Modeling Parameters on Threaded Screws.
Energy Technology Data Exchange (ETDEWEB)
Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-06-01
Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.
Energy Technology Data Exchange (ETDEWEB)
Ng, Chaan S., E-mail: cng@mdanderson.org [Departments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chandler, Adam G., E-mail: adam.chandler@mdanderson.org [Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); CT research, GE Healthcare, Waukesha, Wisconsin (United States); Wei, Wei, E-mail: wwei@mdanderson.org [Departments of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Anderson, Ella F., E-mail: eanderson@mdanderson.org [Departments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Herron, Delise H., E-mail: dherron@mdanderson.org [Departments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Kurzrock, Razelle, E-mail: rkurzrock@ucsd.edu [Departments of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Charnsangavej, Chusilp, E-mail: ccharn@mdanderson.org [Departments of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
2013-10-01
Objectives: To assess the effect of acquisition duration (T{sub acq}) and pre-enhancement set points (T{sub 1}) on computer tomography perfusion (CTp) parameter values in primary and metastatic tumors in the lung. Materials and methods: 24 lung CTp datasets (10 primary; 14 metastatic), acquired using a two phase protocol spanning 125 s, in 12 patients with lung tumors, were analyzed by deconvolution modeling to yield tumor blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability (PS) values. CTp analyses were undertaken for the reference dataset (i.e., T{sub 1} = t{sub 0}) with varying T{sub acq} from 12 to 125 s. This was repeated for shifts in T{sub 1} (±0.5 s, ±1.0 s, ±2.0 s relative to the reference at t{sub 0}). Resultant CTp values were plotted against T{sub acq}; values at 30 s, 50 s, 65 s and 125 s were compared using linear mixed model. Results: All CTp parameter values were noticeably influenced by T{sub acq}, with generally less marked changes beyond 50 s, and with no difference in behavior between primary and secondary tumors. Apart from BV, which attained a plateau at approximately 50 s, the other three CTp parameters did not reach steady-state values within the available 125 s of data, with values at 30 s, 50 s and 65 s significantly different from 125 s (p < 0.004). Shifts in T{sub 1} also affected the CTp parameters values, with positive shifts having greater impact on CTp values than negative shifts. Conclusion: CTp parameter values derived from deconvolution modeling can be markedly affected by T{sub acq}, and pre-enhancement set-points. 50 s acquisition may be adequate for BV, but longer than 125 s is probably required for reliable characterization of the other three CTp parameters.
Precise correction to parameter ρ in the littlest Higgs model
Institute of Scientific and Technical Information of China (English)
Farshid Tabbak; F.Farnoudi
2008-01-01
In this paper tree-level violation of weak isospin parameter,ρ in the flame of the littlest Higgs model is studied.The potentially large deviation from the standard model prediction for the ρ in terms of the littlest Higgs model parameters is calculated.The maximum value for ρ for f ＝ 1 TeV,c ＝ 0.05,c'＝ 0.05and v'= 1.5 GeV is ρ = 1.2973 which means a large enhancement than the SM.
The heuristic value of redundancy models of aging.
Boonekamp, Jelle J; Briga, Michael; Verhulst, Simon
2015-11-01
Molecular studies of aging aim to unravel the cause(s) of aging bottom-up, but linking these mechanisms to organismal level processes remains a challenge. We propose that complementary top-down data-directed modelling of organismal level empirical findings may contribute to developing these links. To this end, we explore the heuristic value of redundancy models of aging to develop a deeper insight into the mechanisms causing variation in senescence and lifespan. We start by showing (i) how different redundancy model parameters affect projected aging and mortality, and (ii) how variation in redundancy model parameters relates to variation in parameters of the Gompertz equation. Lifestyle changes or medical interventions during life can modify mortality rate, and we investigate (iii) how interventions that change specific redundancy parameters within the model affect subsequent mortality and actuarial senescence. Lastly, as an example of data-directed modelling and the insights that can be gained from this, (iv) we fit a redundancy model to mortality patterns observed by Mair et al. (2003; Science 301: 1731-1733) in Drosophila that were subjected to dietary restriction and temperature manipulations. Mair et al. found that dietary restriction instantaneously reduced mortality rate without affecting aging, while temperature manipulations had more transient effects on mortality rate and did affect aging. We show that after adjusting model parameters the redundancy model describes both effects well, and a comparison of the parameter values yields a deeper insight in the mechanisms causing these contrasting effects. We see replacement of the redundancy model parameters by more detailed sub-models of these parameters as a next step in linking demographic patterns to underlying molecular mechanisms.
The value of multivariate model sophistication
DEFF Research Database (Denmark)
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
2014-01-01
We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances....
Weigand, M.; Kemna, A.
2016-06-01
Spectral induced polarization (SIP) data are commonly analysed using phenomenological models. Among these models the Cole-Cole (CC) model is the most popular choice to describe the strength and frequency dependence of distinct polarization peaks in the data. More flexibility regarding the shape of the spectrum is provided by decomposition schemes. Here the spectral response is decomposed into individual responses of a chosen elementary relaxation model, mathematically acting as kernel in the involved integral, based on a broad range of relaxation times. A frequently used kernel function is the Debye model, but also the CC model with some other a priorly specified frequency dispersion (e.g. Warburg model) has been proposed as kernel in the decomposition. The different decomposition approaches in use, also including conductivity and resistivity formulations, pose the question to which degree the integral spectral parameters typically derived from the obtained relaxation time distribution are biased by the approach itself. Based on synthetic SIP data sampled from an ideal CC response, we here investigate how the two most important integral output parameters deviate from the corresponding CC input parameters. We find that the total chargeability may be underestimated by up to 80 per cent and the mean relaxation time may be off by up to three orders of magnitude relative to the original values, depending on the frequency dispersion of the analysed spectrum and the proximity of its peak to the frequency range limits considered in the decomposition. We conclude that a quantitative comparison of SIP parameters across different studies, or the adoption of parameter relationships from other studies, for example when transferring laboratory results to the field, is only possible on the basis of a consistent spectral analysis procedure. This is particularly important when comparing effective CC parameters with spectral parameters derived from decomposition results.
Evaluation of some infiltration models and hydraulic parameters
Energy Technology Data Exchange (ETDEWEB)
Haghighi, F.; Gorji, M.; Shorafa, M.; Sarmadian, F.; Mohammadi, M. H.
2010-07-01
The evaluation of infiltration characteristics and some parameters of infiltration models such as sorptivity and final steady infiltration rate in soils are important in agriculture. The aim of this study was to evaluate some of the most common models used to estimate final soil infiltration rate. The equality of final infiltration rate with saturated hydraulic conductivity (Ks) was also tested. Moreover, values of the estimated sorptivity from the Philips model were compared to estimates by selected pedotransfer functions (PTFs). The infiltration experiments used the doublering method on soils with two different land uses in the Taleghan watershed of Tehran province, Iran, from September to October, 2007. The infiltration models of Kostiakov-Lewis, Philip two-term and Horton were fitted to observed infiltration data. Some parameters of the models and the coefficient of determination goodness of fit were estimated using MATLAB software. The results showed that, based on comparing measured and model-estimated infiltration rate using root mean squared error (RMSE), Hortons model gave the best prediction of final infiltration rate in the experimental area. Laboratory measured Ks values gave significant differences and higher values than estimated final infiltration rates from the selected models. The estimated final infiltration rate was not equal to laboratory measured Ks values in the study area. Moreover, the estimated sorptivity factor by Philips model was significantly different to those estimated by selected PTFs. It is suggested that the applicability of PTFs is limited to specific, similar conditions. (Author) 37 refs.
Environment Modeling Using Runtime Values for JPF-Android
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.
The Lund Model at Nonzero Impact Parameter
Janik, R A; Janik, Romuald A.; Peschanski, Robi
2003-01-01
We extend the formulation of the longitudinal 1+1 dimensional Lund model to nonzero impact parameter using the minimal area assumption. Complete formulae for the string breaking probability and the momenta of the produced mesons are derived using the string worldsheet Minkowskian helicoid geometry. For strings stretched into the transverse dimension, we find probability distribution with slope linear in m_T similar to the statistical models but without any thermalization assumptions.
IMPROVEMENT OF FLUID PIPE LUMPED PARAMETER MODEL
Institute of Scientific and Technical Information of China (English)
Kong Xiaowu; Wei Jianhua; Qiu Minxiu; Wu Genmao
2004-01-01
The traditional lumped parameter model of fluid pipe is introduced and its drawbacks are pointed out.Furthermore, two suggestions are put forward to remove these drawbacks.Firstly, the structure of equivalent circuit is modified, and then the evaluation of equivalent fluid resistance is change to take the frequency-dependent friction into account.Both simulation and experiment prove that this model is precise to characterize the dynamic behaviors of fluid in pipe.
Predictability of extreme values in geophysical models
Directory of Open Access Journals (Sweden)
A. E. Sterk
2012-09-01
Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.
Directory of Open Access Journals (Sweden)
Brdar Mirjana M.
2011-01-01
Full Text Available The adsorption of Cu(II onto poplar sawdust as an adsorbent is analyzed. The experimental data were fitted by the Langmuir isotherm using four linearized forms at the isotherm along with the original one. The least squares regression method was applied. Using the obtained Langmuir constants by each at methods, the enthalpy, entropy and Gibbs free energy at adsoption were calculated. A comparison of the used linear and non-linear regression methods in view at the goodness of the fit is presented. The coefficient of correlation was adopted as a criterionn to select the best method. The impact of the choice at regression model on the resulting estimates of the thermodynamic parameters is discussed. The best fit of the experimental data is obtained by the nonlinear regression. Thus, it is recommended to use the Langmuir parameters calculated by the nonlinear regression for estimating the thermodynamic parameters of adsorptin. The differences in the values obtained by different models are not so large to change the basic conclusion that the adsorption of copper ions on poplar sawdust is a spontaneous endothermic process i.e. that tested adsorbent has an affinity for copper ions.
Reference values for feeding parameters of isopods (Porcellio scaber, Isopoda, Crustacea)
Damjana Drobne; Samo Drobne
2014-01-01
Abstract The advantage of using terrestrial isopods in toxicity studies is that a battery of parameters can be tested at different levels of biological complexity. Feeding parameters for example link organism level response to potential ecological consequences but a problem with using feeding parameters in toxicity tests with terrestrial isopods is their high variability. The aim of our study was to set benchmark values for feeding parameters for isopod Porcellio scaber ( Isopoda , Crustacea ...
Reference values for feeding parameters of isopods ( Porcellio scaber , Isopoda , Crustacea )
Damjana Drobne; Samo Drobne
2014-01-01
Abstract The advantage of using terrestrial isopods in toxicity studies is that a battery of parameters can be tested at different levels of biological complexity. Feeding parameters for example link organism level response to potential ecological consequences but a problem with using feeding parameters in toxicity tests with terrestrial isopods is their high variability. The aim of our study was to set benchmark values for feeding parameters for isopod Porcellio scaber ( Isopoda , Crustacea ...
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rautenstrauch
2004-09-10
This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.
Reference values for feeding parameters of isopods (Porcellio scaber, Isopoda, Crustacea)
Drobne, Damjana; Drobne, Samo
2014-01-01
Abstract The advantage of using terrestrial isopods in toxicity studies is that a battery of parameters can be tested at different levels of biological complexity. Feeding parameters for example link organism level response to potential ecological consequences but a problem with using feeding parameters in toxicity tests with terrestrial isopods is their high variability. The aim of our study was to set benchmark values for feeding parameters for isopod Porcellio scaber (Isopoda, Crustacea) in laboratory-controlled experiments. In the work presented here, the daily feeding rate of the central 50% of the control population of Porcellio scaber and a correlation between feeding rate and isopod weight were set. Values outside these ranges need additional evaluation to increase the relevance of test outcomes. We suggest using benchmark values for feeding parameters as well as the coefficient of variation (a) to identify animals with altered feeding parameters with respect to controls, and (b) to assess the data quality in each experiment. PMID:25561844
Reference values for feeding parameters of isopods (Porcellio scaber, Isopoda, Crustacea
Directory of Open Access Journals (Sweden)
Damjana Drobne
2014-11-01
Full Text Available The advantage of using terrestrial isopods in toxicity studies is that a battery of parameters can be tested at different levels of biological complexity. Feeding parameters for example link organism level response to potential ecological consequences but a problem with using feeding parameters in toxicity tests with terrestrial isopods is their high variability. The aim of our study was to set benchmark values for feeding parameters for isopod Porcellio scaber (Isopoda, Crustacea in laboratory-controlled experiments. In the work presented here, the daily feeding rate of the central 50% of the control population of P. scaber and a correlation between feeding rate and isopod weight were set. Values outside these ranges need additional evaluation to increase the relevance of test outcomes. We suggest using benchmark values for feeding parameters as well as the coefficient of variation (a to identify animals with altered feeding parameters with respect to controls, and (b to assess the data quality in each experiment.
Order Parameters of the Dilute A Models
Warnaar, S O; Seaton, K A; Nienhuis, B
1993-01-01
The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.
Testing Linear Models for Ability Parameters in Item Response Models
Glas, Cees A.W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Parameter Estimation of Photovoltaic Models via Cuckoo Search
Directory of Open Access Journals (Sweden)
Jieming Ma
2013-01-01
Full Text Available Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE value. The proposed method outperforms other algorithms applied in this study.
Modelling of intermittent microwave convective drying: parameter sensitivity
Directory of Open Access Journals (Sweden)
Zhang Zhijun
2017-06-01
Full Text Available The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.
Modelling of intermittent microwave convective drying: parameter sensitivity
Zhang, Zhijun; Qin, Wenchao; Shi, Bin; Gao, Jingxin; Zhang, Shiwei
2017-06-01
The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.
Modelling spin Hamiltonian parameters of molecular nanomagnets.
Gupta, Tulika; Rajaraman, Gopalan
2016-07-12
Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs.
Directory of Open Access Journals (Sweden)
O. M. Horobchenko
2015-12-01
Full Text Available Purpose. During development of intelligent control systems for locomotive there is a need in the evaluation of the current train situation in the terms of traffic safety. In order to estimate the probability of the development of various emergency situations in to the traffic accidents, it is necessary to determine their complexity. The purpose of this paper is to develop the methodology for determining the complexity of emergency situations during the locomotive operation. Methodology. To achieve this purpose the statistical material of traffic safety violations was accumulated. The causes of violations are divided into groups: technical factors, human factors and external influences. Using the theory of hybrid networks it was obtained a model that gives the output complexity parameter of the emergency situation. Network type: multilayer perceptron with hybrid neurons of the first layer and the sigmoid activation function. The methods of the probability theory were used for the analysis of the results. Findings. The approach to the formalization of manufacturing situations that can only be described linguistically was developed, that allowed to use them as input data to the model for emergency situation. It was established and proved that the exponent of complexity for emergency situation during driving the train is a random quantity and obeys to the normal distribution law. It was obtained the graph of the cumulative distribution function, which identified the areas for safe operation and an increased risk of accident. Originality. It was proposed theoretical basis for determining the complexity of emergency situations in the train work and received the maximum complexity value of emergency situations that can be admitted in the operating conditions. Practical value. Constant monitoring of this value allows not only respond to the threat of danger, but also getting it in numerical form and use it as one of the input parameters for the
Proliferation in Non-Hodgkin’S Lymphomas and Its Prognostic Value Related to Staging Parameters
Directory of Open Access Journals (Sweden)
Irene Lorand‐Metze
2004-01-01
Full Text Available In malignant lymphomas, cell kinetics has shown to be related with histologic type as well as with the clinical behaviour. The aim of our study was to investigate the relevance of cell proliferation parameters on overall survival in non‐Hodgkin's lymphomas as well as their relationship with prognostic factors such as International Prognostic Index (IPI. We performed DNA‐flow‐cytometry (S‐phase fraction and detection of DNA‐aneuploidy as well as cytologic examination and the AgNOR technique in material obtained by fine needle aspiration of lymph nodes at diagnosis. The majority of the patients were stage IV by Ann Arbor and intermediate risk by IPI (42/55. When analyzing all patients together, histologic type by the WHO classification, IPI and the presence of a DNA‐aneuploid clone could not separate well patients with a different survival. For all patients, univariate Cox analysis revealed S‐phase (SPF and AgNOR parameters to be of prognostic value. In the multivariate analysis, however, only SPF remained in the final model. Yet, when stratifying for DNA‐ploidy, only the total number of AgNORs/nucleus was an independent parameter. Looking only at the DNA‐diploid cases, the AgNOR pattern remained the most important parameter, whereas for the DNA‐aneuploid cases this was true for SPF. When studying patients with B large cell lymphoma separately, only DNA‐ploidy was a prognostic factor. In summary, cell kinetic parameters reveal important prognostic information in NHL patients. Furthermore, DNA‐aneuploidy seems to interfere with the analysis of the AgNOR pattern.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Modelling of Water Turbidity Parameters in a Water Treatment Plant
Directory of Open Access Journals (Sweden)
A. S. KOVO
2005-01-01
Full Text Available The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alkThe developed model will aid the predictive assessment of water treatment plant performance. The limitations of the models are as a result of insufficient variable considered during the conceptualization.
Reconciling Planck with the local value of H0 in extended parameter space
Directory of Open Access Journals (Sweden)
Eleonora Di Valentino
2016-10-01
Full Text Available The recent determination of the local value of the Hubble constant by Riess et al., 2016 (hereafter R16 is now 3.3 sigma higher than the value derived from the most recent CMB anisotropy data provided by the Planck satellite in a ΛCDM model. Here we perform a combined analysis of the Planck and R16 results in an extended parameter space, varying simultaneously 12 cosmological parameters instead of the usual 6. We find that a phantom-like dark energy component, with effective equation of state w=−1.29−0.12+0.15 at 68% c.l. can solve the current tension between the Planck dataset and the R16 prior in an extended ΛCDM scenario. On the other hand, the neutrino effective number is fully compatible with standard expectations. This result is confirmed when including cosmic shear data from the CFHTLenS survey and CMB lensing constraints from Planck. However, when BAO measurements are included we find that some of the tension with R16 remains, as also is the case when we include the supernova type Ia luminosity distances from the JLA catalog.
Reconciling Planck with the local value of H0 in extended parameter space
Di Valentino, Eleonora; Melchiorri, Alessandro; Silk, Joseph
2016-10-01
The recent determination of the local value of the Hubble constant by Riess et al., 2016 (hereafter R16) is now 3.3 sigma higher than the value derived from the most recent CMB anisotropy data provided by the Planck satellite in a ΛCDM model. Here we perform a combined analysis of the Planck and R16 results in an extended parameter space, varying simultaneously 12 cosmological parameters instead of the usual 6. We find that a phantom-like dark energy component, with effective equation of state w = -1.29-0.12+0.15 at 68% c.l. can solve the current tension between the Planck dataset and the R16 prior in an extended ΛCDM scenario. On the other hand, the neutrino effective number is fully compatible with standard expectations. This result is confirmed when including cosmic shear data from the CFHTLenS survey and CMB lensing constraints from Planck. However, when BAO measurements are included we find that some of the tension with R16 remains, as also is the case when we include the supernova type Ia luminosity distances from the JLA catalog.
Reconciling Planck with the local value of $H_0$ in extended parameter space
Di Valentino, Eleonora; Silk, Joseph
2016-01-01
The recent determination of the local value of the Hubble constant by Riess et al, 2016 (hereafter R16) is now 3.3 sigma higher than the value derived from the most recent CMB anisotropy data provided by the Planck satellite in a LCDM model. Here we perform a combined analysis of the Planck and R16 results in an extended parameter space, varying simultaneously 12 cosmological parameters instead of the usual 6. We find that a phantom-like dark energy component, with effective equation of state $w=-1.29_{-0.12}^{+0.15}$ at 68 % c.l. can solve the current tension between the Planck dataset and the R16 prior in an extended $\\Lambda$CDM scenario. On the other hand, the neutrino effective number is fully compatible with standard expectations. This result is confirmed when including cosmic shear data from the CFHTLenS survey and CMB lensing constraints from Planck. However, when BAO measurements are included we find that some of the tension with R16 remains, as also is the case when we include the supernova type Ia ...
Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J
2014-01-01
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
The value of multivariate model sophistication
DEFF Research Database (Denmark)
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
2014-01-01
in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable...... of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way...
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Li, Zhen; Karniadakis, George
2016-01-01
We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are sparse. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space....
A Note on the Item Information Function of the Four-Parameter Logistic Model
Magis, David
2013-01-01
This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…
A Note on the Item Information Function of the Four-Parameter Logistic Model
Magis, David
2013-01-01
This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…
Mirror symmetry for two-parameter models, 1
Candelas, Philip; Font, A; Katz, S; Morrison, Douglas Robert Ogston; Candelas, Philip; Ossa, Xenia de la; Font, Anamaria; Katz, Sheldon; Morrison, David R.
1994-01-01
We study, by means of mirror symmetry, the quantum geometry of the K\\"ahler-class parameters of a number of Calabi-Yau manifolds that have $b_{11}=2$. Our main interest lies in the structure of the moduli space and in the loci corresponding to singular models. This structure is considerably richer when there are two parameters than in the various one-parameter models that have been studied hitherto. We describe the intrinsic structure of the point in the (compactification of the) moduli space that corresponds to the large complex structure or classical limit. The instanton expansions are of interest owing to the fact that some of the instantons belong to families with continuous parameters. We compute the Yukawa couplings and their expansions in terms of instantons of genus zero. By making use of recent results of Bershadsky et al. we compute also the instanton numbers for instantons of genus one. For particular values of the parameters the models become birational to certain models with one parameter. The co...
Comparison of Parameter Estimation Methods for Transformer Weibull Lifetime Modelling
Institute of Scientific and Technical Information of China (English)
ZHOU Dan; LI Chengrong; WANG Zhongdong
2013-01-01
Two-parameter Weibull distribution is the most widely adopted lifetime model for power transformers.An appropriate parameter estimation method is essential to guarantee the accuracy of a derived Weibull lifetime model.Six popular parameter estimation methods (i.e.the maximum likelihood estimation method,two median rank regression methods including the one regressing X on Y and the other one regressing Y on X,the Kaplan-Meier method,the method based on cumulative hazard plot,and the Li's method) are reviewed and compared in order to find the optimal one that suits transformer's Weibull lifetime modelling.The comparison took several different scenarios into consideration:10 000 sets of lifetime data,each of which had a sampling size of 40 ～ 1 000 and a censoring rate of 90％,were obtained by Monte-Carlo simulations for each scienario.Scale and shape parameters of Weibull distribution estimated by the six methods,as well as their mean value,median value and 90％ confidence band are obtained.The cross comparison of these results reveals that,among the six methods,the maximum likelihood method is the best one,since it could provide the most accurate Weibull parameters,i.e.parameters having the smallest bias in both mean and median values,as well as the shortest length of the 90％ confidence band.The maximum likelihood method is therefore recommended to be used over the other methods in transformer Weibull lifetime modelling.
Reiteration of Hankel singular value decomposition for modeling of complex-valued signal
Staniszewski, Michał; Skorupa, Agnieszka; Boguszewicz, Łukasz; Wicher, Magdalena; Konopka, Marek; Sokół, Maria; Polański, Andrzej
2016-06-01
Modeling signal which forms complex values is a common scientific problem, which is present in many applications, i.e. in medical signals, computer graphics and vision. One of the possible solution is utilization of Hankel Singular Value Decomposition. In the first step complex-valued signal is arranged in a special form called Hankel matrix, which is in the next step decomposed in operation of Singular Value Decomposition. Obtained matrices can be then reformulated in order to get parameters describing system. Basic method can be applied for fitting whole signal but it fails in modeling each particular component of signal. Modification of basic HSVD method, which relies on reiteration and is used for main components, and application of prior knowledge solves presented problem.
MODELING PARAMETERS OF ARC OF ELECTRIC ARC FURNACE
Directory of Open Access Journals (Sweden)
R.N. Khrestin
2015-08-01
Full Text Available Purpose. The aim is to build a mathematical model of the electric arc of arc furnace (EAF. The model should clearly show the relationship between the main parameters of the arc. These parameters determine the properties of the arc and the possibility of optimization of melting mode. Methodology. We have built a fairly simple model of the arc, which satisfies the above requirements. The model is designed for the analysis of electromagnetic processes arc of varying length. We have compared the results obtained when testing the model with the results obtained on actual furnaces. Results. During melting in real chipboard under the influence of changes in temperature changes its properties arc plasma. The proposed model takes into account these changes. Adjusting the length of the arc is the main way to regulate the mode of smelting chipboard. The arc length is controlled by the movement of the drive electrode. The model reflects the dynamic changes in the parameters of the arc when changing her length. We got the dynamic current-voltage characteristics (CVC of the arc for the different stages of melting. We got the arc voltage waveform and identified criteria by which possible identified stage of smelting. Originality. In contrast to the previously known models, this model clearly shows the relationship between the main parameters of the arc EAF: arc voltage Ud, amperage arc id and length arc d. Comparison of the simulation results and experimental data obtained from real particleboard showed the adequacy of the constructed model. It was found that character of change of magnitude Md, helps determine the stage of melting. Practical value. It turned out that the model can be used to simulate smelting in EAF any capacity. Thus, when designing the system of control mechanism for moving the electrode, the model takes into account changes in the parameters of the arc and it can significantly reduce electrode material consumption and energy consumption
Quinn, Terrance; Sinkala, Zachariah
2014-01-01
We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.
Hofmeester, Ilse; Brinker, Astrid E.; Steffens, Martijn G.; Mulder, Zwaan; van Capelle, Jan Willem; Feitz, Wout F.J.; Blanker, Marco H.
2017-01-01
Aims: Reference values of Frequency Volume Chart (FVC) and uroflowmetry parameters for adolescent and adult enuresis patients are lacking. In this study, we aim to describe those parameters, in order to interpret findings from FVCs and uroflowmetries in those patients. Methods: Retrospective, descri
Montopoli, Mario; Cimini, Domenico; Marzano, Frank
2016-04-01
Volcanic eruptions inject both gas and solid particles into the Atmosphere. Solid particles are made by mineral fragments of different sizes (from few microns to meters), generally referred as tephra. Tephra from volcanic eruptions has enormous impacts on social and economical activities through the effects on the environment, climate, public health, and air traffic. The size, density and shape of a particle determine its fall velocity and thus residence time in the Atmosphere. Larger particles tend to fall quickly in the proximity of the volcano, while smaller particles may remain suspended for several days and thus may be transported by winds for thousands of km. Thus, the impact of such hazards involves local as well as large scales effects. Local effects involve mostly the large sized particles, while large scale effects are caused by the transport of the finest ejected tephra (ash) through the atmosphere. Forecasts of ash paths in the atmosphere are routinely run after eruptions using dispersion models. These models make use of meteorological and volcanic source parameters. The former are usually available as output of numerical weather prediction models or large scale reanalysis. Source parameters characterize the volcanic eruption near the vent; these are mainly the ash mass concentration along the vertical column and the top altitude of the volcanic plume, which is strictly related to the flux of the mass ejected at the emission source. These parameters should be known accurately and continuously; otherwise, strong hypothesis are usually needed, leading to large uncertainty in the dispersion forecasts. However, direct observations during an eruption are typically dangerous and impractical. Thus, satellite remote sensing is often exploited to monitor volcanic emissions, using visible (VIS) and infrared (IR) channels available on both Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites. VIS and IR satellite imagery are very useful to monitor
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Directory of Open Access Journals (Sweden)
Otava Gabriel
2006-01-01
Full Text Available Data from literature indicate that there is a correlation between the values of reproductive parameters and the values of parameters of the metabolic profile, even though this cannot be confirmed in many cases. The objective of this work was to examine the connection between certain parameters of the metabolic profile and the restoration of reproductive activity in the postpartal period. Blood samples were taken from the cows involved in the experiment and values of parameters of the metabolic state were determined in 27 cows, of which 14 were primiparous and 13 multiparous. In the blood serum samples, we determined the concentration of glucose, total proteins, albumin, urea, and the activities of certain enzymes (alkaline phosphatase, ASAT and ALAT. It was established on the grounds of the obtained results that the delay in the establishment of reproductive activity in the postpartal period was in correlation with the blood concentrations of urea and albumin. .
Estimating model parameters in nonautonomous chaotic systems using synchronization
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-05-01
In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation.
Estimating model parameters in nonautonomous chaotic systems using synchronization
Energy Technology Data Exchange (ETDEWEB)
Yang, Xiaoli [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)]. E-mail: yangxl205@mail.nwpu.edu.cn; Xu, Wei [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China); Sun, Zhongkui [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)
2007-05-07
In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation.
Parameter optimization in S-system models
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Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Modeling of Parameters of Subcritical Assembly SAD
Petrochenkov, S; Puzynin, I
2005-01-01
The accepted conceptual design of the experimental Subcritical Assembly in Dubna (SAD) is based on the MOX core with a nominal unit capacity of 25 kW (thermal). This corresponds to the multiplication coefficient $k_{\\rm eff} =0.95$ and accelerator beam power 1 kW. A subcritical assembly driven with the existing 660 MeV proton accelerator at the Joint Institute for Nuclear Research has been modelled in order to make choice of the optimal parameters for the future experiments. The Monte Carlo method was used to simulate neutron spectra, energy deposition and doses calculations. Some of the calculation results are presented in the paper.
Prediction of interest rate using CKLS model with stochastic parameters
Energy Technology Data Exchange (ETDEWEB)
Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)
2014-06-19
The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.
Baker Syed; Poskar C; Junker Björn
2011-01-01
Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. Wh...
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2006-06-05
This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This
An analytical high value target acquisition model
Becker, Kevin J.
1986-01-01
Approved for public release; distribution is unlimited An Analytical High Value Target (HVT) acquisition model is developed for a generic anti-ship cruise missile system. the target set is represented as a single HVT within a field of escorts. The HVT's location is described by a bivariate normal probability distribution. the escorts are represented by a spatially homogeneous Poisson random field surrounding the HVT. Model output consists of the probability that at least one missile of...
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan
2010-01-28
We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.
Moose models with vanishing $S$ parameter
Casalbuoni, R; Dominici, Daniele
2004-01-01
In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the $S$ parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on $K$ SU(2) gauge groups, $K+1$ chiral fields and electroweak groups $SU(2)_L$ and $U(1)_Y$ at the ends of the chain of the moose. $S$ vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical non local field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of $S$ through an exponential behavior of the link couplings as suggested by Randall Sundrum metric.
Model parameters for simulation of physiological lipids
McGlinchey, Nicholas
2016-01-01
Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed‐chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid–protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972
Value-Added Modeling in Physical Education
Hushman, Glenn; Hushman, Carolyn
2015-01-01
The educational reform movement in the United States has resulted in a variety of states moving toward a system of value-added modeling (VAM) to measure a teacher's contribution to student achievement. Recently, many states have begun using VAM scores as part of a larger system to evaluate teacher performance. In the past decade, only "core…
A Model for Valuing Military Talents
Institute of Scientific and Technical Information of China (English)
LIU Hong-sheng
2002-01-01
The method of collocating military talents is a difficult problem. It is different from other talents, for the characteristic of military talents. This paper presents a model for valuing military talents,which can assists the military leaders to collocate military talents properly.
Dolka, B; Włodarczyk, R; Zbikowski, A; Dolka, I; Szeleszczuk, P; Kluciński, W
2014-06-01
The knowledge of the correct morphological and biochemical parameters in mute swans is an important indicator of their health status, body condition, adaptation to habitat and useful diagnostic tools in veterinary practice and ecological research. The aim of the study was to obtain hematological parameters in relation to age, sex and serum biochemistry values in wild-living mute swans. We found the significant differences in the erythrocyte count, hematocrit, hemoglobin concentration and erythrocyte sedimentation rate in relation to age of mute swans. There were no differences in hematological values between males and females. The leukogram and H/L ratio did not vary by age and sex in swans. Among of biochemical parameters the slightly increased AST, ALP, CK, K, urea, decreased CHOL and TG values were recorded. As far as we know, this is the first study in which the morphometric parameters of blood cells in mute swans were presented. We found extremely low concentration of lead in blood (at subthreshold level). No blood parasites were found in blood smears. The analysis of body mass and biometric parameters revealed a significant differences dependent on age and sex. No differences in the scaled mass index were found. Our results represent a normal hematologic and blood chemistry values and age-sex related changes, as reference values for the mute swan.
Baseline values of immunologic parameters in the lizard Salvator merianae (Teiidae, Squamata
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Ana Paula Mestre
2017-05-01
Full Text Available The genus Salvator is widely distributed throughout South America. In Argentina, the species most abundant widely distributed is Salvator merianae. Particularly in Santa Fe province, the area occupied by populations of these lizards overlaps with areas where agriculture was extended. With the aim of established baseline values for four immunologic biomarkers widely used, 36 tegu lizards were evaluated tacking into account different age classes and both sexes. Total leukocyte counts were not different between age classes. Of the leucocytes count, eosinophils levels were higher in neonates compared with juvenile and adults; nevertheless, the heterophils group was the most prevalent leukocyte in the peripheral blood in all age classes. Lymphocytes, monocytes, heterophils, azurophils and basophils levels did not differ with age. Natural antibodies titres were higher in the adults compared with neonates and juveniles lizards. Lastly, complement system activity was low in neonates compared with juveniles and adults. Statistical analysis within each age group showed that gender was not a factor in the outcomes. Based on the results, we concluded that S. merianae demonstrated age (but not gender related differences in the immune parameters analyzed. Having established baseline values for these four widely-used immunologic biomarkers, ongoing studies will seek to optimize the use of the S. merianae model in future research.
Iterative integral parameter identification of a respiratory mechanics model
Directory of Open Access Journals (Sweden)
Schranz Christoph
2012-07-01
Full Text Available Abstract Background Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. Methods An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS patients. Results The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. Conclusion These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.
Value as a parameter to consider in operational strategies for CSP plants
de Meyer, Oelof; Dinter, Frank; Govender, Saneshan
2017-06-01
This paper introduced a value parameter to consider when analyzing operational strategies for CSP plants. The electric system in South Africa, used as case study, is severely constrained with an influx of renewables in the early phase of deployment. The energy demand curve for the system is analyzed showing the total wind and solar photovoltaic contributions for winter and summer. Due to the intermittent nature and meteorological operating conditions of wind and solar photovoltaic plants, the value of CSP plants within the electric system is introduced. Analyzing CSP plants based on the value parameter alone will remain only a philosophical view. Currently there is no quantifiable measure to translate the philosophical view or subjective value and it solely remains the position of the stakeholder. By introducing three other parameters, Cost, Plant and System to a holistic representation of the Operating Strategies of generation plants, the Value parameter can be translated into a quantifiable measure. Utilizing the country's current procurement program as case study, CSP operating under the various PPA within the Bid Windows are analyzed. The Value Cost Plant System diagram developed is used to quantify the value parameter. This paper concluded that no value is obtained from CSP plants operating under the Bid Window 1 & 2 Power Purchase Agreement. However, by recognizing the dispatchability potential of CSP plants in Bid Window 3 & 3.5, the value of CSP in the electric system can be quantified utilizing Value Added Relationship VCPS-diagram. Similarly ancillary services to the system were analyzed. One of the relationships that have not yet been explored within the industry is an interdependent relationship. It was emphasized that the cost and value structure is shared between the plant and system. Although this relationship is functional when the plant and system belongs to the same entity, additional value is achieved by marginalizing the cost structure. A
[Basic values of blood coagulation parameters in pigs (Sus scrofa domesticus)].
Hahn, N; Popov-Cenic, S; Dorer, A
1996-01-01
On 23 clinical healthy pigs (2-4 months of age, body weight 13-42 kg) under ketamin-pentobarbital anaesthesia blood plasma coagulation parameters have been investigated. To obtain basic values 26 parameters were measured: number of thrombocytes, parameters of thrombelastogram and resonance-thrombogram, prothrombin time, activated partial thromboplastin time, thrombin time, reptilase time, factors I, II, V, VII, VIII, X, antithrombin III, plasminogen, alpha 1-antitrypsin, alpha 2-antiplasmin, alpha 2-macroglobulin, fibrin degradation products D and E and euglobulin lysis-time. Parameters calculated in percent should be measured against a pig plasma pool. Measurement against a human plasma pool are hardly valid in values higher than 100%. In comparison to man the results indicate modifications of fibrinogenesis and fibrinolysis in pigs.
Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model.
Laury, Marie L; Wang, Lee-Ping; Pande, Vijay S; Head-Gordon, Teresa; Ponder, Jay W
2015-07-23
A set of improved parameters for the AMOEBA polarizable atomic multipole water model is developed. An automated procedure, ForceBalance, is used to adjust model parameters to enforce agreement with ab initio-derived results for water clusters and experimental data for a variety of liquid phase properties across a broad temperature range. The values reported here for the new AMOEBA14 water model represent a substantial improvement over the previous AMOEBA03 model. The AMOEBA14 model accurately predicts the temperature of maximum density and qualitatively matches the experimental density curve across temperatures from 249 to 373 K. Excellent agreement is observed for the AMOEBA14 model in comparison to experimental properties as a function of temperature, including the second virial coefficient, enthalpy of vaporization, isothermal compressibility, thermal expansion coefficient, and dielectric constant. The viscosity, self-diffusion constant, and surface tension are also well reproduced. In comparison to high-level ab initio results for clusters of 2-20 water molecules, the AMOEBA14 model yields results similar to AMOEBA03 and the direct polarization iAMOEBA models. With advances in computing power, calibration data, and optimization techniques, we recommend the use of the AMOEBA14 water model for future studies employing a polarizable water model.
Considerations for parameter optimization and sensitivity in climate models.
Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E
2010-12-14
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
Automatic parameter extraction technique for gate leakage current modeling in double gate MOSFET
Darbandy, Ghader; Gneiting, Thomas; Alius, Heidrun; Alvarado, Joaquín; Cerdeira, Antonio; Iñiguez, Benjamin
2013-11-01
Direct Tunneling (DT) and Trap Assisted Tunneling (TAT) gate leakage current parameters have been extracted and verified considering automatic parameter extraction approach. The industry standard package IC-CAP is used to extract our leakage current model parameters. The model is coded in Verilog-A and the comparison between the model and measured data allows to obtain the model parameter values and parameters correlations/relations. The model and parameter extraction techniques have been used to study the impact of parameters in the gate leakage current based on the extracted parameter values. It is shown that the gate leakage current depends on the interfacial barrier height more strongly than the barrier height of the dielectric layer. There is almost the same scenario with respect to the carrier effective masses into the interfacial layer and the dielectric layer. The comparison between the simulated results and available measured gate leakage current transistor characteristics of Trigate MOSFETs shows good agreement.
Uncertainty Quantification for Optical Model Parameters
Lovell, A E; Sarich, J; Wild, S M
2016-01-01
Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95\\% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. We study a number of reactions involving neutron and deuteron p...
Numerical modeling of partial discharges parameters
Directory of Open Access Journals (Sweden)
Kartalović Nenad M.
2016-01-01
Full Text Available In recent testing of the partial discharges or the use for the diagnosis of insulation condition of high voltage generators, transformers, cables and high voltage equipment develops rapidly. It is a result of the development of electronics, as well as, the development of knowledge about the processes of partial discharges. The aim of this paper is to contribute the better understanding of this phenomenon of partial discharges by consideration of the relevant physical processes in isolation materials and isolation systems. Prebreakdown considers specific processes, and development processes at the local level and their impact on specific isolation material. This approach to the phenomenon of partial discharges needed to allow better take into account relevant discharge parameters as well as better numerical model of partial discharges.
Do land parameters matter in large-scale hydrological modelling?
Gudmundsson, Lukas; Seneviratne, Sonia I.
2013-04-01
Many of the most pending issues in large-scale hydrology are concerned with predicting hydrological variability at ungauged locations. However, current-generation hydrological and land surface models that are used for their estimation suffer from large uncertainties. These models rely on mathematical approximations of the physical system as well as on mapped values of land parameters (e.g. topography, soil types, land cover) to predict hydrological variables (e.g. evapotranspiration, soil moisture, stream flow) as a function of atmospheric forcing (e.g. precipitation, temperature, humidity). Despite considerable progress in recent years, it remains unclear whether better estimates of land parameters can improve predictions - or - if a refinement of model physics is necessary. To approach this question we suggest scrutinizing our perception of hydrological systems by confronting it with the radical assumption that hydrological variability at any location in space depends on past and present atmospheric forcing only, and not on location-specific land parameters. This so called "Constant Land Parameter Hypothesis (CLPH)" assumes that variables like runoff can be predicted without taking location specific factors such as topography or soil types into account. We demonstrate, using a modern statistical tool, that monthly runoff in Europe can be skilfully estimated using atmospheric forcing alone, without accounting for locally varying land parameters. The resulting runoff estimates are used to benchmark state-of-the-art process models. These are found to have inferior performance, despite their explicit process representation, which accounts for locally varying land parameters. This suggests that progress in the theory of hydrological systems is likely to yield larger improvements in model performance than more precise land parameter estimates. The results also question the current modelling paradigm that is dominated by the attempt to account for locally varying land
Robust linear parameter varying induction motor control with polytopic models
Directory of Open Access Journals (Sweden)
Dalila Khamari
2013-01-01
Full Text Available This paper deals with a robust controller for an induction motor which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI based approach and robust Lyapunov feedback controller are associated. This new approach is related to the fact that the synthesis of a linear parameter varying (LPV feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic model because of speed and rotor resistance affine dependence their values can be estimated on line during systems operations. The simulation results are presented to confirm the effectiveness of the proposed approach where robustness stability and high performances have been achieved over the entire operating range of the induction motor.
Herrero-Medrano, J M; Mathur, P K; ten Napel, J; Rashidi, H; Alexandri, P; Knol, E F; Mulder, H A
2015-04-01
Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments
Shanghai Stock Prices as Determined by the Present Value Model
Gregory C. Chow
2003-01-01
Derived from the present-value model of stock prices, our model implies that the log stock price is a linear function of expected log dividends and the expected rate of growth of dividends where expectations are formed adaptively. The model explains very well the prices of 47 stocks traded on the Shanghai Stock Exchange observed at the beginning of 1996, 1997, and 1998. The estimated parameters are remarkably similar to those reported for stocks traded on the Hong Kong Stock Exchange and the ...
Efficient Smoothing for Boundary Value Models
1989-12-29
IEEE Transactions on Automatic Control , vol. 29, pp. 803-821, 1984. [2] A. Bagchi and H. Westdijk, "Smoothing...and likelihood ratio for Gaussian boundary value processes," IEEE Transactions on Automatic Control , vol. 34, pp. 954-962, 1989. [3] R. Nikoukhah et...77-96, 1988. [6] H. L. Weinert and U. B. Desai, "On complementary models and fixed- interval smoothing," IEEE Transactions on Automatic Control ,
Value Concept and Economic Growth Model
Directory of Open Access Journals (Sweden)
Truong Hong Trinh
2014-12-01
Full Text Available This paper approaches the value added method for Gross Domestic Product (GDP measurement that explains the interrelationship between the expenditure approach and the income approach. The economic growth model is also proposed with three key elements of capital accumulation, technological innovation, and institutional reform. Although capital accumulation and technological innovation are two integrated elements in driving economic growth, institutional reforms play a key role in creating incentives that effect the transitional and steady state growth rate in the real world economy. The paper provides a theoretical insight on economic growth to understand incentives and driving forces in economic growth model.
Constraints on the parameters of the Left Right Mirror Model
Cerón, V E; Díaz-Cruz, J L; Maya, M; Ceron, Victoria E.; Cotti, Umberto; Maya, Mario
1998-01-01
We study some phenomenological constraints on the parameters of a left right model with mirror fermions (LRMM) that solves the strong CP problem. In particular, we evaluate the contribution of mirror neutrinos to the invisible Z decay width (\\Gamma_Z^{inv}), and we find that the present experimental value on \\Gamma_Z^{inv}, can be used to place an upper bound on the Z-Z' mixing angle that is consistent with limits obtained previously from other low-energy observables. In this model the charged fermions that correspond to the standard model (SM) mix with its mirror counterparts. This mixing, simultaneously with the Z-Z' one, leads to modifications of the \\Gamma(Z --> f \\bar{f}) decay width. By comparing with LEP data, we obtain bounds on the standard-mirror lepton mixing angles. We also find that the bottom quark mixing parameters can be chosen to fit the experimental values of R_b, and the resulting values for the Z-Z' mixing angle do not agree with previous bounds. However, this disagreement disappears if on...
Normal values and reproducibility of left ventricular filling parameters by radionuclide angiography
Muntinga, HJ; vandenBerg, F; Knol, HR; Niemeyer, MG; Blanksma, PK; Louwes, H; vanderWall, EE
1997-01-01
Background. In physiologic situations age, heart rate (HR) and left ventricular ejection fraction (EF) may influence left ventricular filling rate. In this study, we determined normal values for radionuclide angiography (RNA) derived diastolic filling parameters, the correlations with age, HR and EF
Statistical normal values of visual parameters that characterize binocular function in children.
Jiménez, R; Pérez, M A; García, J A; González, M D
2004-11-01
A wide range of visual parameters used to evaluate binocular function were evaluated in a paediatric population (1056 subjects aged 6-12 years). Mean values are provided for these ages in optometric tests that directly assess the vergence system, horizontal phorias for near and far vision (measured by a modified version of the Thorington method), negative and positive vergence amplitude for near and far vision (step vergence testing), vergence facility (flippers 8 Delta BI/8 Delta BO), and near-point of convergence (penlight push-up technique and red-lens push-up technique), as well as stimulus accommodative convergence/accommodation ratio and stereoacuity (Randot test) which provide an overall evaluation of the vergence, accommodative and oculomotor systems. A statistical comparison (anova and Bonferroni post hoc test) of these values between ages was performed. The differences, although statistically significant, were not clinically meaningful, and therefore we identified two trends in the behaviour of these parameters. For all parameters, except for vergence facility, we established a single mean reference value for the age range studied. The difference between the means for vergence facility indicated the need to divide the population into two age ranges (6-8 and 8-12 years). This study establishes statistical normal values for these parameters in a paediatric population and their means are a valuable instrument for separating children with binocular anomalies from those with normal binocular vision.
Remarks on Slater's asymptotic expansions of Kummer functions for large values of the $a-$parameter
N.M. Temme (Nico)
2013-01-01
textabstractIn Slater's 1960 standard work on confluent hypergeometric functions, also called Kummer functions, a number of asymptotic expansions of these functions can be found. We summarize expansions derived from a differential equation for large values of the $a-$parameter. We show how similar
Directory of Open Access Journals (Sweden)
N. K. Sajeevkumar
2014-09-01
Full Text Available In this article, we derived the Best Linear Unbiased Estimator (BLUE of the location parameter of certain distributions with known coefficient of variation by record values. Efficiency comparisons are also made on the proposed estimator with some of the usual estimators. Finally we give a real life data to explain the utility of results developed in this article.
A strategy for “constraint-based” parameter specification for environmental models
Gharari, S.; Shafiei, M.; Hrachowitz, M.; Fenicia, F.; Gupta, H.V.; Savenije, H.H.G.
2013-01-01
Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available. Here, we explore another way to constrain the parameter values of s
Parameter Optimisation for the Behaviour of Elastic Models over Time
DEFF Research Database (Denmark)
Mosegaard, Jesper
2004-01-01
Optimisation of parameters for elastic models is essential for comparison or finding equivalent behaviour of elastic models when parameters cannot simply be transferred or converted. This is the case with a large range of commonly used elastic models. In this paper we present a general method...... that will optimise parameters based on the behaviour of the elastic models over time....
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Soil-Related Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
A. J. Smith
2004-09-09
was defined as AP-SIII.9Q, ''Scientific Analyses''. This analysis revises the previous version with the same name (BSC 2003 [DIRS 161239]), which was itself a revision of one titled ''Evaluate Soil/Radionuclide Removal by Erosion and Leaching'' (CRWMS M&O 2001 [DIRS 152517]). In Revision 00 of this report, the data generated were fixed values (i.e., taking no account of uncertainty and variability). Revision 01 (BSC 2003 [DIRS 161239]) incorporated uncertainty and variability into the values for the bulk density, elemental partition coefficients, average annual loss of soil from erosion, resuspension enhancement factor, and field capacity water content. The current revision of this document improves the transparency and traceability of the products without changing the details of the analysis. This analysis report supports the treatment of six of the features, events, and processes (FEPs) applicable to the Yucca Mountain reference biosphere (DTN: MO0407SEPFEPLA.000 [DIRS 170760]). The use of the more recent FEP list in DTN: MO0407SEPFEPLA.000 [DIRS 170760] represents a deviation from the detail provided in the TWP (BSC 2004 [DIRS 169573]), which referenced a previous version of the FEP list. The parameters developed in this report support treatment of these six FEPs addressed in the biosphere model that are listed in Table 1-1. Inclusion and treatment of FEPs in the biosphere model is described in the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460], Section 6.2).
Parameter estimation for models of ligninolytic and cellulolytic enzyme kinetics
Energy Technology Data Exchange (ETDEWEB)
Wang, Gangsheng [ORNL; Post, Wilfred M [ORNL; Mayes, Melanie [ORNL; Frerichs, Joshua T [ORNL; Jagadamma, Sindhu [ORNL
2012-01-01
While soil enzymes have been explicitly included in the soil organic carbon (SOC) decomposition models, there is a serious lack of suitable data for model parameterization. This study provides well-documented enzymatic parameters for application in enzyme-driven SOC decomposition models from a compilation and analysis of published measurements. In particular, we developed appropriate kinetic parameters for five typical ligninolytic and cellulolytic enzymes ( -glucosidase, cellobiohydrolase, endo-glucanase, peroxidase, and phenol oxidase). The kinetic parameters included the maximum specific enzyme activity (Vmax) and half-saturation constant (Km) in the Michaelis-Menten equation. The activation energy (Ea) and the pH optimum and sensitivity (pHopt and pHsen) were also analyzed. pHsen was estimated by fitting an exponential-quadratic function. The Vmax values, often presented in different units under various measurement conditions, were converted into the same units at a reference temperature (20 C) and pHopt. Major conclusions are: (i) Both Vmax and Km were log-normal distributed, with no significant difference in Vmax exhibited between enzymes originating from bacteria or fungi. (ii) No significant difference in Vmax was found between cellulases and ligninases; however, there was significant difference in Km between them. (iii) Ligninases had higher Ea values and lower pHopt than cellulases; average ratio of pHsen to pHopt ranged 0.3 0.4 for the five enzymes, which means that an increase or decrease of 1.1 1.7 pH units from pHopt would reduce Vmax by 50%. (iv) Our analysis indicated that the Vmax values from lab measurements with purified enzymes were 1 2 orders of magnitude higher than those for use in SOC decomposition models under field conditions.
Shin, Eun-Seok; Lam, Yat-Yin; Her, Ae-Young; Brachmann, Johannes; Jung, Friedrich; Park, Jai-Wun
2017-02-01
Magnetocardiography (MCG) has been proposed as a non-invasive and functional technique with high accuracy for diagnosis of myocardial ischemia. This study sought to investigate the incremental diagnostic value of combined quantitative and qualitative parameters of MCG to detect coronary artery disease (CAD). Ninety six patients with suspected CAD who underwent coronary angiography were enrolled in the analysis to test the diagnostic accuracy of 2 MCG parameters (a quantitative parameter of the percent change of ST-segment fluctuation score and a qualitative parameter of non-dipole phenomenon). The best cut-off value for the percent change of ST-segment fluctuation score was -51.0%. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.1, 73.9, 82.0, 79.1, and 77.4, in the percent change of ST-segment fluctuation score and 86.5, 84.8, 88.0, 86.7, and 86.3 in non-dipole phenomenon. The area under the curve of receiver-operating characteristics was 0.79 for the percent change of ST-segment fluctuation score and 0.86 for non-dipole phenomenon (pQualitative assessment of non-dipole phenomenon has a better diagnostic value than the quantitative parameter of percent change of ST-segment fluctuation score in the detection of significant CAD. Furthermore, this study found that the incorporation of non-dipole phenomenon into the percent change of ST-segment fluctuation score significantly improved the diagnostic performance of CAD detection. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Finding model parameters: Genetic algorithms and the numerical modelling of quartz luminescence
Energy Technology Data Exchange (ETDEWEB)
Adamiec, Grzegorz [Department of Radioisotopes, Institute of Physics, Silesian University of Technology, ul. Krzywoustego 2, 44-100 Gliwice (Poland)]. E-mail: grzegorz.adamiec@polsl.pl; Bluszcz, Andrzej [Department of Radioisotopes, Institute of Physics, Silesian University of Technology, ul. Krzywoustego 2, 44-100 Gliwice (Poland); Bailey, Richard [Department of Geography, Royal Holloway, University of London, Egham, Surrey, TW20 0EX (United Kingdom); Garcia-Talavera, Marta [LIBRA, Centro I-D, Campus Miguel Delibes, 47011 Valladolid (Spain)
2006-08-15
The paper presents an application of genetic algorithms (GAs) to the problem of finding appropriate parameter values for the numerical simulation of quartz thermoluminescence (TL). We show that with the use of GAs it is possible to achieve a very good match between simulated and experimentally measured characteristics of quartz, for example the thermal activation characteristics of fired quartz. The rate equations of charge transport in the numerical model of luminescence in quartz contain a large number of parameters (trap depths, frequency factors, populations, charge capture probabilities, optical detrapping probabilities, and recombination probabilities). Given that comprehensive models consist of over 10 traps, finding model parameters proves a very difficult task. Manual parameter changes are very time consuming and allow only a limited degree of accuracy. GAs provide a semi-automatic way of finding appropriate parameters.
Energy Technology Data Exchange (ETDEWEB)
Swaminathan-Gopalan, Krishnan; Stephani, Kelly A., E-mail: ksteph@illinois.edu [Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States)
2016-02-15
A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach. The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.
Swaminathan-Gopalan, Krishnan; Stephani, Kelly A.
2016-02-01
A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach. The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.
From Business Value Model to Coordination Process Model
Fatemi, Hassan; van Sinderen, Marten; Wieringa, Roel
The increased complexity of business webs calls for modeling the collaboration of enterprises from different perspectives, in particular the business and process perspectives, and for mutually aligning these perspectives. Business value modeling and coordination process modeling both are necessary for a good e-business design, but these activities have different goals and use different concepts. Nevertheless, the resulting models should be consistent with each other because they refer to the same system from different perspectives. Hence, checking the consistency between these models or producing one based on the other would be of high value. In this paper we discuss the issue of achieving consistency in multi-level e-business design and give guidelines to produce consistent coordination process models from business value models in a stepwise manner.
Prognostic value of diffusion tensor imaging parameters for Gamma Knife radiosurgery in meningiomas.
Speckter, Herwin; Bido, Jose; Hernandez, Giancarlo; Mejía, Diones Rivera; Suazo, Luis; Valenzuela, Santiago; Perez-Then, Eddy; Stoeter, Peter
2016-12-01
OBJECTIVE Diffusion tensor imaging (DTI) parameters are able to differentiate between meningioma subtypes. The hypothesis that there is a correlation between DTI parameters and the change in tumor size after Gamma Knife radiosurgery (GKRS) was analyzed. METHODS DTI parameters were measured using MRI before GKRS in 26 patients with meningiomas. The findings were correlated with the change in tumor size after treatment as measured at the last follow-up (range 12.5-45 months). RESULTS Only those meningiomas that showed the highest fractional anisotropy (FA), the lowest spherical index of the tensor ellipsoid (Cs), and the lowest radial diffusivity (RD) either increased or remained stable in terms of volume, whereas all other meningiomas decreased in volume. The correlation between the DTI parameters (correlation values of -0.81 for FA, 0.75 for Cs, 0.66 for RD, and 0.66 for mean diffusivity) and the rate of volume change per month was significant (p ≤ 0.001). Other factors, including original tumor size, prescription dose, and patient age, did not correlate significantly. CONCLUSIONS Meningiomas that show high FA values-as well as low Cs, low RD, and low mean diffusivity values-do not respond as well to GKRS in comparison with meningiomas with low FA values. This finding might be due to their higher content level of fibrous tissue. In particular, the meningioma with the highest FA value (0.444) considerably increased in volume (by 32.3% after 37 months), whereas the meningioma with the lowest FA value (0.151) showed the highest rate of reduction (3.3% per month) in this study.
[Calculation of parameters in forest evapotranspiration model].
Wang, Anzhi; Pei, Tiefan
2003-12-01
Forest evapotranspiration is an important component not only in water balance, but also in energy balance. It is a great demand for the development of forest hydrology and forest meteorology to simulate the forest evapotranspiration accurately, which is also a theoretical basis for the management and utilization of water resources and forest ecosystem. Taking the broadleaved Korean pine forest on Changbai Mountain as an example, this paper constructed a mechanism model for estimating forest evapotranspiration, based on the aerodynamic principle and energy balance equation. Using the data measured by the Routine Meteorological Measurement System and Open-Path Eddy Covariance Measurement System mounted on the tower in the broadleaved Korean pine forest, the parameters displacement height d, stability functions for momentum phi m, and stability functions for heat phi h were ascertained. The displacement height of the study site was equal to 17.8 m, near to the mean canopy height, and the functions of phi m and phi h changing with gradient Richarson number R i were constructed.
Chen, Ying; Lin, Li
2017-07-01
Preeclampsia is a relatively common complication of pregnancy and considered to be associated with different degrees of coagulation dysfunction. This study was developed to evaluate the potential value of coagulation parameters for suggesting preeclampsia during the third trimester of pregnancy. Data from 188 healthy pregnant women, 125 patients with preeclampsia in the third trimester and 120 age-matched nonpregnant women were analyzed. Prothrombin time, prothrombin activity, activated partial thromboplastin time, fibrinogen (Fg), antithrombin, platelet count, mean platelet volume, platelet distribution width and plateletcrit were tested. All parameters, excluding prothrombin time, platelet distribution width and plateletcrit, differed significantly between healthy pregnant women and those with preeclampsia. Platelet count, antithrombin and Fg were significantly lower and mean platelet volume and prothrombin activity were significantly higher in patients with preeclampsia (P preeclampsia was 0.872 for Fg with an optimal cutoff value of ≤2.87g/L (sensitivity = 0.68 and specificity = 0.98). For severe preeclampsia, the area under the curve for Fg reached up to 0.922 with the same optimal cutoff value (sensitivity = 0.84, specificity = 0.98, positive predictive value = 0.96 and negative predictive value = 0.93). Fg is a biomarker suggestive of preeclampsia in the third trimester of pregnancy, and our data provide a potential cutoff value of Fg ≤ 2.87g/L for screening preeclampsia, especially severe preeclampsia. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.
Proving the ecosystem value through hydrological modelling
Dorner, W.; Spachinger, K.; Porter, M.; Metzka, R.
2008-11-01
Ecosystems provide valuable functions. Also natural floodplains and river structures offer different types of ecosystem functions such as habitat function, recreational area and natural detention. From an economic stand point the loss (or rehabilitation) of these natural systems and their provided natural services can be valued as a damage (or benefit). Consequently these natural goods and services must be economically valued in project assessments e.g. cost-benefit-analysis or cost comparison. Especially in smaller catchments and river systems exists significant evidence that natural flood detention reduces flood risk and contributes to flood protection. Several research projects evaluated the mitigating effect of land use, river training and the loss of natural flood plains on development, peak and volume of floods. The presented project analysis the hypothesis that ignoring natural detention and hydrological ecosystem services could result in economically inefficient solutions for flood protection and mitigation. In test areas, subcatchments of the Danube in Germany, a combination of hydrological and hydrodynamic models with economic evaluation techniques was applied. Different forms of land use, river structure and flood protection measures were assed and compared from a hydrological and economic point of view. A hydrodynamic model was used to simulate flows to assess the extent of flood affected areas and damages to buildings and infrastructure as well as to investigate the impacts of levees and river structure on a local scale. These model results provided the basis for an economic assessment. Different economic valuation techniques, such as flood damage functions, cost comparison method and substation-approach were used to compare the outcomes of different hydrological scenarios from an economic point of view and value the ecosystem service. The results give significant evidence that natural detention must be evaluated as part of flood mitigation projects
Zhang, Xuefeng; Zhang, Shaoqing; Liu, Zhengyu; Wu, Xinrong; Han, Guijun
2016-09-01
Imperfect physical parameterization schemes are an important source of model bias in a coupled model and adversely impact the performance of model simulation. With a coupled ocean-atmosphere-land model of intermediate complexity, the impact of imperfect parameter estimation on model simulation with biased physics has been studied. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation and "truth" models. To mitigate model bias, the parameters employed in the biased longwave radiation scheme are optimized using three different methods: least-squares parameter fitting (LSPF), single-valued parameter estimation and geography-dependent parameter optimization (GPO), the last two of which belong to the coupled model parameter estimation (CMPE) method. While the traditional LSPF method is able to improve the performance of coupled model simulations, the optimized parameter values from the CMPE, which uses the coupled model dynamics to project observational information onto the parameters, further reduce the bias of the simulated climate arising from biased physics. Further, parameters estimated by the GPO method can properly capture the climate-scale signal to improve the simulation of climate variability. These results suggest that the physical parameter estimation via the CMPE scheme is an effective approach to restrain the model climate drift during decadal climate predictions using coupled general circulation models.
Yu, Xiaolin; Zhang, Shaoqing; Lin, Xiaopei; Li, Mingkui
2017-03-01
The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.
Tatar, Emel C; Sahin, Mustafa; Demiral, Dilek; Bayir, Omer; Saylam, Guleser; Ozdek, Ali; Korkmaz, Mehmet Hakan
2016-05-01
Objective measurements are quite important for assessment of voice disorders. The first aim of this study was to establish a prototype database of normative values of voice analysis parameters in healthy Turkish adult female population. The second aim was to evaluate the variations of these parameters during physiological menstrual cycle. This is a prospective, single-blind study. Eighty-nine healthy women (mean age, 31.5 ± 6.0 years) with normal physical findings and without vocal abuse or dysphonia were participated. Detailed physical and videostroboscopic larynx examination was done. Participants' voice samples of sustained /a/ and /i/ vowels were recorded, and voice analysis was done. GRBAS scale was done by four otolaryngologists, and subjects were asked to score their own voice quality using Voice Handicap Index-10 (VHI-10) at the premenstruation, during menstruation, and postmenstruation periods. Eighty-nine healthy Turkish women's some normative acoustic vocal parameters of three different phases of menstrual cycle were reported. The data indicated that during the premenstruation period; the mean jitter %, shimmer % and noise-to-harmonic ratio values were significantly higher than that of other two periods. Variations of computerized acoustic vocal analysis parameters through menstruation cycle were in concordance with the perceptual voice assessment (GRBAS) and the questionnaire of subjects' perception of their own voices (VHI-10). In this study the normative values of voice analysis parameters of healthy adult Turkish women was reported. Adding computerized voice analysis parameters may improve the assessment and screening of voice in routine clinical practice because this is a simple and reliable method. Evaluation of voice can be performed regardless of the phases of menstrual cycle, but not performing acoustic analysis during the premenstrual period may prevent some of the unintended errors. Copyright © 2016 The Voice Foundation. Published by
Bifurcations, chaos, and sensitivity to parameter variations in the Sato cardiac cell model
Otte, Stefan; Berg, Sebastian; Luther, Stefan; Parlitz, Ulrich
2016-08-01
The dynamics of a detailed ionic cardiac cell model proposed by Sato et al. (2009) is investigated in terms of periodic and chaotic action potentials, bifurcation scenarios, and coexistence of attractors. Starting from the model's standard parameter values bifurcation diagrams are computed to evaluate the model's robustness with respect to (small) parameter changes. While for some parameters the dynamics turns out to be practically independent from their values, even minor changes of other parameters have a very strong impact and cause qualitative changes due to bifurcations or transitions to coexisting attractors. Implications of this lack of robustness are discussed.
DEFF Research Database (Denmark)
Sørensen, Martin Kryspin; Durck, Tina Trier; Bork, Kristian Hveysel
2016-01-01
normative values for adults and investigates the correlation between these MDVP parameters in relation to the "standardized" trauma of endotracheal intubation. METHODS: Preoperative and postoperative assessments of vocal fold pathology with flexible videolaryngoscopy and voice analysis with MDVP using......PURPOSE: The Multi-Dimensional Voice Program (MDVP) is used for assessment of voice quality. A simple procedure for MDVP recordings was used in a randomized clinical trial (RCT) on induced vocal fold trauma due to intubation. This secondary study compares the common MDVP parameters with other...
Directory of Open Access Journals (Sweden)
Rahmawati Ibrahim
2017-08-01
Full Text Available Coliform bacteria can be used as an indicator of the presence of pathogenic bacteria, such as E.coli bacteria that cause diarrhea. The aimed of this study is to determine the relationship between physical-chemical parameters namely temperature, pH, DO and BOD to the density of coliform bacteria in Jawi River, Pontianak. The sampling was conducted at one point each in the upstream, midstream and downstream area of the Jawi River during two tidal conditions of the Kapuas River in September 2016 at 09:40 (at low tide and at 15:40 (at high tide. The correlation of physical-chemical parameter to coliform value was tested Pearson Product Moment. The results showed that coliform bacterial density increased from upstream to downstream with 150-1500 MPN/100 ml at high tide and 930-11000 MPN/100 ml at low tide. The results showed that the coliform bacterial density value had a positive relation with pH and BOD parameters and negative relation with temperature and DO parameters. So, it can be concluded that there is a correlation between physical parameters, such as temperature and chemical parameters such as pH, DO and BOD to microbiological parameters especially Coliform bacterial density. The benefit of this study is to give information about water quality of Jawi River and its correlation with density of Coliform bacterial, so that people are expected to pay more attention to the use of clean water to avoid the disease caused by coliform.
Using many pilot points and singular value decomposition in groundwater model calibration
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
over the model area. Singular value decomposition (SVD) of the normal matrix is used to reduce the large number of pilot point parameters to a smaller number of so-called super parameters that can be estimated by nonlinear regression from the available observations. A number of eigenvectors...... corresponding to significant Eigen values (resulting from the decomposition) is used to transform the model from having many pilot point parameters to having a few super parameters. A synthetic case model is used to analyze and demonstrate the application of the presented method of model parameterization...
On the modeling of internal parameters in hyperelastic biological materials
Giantesio, Giulia
2016-01-01
This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.
A robust approach for the determination of Gurson model parameters
Directory of Open Access Journals (Sweden)
R. Sepe
2016-07-01
Full Text Available Among the most promising models introduced in recent years, with which it is possible to obtain very useful results for a better understanding of the physical phenomena involved in the macroscopic mechanism of crack propagation, the one proposed by Gurson and Tvergaard links the propagation of a crack to the nucleation, growth and coalescence of micro-voids, which is likely to connect the micromechanical characteristics of the component under examination to crack initiation and propagation up to a macroscopic scale. It must be pointed out that, even if the statistical character of some of the many physical parameters involved in the said model has been put in evidence, no serious attempt has been made insofar to link the corresponding statistic to the experimental and macroscopic results, as for example crack initiation time, material toughness, residual strength of the cracked component (R-Curve, and so on. In this work, such an analysis was carried out in a twofold way: the former concerned the study of the influence exerted by each of the physical parameters on the material toughness, and the latter concerned the use of the Stochastic Design Improvement (SDI technique to perform a “robust” numerical calibration of the model evaluating the nominal values of the physical and correction parameters, which fit a particular experimental result even in the presence of their “natural” variability.
Determining extreme parameter correlation in ground water models
DEFF Research Database (Denmark)
Hill, Mary Cole; Østerby, Ole
2003-01-01
In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation...... correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters...
Yang, Chih-Chung; Bose, N K
2005-05-01
Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.
Radiatively Important Parameters Best Estimate (RIPBE): An ARM Value-Added Product
Energy Technology Data Exchange (ETDEWEB)
McFarlane, S; Shippert, T; Mather, J
2011-06-30
The Radiatively Important Parameters Best Estimate (RIPBE) VAP was developed to create a complete set of clearly identified set of parameters on a uniform vertical and temporal grid to use as input to a radiative transfer model. One of the main drivers for RIPBE was as input to the Broadband Heating Rate Profile (BBHRP) VAP, but we also envision using RIPBE files for user-run radiative transfer codes, as part of cloud/aerosol retrieval testbeds, and as input to averaged datastreams for model evaluation.
The reference values for semen parameters of 1213 fertile men in Guangdong Province in China
Directory of Open Access Journals (Sweden)
Yun-Ge Tang
2015-04-01
Full Text Available Semen samples were collected from 1213 fertile men whose partners had a time-to-pregnancy (TTP ≤12 months in Guangdong Province in Southern China, and semen parameters including semen volume, sperm concentration, total counts, motility, and morphology were evaluated according to the World Health Organization (WHO 2010 guideline. All semen parameters analyzed were normal in ~62.2% of the total samples, whereas ~37.8% showed at least one of the semen parameters below normal threshold values. The fifth centiles (with 95% confidence intervals were 1.3 (1.2-1.5 ml for semen volume, 20 × 10 6 (18×10 6 -20×10 6 ml−1 for sperm concentration, 40 × 10 6 (38×10 6 -44×10 6 per ejaculate for total sperm counts, 48% (47%-53% for vitality, 39% (36%-43% for total motility, 25% (23%-27% for sperm progressive motility, 5.0% (4%-5% for normal morphology. The pH values ranged from 7.2 to 8.0 with the mean ± standard deviation at 7.32 ± 0.17. No effects of age and body mass index were found on semen parameters. Occupation, smoking and alcohol abuse, varicocele appeared to decrease semen quality. Sperm concentration, but not sperm morphology, is positively correlated with TTP, whereas vitality is negatively correlated with TTP. Our study provides the latest reference values for the semen parameters of Chinese fertile men in Guangdong Province, which are close to those described in the new WHO guidelines (5 th Edition.
Multi-parameter models of innovation diffusion on complex networks
McCullen, Nicholas J; Bale, Catherine S E; Foxon, Tim J; Gale, William F
2012-01-01
A model, applicable to a range of innovation diffusion applications with a strong peer to peer component, is developed and studied, along with methods for its investigation and analysis. A particular application is to individual households deciding whether to install an energy efficiency measure in their home. The model represents these individuals as nodes on a network, each with a variable representing their current state of adoption of the innovation. The motivation to adopt is composed of three terms, representing personal preference, an average of each individual's network neighbours' states and a system average, which is a measure of the current social trend. The adoption state of a node changes if a weighted linear combination of these factors exceeds some threshold. Numerical simulations have been carried out, computing the average uptake after a sufficient number of time-steps over many realisations at a range of model parameter values, on various network topologies, including random (Erdos-Renyi), s...
A novel criterion for determination of material model parameters
Andrade-Campos, A.; de-Carvalho, R.; Valente, R. A. F.
2011-05-01
Parameter identification problems have emerged due to the increasing demanding of precision in the numerical results obtained by Finite Element Method (FEM) software. High result precision can only be obtained with confident input data and robust numerical techniques. The determination of parameters should always be performed confronting numerical and experimental results leading to the minimum difference between them. However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data (Lin et al., 2002; Cao and Lin, 2008; among others). The objective functions should be able to efficiently lead the optimisation process. An ideal objective function should have the following properties: (i) all the experimental data points on the curve and all experimental curves should have equal opportunity to be optimised; and (ii) different units and/or the number of curves in each sub-objective should not affect the overall performance of the fitting. These two criteria should be achieved without manually choosing the weighting factors. However, for some non-analytical specific problems, this is very difficult in practice. Null values of experimental or numerical values also turns the task difficult. In this work, a novel objective function for constitutive model parameter identification is presented. It is a generalization of the work of Cao and Lin and it is suitable for all kinds of constitutive models and mechanical tests, including cyclic tests and Baushinger tests with null values.
Model comparisons and genetic and environmental parameter ...
African Journals Online (AJOL)
arc
South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...
2007-01-01
In the present work, we study a multi-point boundary-value problem for an ordinary differential equation with matrix coefficients containing a spectral parameter in the boundary conditions. Assuming some regularity conditions, we show that the characteristic determinant has an infinite number of zeros, and specify their asymptotic behavior. Using the asymptotic behavior of Green matrix on contours expending at infinity, we establish the series expansion formula of sufficiently smooth function...
Bhatia R; Garg R.; Gaur S; Kar A; Shukla R; Agarwal A; Verma R
2004-01-01
OBJECTIVE: This prospective study was planned to study the prognostic value of routine clinical, hematological and biochemical parameters, including platelet aggregation in patients of acute stroke, on fatality occurring during the first 30 days. MATERIAL AND METHODS: In this study 116 consecutive patients (77 males and 39 females) of stroke (within 72 hours of onset) were included. After clinical evaluation and neuroimaging, blood investigations, hemoglobin, total leukocyte count, platelet c...
Positive solutions for a nonlinear periodic boundary-value problem with a parameter
Directory of Open Access Journals (Sweden)
Jingliang Qiu
2012-08-01
Full Text Available Using topological degree theory with a partially ordered structure of space, sufficient conditions for the existence and multiplicity of positive solutions for a second-order nonlinear periodic boundary-value problem are established. Inspired by ideas in Guo and Lakshmikantham [6], we study the dependence of positive periodic solutions as a parameter approaches infinity, $$ lim_{lambdao +infty}|x_{lambda}|=+infty,quadhbox{or}quad lim_{lambdao+infty}|x_{lambda}|=0. $$
Reference Values of Oxidative Stress Parameters in Adult Iranian Fat-Tailed Sheep
Directory of Open Access Journals (Sweden)
S. Nazifi*, N. Ghafari1, F. Farshneshani, M. Rahsepar and S. M. Razavi1
2010-01-01
Full Text Available The present study was performed on 111 adult clinically healthy Iranian fat-tailed sheep from both sexes (28 male and 83 female. Blood concentrations of oxidative stress parameters viz. malondialdehyde (MDA, superoxide dismutase (SOD, catalase (CAT and glutathione peroxidase (GPX were determined. The reference values for oxidative stress parameters of adult clinically healthy Iranian fat-tailed sheep were as followed: MDA 0.53-0.60 mmol/l, SOD 948.65-1011.50 U/gHb, CAT 1834.29-1915.63 U/gHb and GPX 191.67-196.52 U/gHb. There were no significant differences in oxidative stress parameters among animal of the two sexes.
Rezaei, Meisam; Seuntjens, Piet; Shahidi, Reihaneh; Joris, Ingeborg; Boënne, Wesley; Cornelis, Wim
2016-04-01
Soil hydraulic parameters, which can be derived from in situ and/or laboratory experiments, are key input parameters for modeling water flow in the vadose zone. In this study, we measured soil hydraulic properties with typical laboratory measurements and field tension infiltration experiments using Wooding's analytical solution and inverse optimization along the vertical direction within two typical podzol profiles with sand texture in a potato field. The objective was to identify proper sets of hydraulic parameters and to evaluate their relevance on hydrological model performance for irrigation management purposes. Tension disc infiltration experiments were carried out at five different depths for both profiles at consecutive negative pressure heads of 12, 6, 3 and 0.1 cm. At the same locations and depths undisturbed samples were taken to determine the water retention curve with hanging water column and pressure extractors and lab saturated hydraulic conductivity with the constant head method. Both approaches allowed to determine the Mualem-van Genuchten (MVG) hydraulic parameters (residual water content θr, saturated water content θs,, shape parameters α and n, and field or lab saturated hydraulic conductivity Kfs and Kls). Results demonstrated horizontal differences and vertical variability of hydraulic properties. Inverse optimization resulted in excellent matches between observed and fitted infiltration rates in combination with final water content at the end of the experiment, θf, using Hydrus 2D/3D. It also resulted in close correspondence of and Kfs with those from Logsdon and Jaynes' (1993) solution of Wooding's equation. The MVG parameters Kfs and α estimated from the inverse solution (θr set to zero), were relatively similar to values from Wooding's solution which were used as initial value and the estimated θs corresponded to (effective) field saturated water content θf. We found the Gardner parameter αG to be related to the optimized van
Numerical model for thermal parameters in optical materials
Sato, Yoichi; Taira, Takunori
2016-04-01
Thermal parameters of optical materials, such as thermal conductivity, thermal expansion, temperature coefficient of refractive index play a decisive role for the thermal design inside laser cavities. Therefore, numerical value of them with temperature dependence is quite important in order to develop the high intense laser oscillator in which optical materials generate excessive heat across mode volumes both of lasing output and optical pumping. We already proposed a novel model of thermal conductivity in various optical materials. Thermal conductivity is a product of isovolumic specific heat and thermal diffusivity, and independent modeling of these two figures should be required from the viewpoint of a clarification of physical meaning. Our numerical model for thermal conductivity requires one material parameter for specific heat and two parameters for thermal diffusivity in the calculation of each optical material. In this work we report thermal conductivities of various optical materials as Y3Al5O12 (YAG), YVO4 (YVO), GdVO4 (GVO), stoichiometric and congruent LiTaO3, synthetic quartz, YAG ceramics and Y2O3 ceramics. The dependence on Nd3+-doping in laser gain media in YAG, YVO and GVO is also studied. This dependence can be described by only additional three parameters. Temperature dependence of thermal expansion and temperature coefficient of refractive index for YAG, YVO, and GVO: these are also included in this work for convenience. We think our numerical model is quite useful for not only thermal analysis in laser cavities or optical waveguides but also the evaluation of physical properties in various transparent materials.
NEW DOCTORAL DEGREE Parameter estimation problem in the Weibull model
Marković, Darija
2009-01-01
In this dissertation we consider the problem of the existence of best parameters in the Weibull model, one of the most widely used statistical models in reliability theory and life data theory. Particular attention is given to a 3-parameter Weibull model. We have listed some of the many applications of this model. We have described some of the classical methods for estimating parameters of the Weibull model, two graphical methods (Weibull probability plot and hazard plot), and two analyt...
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Coupled 1D-2D hydrodynamic inundation model for sewer overflow: Influence of modeling parameters
Directory of Open Access Journals (Sweden)
Adeniyi Ganiyu Adeogun
2015-10-01
Full Text Available This paper presents outcome of our investigation on the influence of modeling parameters on 1D-2D hydrodynamic inundation model for sewer overflow, developed through coupling of an existing 1D sewer network model (SWMM and 2D inundation model (BREZO. The 1D-2D hydrodynamic model was developed for the purpose of examining flood incidence due to surcharged water on overland surface. The investigation was carried out by performing sensitivity analysis on the developed model. For the sensitivity analysis, modeling parameters, such as mesh resolution Digital Elevation Model (DEM resolution and roughness were considered. The outcome of the study shows the model is sensitive to changes in these parameters. The performance of the model is significantly influenced, by the Manning's friction value, the DEM resolution and the area of the triangular mesh. Also, changes in the aforementioned modeling parameters influence the Flood characteristics, such as the inundation extent, the flow depth and the velocity across the model domain.
The Symmetric Solutions of Affiliated Value Model
Institute of Scientific and Technical Information of China (English)
Che Ka-jia; Li Zhi-chen
2004-01-01
In a symmetric affiliated value model, this paper analyses High-Technology industrial firms' competitive strategy in research and development (R&D). We obtain the symmetric Bayesian Nash Equilibrium functions with or without government's prize:b1(x)=v(x,x)Fn-1(x|x)-∫x0Fn-1(y|y)dv(y,y), b2(x)=∫x0[v(y,y)+v0]dFn-1(y|y), and b3(x)=∫x0v(y,y)(fn-1(y|y))/(1-Fn-1(y|y))dy. We find the firm's investment level will increase in prize, only when the constant prize v0≥v(y,y)(Fn-1(y|y))/(1-Fn-1(y|y)), does the firm invest more aggressively with constant prize than with variable prize.
Issues in Value-at-Risk Modeling and Evaluation
J. Danielsson; C.G. de Vries (Casper); B.N. Jorgensen (Bjørn); P.F. Christoffersen (Peter); F.X. Diebold (Francis); T. Schuermann (Til); J.A. Lopez (Jose); B. Hirtle (Beverly)
1998-01-01
textabstractDiscusses the issues in value-at-risk modeling and evaluation. Value of value at risk; Horizon problems and extreme events in financial risk management; Methods of evaluating value-at-risk estimates.
Bao, Xingxian; Cao, Aixia; Zhang, Jing
2016-07-01
Modal parameters estimation plays an important role for structural health monitoring. Accurately estimating the modal parameters of structures is more challenging as the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of solving the partially described inverse singular value problem (PDISVP) combined with the complex exponential (CE) method to estimate the modal parameters. The PDISVP solving method is to reconstruct an L2-norm optimized (filtered) data matrix from the measured (noisy) data matrix, when the prescribed data constraints are one or several sets of singular triplets of the matrix. The measured data matrix is Hankel structured, which is constructed based on the measured impulse response function (IRF). The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. Once the filtered IRF is obtained, the CE method can be applied to extract the modal parameters. Two physical experiments, including a steel cantilever beam with 10 accelerometers mounted, and a steel plate with 30 accelerometers mounted, excited by an impulsive load, respectively, are investigated to test the applicability of the proposed scheme. In addition, the consistency diagram is proposed to exam the agreement among the modal parameters estimated from those different accelerometers. Results indicate that the PDISVP-CE method can significantly remove noise from measured signals and accurately estimate the modal frequencies and damping ratios.
An improved state-parameter analysis of ecosystem models using data assimilation
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
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen;
2008-01-01
Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D s...
Parameter uncertainty in CGE Modeling of the environmental impacts of economic policies
Energy Technology Data Exchange (ETDEWEB)
Abler, D.G.; Shortle, J.S. [Agricultural Economics, Pennsylvania State University, University Park, PA (United States); Rodriguez, A.G. [University of Costa Rica, San Jose (Costa Rica)
1999-07-01
This study explores the role of parameter uncertainty in Computable General Equilibrium (CGE) modeling of the environmental impacts of macroeconomic and sectoral policies, using Costa Rica as a case for study. A CGE model is constructed which includes eight environmental indicators covering deforestation, pesticides, overfishing, hazardous wastes, inorganic wastes, organic wastes, greenhouse gases, and air pollution. The parameters are treated as random variables drawn from prespecified distributions. Evaluation of each policy option consists of a Monte Carlo experiment. The impacts of the policy options on the environmental indicators are relatively robust to different parameter values, in spite of the wide range of parameter values employed. 33 refs.
Compositional modelling of distributed-parameter systems
Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.
2005-01-01
The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the
Parameter Estimation and Experimental Design in Groundwater Modeling
Institute of Scientific and Technical Information of China (English)
SUN Ne-zheng
2004-01-01
This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.
Directory of Open Access Journals (Sweden)
Slijepčević Dajana
2014-01-01
Full Text Available One of very important prerequisites for achieving good results in races, in addition to genetic predisposition, quality training and good health, are optimal values for number of erythrocytes, concentration of haemoglobin and hematocrit, of which depends efficient oxygen supply of muscles during great efforts. The stated values, along with data on heart rate, are useful indicators of the degree of horse fitness and readiness for horse race. The influence of physical exertion on the values of basic hematological parameters as well as on heart rate, was investigated on 6 trotters, in training at the Belgrade racetrack (one head of Italian trotter, male, 3 years old; 3 heads of American trotter, male, 3,4 and 6 years old and two heads of Serbian trotter, female, 4 and 5 years old. The blood samples for hematological tests were taken by punction of jugular vein in resting phase - immediately before the commencement of work, after light trot warming for 3000 m and fast trot for 1000 m, with 30 minutes rest between the two runnings. The heart rate was monitored continuously by radio telemetry cardiometer, from the moment they were taken from their boxes and harnessing to the completion of work. The obtained results confirm the relationship between the rise of heart rate and hematocrit values: maximal hematocrit values were determined after the first running (0.49±0.05, in regard to 0.42±0.03 in resting phase, but 30 minutes after the second running there was a slight drop of hematocrit values (0.46±0.04. The blood samples in both cases were taken after fast trot during which there were recorded maximal pulse values, so in the moment of sampling the pulse lowered close to the values in resting - after the first running from 192.23±19.66, and after the second from 180.33±17.22 to 40.67±5.76.
Gugjoo, M B; Hoque, M; Saxena, A C; Shamsuz Zama, M M; Dey, S
2014-01-01
Breed-wise standard echocardiographic values in dogs have been reported as there is variation in body and chest conformation which limits the application of data of one breed for other breed. Labrador Retrievers being originated from hunting dogs, might have different echocardiographic values from standard normal range of other dog breeds. So, the present study was aimed to determine the M-mode echocardiographic reference ranges in Labrador Retriever dogs and to evaluate the effect of body weight and gender on these parameters. The data obtained were also compared with that of the other dog breeds. Conscious clinically healthy Labrador Retriever dogs (n=24) of both sexes were made the subject of the study. All the measurements were made from a right parasternal long axis left ventricular outflow tract view and the parameters measured were: left ventricular dimensions, left ventricular function, left ventricular volumes, left atrial and aortic root diameter and mitral valve parameters. Data obtained were also compared with that available for other dog breeds. Significant correlation (P0.5); moderate for LVPWd, LVPWs, EPSS, EF Slope and SV (r=0.3 to 0.5); weak for EDV and ESV (rcardiology.
Laio, Francesco
2004-09-01
The use of goodness of fit tests based on Cramer-von Mises and Anderson-Darling statistics is discussed, with reference to the composite hypothesis that a sample of observations comes from a distribution, FH, whose parameters are unspecified. When this is the case, the critical region of the test has to be redetermined for each hypothetical distribution FH. To avoid this difficulty, a transformation is proposed that produces a new test statistic which is independent of FH. This transformation involves three coefficients that are determined using the asymptotic theory of tests based on the empirical distribution function. A single table of coefficients is thus sufficient for carrying out the test with different hypothetical distributions; a set of probability models of common use in extreme value analysis is considered here, including the following: extreme value 1 and 2, normal and lognormal, generalized extreme value, three-parameter gamma, and log-Pearson type 3, in all cases with parameters estimated using maximum likelihood. Monte Carlo simulations are used to determine small sample corrections and to assess the power of the tests compared to alternative approaches.
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen
2012-01-01
Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more a...
Directory of Open Access Journals (Sweden)
Adrian NAGEL
2013-06-01
Full Text Available Purpose: To establish the reference values and gender differences regarding fitness, strength, and time reaction of the Romanian elite junior tennis players. Material and Methods: Thirty four junior tennis players (19 male, 15 female with a mean age of 15 years were selected from the database of the Cardiopulmonary Effort Testing and Physical Performance Laboratory from Faculty of Physical Education and Sport, West University of Timisoara. To be included in the study, a tennis player had to belong to the Romanian Tennis Federation as a junior, be among the 50 best players in his/her category, and have no pathology at the evaluation moment. All subjects underwent functional evaluation by performing a maximal cardiopulmonary exercise test, the counter movement jump test and complex opto-acoustic reaction test. Results: The functional performance are significantly different for males and females in all parameters studied, with male tennis players showing better performance than female tennis players. Conclusions: The mean values of functional parameters of the Romanian elite junior tennis players are lower than international competitive tennis athletes. There are significant gender differences in junior tennis players. We hope that the present study results provide useful reference values for coaches and physical trainers in order to improve functional performance of their junior tennis players.
Directory of Open Access Journals (Sweden)
Milovanović Branislav
2007-01-01
Full Text Available Introduction: There are different proofs about association of autonomic nervous system dysfunction, especially nonlinear parameters, with higher mortality after myocardial infarction. Objective The objective of the study was to determine predictive value of Poincare plot as nonlinear parameter and other significant standard risk predictors: ejection fraction of the left ventricle, late potentials, ventricular arrhythmias, and QT interval. Method The study included 1081 patients with mean follow up of 28 months (ranging fom 0-80 months. End-point of the study was cardiovascular mortality. The following diagnostic methods were used during the second week: ECG with commercial software Schiller AT-10: short time spectral analysis of RR variability with analysis of Poincare plot as nonlinear parameter and late potentials; 24-hour ambulatory ECG monitoring: QT interval, RR interval, QT/RR slope, ventricular arrhythmias (Lown >II; echocardiography examinations: systolic disorder (defined as EF<40 %. Results There were 103 (9.52% cardiovascular deaths during the follow-up. In univariate analysis, the following parameters were significantly correlated with mortality: mean RR interval < 800 ms, QT and RR interval space relationship as mean RR interval < 800 ms and QT interval > 350 ms, positive late potentials, systolic dysfunction, Poincare plot as a point, ventricular arrhythmias (Lown > II. In multivariate analysis, the significant risk predictors were: Poincare plot as a point and mean RR interval lower than 800 ms. Conclusion Mean RR interval lower than 800 ms and nonlinear and space presentation of RR interval as a point Poincare plot were multivariate risk predictors.
The Advancement Value Chain: An Exploratory Model
Leonard, Edward F., III
2005-01-01
Since the introduction of the value chain concept in 1985, several varying, yet virtually similar, value chains have been developed for the business enterprise. Shifting to higher education, can a value chain be found that links together the various activities of advancement so that an institution's leaders can actually look at the philanthropic…
The Advancement Value Chain: An Exploratory Model
Leonard, Edward F., III
2005-01-01
Since the introduction of the value chain concept in 1985, several varying, yet virtually similar, value chains have been developed for the business enterprise. Shifting to higher education, can a value chain be found that links together the various activities of advancement so that an institution's leaders can actually look at the philanthropic…
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
Multiobjective Automatic Parameter Calibration of a Hydrological Model
Directory of Open Access Journals (Sweden)
Donghwi Jung
2017-03-01
Full Text Available This study proposes variable balancing approaches for the exploration (diversification and exploitation (intensification of the non-dominated sorting genetic algorithm-II (NSGA-II with simulated binary crossover (SBX and polynomial mutation (PM in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization in a benchmark hydrological calibration problem for the Leaf River (1950 km2 near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
Application of Powell's optimization method to surge arrester circuit models' parameters
Energy Technology Data Exchange (ETDEWEB)
Christodoulou, C.A.; Stathopulos, I.A. [National Technical University of Athens, School of Electrical and Computer Engineering, 9 Iroon Politechniou St., Zografou Campus, 157 80 Athens (Greece); Vita, V.; Ekonomou, L.; Chatzarakis, G.E. [A.S.PE.T.E. - School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece)
2010-08-15
Powell's optimization method has been used for the evaluation of the surge arrester models parameters. The proper modelling of metal-oxide surge arresters and the right selection of equivalent circuit parameters are very significant issues, since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters' dynamic behavior. The proposed approach selects optimum arrester model equivalent circuit parameter values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer. Application of the method in performed on a 120 kV metal oxide arrester. The use of the obtained optimum parameter values reduces significantly the relative error between the simulated and manufacturer's peak residual voltage value, presenting the effectiveness of the method. (author)
ANALYSIS OF THE VALUES OF OXIDATIVE STRESS PARAMETERS IN SALIVA OF CHILDREN WITH GINGIVITIS
Directory of Open Access Journals (Sweden)
Olivera Tričković-Janjić
2016-06-01
Full Text Available Early detection and analysis of the values of the parameters of oxidative stress, malondialdehyde (MDA and reactive carbonyl groups (RCG in saliva, as possible biochemical markers in the diagnosis and prognosis of periodontal disease, may be of particular importance in children. For this reason, the aim of this study was to examine the levels of lipoproteins in the saliva of children without gingivitis and with gingivitis, as well as the degree of gingival inflammation. The testing was conducted in 120 children aged 12.2 years, with permanent dentition. Gingival index by Löe-Silness was used for the gingival estimation. A modified method with thiobarbituric acid was used for the determination of MDA in unstimulated saliva. Colorimetric reaction with 2.4 dinitrophenylhydrazine (2.4 DNPH was applied for the determination of RCG. Results of the analysis of the average values of prooxidizer in the saliva of the patients in a study and the control group showed as statistically significantly higher in the patients of the study group, in RCG concentration (UMW=667.5, z=-4.137, p<0.001 as well as in the level of MDA (UMW=452.5, z=-5.44, p<0.001. The results of the analysis of the MDA level showed an increase in average values with increasing degree of gingival inflammation with statistical significance between the groups confirmed by the KruskalWallis test (χ2 KW=32.45, p<0.001 but not by the Mann-Whitney test. Results of the analysis of concentration of carbonyl groups in patients with varying degrees of gingival inflammation showed an increase with statistically significant differences in the values of this parameter among all groups of patients (χ2 KW=45.23, p<0.001 and by the MannWhitney test the highest among the patients with healthy gingiva and patients with severe gingival inflammation (UMW=113.00, z=-4.98, p<0.001. The presence and increase in the parameters of oxidative stress of malondialdehyde and carbonyl groups in the saliva of children
Directory of Open Access Journals (Sweden)
Mohamed Denche
2007-01-01
Full Text Available In the present work, we study a multi-point boundary-value problem for an ordinary differential equation with matrix coefficients containing a spectral parameter in the boundary conditions. Assuming some regularity conditions, we show that the characteristic determinant has an infinite number of zeros, and specify their asymptotic behavior. Using the asymptotic behavior of Green matrix on contours expending at infinity, we establish the series expansion formula of sufficiently smooth functions in terms of residuals solutions to the given problem. This formula actually gives the completeness of root functions as well as the possibility of calculating the coefficients of the series.
Parameter redundancy in discrete state‐space and integrated models
McCrea, Rachel S.
2016-01-01
Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. PMID:27362826
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Ternary interaction parameters in calphad solution models
Energy Technology Data Exchange (ETDEWEB)
Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering
2014-07-01
For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)
Modelling of bio-optical parameters of open ocean waters
Directory of Open Access Journals (Sweden)
Vadim N. Pelevin
2001-12-01
Full Text Available An original method for estimating the concentration of chlorophyll pigments, absorption of yellow substance and absorption of suspended matter without pigments and yellow substance in detritus using spectral diffuse attenuation coefficient for downwelling irradiance and irradiance reflectance data has been applied to sea waters of different types in the open ocean (case 1. Using the effective numerical single parameter classification with the water type optical index m as a parameter over the whole range of the open ocean waters, the calculations have been carried out and the light absorption spectra of sea waters tabulated. These spectra are used to optimize the absorption models and thus to estimate the concentrations of the main admixtures in sea water. The value of m can be determined from direct measurements of the downward irradiance attenuation coefficient at 500 nm or calculated from remote sensing data using the regressions given in the article. The sea water composition can then be readily estimated from the tables given for any open ocean area if that one parameter m characterizing the basin is known.
Modeling Source Water Threshold Exceedances with Extreme Value Theory
Rajagopalan, B.; Samson, C.; Summers, R. S.
2016-12-01
Variability in surface water quality, influenced by seasonal and long-term climate changes, can impact drinking water quality and treatment. In particular, temperature and precipitation can impact surface water quality directly or through their influence on streamflow and dilution capacity. Furthermore, they also impact land surface factors, such as soil moisture and vegetation, which can in turn affect surface water quality, in particular, levels of organic matter in surface waters which are of concern. All of these will be exacerbated by anthropogenic climate change. While some source water quality parameters, particularly Total Organic Carbon (TOC) and bromide concentrations, are not directly regulated for drinking water, these parameters are precursors to the formation of disinfection byproducts (DBPs), which are regulated in drinking water distribution systems. These DBPs form when a disinfectant, added to the water to protect public health against microbial pathogens, most commonly chlorine, reacts with dissolved organic matter (DOM), measured as TOC or dissolved organic carbon (DOC), and inorganic precursor materials, such as bromide. Therefore, understanding and modeling the extremes of TOC and Bromide concentrations is of critical interest for drinking water utilities. In this study we develop nonstationary extreme value analysis models for threshold exceedances of source water quality parameters, specifically TOC and bromide concentrations. In this, the threshold exceedances are modeled as Generalized Pareto Distribution (GPD) whose parameters vary as a function of climate and land surface variables - thus, enabling to capture the temporal nonstationarity. We apply these to model threshold exceedance of source water TOC and bromide concentrations at two locations with different climate and find very good performance.
Prediction of Liquid-Liquid Equilibrium Using the Group Solubility Parameter Model
Institute of Scientific and Technical Information of China (English)
ZHAO Mo; CHEN Fuming
2005-01-01
The group solubility parameter (GSP) model was used to analyze the liquid-liquid equilibrium (LLE) of ternary and quaternary systems. The GSP parameters are divided into four dimensions representing the four major intermolecular forces. The values of the parameters were determined by regression using the nonlinear SIMPLEX optimization method to fit the LLE data of 548 ternary and 26 quaternary systems selected from the literature. LLE predictions of 8 ternary systems were then made using the fit parameters. Comparison of the results with predictions using the modified UNIFAC model shows that the GSP model has less adjustable parameters to achieve a similar accuracy and that the parameter values are easily acquired by analysis of available data.
Directory of Open Access Journals (Sweden)
Bhatia R
2004-04-01
Full Text Available OBJECTIVE: This prospective study was planned to study the prognostic value of routine clinical, hematological and biochemical parameters, including platelet aggregation in patients of acute stroke, on fatality occurring during the first 30 days. MATERIAL AND METHODS: In this study 116 consecutive patients (77 males and 39 females of stroke (within 72 hours of onset were included. After clinical evaluation and neuroimaging, blood investigations, hemoglobin, total leukocyte count, platelet count, platelet aggregation, erythrocyte sedimentation rate (ESR, blood sugar, urea, creatinine, sodium, potassium, serum cholesterol, serum bilirubin, aspartate aminotransferase (SGOT, alanine aminotransferase (SGPT, albumin, and globulin estimations were performed. The patients were followed up for a maximum period of 30 days from the onset of stroke, and patients who expired were grouped as ′expired′ and the rest as ′survivors′. Logistic regression analysis was carried out among the significant parameters to identify independent predictors of 30-day fatality. RESULTS: Univariate analysis demonstrated that among hematological parameters, high total leukocyte count and ESR, at admission, correlated significantly with an undesirable outcome during the initial 30 days. Among biochemical parameters, elevated urea, creatinine, serum transaminases (SGOT and SGPT and globulin levels correlated significantly with death. Logistic regression analysis demonstrated that a low Glasgow Coma Scale (GCS score along with biochemical parameters such as high serum creatinine, SGPT, ESR and total leukocyte count correlated with death. CONCLUSION: Impaired consciousness, high total leukocyte count, raised ESR, elevated creatinine and SGPT levels, estimated within 24 hours of hospitalization, are the most important indicators of 30-day mortality in patients with first-time ischemic stroke.
One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values
Jin Xiao; Bing Zhu; Geer Teng; Changzheng He; Dunhu Liu
2014-01-01
Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classif...
Kuznetsov, I A; Kuznetsov, A V
2017-05-01
In this paper, we first develop a model of axonal transport of tubulin-associated unit (tau) protein. We determine the minimum number of parameters necessary to reproduce published experimental results, reducing the number of parameters from 18 in the full model to eight in the simplified model. We then address the following questions: Is it possible to estimate parameter values for this model using the very limited amount of published experimental data? Furthermore, is it possible to estimate confidence intervals for the determined parameters? The idea that is explored in this paper is based on using bootstrapping. Model parameters were estimated by minimizing the objective function that simulates the discrepancy between the model predictions and experimental data. Residuals were then identified by calculating the differences between the experimental data and model predictions. New, surrogate 'experimental' data were generated by randomly resampling residuals. By finding sets of best-fit parameters for a large number of surrogate data the histograms for the model parameters were produced. These histograms were then used to estimate confidence intervals for the model parameters, by using the percentile bootstrap. Once the model was calibrated, we applied it to analysing some features of tau transport that are not accessible to current experimental techniques.
Olondo, C; Legarda, F; Herranz, M; Idoeta, R
2017-01-05
This paper shows the procedure performed to validate the migration equation and the migration parameters' values presented in a previous paper (Legarda et al., 2011) regarding the migration of (137)Cs in Spanish mainland soils. In this paper, this model validation has been carried out checking experimentally obtained activity concentration values against those predicted by the model. This experimental data come from the measured vertical activity profiles of 8 new sampling points which are located in northern Spain. Before testing predicted values of the model, the uncertainty of those values has been assessed with the appropriate uncertainty analysis. Once establishing the uncertainty of the model, both activity concentration values, experimental versus model predicted ones, have been compared. Model validation has been performed analyzing its accuracy, studying it as a whole and also at different depth intervals. As a result, this model has been validated as a tool to predict (137)Cs behaviour in a Mediterranean environment.
Application of a free parameter model to plastic scintillation samples
Energy Technology Data Exchange (ETDEWEB)
Tarancon Sanz, Alex, E-mail: alex.tarancon@ub.edu [Departament de Quimica Analitica, Universitat de Barcelona, Diagonal 647, E-08028 Barcelona (Spain); Kossert, Karsten, E-mail: Karsten.Kossert@ptb.de [Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig (Germany)
2011-08-21
In liquid scintillation (LS) counting, the CIEMAT/NIST efficiency tracing method and the triple-to-double coincidence ratio (TDCR) method have proved their worth for reliable activity measurements of a number of radionuclides. In this paper, an extended approach to apply a free-parameter model to samples containing a mixture of solid plastic scintillation microspheres and radioactive aqueous solutions is presented. Several beta-emitting radionuclides were measured in a TDCR system at PTB. For the application of the free parameter model, the energy loss in the aqueous phase must be taken into account, since this portion of the particle energy does not contribute to the creation of scintillation light. The energy deposit in the aqueous phase is determined by means of Monte Carlo calculations applying the PENELOPE software package. To this end, great efforts were made to model the geometry of the samples. Finally, a new geometry parameter was defined, which was determined by means of a tracer radionuclide with known activity. This makes the analysis of experimental TDCR data of other radionuclides possible. The deviations between the determined activity concentrations and reference values were found to be lower than 3%. The outcome of this research work is also important for a better understanding of liquid scintillation counting. In particular the influence of (inverse) micelles, i.e. the aqueous spaces embedded in the organic scintillation cocktail, can be investigated. The new approach makes clear that it is important to take the energy loss in the aqueous phase into account. In particular for radionuclides emitting low-energy electrons (e.g. M-Auger electrons from {sup 125}I), this effect can be very important.
Directory of Open Access Journals (Sweden)
Jonathan R Karr
2015-05-01
Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.
Parameter estimation of hydrologic models using data assimilation
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
GIS-Based Hydrogeological-Parameter Modeling
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A regression model is proposed to relate the variation of water well depth with topographic properties (area and slope), the variation of hydraulic conductivity and vertical decay factor. The implementation of this model in GIS environment (ARC/TNFO) based on known water data and DEM is used to estimate the variation of hydraulic conductivity and decay factor of different lithoiogy units in watershed context.
DEFF Research Database (Denmark)
Pauwels, Valentijn; Balenzano, Anna; Satalino, Giuseppe
2009-01-01
It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has...... been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such its, for example, hydraulic conductivity, values, through remote sensing. This is due to the fact...... that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through...
Parameter estimation and sensitivity analysis for a mathematical model with time delays of leukemia
Cândea, Doina; Halanay, Andrei; Rǎdulescu, Rodica; Tǎlmaci, Rodica
2017-01-01
We consider a system of nonlinear delay differential equations that describes the interaction between three competing cell populations: healthy, leukemic and anti-leukemia T cells involved in Chronic Myeloid Leukemia (CML) under treatment with Imatinib. The aim of this work is to establish which model parameters are the most important in the success or failure of leukemia remission under treatment using a sensitivity analysis of the model parameters. For the most significant parameters of the model which affect the evolution of CML disease during Imatinib treatment we try to estimate the realistic values using some experimental data. For these parameters, steady states are calculated and their stability is analyzed and biologically interpreted.
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens;
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...
Mirror symmetry for two parameter models, 2
Candelas, Philip; Katz, S; Morrison, Douglas Robert Ogston; Philip Candelas; Anamaria Font; Sheldon Katz; David R Morrison
1994-01-01
We describe in detail the space of the two K\\"ahler parameters of the Calabi--Yau manifold \\P_4^{(1,1,1,6,9)}[18] by exploiting mirror symmetry. The large complex structure limit of the mirror, which corresponds to the classical large radius limit, is found by studying the monodromy of the periods about the discriminant locus, the boundary of the moduli space corresponding to singular Calabi--Yau manifolds. A symplectic basis of periods is found and the action of the Sp(6,\\Z) generators of the modular group is determined. From the mirror map we compute the instanton expansion of the Yukawa couplings and the generalized N=2 index, arriving at the numbers of instantons of genus zero and genus one of each degree. We also investigate an SL(2,\\Z) symmetry that acts on a boundary of the moduli space.
Relevant parameters in models of cell division control
Grilli, Jacopo; Osella, Matteo; Kennard, Andrew S.; Lagomarsino, Marco Cosentino
2017-03-01
A recent burst of dynamic single-cell data makes it possible to characterize the stochastic dynamics of cell division control in bacteria. Different models were used to propose specific mechanisms, but the links between them are poorly explored. The lack of comparative studies makes it difficult to appreciate how well any particular mechanism is supported by the data. Here, we describe a simple and generic framework in which two common formalisms can be used interchangeably: (i) a continuous-time division process described by a hazard function and (ii) a discrete-time equation describing cell size across generations (where the unit of time is a cell cycle). In our framework, this second process is a discrete-time Langevin equation with simple physical analogues. By perturbative expansion around the mean initial size (or interdivision time), we show how this framework describes a wide range of division control mechanisms, including combinations of time and size control, as well as the constant added size mechanism recently found to capture several aspects of the cell division behavior of different bacteria. As we show by analytical estimates and numerical simulations, the available data are described precisely by the first-order approximation of this expansion, i.e., by a "linear response" regime for the correction of size fluctuations. Hence, a single dimensionless parameter defines the strength and action of the division control against cell-to-cell variability (quantified by a single "noise" parameter). However, the same strength of linear response may emerge from several mechanisms, which are distinguished only by higher-order terms in the perturbative expansion. Our analytical estimate of the sample size needed to distinguish between second-order effects shows that this value is close to but larger than the values of the current datasets. These results provide a unified framework for future studies and clarify the relevant parameters at play in the control of
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
Pantoja, T M A; Da Rosas, F C W; Dos Silva, V M F; Santos, A M F
2010-08-01
The Amazonian manatee, Trichechus inunguis (Natterer 1883) is endemic to the Amazon Basin and is currently considered a vulnerable species. In order to establish normality ranges of urinary parameters to help monitor the health of this species in captivity, chemical urinalyses were performed on twelve males and nine females of various age groups. Urine was collected once a month for twelve months in the tanks just after being drained, by placing stainless steel containers under the genital slit of females and applying abdominal massages to males in order to stimulate urination. Quantitative data of glucose, urea, creatinine, uric acid and amylase levels were obtained using colorimetric spectrophotometry. Dip strips were also useful for routine analyses, despite only providing qualitative results. Normal range to glucose levels, regardless of sex or age class, was 3.0 to 3.6 mgxdL-1, coinciding with qualitative values of glucose measured by dip strips. Statistical differences observed in some parameter levels suggest that some urine parameters analysed must take into consideration the sex and the age class of the animal studied, being these differences less remarkable in creatinine and amylase levels. To this last one, statistical difference was detected only in the calve's urine (7.0 to 11.5 mgxdL-1) compared to other age classes samples (4.1 to 5.3 mgxdL-1). The results presented here may be used as comparative data in future research on urinalysis in related species.
Directory of Open Access Journals (Sweden)
TMA. Pantoja
Full Text Available The Amazonian manatee, Trichechus inunguis (Natterer 1883 is endemic to the Amazon Basin and is currently considered a vulnerable species. In order to establish normality ranges of urinary parameters to help monitor the health of this species in captivity, chemical urinalyses were performed on twelve males and nine females of various age groups. Urine was collected once a month for twelve months in the tanks just after being drained, by placing stainless steel containers under the genital slit of females and applying abdominal massages to males in order to stimulate urination. Quantitative data of glucose, urea, creatinine, uric acid and amylase levels were obtained using colorimetric spectrophotometry. Dip strips were also useful for routine analyses, despite only providing qualitative results. Normal range to glucose levels, regardless of sex or age class, was 3.0 to 3.6 mg.dL-1, coinciding with qualitative values of glucose measured by dip strips. Statistical differences observed in some parameter levels suggest that some urine parameters analysed must take into consideration the sex and the age class of the animal studied, being these differences less remarkable in creatinine and amylase levels. To this last one, statistical difference was detected only in the calve's urine (7.0 to 11.5 mg.dL-1 compared to other age classes samples (4.1 to 5.3 mg.dL-1. The results presented here may be used as comparative data in future research on urinalysis in related species.
Energy Technology Data Exchange (ETDEWEB)
Goh, Vicky [Mount Vernon Hospital, Paul Strickland Scanner Centre, Northwood, Middlesex (United Kingdom); Shastry, Manu; Endozo, Raymondo; Groves, Ashley M. [University College Hospital, Institute of Nuclear Medicine, London (United Kingdom); Engledow, Alec; Peck, Jacqui [University College Hospital, Department of Surgery, London (United Kingdom); Reston, Jonathan; Wellsted, David M. [University of Hertfordshire, Centre for Lifespan and Chronic Illness Research (CLiCIR), Hatfield (United Kingdom); Rodriguez-Justo, Manuel [University College Hospital, Department of Histopathology, London (United Kingdom); Taylor, Stuart A.; Halligan, Steve [University College Hospital, Specialist Radiology, London (United Kingdom)
2011-04-15
To determine how commercial software platform upgrades impact on derived parameters for colorectal cancer. Following ethical approval, 30 patients with suspected colorectal cancer underwent Perfusion CT using integrated 64 detector PET/CT before surgery. Analysis was performed using software based on modified distributed parameter analysis (Perfusion software version 4; Perfusion 4.0), then repeated using the previous version (Perfusion software version 3; Perfusion 3.0). Tumour blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface area product (PS) were determined for identical regions-of-interest. Slice-by-slice and 'whole tumour' variance was assessed by Bland-Altman analysis. Mean BF, BV and PS was 20.4%, 59.5%, and 106% higher, and MTT 14.3% shorter for Perfusion 4.0 than Perfusion 3.0. The mean difference (95% limits of agreement) were +13.5 (-44.9 to 72.0), +2.61 (-0.06 to 5.28), -1.23 (-6.83 to 4.36), and +14.2 (-4.43 to 32.8) for BF, BV, MTT and PS respectively. Within subject coefficient of variation was 36.6%, 38.0%, 27.4% and 60.6% for BF, BV, MTT and PS respectively indicating moderate to poor agreement. Software version upgrades of the same software platform may result in significantly different parameter values, requiring adjustments for cross-version comparison. (orig.)
On linear models and parameter identifiability in experimental biological systems.
Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A
2014-10-07
A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
Romanuke Vadim V.
2016-01-01
Adjustment of an unknown parameter of the multistage expert procedure is considered. The lower and upper boundaries of the parameter are counted to be known. A key condition showing that experts’ estimations are satisfactory in the current procedure is an inequality, in which the value based on the estimations is not greater than the parameter. The algorithms of hard and soft adjusting are developed. If the inequality is true and its both terms are too close for a long sequence of expert proc...
Ward, Adam S.; Kelleher, Christa A.; Mason, Seth J. K.; Wagener, Thorsten; McIntyre, Neil; McGlynn, Brian L.; Runkel, Robert L.; Payn, Robert A.
2017-01-01
Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
CHAMP: Changepoint Detection Using Approximate Model Parameters
2014-06-01
positions as a Markov chain in which the transition probabilities are defined by the time since the last changepoint: p(τi+1 = t|τi = s) = g(t− s), (1...experimentally verified using artifi- cially generated data and are compared to those of Fearnhead and Liu [5]. 2 Related work Hidden Markov Models (HMMs) are...length α, and maximum number of particles M . Output: Viterbi path of changepoint times and models // Initialize data structures 1: max path, prev queue
Influence of cone beam CT scanning parameters on grey value measurements at an implant site.
Parsa, A; Ibrahim, N; Hassan, B; Motroni, A; van der Stelt, P; Wismeijer, D
2013-01-01
The aim of this study was to determine the grey value variation at the implant site with different scan settings, including field of view (FOV), spatial resolution, number of projections, exposure time and dose selections in two cone beam CT (CBCT) systems and to compare the results with those obtained from a multislice CT system. A partially edentulous human mandibular cadaver was scanned by three CT modalities: multislice CT (MSCT) (Philips, Best, the Netherlands), and two CBCT systems: (Accuitomo 170(®), Morita, Japan) and (NewTom 5G(®), QR, Verona, Italy). Using different scan settings 36 and 24 scans were obtained from the Accuitomo and the NewTom, respectively. The scans were converted to digital imaging and communications in medicine 3 format. The analysis of the data was performed using 3Diagnosys(®) software (v. 3.1, 3diemme, Cantù, Italy) and Geomagic studio(®) 2012 (Morrisville, NC). On the MSCT scan, one probe designating the site for pre-operative implant placement was inserted. The inserted probe on MSCT was transformed to the same region on each CBCT scan using a volume-based three-dimensional registration algorithm. The mean voxel grey value of the region around the probe was derived separately for each CBCT. The influence of scanning parameters on the measured mean voxel grey values was assessed. Grey values in both CBCT systems significantly deviated from Hounsfield unit values measured with MSCT (p = 0.0001). In both CBCT systems, scan FOV and spatial resolution selections had a statistically significant influence on grey value measurements (p = 0.0001). The number of projections selection had a statistically significant influence in the Accuitomo system (p = 0.0001) while exposure time and dose selections had no statistically significant influence on grey value measurements in the NewTom (p = 0.43 and p = 0.37, respectively). Grey-level values from CBCT images are influenced by device and scanning settings.
Regional environmental assessments with process-based models require realistic estimates of plant parameters for the primary plant functional groups in the region. “Functional group” in this context is an operational term, based on similarities in plant type and in plant parameter values. Likewise...
The role of non-epistemic values in engineering models.
Diekmann, Sven; Peterson, Martin
2013-03-01
We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only "secondary values" that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that models are neither value-free, nor depend exclusively on epistemic values or use non-epistemic values as tie-breakers.
Meissler Michael; Fischer Axel; Fehrenberg Claudia; Grosse-Siestrup Christian; Unger Volker; Groneberg David A
2007-01-01
Abstract Background Models of isolated and perfused kidneys are used to study the effects of drugs, hazardous or toxic substances on renal functions. Since physiological and morphological parameters of small laboratory animal kidneys are difficult to compare to human renal parameters, porcine kidney perfusion models have been developed to simulate closer conditions to the human situation, but exact values of renal parameters for different collection and perfusion conditions have not been repo...
WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...
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[3, 9]. However, mainly due to the simplicity of Winkler's model in practical applications and .... this case, the coefficient B takes the dimension of a ... In plane-strain problems, the assumption of ... loaded circular region; s is the radial coordinate.
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Ali, Nur Soffiah Sahubar; Ramli, Ahmad; Rahman, Nuzlinda Abdul
2017-08-01
In Radial Basis Function (RBF), user can define a parameter value,c which adjust the shape and smoothness of a data fitting. A larger parameter values increases the training error and may lead to unpleasant result. Therefore, relying on training error and visual evaluation may be difficult in some cases. Bootstrap estimation method on 2 dimensional data is applied for RBF. Simulated data points are generated with some added noise. In this paper, training error and bootstrap error for various parameter values on selected data is evaluated. Using different parameter value, we compute bootstrap error for the RBF fitting. Bootstrap error gives the optimum parameter value which produces the best fit for a specific data set.
On retrial queueing model with fuzzy parameters
Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng
2007-01-01
This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.
Solar parameters for modeling interplanetary background
Bzowski, M; Tokumaru, M; Fujiki, K; Quemerais, E; Lallement, R; Ferron, S; Bochsler, P; McComas, D J
2011-01-01
The goal of the Fully Online Datacenter of Ultraviolet Emissions (FONDUE) Working Team of the International Space Science Institute in Bern, Switzerland, was to establish a common calibration of various UV and EUV heliospheric observations, both spectroscopic and photometric. Realization of this goal required an up-to-date model of spatial distribution of neutral interstellar hydrogen in the heliosphere, and to that end, a credible model of the radiation pressure and ionization processes was needed. This chapter describes the solar factors shaping the distribution of neutral interstellar H in the heliosphere. Presented are the solar Lyman-alpha flux and the solar Lyman-alpha resonant radiation pressure force acting on neutral H atoms in the heliosphere, solar EUV radiation and the photoionization of heliospheric hydrogen, and their evolution in time and the still hypothetical variation with heliolatitude. Further, solar wind and its evolution with solar activity is presented in the context of the charge excha...
Uncertainty in the relationship between flow and parameters in models of pollutant transport
Romanowicz, R.; Osuch, M.; Wallis, S.; Napiórkowski, J. J.
2009-04-01
Fluorescent dye-tracer studies are usually performed under steady-state flow conditions. However, the model parameters, estimated using the tracer data, depend on the discharges. This paper investigates uncertainties in the relationship between discharges and parameters of a transient storage (TS) and an aggregated dead zone (ADZ) models. We apply a Bayesian statistical approach to derive the cumulative distribution of a range of model parameters conditioned on discharges. The data consist of eighteen tracer concentration profiles taken at different flow values at two cross-sections from the Murray Burn, a stream flowing through the Heriot-Watt University Campus at Riccarton in Edinburgh, Scotland. A number of studies have been reported of the dependence of TS and ADZ model parameters on discharge but there are very few studies on the uncertainty related to that parameterization, which is the aim of this work. As the TS model is purely deterministic and the ADZ model is stochastic, different approaches are required to estimate the uncertainty in the dependence of their parameters on flow. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is suitable for the deterministic models and is therefore applied to the TS model. The method applies Monte Carlo sampling of parameter space used in multiple simulations of a deterministic transient storage model. The relationship between model parameters and flow has the form of a nonlinear regression model based on multiple random realizations of the deterministic transport model. The parameterization of that relationship and its introduction into the TS model allow for the conditioning of parameter estimates and as a result, also model predictions on the whole set of available observations. In the case of the ADZ model, the approach is based on Monte Carlo sampling of ADZ model parameters, taking into account heteroscedastic variance of the observations and estimates of the covariance of the model parameters
Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models
Hori, Kentaro
2013-01-01
We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...
Standard model parameters and the search for new physics
Energy Technology Data Exchange (ETDEWEB)
Marciano, W.J.
1988-04-01
In these lectures, my aim is to present an up-to-date status report on the standard model and some key tests of electroweak unification. Within that context, I also discuss how and where hints of new physics may emerge. To accomplish those goals, I have organized my presentation as follows: I discuss the standard model parameters with particular emphasis on the gauge coupling constants and vector boson masses. Examples of new physics appendages are also briefly commented on. In addition, because these lectures are intended for students and thus somewhat pedagogical, I have included an appendix on dimensional regularization and a simple computational example that employs that technique. Next, I focus on weak charged current phenomenology. Precision tests of the standard model are described and up-to-date values for the Cabibbo-Kobayashi-Maskawa (CKM) mixing matrix parameters are presented. Constraints implied by those tests for a 4th generation, supersymmetry, extra Z/prime/ bosons, and compositeness are also discussed. I discuss weak neutral current phenomenology and the extraction of sin/sup 2/ /theta//sub W/ from experiment. The results presented there are based on a recently completed global analysis of all existing data. I have chosen to concentrate that discussion on radiative corrections, the effect of a heavy top quark mass, and implications for grand unified theories (GUTS). The potential for further experimental progress is also commented on. I depart from the narrowest version of the standard model and discuss effects of neutrino masses and mixings. I have chosen to concentrate on oscillations, the Mikheyev-Smirnov- Wolfenstein (MSW) effect, and electromagnetic properties of neutrinos. On the latter topic, I will describe some recent work on resonant spin-flavor precession. Finally, I conclude with a prospectus on hopes for the future. 76 refs.
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; ten Brummelhuis, P.G.J.; ten Brummelhuis, Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The
Exploring the interdependencies between parameters in a material model.
Energy Technology Data Exchange (ETDEWEB)
Silling, Stewart Andrew; Fermen-Coker, Muge
2014-01-01
A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.
An Alternative Three-Parameter Logistic Item Response Model.
Pashley, Peter J.
Birnbaum's three-parameter logistic function has become a common basis for item response theory modeling, especially within situations where significant guessing behavior is evident. This model is formed through a linear transformation of the two-parameter logistic function in order to facilitate a lower asymptote. This paper discusses an…
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; Brummelhuis, ten Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperboli
A compact cyclic plasticity model with parameter evolution
DEFF Research Database (Denmark)
Krenk, Steen; Tidemann, L.
2017-01-01
, and it is demonstrated that this simple formulation enables very accurate representation of experimental results. An extension of the theory to account for model parameter evolution effects, e.g. in the form of changing yield level, is included in the form of extended evolution equations for the model parameters...
Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli
2013-01-01
Background: Infertile women might get pregnant sometime after fertility treatment, but today, there is no prediction model on who will eventually have children. The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value for long-term possibility of live birth. Methods: In this cross-sectional study, with 71 infertile women with diagnosis unexplained infe...
Mean-value second-order uncertainty analysis method: application to water quality modelling
Mailhot, Alain; Villeneuve, Jean-Pierre
Uncertainty analysis in hydrology and water quality modelling is an important issue. Various methods have been proposed to estimate uncertainties on model results based on given uncertainties on model parameters. Among these methods, the mean-value first-order second-moment (MFOSM) method and the advanced mean-value first-order second-moment (AFOSM) method are the most common ones. This paper presents a method based on a second-order approximation of a model output function. The application of this method requires the estimation of first- and second-order derivatives at a mean-value point in the parameter space. Application to a Streeter-Phelps prototype model is presented. Uncertainties on two and six parameters are considered. Exceedance probabilities (EP) of dissolved oxygen concentrations are obtained and compared with EP computed using Monte Carlo, AFOSM and MFOSM methods. These results show that the mean-value second-order method leads to better estimates of EP.
State and Parameter Estimation for a Coupled Ocean--Atmosphere Model
Ghil, M.; Kondrashov, D.; Sun, C.
2006-12-01
The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.
Directory of Open Access Journals (Sweden)
Sylvia Chan-Olmsted
2016-07-01
Full Text Available This study models the dynamic nature of today’s media markets using the framework of value-adding activities in the provision and consumption of media products. The proposed user-centric approach introduces the notion that the actions of external users, social media, and interfaces affect the internal value activities of media firms via a feedback loop, and therefore should themselves be considered value activities. The model also suggests a more comprehensive list of indicators for value assessment.
[Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms].
Chen, Ying; Hu, Xuyi; Zhu, Qiguang
2015-12-01
Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.
Automatic parameter extraction techniques in IC-CAP for a compact double gate MOSFET model
Darbandy, Ghader; Gneiting, Thomas; Alius, Heidrun; Alvarado, Joaquín; Cerdeira, Antonio; Iñiguez, Benjamin
2013-05-01
In this paper, automatic parameter extraction techniques of Agilent's IC-CAP modeling package are presented to extract our explicit compact model parameters. This model is developed based on a surface potential model and coded in Verilog-A. The model has been adapted to Trigate MOSFETs, includes short channel effects (SCEs) and allows accurate simulations of the device characteristics. The parameter extraction routines provide an effective way to extract the model parameters. The techniques minimize the discrepancy and error between the simulation results and the available experimental data for more accurate parameter values and reliable circuit simulation. Behavior of the second derivative of the drain current is also verified and proves to be accurate and continuous through the different operating regimes. The results show good agreement with measured transistor characteristics under different conditions and through all operating regimes.
Bayesian estimation of parameters in a regional hydrological model
Directory of Open Access Journals (Sweden)
K. Engeland
2002-01-01
Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis
Mean Value Modelling of Turbocharged SI Engines
DEFF Research Database (Denmark)
Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.
1998-01-01
The development of a computer simulation to predict the performance of a turbocharged spark ignition engine during transient operation. New models have been developed for the turbocharged and the intercooling system. An adiabatic model for the intake manifold is presented....
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Bayesian parameter inference and model selection by population annealing in systems biology.
Murakami, Yohei
2014-01-01
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.
When the optimal is not the best: parameter estimation in complex biological models.
Directory of Open Access Journals (Sweden)
Diego Fernández Slezak
Full Text Available BACKGROUND: The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values. RESULTS: We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. CONCLUSIONS: The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally.
Chirality of tensor perturbations for complex values of the Immirzi parameter
Bethke, Laura
2011-01-01
In this paper we generalise previous work on tensor perturbations in a de Sitter background in terms of Ashtekar variables to cover all complex values of the Immirzi parameter gamma (previous work was restricted to imaginary gamma). Particular attention is paid to the case of real gamma. Following the same approach as in the imaginary case, we can obtain physical graviton states by invoking reality and torsion free conditions. The Hamiltonian in terms of graviton states has the same form whether gamma has a real part or not; however changes occur for the vacuum energy and fluctuations. Specifically, we observe a gamma dependent chiral asymmetry in the vacuum fluctuations only if gamma has an imaginary part. Ordering prescriptions also change this asymmetry. We thus present a measurable result for CMB polarisation experiments that could shed light on the workings of quantum gravity.
Cognitive and value parameters of students’ perceptions of the effects of psychoactive substances
Directory of Open Access Journals (Sweden)
Dontsov, Aleksander I.
2016-09-01
Full Text Available This article sets forth the main results of a study analyzing attitudes toward psychoactivesubstance (PAS effects. These findings demonstrate the conditionality of social, historic, and cultural views of PAS effects. Despite the threat posed by increasing high school and university students’ drug involvement, exploration of this phenomenon in the format of scientific discourse has been limited so far. In 2014–2015, in Yekaterinburg, Moscow, and Krasnoyarsk a survey to evaluate perceptions of high school and university students about PAS effects was conducted (289 respondents, aged 16–22.The methods used included the semantic differential (Peabody Picture Vocabulary Test modified by A.G. Shmelyov, a modified version of the Rokeach Value Survey, word associations, and content analysis. The use of psychoactive substances is a specific social practice emerging in a certain social context that includes both drug-addicted and PAS-free young people. Examination of the factors affecting the formation of views about PAS effects and the dynamics of youth values is possible by using a bio-psycho-socio paradigm for performing a complex analysis of cognitive, behavioral, and value parameters. As documented in the respondents’ perceptions, distinctive features that are characteristic of drug addicts and that are seen in their behavior area loss of control over behavior, emotions, and volition; changes in value systems; and a tendency to develop a manipulative communication style. Within the system of their social perceptions the respondents endowed drug-addicted persons with pronounced negative characteristics (“aggressiveness,” “addiction,” “stupidity,” “light-mindedness”. Still, they stated that drug abusers are capable of being active, decisive, cheerful, generous, and flexible. The value analysis demonstrated that terminal values appreciated by the school and university students included health, true friends, love, happy family
Directory of Open Access Journals (Sweden)
Xiao-mei LIU
2014-08-01
Full Text Available Objective To explore the value of stroke volume variation (SVV, pulse pressure variation (PPV and pleth variability index (PVI in evaluating the volume status under pneumoperitoneum and their correlation. Methods Twenty-six patients with stomach cancer receiving laparoscopy-assisted radical gastrectomy under general anesthesia were involved in this study. The indexes of SVV, PPV and PVI were monitored continuously after general anesthesia. A volume loading test was performed after pneumoperitoneum, and the hemodynamic parameters were determined before and after fluid infusion (6% HES, 7ml/kg, and the patients were divided into response group (n=16 and non-response group (n=10 according to whether the increase in stroke volume index (ΔSVI was ≥15% or not. The receiver operator characteristic (ROC curves of SVV, PPV and PVI were drawn to evaluate the value of SVV, PPV and PVI in evaluating volume status under artificial pneumoper itoneum and the correlation between them. Results The analysis of ROC curves showed that SVV, PPV and PVI could all be used to estimate the volume status effectively, the area under the curve (AUC of them was 0.978, 0.963 and 0.928 respectively, and there was a significant correlation between the three parameters (SVV and PPV: r=0.921, P<0.01; SVV and PVI: r=0.686, P<0.01; PPV and PVI: r=0.577, P<0.01. Conclusion SVV, PPV and PVI could be used to accurately evaluate the volume status under artificial pneumoperitoneum, and there is a good correlation between them. DOI: 10.11855/j.issn.0577-7402.2014.07.08
Some tests for parameter constancy in cointegrated VAR-models
DEFF Research Database (Denmark)
Hansen, Henrik; Johansen, Søren
1999-01-01
Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ...
Spatio-temporal modeling of nonlinear distributed parameter systems
Li, Han-Xiong
2011-01-01
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s
Revisiting linear plasma waves for finite value of the plasma parameter
Grismayer, Thomas; Fahlen, Jay; Decyk, Viktor; Mori, Warren
2010-11-01
We investigate through theory and PIC simulations the Landau-damping of plasma waves with finite plasma parameter. We concentrate on the linear regime, γφB, where the waves are typically small and below the thermal noise. We simulate these condition using 1,2,3D electrostatic PIC codes (BEPS), noting that modern computers now allow us to simulate cases where (nλD^3 = [1e2;1e6]). We study these waves by using a subtraction technique in which two simulations are carried out. In the first, a small wave is initialized or driven, in the second no wave is excited. The results are subtracted to provide a clean signal that can be studied. As nλD^3 is decreased, the number of resonant electrons can be small for linear waves. We show how the damping changes as a result of having few resonant particles. We also find that for small nλD^3 fluctuations can cause the electrons to undergo collisions that eventually destroy the initial wave. A quantity of interest is the the life time of a particular mode which depends on the plasma parameter and the wave number. The life time is estimated and then compared with the numerical results. A surprising result is that even for large values of nλD^3 some non-Vlasov discreteness effects appear to be important.
Directory of Open Access Journals (Sweden)
Baker Syed
2011-01-01
Full Text Available Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF, rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-10-11
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Morin, José A.; Ibarra, Borja; Cao, Francisco J.
2016-05-01
Single-molecule manipulation experiments of molecular motors provide essential information about the rate and conformational changes of the steps of the reaction located along the manipulation coordinate. This information is not always sufficient to define a particular kinetic cycle. Recent single-molecule experiments with optical tweezers showed that the DNA unwinding activity of a Phi29 DNA polymerase mutant presents a complex pause behavior, which includes short and long pauses. Here we show that different kinetic models, considering different connections between the active and the pause states, can explain the experimental pause behavior. Both the two independent pause model and the two connected pause model are able to describe the pause behavior of a mutated Phi29 DNA polymerase observed in an optical tweezers single-molecule experiment. For the two independent pause model all parameters are fixed by the observed data, while for the more general two connected pause model there is a range of values of the parameters compatible with the observed data (which can be expressed in terms of two of the rates and their force dependencies). This general model includes models with indirect entry and exit to the long-pause state, and also models with cycling in both directions. Additionally, assuming that detailed balance is verified, which forbids cycling, this reduces the ranges of the values of the parameters (which can then be expressed in terms of one rate and its force dependency). The resulting model interpolates between the independent pause model and the indirect entry and exit to the long-pause state model
Identification of hydrological model parameter variation using ensemble Kalman filter
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao
2016-12-01
Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.
The Added Value of Business Models
Vliet, Harry van
2014-01-01
An overview of innovations in a particular area, for example retail developments in the fashion sector (Van Vliet, 2014), and a subsequent discussion about the probability as to whether these innovations will realise a ‘breakthrough’, has to be supplemented with the question of what the added value
Modeling value creation with enterprise architecture
Singh, Prince Mayurank; Jonkers, H.; Iacob, Maria Eugenia; van Sinderen, Marten J.
2014-01-01
Firms may not succeed in business if strategies are not properly implemented in practice. Every firm needs to know, represent and master its value creation logic, not only to stay in business but also to keep growing. This paper is about focusing on an important topic in the field of strategic
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
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Guanqun eZhang
2011-11-01
Full Text Available A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel while being defined by only a few parameters (unlike comprehensive distributed-parameter models. As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
Georgiou, Stelios N; Fousiani, Kyriaki; Michaelides, Michalis; Stavrinides, Panayiotis
2013-01-01
The purpose of the present study was to examine the existing association between cultural value orientation, authoritarian parenting, and bullying and victimization at school. The participants (N = 231) were early adolescents, randomly selected from 11 different schools in urban and rural areas of Cyprus. Participants completed self reports measuring cultural value orientation, authoritarian parenting, bullying, and victimization. These instruments were the following: the cultural value scale (CVS), the parental authority questionnaire (PAQ), and the revised bullying and victimization questionnaire (BVQ-R). Structural equation modeling (SEM) was used to examine mediation effects. It was found that vertical individualism acted as a mediator between authoritarian parenting and bullying. Statistically significant positive correlations were found between authoritarian parenting and the vertical dimensions of both cultural value orientations (individualism and collectivism), but not with the horizontal dimensions of either cultural orientation. Further, authoritarian parenting was also positively associated with bullying and victimization at school. The main contribution of the present study is the finding that vertical individualism significantly mediates the relationship between authoritarian parental style and bullying propensity.
Genetic parameters and breeding values for semen characteristics in Hanoverian stallions.
Labitzke, D; Sieme, H; Martinsson, G; Distl, O
2014-08-01
The objectives of this study were to show whether semen traits of 30 Hanoverian stallions regularly used in AI may be useful for breeding purposes. Semen characteristics were studied using 15 149 ejaculates from 30 Hanoverian stallions of the State Stud Celle of Lower Saxony. Semen samples were collected between 2005 and 2009. Traits analysed were gel-free volume, sperm concentration, total and motile sperm number and progressive motility. A linear multivariate animal model was employed to estimate heritabilities and permanent environmental variances for stallions. The same model was used to predict breeding values for all traits simultaneously. Heritabilities were high for gel-free volume (h(2) = 0.43) and moderate for total number of sperm (h(2) = 0.29) and progressive motility (h(2) = 0.20). Gel-free volume, sperm concentration and total number of sperm were genetically negatively correlated with progressive motility. The effect of the permanent environment for stallions accounted for 9-55% of the trait variance. The total variance among stallions explained 37-69% of the trait variance. The average reliabilities of the breeding values were 0.43-0.76 for the 30 Hanoverian stallions. In conclusion, the study could demonstrate large effects of stallions, routinely employed in a breeding programme, on semen characteristics analysed here. We could demonstrate that estimated breeding values (EBV) with sufficient high reliabilities can be predicted using data from these stallions and these EBV are useful in horse breeding programmes to achieve genetic improvement in semen quality.
Parameter estimation and investigation of a bolted joint model
Shiryayev, O. V.; Page, S. M.; Pettit, C. L.; Slater, J. C.
2007-11-01
Mechanical joints are a primary source of variability in the dynamics of built-up structures. Physical phenomena in the joint are quite complex and therefore too impractical to model at the micro-scale. This motivates the development of lumped parameter joint models with discrete interfaces so that they can be easily implemented in finite element codes. Among the most important considerations in choosing a model for dynamically excited systems is its ability to model energy dissipation. This translates into the need for accurate and reliable methods to measure model parameters and estimate their inherent variability from experiments. The adjusted Iwan model was identified as a promising candidate for representing joint dynamics. Recent research focused on this model has exclusively employed impulse excitation in conjunction with neural networks to identify the model parameters. This paper presents an investigation of an alternative parameter estimation approach for the adjusted Iwan model, which employs data from oscillatory forcing. This approach is shown to produce parameter estimates with precision similar to the impulse excitation method for a range of model parameters.
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Uniqueness, scale, and resolution issues in groundwater model parameter identification
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Tian-chyi J. Yeh
2015-07-01
Full Text Available This paper first visits uniqueness, scale, and resolution issues in groundwater flow forward modeling problems. It then makes the point that non-unique solutions to groundwater flow inverse problems arise from a lack of information necessary to make the problems well defined. Subsequently, it presents the necessary conditions for a well-defined inverse problem. They are full specifications of (1 flux boundaries and sources/sinks, and (2 heads everywhere in the domain at at least three times (one of which is t = 0, with head change everywhere at those times must being nonzero for transient flow. Numerical experiments are presented to corroborate the fact that, once the necessary conditions are met, the inverse problem has a unique solution. We also demonstrate that measurement noise, instability, and sensitivity are issues related to solution techniques rather than the inverse problems themselves. In addition, we show that a mathematically well-defined inverse problem, based on an equivalent homogeneous or a layered conceptual model, may yield physically incorrect and scenario-dependent parameter values. These issues are attributed to inconsistency between the scale of the head observed and that implied by these models. Such issues can be reduced only if a sufficiently large number of observation wells are used in the equivalent homogeneous domain or each layer. With a large number of wells, we then show that increase in parameterization can lead to a higher-resolution depiction of heterogeneity if an appropriate inverse methodology is used. Furthermore, we illustrate that, using the same number of wells, a highly parameterized model in conjunction with hydraulic tomography can yield better characterization of the aquifer and minimize the scale and scenario-dependent problems. Lastly, benefits of the highly parameterized model and hydraulic tomography are tested according to their ability to improve predictions of aquifer responses induced by
Identification of parameters of discrete-continuous models
Energy Technology Data Exchange (ETDEWEB)
Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
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Yang W
2015-04-01
Full Text Available Wanjie Yang,1 Qingguo Feng,1 Youzhong An,2 Xuefeng Zhao,1 Kai Wei,1 Chang Li,1 Wei Wang,1 Hongyun Teng1 1Department of Critical Care, The Fifth Central Hospital of Tianjin, Tianjin, People’s Republic of China; 2Department of Critical Care, The People’s Hospital, Peking University, Beijing, People’s Republic of China Aim: To investigate the effects of differential sites for cold saline injection on transpulmonary thermodilution parameter values.Methods: This was a prospective study. Twelve patients were recruited for the following examinations: control condition (injection site at proximal injection end of the Swan-Ganz catheter, proximal end condition (injection site at sheath of the Swan-Ganz catheter, and distal end condition (injection site at PA end of the Swan-Ganz catheter. Sixty measurements were performed for each condition. The cardiac index, global end diastolic volume index (GEDI, and extravascular lung water index for the three different injection sites were recorded from each patient. In addition, the mean transmission time (MTt, downslope time, and area under the curve obtained from PiCCO-VoLEF-Win software were compared among different groups.Results: There were no differences in cardiac index and extravascular lung water index values among the three conditions (P>0.05. There were no differences in GEDI between the proximal end condition and control condition (P>0.05, while the GEDI was significantly lower for the distal end condition (493.33±254.65 mL/m2 than for the control condition (645.53±234.46 mL/m2 (P<0.05 and proximal end condition (717.96±321.63 mL/m2 (P<0.01. There were no differences in downslope time and area under the curve among the three conditions (P>0.05. There were no differences in MTt between the proximal end condition and control condition (P>0.05, while the MTt was significantly lower for distal end condition (40.22±16.37 seconds than for the control condition (42.91±17.93 seconds (P<0.05 and
Energy Technology Data Exchange (ETDEWEB)
Zou, Li [Dalian Univ. of Technology, Dalian City (China). State Key Lab. of Structural Analysis for Industrial Equipment; Liang, Songxin; Li, Yawei [Dalian Univ. of Technology, Dalian City (China). School of Mathematical Sciences; Jeffrey, David J. [Univ. of Western Ontario, London (Canada). Dept. of Applied Mathematics
2017-06-01
Nonlinear boundary value problems arise frequently in physical and mechanical sciences. An effective analytic approach with two parameters is first proposed for solving nonlinear boundary value problems. It is demonstrated that solutions given by the two-parameter method are more accurate than solutions given by the Adomian decomposition method (ADM). It is further demonstrated that solutions given by the ADM can also be recovered from the solutions given by the two-parameter method. The effectiveness of this method is demonstrated by solving some nonlinear boundary value problems modeling beam-type nano-electromechanical systems.
Zou, Li; Liang, Songxin; Li, Yawei; Jeffrey, David J.
2017-03-01
Nonlinear boundary value problems arise frequently in physical and mechanical sciences. An effective analytic approach with two parameters is first proposed for solving nonlinear boundary value problems. It is demonstrated that solutions given by the two-parameter method are more accurate than solutions given by the Adomian decomposition method (ADM). It is further demonstrated that solutions given by the ADM can also be recovered from the solutions given by the two-parameter method. The effectiveness of this method is demonstrated by solving some nonlinear boundary value problems modeling beam-type nano-electromechanical systems.
A parameter model for dredge plume sediment source terms
Decrop, Boudewijn; De Mulder, Tom; Toorman, Erik; Sas, Marc
2017-01-01
The presented model allows for fast simulations of the near-field behaviour of overflow dredging plumes. Overflow dredging plumes occur when dredging vessels employ a dropshaft release system to discharge the excess sea water, which is pumped into the trailing suction hopper dredger (TSHD) along with the dredged sediments. The fine sediment fraction in the loaded water-sediment mixture does not fully settle before it reaches the overflow shaft. By consequence, the released water contains a fine sediment fraction of time-varying concentration. The sediment grain size is in the range of clays, silt and fine sand; the sediment concentration varies roughly between 10 and 200 g/l in most cases, peaking at even higher value with short duration. In order to assess the environmental impact of the increased turbidity caused by this release, plume dispersion predictions are often carried out. These predictions are usually executed with a large-scale model covering a complete coastal zone, bay, or estuary. A source term of fine sediments is implemented in the hydrodynamic model to simulate the fine sediment dispersion. The large-scale model mesh resolution and governing equations, however, do not allow to simulate the near-field plume behaviour in the vicinity of the ship hull and propellers. Moreover, in the near-field, these plumes are under influence of buoyancy forces and air bubbles. The initial distribution of sediments is therefore unknown and has to be based on crude assumptions at present. The initial (vertical) distribution of the sediment source is indeed of great influence on the final far-field plume dispersion results. In order to study this near-field behaviour, a highly-detailed computationally fluid dynamics (CFD) model was developed. This model contains a realistic geometry of a dredging vessel, buoyancy effects, air bubbles and propeller action, and was validated earlier by comparing with field measurements. A CFD model requires significant simulation times
Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli
2013-07-11
Infertile women might get pregnant sometime after fertility treatment, but today, there is no prediction model on who will eventually have children. The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value for long-term possibility of live birth. In this cross-sectional study, with 71 infertile women with diagnosis unexplained infertility and male infertility, blood samples were obtained during the proliferative and secretory phases of an arbitrary menstrual cycle. Serum concentrations of FSH, LH, AMH, inhibin B, estradiol, progesterone, PRL and TSH were determined. The predictive value of ovulation and hormonal analysis was determined by identifying the proportion of women with at least one live birth. Mann Whitney U test, chi2 test and Spearman's correlation were used for statistical analysis. A value of p hormone values and live birth rates between women with unexplained infertility and male infertility. The best sole predictors of live birth were age of the women, followed by ovulatory cycle, defined as serum progesterone concentration of greater than or equal to 32 nmol/L, and a serum TSH concentration of less than or equal to 2.5 mIU/L. Combining the age with the ovulatory cycle and serum TSH less than or equal to 2.5 mIU/L or serum AMH greater than or equal to 10 pmol/L the predictive value was close to 90%. Age in combination with the presence of an ovulatory cycle and serum TSH or serum AMH is predictive for long-term live birth. The advantage of serum AMH compared with serum TSH is the very little variation throughout the menstrual cycle, which makes it a useful tool in infertility diagnosis.
Maheshwari, Arpit; Dumitrescu, Mihaela Aneta; Destro, Matteo; Santarelli, Massimo
2016-03-01
Battery models are riddled with incongruous values of parameters considered for validation. In this work, thermally coupled electrochemical model of the pouch is developed and discharge tests on a LiFePO4 pouch cell at different discharge rates are used to optimize the LiFePO4 battery model by determining parameters for which there is no consensus in literature. A discussion on parameter determination, selection and comparison with literature values has been made. The electrochemical model is a P2D model, while the thermal model considers heat transfer in 3D. It is seen that even with no phase change considered for LiFePO4 electrode, the model is able to simulate the discharge curves over a wide range of discharge rates with a single set of parameters provided a dependency of the radius of the LiFePO4 electrode on discharge rate. The approach of using a current dependent radius is shown to be equivalent to using a current dependent diffusion coefficient. Both these modelling approaches are a representation of the particle size distribution in the electrode. Additionally, the model has been thermally validated, which increases the confidence level in the selection of values of parameters.
Martelli, Fabrizio; Del Bianco, Samuele; Zaccanti, Giovanni
2011-02-01
We propose the use of a retrieval operator for biomedical applications in near-infrared spectroscopy. The proposed retrieval operator is based on the "Optimal Estimation" method. The main characteristic of this method relates to the possibility to include prior information both on target and on forward model parameters of the inversion procedure. The possibility of the retrieval operator to elaborate a-priori information can in principle be a benefit for the whole retrieval procedure. This means that a larger number of target parameters can be retrieved, or that a better accuracy can be achieved in retrieving the target parameters. The final goal of this inversion procedure is to have an improved estimate of the target parameters. The procedure has been tested on time-resolved simulated experiments obtained with a Monte Carlo code. The results obtained show that an improved performance of the inversion procedure is achieved when prior information on target and forward model parameters is available. With the use of a priori information on target parameters we have in average a lower difference between the retrieved values of the parameters and their true values, and the error bars determined by the inversion procedure on the retrieved parameters are significantly lower. At the same time a good estimate of the errors on the forward model parameters can significantly improve the retrieval of the target parameters.
Checking the new IRI model The bottomside B parameters
Mosert, M; Ezquer, R; Lazo, B; Miro, G
2002-01-01
Electron density profiles obtained at Pruhonice (50.0, 15.0), El Arenosillo (37.1, 353.2) and Havana (23, 278) were used to check the bottom-side B parameters BO (thickness parameter) and B1 (shape parameter) predicted by the new IRI - 2000 version. The electron density profiles were derived from ionograms using the ARP technique. The data base includes daytime and nighttime ionograms recorded under different seasonal and solar activity conditions. Comparisons with IRI predictions were also done. The analysis shows that: a) The parameter B1 given by IRI 2000 reproduces better the observed ARP values than the IRI-90 version and b) The observed BO values are in general well reproduced by both IRI versions: IRI-90 and IRI-2000.
Estimation of the input parameters in the Feller neuronal model
Ditlevsen, Susanne; Lansky, Petr
2006-06-01
The stochastic Feller neuronal model is studied, and estimators of the model input parameters, depending on the firing regime of the process, are derived. Closed expressions for the first two moments of functionals of the first-passage time (FTP) through a constant boundary in the suprathreshold regime are derived, which are used to calculate moment estimators. In the subthreshold regime, the exponentiality of the FTP is utilized to characterize the input parameters. The methods are illustrated on simulated data. Finally, approximations of the first-passage-time moments are suggested, and biological interpretations and comparisons of the parameters in the Feller and the Ornstein-Uhlenbeck models are discussed.
Parameters Calculation of ZnO Surge Arrester Models by Genetic Algorithms
Directory of Open Access Journals (Sweden)
A. Bayadi
2006-09-01
Full Text Available This paper proposes to provide a new technique based on the genetic algorithm to obtain the best possible series of values of the parameters of the ZnO surge arresters models. The validity of the predicted parameters is then checked by comparing the results predicted with the experimental results available in the literature. Using the ATP-EMTP package an application of the arrester model on network system studies is presented and discussed.
Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
2015-01-01
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values dep...
Single Parameter Model for Free Recall And the Nature of Partially Filled Working Memory
Tarnow, Dr Eugen
2009-01-01
I present a single parameter model of free recall and fit the one parameter, the probability per time unit of an item in working memory entering the next memory store (similar to Atkinson and Shiffrin, 1968), to the original Murdock (1962) data. Working memory is modeled as having space for a maximum of 4 items (Cowan, 2001). The first four probability values convey precise information about how items in the partially filled working memory enter the next memory store. In particular, one ...
Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells
Directory of Open Access Journals (Sweden)
Rongjie Wang
2015-07-01
Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation of struct......This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation...... response during excitation and the geometrical damping related to free vibrations of a hexagonal footing. The optimal order of a lumped-parameter model is determined for each degree of freedom, i.e. horizontal and vertical translation as well as torsion and rocking. In particular, the necessity of coupling...... between horizontal sliding and rocking is discussed....
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values
Directory of Open Access Journals (Sweden)
Jin Xiao
2014-01-01
Full Text Available Scientific customer value segmentation (CVS is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.
Estimating winter wheat phenological parameters: Implications for crop modeling
Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...
Bates, P. D.; Neal, J. C.; Fewtrell, T. J.
2012-12-01
In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Spatial scale effects on model parameter estimation and predictive uncertainty in ungauged basins
CSIR Research Space (South Africa)
Hughes, DA
2013-06-01
Full Text Available The most appropriate scale to use for hydrological modelling depends on the structure of the chosen model, the purpose of the results and the resolution of the available data used to quantify parameter values and provide the climatic forcing data...
Engineering the Complex-Valued Constitutive Parameters of Metamaterials for Perfect Absorption.
Wang, Pengwei; Chen, Naibo; Tang, Chaojun; Chen, Jing; Liu, Fanxin; Sheng, Saiqian; Yan, Bo; Sui, Chenghua
2017-12-01
We theoretically studied how to directly engineer the constitutive parameters of metamaterials for perfect absorbers of electromagnetic waves. As an example, we numerically investigated the necessary refractive index n and extinction coefficient k and the relative permittivity ε and permeability μ of a metamaterial anti-reflection layer, which could cancel the reflection from a hydrogenated amorphous silicon (α-Si:H) thin film on a metal substrate, within the visible wavelength range from 300 to 800 nm. We found that the metamaterial anti-reflection layer should have a negative refractive index (n 0) for long-wavelength visible light. The relative permittivity ε and permeability μ could be fitted by the Lorentz model, which exhibited electric and magnetic resonances, respectively.
Engineering the Complex-Valued Constitutive Parameters of Metamaterials for Perfect Absorption
Wang, Pengwei; Chen, Naibo; Tang, Chaojun; Chen, Jing; Liu, Fanxin; Sheng, Saiqian; Yan, Bo; Sui, Chenghua
2017-04-01
We theoretically studied how to directly engineer the constitutive parameters of metamaterials for perfect absorbers of electromagnetic waves. As an example, we numerically investigated the necessary refractive index n and extinction coefficient k and the relative permittivity ɛ and permeability μ of a metamaterial anti-reflection layer, which could cancel the reflection from a hydrogenated amorphous silicon (α-Si:H) thin film on a metal substrate, within the visible wavelength range from 300 to 800 nm. We found that the metamaterial anti-reflection layer should have a negative refractive index ( n 0) for long-wavelength visible light. The relative permittivity ɛ and permeability μ could be fitted by the Lorentz model, which exhibited electric and magnetic resonances, respectively.
The effect of aerobic exposure on nutritive value and fermentation parameters of maize silage
Directory of Open Access Journals (Sweden)
Marián MAJLÁT
2016-06-01
Full Text Available The objective of the study was to investigate the effect of 24 hours aerobic exposure on the nutritive value and fermentation parameters of maize silage under operating conditions in autumn. The maize silage was treated with the addition of granulated biological silage additive (Lactobacillus plantarum, Lactobacillus buchneri and Pediococcus pentosaceus in a 0.25 kg*t-1 dose of matter. The maize silage was stored in unsheltered, impassable 36x23x6m silo, covered with thin translucent underlying sheet and black upper sheet with tires laid on the top at the University farm, Ltd. Kolíňany – large scale dairy farm Oponice. Sampling of the silage from silo was realized by block cutter on 5th of October 2011 at 4 a.m. (before feeding and subsequently, maize silage was stored at the feeding table in dairy cow's stall. The sampling of the maize silage happened immediately after collection (H0 and after 24 hours of storage at the feeding table (H24. The air temperature in dairy cow's stall was 13.5 °C (H0 and after 24 hours of storage (H24 15 °C. The temperature of the silage in 30cm depth was 28 °C (H0 and 40.3 °C (H24. After 24 hours of aerobic exposure, the content of dry matter, starch, hemicelluloses and neutral detergent fiber in maize silage was statistically significantly (P < 0.05 increased. Our results indicate a reduction in vitro organic matter digestibility and a decrease in energy and nitrogen values during aerobic exposure. We found statistically significant (P < 0.05 decrease in lactic acid content, the degree of proteolysis and an increase in acetic acid (by 41% in maize silage with Lactobacillus plantarum, Lactobacillus buchneri and Pediococcus pentosaceus during 24 hours aerobic exposure.
Rock thermal conductivity as key parameter for geothermal numerical models
Di Sipio, Eloisa; Chiesa, Sergio; Destro, Elisa; Galgaro, Antonio; Giaretta, Aurelio; Gola, Gianluca; Manzella, Adele
2013-04-01
The geothermal energy applications are undergoing a rapid development. However, there are still several challenges in the successful exploitation of geothermal energy resources. In particular, a special effort is required to characterize the thermal properties of the ground along with the implementation of efficient thermal energy transfer technologies. This paper focuses on understanding the quantitative contribution that geosciences can receive from the characterization of rock thermal conductivity. The thermal conductivity of materials is one of the main input parameters in geothermal modeling since it directly controls the steady state temperature field. An evaluation of this thermal property is required in several fields, such as Thermo-Hydro-Mechanical multiphysics analysis of frozen soils, designing ground source heat pumps plant, modeling the deep geothermal reservoirs structure, assessing the geothermal potential of subsoil. Aim of this study is to provide original rock thermal conductivity values useful for the evaluation of both low and high enthalpy resources at regional or local scale. To overcome the existing lack of thermal conductivity data of sedimentary, igneous and metamorphic rocks, a series of laboratory measurements has been performed on several samples, collected in outcrop, representative of the main lithologies of the regions included in the VIGOR Project (southern Italy). Thermal properties tests were carried out both in dry and wet conditions, using a C-Therm TCi device, operating following the Modified Transient Plane Source method.Measurements were made at standard laboratory conditions on samples both water saturated and dehydrated with a fan-forced drying oven at 70 ° C for 24 hr, for preserving the mineral assemblage and preventing the change of effective porosity. Subsequently, the samples have been stored in an air-conditioned room while bulk density, solid volume and porosity were detected. The measured thermal conductivity
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Dynamic Load Model using PSO-Based Parameter Estimation
Taoka, Hisao; Matsuki, Junya; Tomoda, Michiya; Hayashi, Yasuhiro; Yamagishi, Yoshio; Kanao, Norikazu
This paper presents a new method for estimating unknown parameters of dynamic load model as a parallel composite of a constant impedance load and an induction motor behind a series constant reactance. An adequate dynamic load model is essential for evaluating power system stability, and this model can represent the behavior of actual load by using appropriate parameters. However, the problem of this model is that a lot of parameters are necessary and it is not easy to estimate a lot of unknown parameters. We propose an estimating method based on Particle Swarm Optimization (PSO) which is a non-linear optimization method by using the data of voltage, active power and reactive power measured at voltage sag.
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Directory of Open Access Journals (Sweden)
Romanuke Vadim V.
2016-07-01
Full Text Available Adjustment of an unknown parameter of the multistage expert procedure is considered. The lower and upper boundaries of the parameter are counted to be known. A key condition showing that experts’ estimations are satisfactory in the current procedure is an inequality, in which the value based on the estimations is not greater than the parameter. The algorithms of hard and soft adjusting are developed. If the inequality is true and its both terms are too close for a long sequence of expert procedures, the adjusting can be early stopped. The algorithms are reversible, implying inversion to the reverse inequality and sliding up off the lower boundary.
SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology
Directory of Open Access Journals (Sweden)
Alexander Dörr
2014-12-01
Full Text Available The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.
Modeling churn using customer lifetime value
Glady, Nicolas; Baesens, Bart; Croux, Christophe
2009-01-01
The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric vi...
Modeling customer loyalty using customer lifetime value.
Glady, N.; Baesens, Bart; Croux, Christophe
2006-01-01
The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution will be to redefine the notion of customer's loyalty by considering it from a customer-cen...
Modeling the Marginal Value of Rainforest Losses
Strand, Jon
2015-01-01
A rainforest can be modeled as a dynamic asset subject to various risks, including risk of fire. Any small part of the forest can be in one of two states: either untouched by forest fire, or already damaged by fire, in which case there is both a local forest loss and increased dryness over a broader area. In this paper, two Bellman equations are constructed, one for unharmed forest and a s...
Directory of Open Access Journals (Sweden)
G.B. Camilo
2015-01-01
Full Text Available The aims of this study were to evaluate the forced oscillation technique (FOT and pulmonary densitovolumetry in acromegalic patients and to examine the correlations between these findings. In this cross-sectional study, 29 non-smoking acromegalic patients and 17 paired controls were subjected to the FOT and quantification of lung volume using multidetector computed tomography (Q-MDCT. Compared with the controls, the acromegalic patients had a higher value for resonance frequency [15.3 (10.9-19.7 vs 11.4 (9.05-17.6 Hz, P=0.023] and a lower value for mean reactance [0.32 (0.21-0.64 vs 0.49 (0.34-0.96 cm H2O/L/s2, P=0.005]. In inspiratory Q-MDCT, the acromegalic patients had higher percentages of total lung volume (TLV for nonaerated and poorly aerated areas [0.42% (0.30-0.51% vs 0.25% (0.20-0.32%, P=0.039 and 3.25% (2.48-3.46% vs 1.70% (1.45-2.15%, P=0.001, respectively]. Furthermore, the acromegalic patients had higher values for total lung mass in both inspiratory and expiratory Q-MDCT [821 (635-923 vs 696 (599-769 g, P=0.021 and 844 (650-945 vs 637 (536-736 g, P=0.009, respectively]. In inspiratory Q-MDCT, TLV showed significant correlations with all FOT parameters. The TLV of hyperaerated areas showed significant correlations with intercept resistance (rs=−0.602, P<0.001 and mean resistance (rs=−0.580, P<0.001. These data showed that acromegalic patients have increased amounts of lung tissue as well as nonaerated and poorly aerated areas. Functionally, there was a loss of homogeneity of the respiratory system. Moreover, there were correlations between the structural and functional findings of the respiratory system, consistent with the pathophysiology of the disease.
Camilo, G.B.; Carvalho, A.R.S.; Machado, D.C.; Mogami, R.; Kasuki, L.; Gadelha, M.R.; Melo, P.L.; Lopes, A.J.
2015-01-01
The aims of this study were to evaluate the forced oscillation technique (FOT) and pulmonary densitovolumetry in acromegalic patients and to examine the correlations between these findings. In this cross-sectional study, 29 non-smoking acromegalic patients and 17 paired controls were subjected to the FOT and quantification of lung volume using multidetector computed tomography (Q-MDCT). Compared with the controls, the acromegalic patients had a higher value for resonance frequency [15.3 (10.9-19.7) vs 11.4 (9.05-17.6) Hz, P=0.023] and a lower value for mean reactance [0.32 (0.21-0.64) vs 0.49 (0.34-0.96) cm H2O/L/s2, P=0.005]. In inspiratory Q-MDCT, the acromegalic patients had higher percentages of total lung volume (TLV) for nonaerated and poorly aerated areas [0.42% (0.30-0.51%) vs 0.25% (0.20-0.32%), P=0.039 and 3.25% (2.48-3.46%) vs 1.70% (1.45-2.15%), P=0.001, respectively]. Furthermore, the acromegalic patients had higher values for total lung mass in both inspiratory and expiratory Q-MDCT [821 (635-923) vs 696 (599-769) g, P=0.021 and 844 (650-945) vs 637 (536-736) g, P=0.009, respectively]. In inspiratory Q-MDCT, TLV showed significant correlations with all FOT parameters. The TLV of hyperaerated areas showed significant correlations with intercept resistance (rs=−0.602, P<0.001) and mean resistance (rs=−0.580, P<0.001). These data showed that acromegalic patients have increased amounts of lung tissue as well as nonaerated and poorly aerated areas. Functionally, there was a loss of homogeneity of the respiratory system. Moreover, there were correlations between the structural and functional findings of the respiratory system, consistent with the pathophysiology of the disease. PMID:26445330
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan D.; Stieglitz, Marc
2015-06-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Optimisation of dispersion parameters of Gaussian plume model for CO₂ dispersion.
Liu, Xiong; Godbole, Ajit; Lu, Cheng; Michal, Guillaume; Venton, Philip
2015-11-01
The carbon capture and storage (CCS) and enhanced oil recovery (EOR) projects entail the possibility of accidental release of carbon dioxide (CO2) into the atmosphere. To quantify the spread of CO2 following such release, the 'Gaussian' dispersion model is often used to estimate the resulting CO2 concentration levels in the surroundings. The Gaussian model enables quick estimates of the concentration levels. However, the traditionally recommended values of the 'dispersion parameters' in the Gaussian model may not be directly applicable to CO2 dispersion. This paper presents an optimisation technique to obtain the dispersion parameters in order to achieve a quick estimation of CO2 concentration levels in the atmosphere following CO2 blowouts. The optimised dispersion parameters enable the Gaussian model to produce quick estimates of CO2 concentration levels, precluding the necessity to set up and run much more complicated models. Computational fluid dynamics (CFD) models were employed to produce reference CO2 dispersion profiles in various atmospheric stability classes (ASC), different 'source strengths' and degrees of ground roughness. The performance of the CFD models was validated against the 'Kit Fox' field measurements, involving dispersion over a flat horizontal terrain, both with low and high roughness regions. An optimisation model employing a genetic algorithm (GA) to determine the best dispersion parameters in the Gaussian plume model was set up. Optimum values of the dispersion parameters for different ASCs that can be used in the Gaussian plume model for predicting CO2 dispersion were obtained.
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Transformations among CE–CVM model parameters for multicomponent systems
Indian Academy of Sciences (India)
B Nageswara Sarma; Shrikant Lele
2005-06-01
In the development of thermodynamic databases for multicomponent systems using the cluster expansion–cluster variation methods, we need to have a consistent procedure for expressing the model parameters (CECs) of a higher order system in terms of those of the lower order subsystems and to an independent set of parameters which exclusively represent interactions of the higher order systems. Such a procedure is presented in detail in this communication. Furthermore, the details of transformations required to express the model parameters in one basis from those defined in another basis for the same system are also presented.
Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model
Energy Technology Data Exchange (ETDEWEB)
Rengers, Francis; Lunacek, Monte; Tucker, Gregory
2016-06-01
Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.
Christensen, H. M.; Moroz, I.; Palmer, T.
2015-12-01
It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic ensemble forecasts, and a number of different techniques have been proposed for this purpose. Stochastic convection parameterization schemes use random numbers to represent the difference between a deterministic parameterization scheme and the true atmosphere, accounting for the unresolved sub grid-scale variability associated with convective clouds. An alternative approach varies the values of poorly constrained physical parameters in the model to represent the uncertainty in these parameters. This study presents new perturbed parameter schemes for use in the European Centre for Medium Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parametrisation scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are changed between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, Stochastically Perturbed Parametrisation Tendencies (SPPT), and to a model which does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parametrisation in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skilful representations of model uncertainty due to convection parametrisation. Reference: H. M. Christensen, I
Zimmer, Christoph; Sahle, Sven
2016-04-01
Parameter estimation for models with intrinsic stochasticity poses specific challenges that do not exist for deterministic models. Therefore, specialized numerical methods for parameter estimation in stochastic models have been developed. Here, we study whether dedicated algorithms for stochastic models are indeed superior to the naive approach of applying the readily available least squares algorithm designed for deterministic models. We compare the performance of the recently developed multiple shooting for stochastic systems (MSS) method designed for parameter estimation in stochastic models, a stochastic differential equations based Bayesian approach and a chemical master equation based techniques with the least squares approach for parameter estimation in models of ordinary differential equations (ODE). As test data, 1000 realizations of the stochastic models are simulated. For each realization an estimation is performed with each method, resulting in 1000 estimates for each approach. These are compared with respect to their deviation to the true parameter and, for the genetic toggle switch, also their ability to reproduce the symmetry of the switching behavior. Results are shown for different set of parameter values of a genetic toggle switch leading to symmetric and asymmetric switching behavior as well as an immigration-death and a susceptible-infected-recovered model. This comparison shows that it is important to choose a parameter estimation technique that can treat intrinsic stochasticity and that the specific choice of this algorithm shows only minor performance differences.
Scheibehenne, Benjamin; Pachur, Thorsten
2015-04-01
To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice-cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.
SPOTting Model Parameters Using a Ready-Made Python Package.
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
SPOTting Model Parameters Using a Ready-Made Python Package.
Directory of Open Access Journals (Sweden)
Tobias Houska
Full Text Available The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool, an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI. We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
Numerical modeling of piezoelectric transducers using physical parameters.
Cappon, Hans; Keesman, Karel J
2012-05-01
Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and experimental data can be used to acquire valid estimates of the material parameters. In our design application, a finite element (FE) model of an ultrasonic particle separator, driven by an ultrasonic transducer in thickness mode, is required. A limited set of material parameters for the piezoelectric transducer were obtained from the manufacturer, thus preserving prior physical knowledge to a large extent. The remaining unknown parameters were estimated from impedance analysis with a simple experimental setup combined with a numerical optimization routine using 2-D and 3-D FE models. Thus, a full set of physically interpretable material parameters was obtained for our specific purpose. The approach provides adequate accuracy of the estimates of the material parameters, near 1%. These parameter estimates will subsequently be applied in future design simulations, without the need to go through an entire series of characterization experiments. Finally, a sensitivity study showed that small variations of 1% in the main parameters caused changes near 1% in the eigenfrequency, but changes up to 7% in the admittance peak, thus influencing the efficiency of the system. Temperature will already cause these small variations in response; thus, a frequency control unit is required when actually manufacturing an efficient ultrasonic separation system.
Spatial variability of the parameters of a semi-distributed hydrological model
de Lavenne, Alban; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria-Helena
2016-05-01
Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).
An Effective Parameter Screening Strategy for High Dimensional Watershed Models
Khare, Y. P.; Martinez, C. J.; Munoz-Carpena, R.
2014-12-01
Watershed simulation models can assess the impacts of natural and anthropogenic disturbances on natural systems. These models have become important tools for tackling a range of water resources problems through their implementation in the formulation and evaluation of Best Management Practices, Total Maximum Daily Loads, and Basin Management Action Plans. For accurate applications of watershed models they need to be thoroughly evaluated through global uncertainty and sensitivity analyses (UA/SA). However, due to the high dimensionality of these models such evaluation becomes extremely time- and resource-consuming. Parameter screening, the qualitative separation of important parameters, has been suggested as an essential step before applying rigorous evaluation techniques such as the Sobol' and Fourier Amplitude Sensitivity Test (FAST) methods in the UA/SA framework. The method of elementary effects (EE) (Morris, 1991) is one of the most widely used screening methodologies. Some of the common parameter sampling strategies for EE, e.g. Optimized Trajectories [OT] (Campolongo et al., 2007) and Modified Optimized Trajectories [MOT] (Ruano et al., 2012), suffer from inconsistencies in the generated parameter distributions, infeasible sample generation time, etc. In this work, we have formulated a new parameter sampling strategy - Sampling for Uniformity (SU) - for parameter screening which is based on the principles of the uniformity of the generated parameter distributions and the spread of the parameter sample. A rigorous multi-criteria evaluation (time, distribution, spread and screening efficiency) of OT, MOT, and SU indicated that SU is superior to other sampling strategies. Comparison of the EE-based parameter importance rankings with those of Sobol' helped to quantify the qualitativeness of the EE parameter screening approach, reinforcing the fact that one should use EE only to reduce the resource burden required by FAST/Sobol' analyses but not to replace it.
Directory of Open Access Journals (Sweden)
Agnaldo Donizete Ferreira de Carvalho
2008-01-01
Full Text Available The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction andestimation of genetic parameters and breeding values in soybean progenies. F2:3 and F4:5 progenies were evaluated in the2005/06 growing season and the F2:4 and F4:6 generations derived thereof were evaluated in 2006/07. These progenies wereoriginated from two semi-early experimental lines that differ in grain yield. The experiments were conducted in a lattice designand plots consisted of a 2 m row, spaced 0.5 m apart. The trait grain yield per plot was evaluated. It was observed that earlyselection is more efficient for the discrimination of the best lines from the F4 generation onwards. No practical differences wereobserved between the least square and REML/BLUP procedures in the case of the models and simplifications for REML/BLUPused here.
Energy Technology Data Exchange (ETDEWEB)
Hamimid, M., E-mail: Hamimid_mourad@hotmail.com [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Mimoune, S.M., E-mail: s.m.mimoune@mselab.org [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Feliachi, M., E-mail: mouloud.feliachi@univ-nantes.fr [IREENA-IUT, CRTT, 37 Boulevard de l' Universite, BP 406, 44602 Saint Nazaire Cedex (France)
2012-07-01
In this present work, the minor hysteresis loops model based on parameters scaling of the modified Jiles-Atherton model is evaluated by using judicious expressions. These expressions give the minor hysteresis loops parameters as a function of the major hysteresis loop ones. They have exponential form and are obtained by parameters identification using the stochastic optimization method 'simulated annealing'. The main parameters influencing the data fitting are three parameters, the pinning parameter k, the mean filed parameter {alpha} and the parameter which characterizes the shape of anhysteretic magnetization curve a. To validate this model, calculated minor hysteresis loops are compared with measured ones and good agreements are obtained.
MODELING OF FUEL SPRAY CHARACTERISTICS AND DIESEL COMBUSTION CHAMBER PARAMETERS
Directory of Open Access Journals (Sweden)
G. M. Kukharonak
2011-01-01
Full Text Available The computer model for coordination of fuel spray characteristics with diesel combustion chamber parameters has been created in the paper. The model allows to observe fuel sprays develоpment in diesel cylinder at any moment of injection, to calculate characteristics of fuel sprays with due account of a shape and dimensions of a combustion chamber, timely to change fuel injection characteristics and supercharging parameters, shape and dimensions of a combustion chamber. Moreover the computer model permits to determine parameters of holes in an injector nozzle that provides the required fuel sprays characteristics at the stage of designing a diesel engine. Combustion chamber parameters for 4ЧН11/12.5 diesel engine have been determined in the paper.
Regionalization parameters of conceptual rainfall-runoff model
Osuch, M.
2003-04-01
Main goal of this study was to develop techniques for the a priori estimation parameters of hydrological model. Conceptual hydrological model CLIRUN was applied to around 50 catchment in Poland. The size of catchments range from 1 000 to 100 000 km2. The model was calibrated for a number of gauged catchments with different catchment characteristics. The parameters of model were related to different climatic and physical catchment characteristics (topography, land use, vegetation and soil type). The relationships were tested by comparing observed and simulated runoff series from the gauged catchment that were not used in the calibration. The model performance using regional parameters was promising for most of the calibration and validation catchments.
Rácz, A; Bajusz, D; Héberger, K
2015-01-01
Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.
Neutrino masses and mixing parameters in a left-right model with mirror fermions
Gaitan, R; Rivera-Rebolledo, J M; de C'ordoba, P F
2006-01-01
In this work we consider a left-right model containing mirror fermions with gauge group SU(3)$_{C} \\otimes SU(2)_{L} \\otimes SU(2)_{R} \\otimes U(1)_{Y^\\prime}$. The model has several free parameters which here we have calculated by using the recent values for the squared-neutrino mass differences. Lower bound for the mirror vacuum expectation value helped us to obtain crude estimations for some of these parameters. Also we estimate the order of magnitude of the masses of the standard and mirror neutrinos.
The HackensackUMC Value-Based Care Model: Building Essentials for Value-Based Purchasing.
Douglas, Claudia; Aroh, Dianne; Colella, Joan; Quadri, Mohammed
2016-01-01
The Affordable Care Act, 2010, and the subsequent shift from a quantity-focus to a value-centric reimbursement model led our organization to create the HackensackUMC Value-Based Care Model to improve our process capability and performance to meet and sustain the triple aims of value-based purchasing: higher quality, lower cost, and consumer perception. This article describes the basics of our model and illustrates how we used it to reduce the costs of our patient sitter program.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....
Energy Technology Data Exchange (ETDEWEB)
Gopinath, A.; Puhan, Sukumar; Nagarajan, G. [Internal Combustion Engineering Division, Department of Mechanical Engineering, Anna University, Chennai 600 025, Tamil Nadu (India)
2009-07-15
Biodiesel is an alternative fuel consisting of alkyl esters of fatty acids from vegetable oils or animal fats. The properties of biodiesel depend on the type of vegetable oil used for the transesterification process. The objective of the present work is to theoretically predict the iodine value and the saponification value of different biodiesels from their fatty acid methyl ester composition. The fatty acid ester compositions and the above values of different biodiesels were taken from the available published data. A multiple linear regression model was developed to predict the iodine value and saponification value of different biodiesels. The predicted results showed that the prediction errors were less than 3.4% compared to the available published data. The predicted values were also verified by substituting in the available published model which was developed to predict the higher heating values of biodiesel fuels from their iodine value and the saponification value. The resulting heating values of biodiesels were then compared with the published heating values and reported. (author)
Prediction of genotypic values and estimation of genetic parameters in common bean
Directory of Open Access Journals (Sweden)
Alisson Fernando Chiorato
2008-06-01
Full Text Available Eighteen common bean (Phaseolus vulgaris L. genotypes were evaluated in 25 environments of the state of São Paulo in 2001 and 2002. The estimation of genetic parameters by the Restricted Maximum Likelihood (REML and the prediction of genotypic values via Best Linear Unbiased Prediction (BLUP were obtained by software Selegen-REML/BLUP. The estimate of the broad-sense heritability was low for the grain yield (0.03, since it took individual plots into consideration and was free of the effects of interaction with years, cultivation periods and site. Nevertheless, the heritability at the level of line means across the various environments was high (0.75, allowing a high accuracy (0.87 in the selection of lines for planting in the environment mean. Among the 18 genotypes, the predicted genotypic values of nine were higher than the general mean. The genetic gain predicted with the selection of the best line, in this case line Gen 96A31 of the IAC, was 16.25%.Dezoito genótipos de feijoeiro (Phaseolus vulgaris L. foram avaliados em 25 ambientes do estado de São Paulo durante os anos de 2001 e 2002. As estimativas de parâmetros genéticos por REML e a predição de valores genotípicos via BLUP foram obtidas por meio do aplicativo computacional Selegen REML/BLUP, seguindo o modelo misto para linhagens. A estimativa da herdabilidade no sentido amplo para produção de grãos foi baixa (0,03, por ser em nível de parcelas individuais e livre dos efeitos da interação com anos, épocas e locais. No entanto, a herdabilidade ao nível de médias de linhagens ao longo dos vários ambientes foi alta (0,75, permitindo alta acurácia (0,87 na seleção de linhagens para plantio no ambiente médio. Dentre os 18 genótipos, nove apresentaram valores genotípicos preditos superiores à média geral. O ganho genético predito com a seleção da melhor linhagem, no caso, a linhagem Gen 96A31 do IAC, foi de 16,25%.
An evolutionary computing approach for parameter estimation investigation of a model for cholera.
Akman, Olcay; Schaefer, Elsa
2015-01-01
We consider the problem of using time-series data to inform a corresponding deterministic model and introduce the concept of genetic algorithms (GA) as a tool for parameter estimation, providing instructions for an implementation of the method that does not require access to special toolboxes or software. We give as an example a model for cholera, a disease for which there is much mechanistic uncertainty in the literature. We use GA to find parameter sets using available time-series data from the introduction of cholera in Haiti and we discuss the value of comparing multiple parameter sets with similar performances in describing the data.
On Calculating the Hougaard Measure of Skewness in a Nonlinear Regression Model with Two Parameters
Directory of Open Access Journals (Sweden)
S. A. EL-Shehawy
2009-01-01
Full Text Available Problem statement: This study presented an alternative computational algorithm for determining the values of the Hougaard measure of skewness as a nonlinearity measure in a Nonlinear Regression model (NLR-model with two parameters. Approach: These values indicated a degree of a nonlinear behavior in the estimator of the parameter in a NLR-model. Results: We applied the suggested algorithm on an example of a NLR-model in which there is a conditionally linear parameter. The algorithm is mainly based on many earlier studies in measures of nonlinearity. The algorithm was suited for implementation using computer algebra systems such as MAPLE, MATLAB and MATHEMATICA. Conclusion/Recommendations: The results with the corresponding output the same considering example will be compared with the results in some earlier studies.
Directory of Open Access Journals (Sweden)
Khaled MAMMAR
2013-11-01
Full Text Available In this paper, a new approach based on Experimental of design methodology (DoE is used to estimate the optimal of unknown model parameters proton exchange membrane fuel cell (PEMFC. This proposed approach combines the central composite face-centered (CCF and numerical PEMFC electrochemical. Simulation results obtained using electrochemical model help to predict the cell voltage in terms of inlet partial pressures of hydrogen and oxygen, stack temperature, and operating current. The value of the previous model and (CCF design methodology is used for parametric analysis of electrochemical model. Thus it is possible to evaluate the relative importance of each parameter to the simulation accuracy. However this methodology is able to define the exact values of the parameters from the manufacture data. It was tested for the BCS 500-W stack PEM Generator, a stack rated at 500 W, manufactured by American Company BCS Technologies FC.
Energy Technology Data Exchange (ETDEWEB)
Butcher, B.M.
1997-08-01
A summary of the input parameter values used in final predictions of closure and waste densification in the Waste Isolation Pilot Plant disposal room is presented, along with supporting references. These predictions are referred to as the final porosity surface data and will be used for WIPP performance calculations supporting the Compliance Certification Application to be submitted to the U.S. Environmental Protection Agency. The report includes tables and list all of the input parameter values, references citing their source, and in some cases references to more complete descriptions of considerations leading to the selection of values.
Hu, Jin; Zeng, Chunna
2017-02-01
The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.
Construction of constant-Q viscoelastic model with three parameters
Institute of Scientific and Technical Information of China (English)
SUN Cheng-yu; YIN Xing-yao
2007-01-01
The popularly used viscoelastic models have some shortcomings in describing relationship between quality factor (Q) and frequency, which is not consistent with the observation data. Based on the theory of viscoelasticity, a new approach to construct constant-Q viscoelastic model in given frequency band with three parameters is developed. The designed model describes the frequency-independence feature of quality factor very well, and the effect of viscoelasticity on seismic wave field can be studied relatively accurate in theory with this model. Furthermore, the number of required parameters in this model has been reduced fewer than that of other constant-Q models, this can simplify the solution of the viscoelastic problems to some extent. At last, the accuracy and application range have been analyzed through numerical tests. The effect of viscoelasticity on wave propagation has been briefly illustrated through the change of frequency spectra and waveform in several different viscoelastic models.
Diagnosing ΛHDE model with statefinder hierarchy and fractional growth parameter
Zhou, LanJun; Wang, Shuang
2016-07-01
Recently, a new dark energy model called ΛHDE was proposed. In this model, dark energy consists of two parts: cosmological constant Λ and holographic dark energy (HDE). Two key parameters of this model are the fractional density of cosmological constant ΩΛ0, and the dimensionless HDE parameter c. Since these two parameters determine the dynamical properties of DE and the destiny of universe, it is important to study the impacts of different values of ΩΛ0 and c on the ΛHDE model. In this paper, we apply various DE diagnostic tools to diagnose ΛHDE models with different values of ΩΛ0 and c; these tools include statefinder hierarchy {S 3 (1) , S 4 (1) }, fractional growth parameter ɛ, and composite null diagnostic (CND), which is a combination of {S 3 (1) , S 4 (1) } and ɛ. We find that: (1) adopting different values of ΩΛ0 only has quantitative impacts on the evolution of the ΛHDE model, while adopting different c has qualitative impacts; (2) compared with S 3 (1) , S 4 (1) can give larger differences among the cosmic evolutions of the ΛHDE model associated with different ΩΛ0 or different c; (3) compared with the case of using a single diagnostic, adopting a CND pair has much stronger ability to diagnose the ΛHDE model.
Global-scale regionalization of hydrologic model parameters
Beck, Hylke E.; van Dijk, Albert I. J. M.; de Roo, Ad; Miralles, Diego G.; McVicar, Tim R.; Schellekens, Jaap; Bruijnzeel, L. Adrian
2016-05-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments > 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.
Bayesian parameter estimation for nonlinear modelling of biological pathways
Directory of Open Access Journals (Sweden)
Ghasemi Omid
2011-12-01
Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies
Smith, Carrie E.; Cribbie, Robert A.
2013-01-01
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Geometry parameters for musculoskeletal modelling of the shoulder system
Van der Helm, F C; Veeger, DirkJan (H. E. J.); Pronk, G M; Van der Woude, L H; Rozendal, R H
1992-01-01
A dynamical finite-element model of the shoulder mechanism consisting of thorax, clavicula, scapula and humerus is outlined. The parameters needed for the model are obtained in a cadaver experiment consisting of both shoulders of seven cadavers. In this paper, in particular, the derivation of geomet
Comparative Analysis of Visco-elastic Models with Variable Parameters
Directory of Open Access Journals (Sweden)
Silviu Nastac
2010-01-01
Full Text Available The paper presents a theoretical comparative study for computational behaviour analysis of vibration isolation elements based on viscous and elastic models with variable parameters. The changing of elastic and viscous parameters can be produced by natural timed evolution demo-tion or by heating developed into the elements during their working cycle. It was supposed both linear and non-linear numerical viscous and elastic models, and their combinations. The results show the impor-tance of numerical model tuning with the real behaviour, as such the characteristics linearity, and the essential parameters for damping and rigidity. Multiple comparisons between linear and non-linear simulation cases dignify the basis of numerical model optimization regarding mathematical complexity vs. results reliability.
Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction
Rezaeian Zadeh, Mehdi; Zia Hosseinipour, E.; Abghari, Hirad; Nikian, Ashkan; Shaeri Karimi, Sara; Moradzadeh Azar, Foad
2010-05-01
Hydrological models can help us to predict stream flows and associated runoff volumes of rainfall events within a watershed. There are many different reasons why we need to model the rainfall-runoff processes of for a watershed. However, the main reason is the limitation of hydrological measurement techniques and the costs of data collection at a fine scale. Generally, we are not able to measure all that we would like to know about a given hydrological systems. This is very particularly the case for ungauged catchments. Since the ultimate aim of prediction using models is to improve decision-making about a hydrological problem, therefore, having a robust and efficient modeling tool becomes an important factor. Among several hydrologic modeling approaches, continuous simulation has the best predictions because it can model dry and wet conditions during a long-term period. Continuous hydrologic models, unlike event based models, account for a watershed's soil moisture balance over a long-term period and are suitable for simulating daily, monthly, and seasonal streamflows. In this paper, we describe a soil moisture accounting (SMA) algorithm added to the hydrologic modeling system (HEC-HMS) computer program. As is well known in the hydrologic modeling community one of the ways for improving a model utility is the reduction of input parameters. The enhanced model developed in this study is applied to Khosrow Shirin Watershed, located in the north-west part of Fars Province in Iran, a data limited watershed. The HMS SMA algorithm divides the potential path of rainfall onto a watershed into five zones. The results showed that the output of HMS SMA is insensitive with the variation of many parameters such as soil storage and soil percolation rate. The study's objective is to remove insensitive parameters from the model input using Multi-objective sensitivity analysis. Keywords: Continuous Hydrologic Modeling, HMS SMA, Multi-objective sensitivity analysis, SMA Parameters
Condition Parameter Modeling for Anomaly Detection in Wind Turbines
Directory of Open Access Journals (Sweden)
Yonglong Yan
2014-05-01
Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Automatic Determination of the Conic Coronal Mass Ejection Model Parameters
Pulkkinen, A.; Oates, T.; Taktakishvili, A.
2009-01-01
Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-01-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is...
CADLIVE optimizer: web-based parameter estimation for dynamic models
Directory of Open Access Journals (Sweden)
Inoue Kentaro
2012-08-01
Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.
Ye, Yan; Song, Xiaomeng; Zhang, Jianyun; Kong, Fanzhe; Ma, Guangwen
2014-06-01
Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi-objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling (multivariate adaptive regression splines, MARS) for a conceptual rainfall-runoff model (Xin'anjiang model, XAJ) was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume (REPF and RERV). The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate modeling. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation
The Role of Non-Epistemic Values in Engineering Models
Diekmann, Sven; Peterson, Martin
2011-01-01
We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only “secondary values” that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that models are ne...
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders
1990-01-01
In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore...
X-Parameter Based Modelling of Polar Modulated Power Amplifiers
DEFF Research Database (Denmark)
Wang, Yelin; Nielsen, Troels Studsgaard; Sira, Daniel
2013-01-01
X-parameters are developed as an extension of S-parameters capable of modelling non-linear devices driven by large signals. They are suitable for devices having only radio frequency (RF) and DC ports. In a polar power amplifier (PA), phase and envelope of the input modulated signal are applied...... at separate ports and the envelope port is neither an RF nor a DC port. As a result, X-parameters may fail to characterise the effect of the envelope port excitation and consequently the polar PA. This study introduces a solution to the problem for a commercial polar PA. In this solution, the RF-phase path...
A Bayesian framework for parameter estimation in dynamical models.
Directory of Open Access Journals (Sweden)
Flávio Codeço Coelho
Full Text Available Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
Marton, F. C.
2001-12-01
The thermal, mineralogical, and buoyancy structures of thermal-kinetic models of subducting slabs are highly dependent upon a number of parameters, especially if the metastable persistence of olivine in the transition zone is investigated. The choice of starting thermal model for the lithosphere, whether a cooling halfspace (HS) or plate model, can have a significant effect, resulting in metastable wedges of olivine that differ in size by up to two to three times for high values of the thermal parameter (ǎrphi). Moreover, as ǎrphi is the product of the age of the lithosphere at the trench, convergence rate, and dip angle, slabs with similar ǎrphis can show great variations in structures as these constituents change. This is especially true for old lithosphere, as the lithosphere continually cools and thickens with age for HS models, but plate models, with parameters from Parson and Sclater [1977] (PS) or Stein and Stein [1992] (GDH1), achieve a thermal steady-state and constant thickness in about 70 My. In addition, the latent heats (q) of the phase transformations of the Mg2SiO4 polymorphs can also have significant effects in the slabs. Including q feedback in models raises the temperature and reduces the extent of metastable olivine, causing the sizes of the metastable wedges to vary by factors of up to two times. The effects of the choice of thermal model, inclusion and non-inclusion of q feedback, and variations in the constituents of ǎrphi are investigated for several model slabs.
Optimal experiment selection for parameter estimation in biological differential equation models
Directory of Open Access Journals (Sweden)
Transtrum Mark K
2012-07-01
Full Text Available Abstract Background Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection. Results A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information. Conclusions We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.
Shape parameter estimate for a glottal model without time position
Degottex, Gilles; Roebel, Axel; Rodet, Xavier
2009-01-01
cote interne IRCAM: Degottex09a; None / None; National audience; From a recorded speech signal, we propose to estimate a shape parameter of a glottal model without estimating his time position. Indeed, the literature usually propose to estimate the time position first (ex. by detecting Glottal Closure Instants). The vocal-tract filter estimate is expressed as a minimum-phase envelope estimation after removing the glottal model and a standard lips radiation model. Since this filter is mainly b...
Light-Front Spin-1 Model: Parameters Dependence
Mello, Clayton S; de Melo, J P B C; Frederico, T
2015-01-01
We study the structure of the $\\rho$-meson within a light-front model with constituent quark degrees of freedom. We calculate electroweak static observables: magnetic and quadrupole moments, decay constant and charge radius. The prescription used to compute the electroweak quantities is free of zero modes, which makes the calculation implicitly covariant. We compare the results of our model with other ones found in the literature. Our model parameters give a decay constant close to the experimental one.
Cosmological Models with Variable Deceleration Parameter in Lyra's Manifold
Pradhan, A; Singh, C B
2006-01-01
FRW models of the universe have been studied in the cosmological theory based on Lyra's manifold. A new class of exact solutions has been obtained by considering a time dependent displacement field for variable deceleration parameter from which three models of the universe are derived (i) exponential (ii) polynomial and (iii) sinusoidal form respectively. The behaviour of these models of the universe are also discussed. Finally some possibilities of further problems and their investigations have been pointed out.
Improved estimation of MR relaxation parameters using complex-valued data
Umesh Rudrapatna, S.; Bakker, C J G; Viergever, M A; van der Toorn, A; Dijkhuizen, R M
PURPOSE: In MR image analysis, T1 , T2 , and T2* maps are generally calculated using magnitude MR data. Without knowledge of the underlying noise variance, parameter estimates at low signal to noise ratio (SNR) are usually biased. This leads to confounds in studies that compare parameters across
Riml, J.; Wörman, A.
2009-12-01
Knowledge about both hydrochemical processes and watershed characteristics are key factors when trying to model transportation and retention of nutrients in a river system. The proposed parameterization method opens for the possibility to introduce independently measured parameters in lumped (compartmental) models. The analysis provides a better understanding of the model structure and aids in the calculation of optimal parameter values. The investigation uses a 1D distributed network model and parameterizes the result in a form appropriate for a compartmental model structure that has been developed for Swedish conditions during decades. The main tool for the analysis is the comparison of temporal moments between the two model structures. The parameterization gives information about the importance of river hydraulics but also about the effect of geomorphological processes such as the river network structure and parameter variability within the watershed. The methodology does also reveal information about predominating processes during distinctive hydrological conditions.
Algorithm for Tree Growth Modeling Based on Random Parameters and ARMA
Directory of Open Access Journals (Sweden)
Lichun Jiang
2013-08-01
Full Text Available Chapman-Richards function is used to model growth data of dahurian larch (Larix gmelinii Rupr. from longitudinal measurements using nonlinear mixed-effects modeling approach. The parameter variation in the model was divided into random effects, fixed effects and variance-covariance structure. The values for fixed effects parameters and the variance-covariance matrix of random effects were estimated using NLME function in S-plus software. Autocorrelation structure was considered for explaining the dependency among multiple measurements within the individuals. Information criterion statistics (AIC, BIC and Likelihood ratio test are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software. Results showed that the Chapman-Richards model with three random parameters could typically depict the dahurian larch tree growth in northeastern China. The mixed-effects model provided better performance and more precise estimations than the fixed-effects model.
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
IP-Sat: Impact-Parameter dependent Saturation model; revised
Rezaeian, Amir H; Van de Klundert, Merijn; Venugopalan, Raju
2013-01-01
In this talk, we present a global analysis of available small-x data on inclusive DIS and exclusive diffractive processes, including the latest data from the combined HERA analysis on reduced cross sections within the Impact-Parameter dependent Saturation (IP-Sat) Model. The impact-parameter dependence of dipole amplitude is crucial in order to have a unified description of both inclusive and exclusive diffractive processes. With the parameters of model fixed via a fit to the high-precision reduced cross-section, we compare model predictions to data for the structure functions, the longitudinal structure function, the charm structure function, exclusive vector mesons production and Deeply Virtual Compton Scattering (DVCS). Excellent agreement is obtained for the processes considered at small x in a wide range of Q^2.
QCD-inspired determination of NJL model parameters
Springer, Paul; Rechenberger, Stefan; Rennecke, Fabian
2016-01-01
The QCD phase diagram at finite temperature and density has attracted considerable interest over many decades now, not least because of its relevance for a better understanding of heavy-ion collision experiments. Models provide some insight into the QCD phase structure but usually rely on various parameters. Based on renormalization group arguments, we discuss how the parameters of QCD low-energy models can be determined from the fundamental theory of the strong interaction. We particularly focus on a determination of the temperature dependence of these parameters in this work and comment on the effect of a finite quark chemical potential. We present first results and argue that our findings can be used to improve the predictive power of future model calculations.
On the added value of WUDAPT for Urban Climate Modelling
Brousse, Oscar; Martilli, Alberto; Mills, Gerald; Bechtel, Benjamin; Hammerberg, Kris; Demuzere, Matthias; Wouters, Hendrik; Van Lipzig, Nicole; Ren, Chao; Feddema, Johannes J.; Masson, Valéry; Ching, Jason
2017-04-01
Over half of the planet's population now live in cities and is expected to grow up to 65% by 2050 (United Nations, 2014), most of whom will actually occupy new emerging cities of the global South. Cities' impact on climate is known to be a key driver of environmental change (IPCC, 2014) and has been studied for decades now (Howard, 1875). Still very little is known about our cities' structure around the world, preventing urban climate simulations to be done and hence guidance to be provided for mitigation. Assessing the need to bridge the urban knowledge gap for urban climate modelling perspectives, the World Urban Database and Access Portal Tool - WUDAPT - project (Ching et al., 2015; Mills et al., 2015) developed an innovative technique to map cities globally rapidly and freely. The framework established by Bechtel and Daneke (2012) derives Local Climate Zones (Stewart and Oke, 2012) city maps out of LANDSAT 8 OLI-TIRS imagery (Bechtel et al., 2015) through a supervised classification by a Random Forest Classification algorithm (Breiman, 2001). The first attempt to implement Local Climate Zones (LCZ) out of the WUDAPT product within a major climate model was carried out by Brousse et al. (2016) over Madrid, Spain. This study proved the applicability of LCZs as an enhanced urban parameterization within the WRF model (Chen et al. 2011) employing the urban canopy model BEP-BEM (Martilli, 2002; Salamanca et al., 2010), using the averaged values of the morphological and physical parameters' ranges proposed by Stewart and Oke (2012). Other studies have now used the Local Climate Zones for urban climate modelling purposes (Alexander et al., 2016; Wouters et al. 2016; Hammerberg et al., 2017; Brousse et al., 2017) and demonstrated the added value of the WUDAPT dataset. As urban data accessibility is one of the major challenge for simulations in emerging countries, this presentation will show results of simulations using LCZs and the capacity of the WUDAPT framework to be
Mean Value Modelling of an SI Engine with EGR
DEFF Research Database (Denmark)
Føns, Michael; Muller, Martin; Chevalier, Alain
1999-01-01
Mean Value Engine Models (MVEMs) are simplified, dynamic engine models which are physically based. Such models are useful for control studies, for engine control system analysis and for model based control systems. Very few published MVEMs have included the effects of Exhaust Gas Recirculation (E...
SPOTting model parameters using a ready-made Python package
Houska, Tobias; Kraft, Philipp; Breuer, Lutz
2015-04-01
The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations
Hanson, Andrea; Reed, Erik; Cavanagh, Peter
2011-01-01
Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan; Stieglitz, Marc
2015-04-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. In our view, such comparison is especially pertinent in the context of increasing appeal and popularity of the "trading space for time" approaches that are proposed for assessing the hydrological implications of anthropogenic climate change. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal
A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments.
Zeng, Qiang; Wen, Huiying; Huang, Helai; Abdel-Aty, Mohamed
2017-03-01
This study develops a Bayesian spatial random parameters Tobit model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed model with that of a (fixed parameters) Tobit model and a spatial (fixed parameters) Tobit model in the Bayesian context. Significant spatial effect is found in both spatial models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis.
Pawar, D D; Mapari, R V
2016-01-01
The main purpose of the present paper is to investigate LRS Bianchi type I metric in the presence of perfect fluid and dark energy. In order to obtain a deterministic solution of the field equations we have assumed that, the two sources of the perfect fluid and dark energy interact minimally with separate conservation of their energy momentum tensors. The EoS parameter of the perfect fluid is also assumed to be constant. In addition to these we have used a special law of variation of Hubble parameter proposed by Berman that yields constant deceleration parameter. For two different values of the constant deceleration parameters we have obtained two different cosmological models. The physical behaviors of both the models have been discussed by using some physical parameters.
Linear regression models of floor surface parameters on friction between Neolite and quarry tiles.
Chang, Wen-Ruey; Matz, Simon; Grönqvist, Raoul; Hirvonen, Mikko
2010-01-01
For slips and falls, friction is widely used as an indicator of surface slipperiness. Surface parameters, including surface roughness and waviness, were shown to influence friction by correlating individual surface parameters with the measured friction. A collective input from multiple surface parameters as a predictor of friction, however, could provide a broader perspective on the contributions from all the surface parameters evaluated. The objective of this study was to develop regression models between the surface parameters and measured friction. The dynamic friction was measured using three different mixtures of glycerol and water as contaminants. Various surface roughness and waviness parameters were measured using three different cut-off lengths. The regression models indicate that the selected surface parameters can predict the measured friction coefficient reliably in most of the glycerol concentrations and cut-off lengths evaluated. The results of the regression models were, in general, consistent with those obtained from the correlation between individual surface parameters and the measured friction in eight out of nine conditions evaluated in this experiment. A hierarchical regression model was further developed to evaluate the cumulative contributions of the surface parameters in the final iteration by adding these parameters to the regression model one at a time from the easiest to measure to the most difficult to measure and evaluating their impacts on the adjusted R(2) values. For practical purposes, the surface parameter R(a) alone would account for the majority of the measured friction even if it did not reach a statistically significant level in some of the regression models.
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-02-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is more significant. These results give new insights into the ETAS model and the efficiency of the maximum-likelihood method within this context.
J-A Hysteresis Model Parameters Estimation using GA
Directory of Open Access Journals (Sweden)
Bogomir Zidaric
2005-01-01
Full Text Available This paper presents the Jiles and Atherton (J-A hysteresis model parameter estimation for soft magnetic composite (SMC material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA. Genetic algorithms operate in a given area of possible solutions. Finding the best solution of a problem in wide area of possible solutions is uncertain. A new approach in use of genetic algorithms is proposed to overcome this uncertainty. The basis of this approach is in genetic algorithm built in another genetic algorithm.
A new estimate of the parameters in linear mixed models
Institute of Scientific and Technical Information of China (English)
王松桂; 尹素菊
2002-01-01
In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.
Models wagging the dog: are circuits constructed with disparate parameters?
Nowotny, Thomas; Szücs, Attila; Levi, Rafael; Selverston, Allen I
2007-08-01
In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.
Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine
Directory of Open Access Journals (Sweden)
Bambang Wahono
2014-01-01
Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.
M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); D.J.C. van Dijk (Dick)
2007-01-01
textabstractWe forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation
Institute of Scientific and Technical Information of China (English)
Lukas Graber; Diomar Infante; Michael Steurer; William W. Brey
2011-01-01
Careful analysis of transients in shipboard power systems is important to achieve long life times of the com ponents in future all-electric ships. In order to accomplish results with high accuracy, it is recommended to validate cable models as they have significant influence on the amplitude and frequency spectrum of voltage transients. The authors propose comparison of model and measurement using scattering parameters. They can be easily obtained from measurement and simulation and deliver broadband information about the accuracy of the model. The measurement can be performed using a vector network analyzer. The process to extract scattering parameters from simulation models is explained in detail. Three different simulation models of a 5 kV XLPE power cable have been validated. The chosen approach delivers an efficient tool to quickly estimate the quality of a model.
Dykierek, P; Stadtmüller, G; Schramm, P; Bahro, M; van Calker, D; Braus, D F; Steigleider, P; Löw, H; Hohagen, F; Gattaz, W F; Berger, M; Riemann, D
1998-01-01
Pseudodementia as a common trait in elderly depressives presents a major problem in gerontopsychiatry, especially for the differential diagnosis between Old-Age Depression (OAD) and Dementia of the Alzheimer Type (DAT). The present polysomnographic study examined parameters of sleep continuity, sleep architecture, and REM sleep to differentiate DAT from OAD. The investigation was based on the theoretical framework of the cholinergic-aminergic imbalance model of depression, the cholinergic deficit hypothesis of Alzheimer's disease and the reciprocal interaction model of Non-REM/REM sleep regulation, according to which REM sleep parameters should have high discriminative value to differentiate OAD and DAT. We investigated 35 DAT patients, 39 OAD patients and 42 healthy controls for two consecutive nights in the sleep laboratory. The DAT patients were in relatively early/mild stages of the disease, the severity of depression in the OAD group was moderate to severe. Depressed patients showed characteristic 'depression-like' EEG sleep alterations, i.e. a lower sleep efficiency, a higher amount of nocturnal awakenings and decreased sleep stage 2. Sleep continuity and architecture in DAT was less disturbed. Nearly all REM sleep measures differentiated significantly between the diagnostic groups. OAD patients showed a shortened REM latency, increased REM density and a high rate of Sleep Onset REM periods (SOREM), whereas in DAT REM density was decreased in comparison to control subjects. REM latency in DAT was not prolonged as expected. To assess the discriminative power of REM sleep variables a series of discriminant analyses were conducted. Overall, 86% of patients were correctly classified, using REM density and REM latency measures. Our findings suggest that REM density as an indicator of phasic activity appears to be more sensitive as a biological marker for the differential diagnosis of OAD and DAT than REM latency. The results support the role of central cholinergic
Model parameter uncertainty analysis for an annual field-scale P loss model
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
Analysis report for WIPP colloid model constraints and performance assessment parameters
Energy Technology Data Exchange (ETDEWEB)
Mariner, Paul E.; Sassani, David Carl
2014-03-01
An analysis of the Waste Isolation Pilot Plant (WIPP) colloid model constraints and parameter values was performed. The focus of this work was primarily on intrinsic colloids, mineral fragment colloids, and humic substance colloids, with a lesser focus on microbial colloids. Comments by the US Environmental Protection Agency (EPA) concerning intrinsic Th(IV) colloids and Mg-Cl-OH mineral fragment colloids were addressed in detail, assumptions and data used to constrain colloid model calculations were evaluated, and inconsistencies between data and model parameter values were identified. This work resulted in a list of specific conclusions regarding model integrity, model conservatism, and opportunities for improvement related to each of the four colloid types included in the WIPP performance assessment.
A value model for evaluating homeland security decisions.
Keeney, Ralph L; von Winterfeldt, Detlof
2011-09-01
One of the most challenging tasks of homeland security policymakers is to allocate their limited resources to reduce terrorism risks cost effectively. To accomplish this task, it is useful to develop a comprehensive set of homeland security objectives, metrics to measure each objective, a utility function, and value tradeoffs relevant for making homeland security investments. Together, these elements form a homeland security value model. This article develops a homeland security value model based on literature reviews, a survey, and experience with building value models. The purposes of the article are to motivate the use of a value model for homeland security decision making and to illustrate its use to assess terrorism risks, assess the benefits of countermeasures, and develop a severity index for terrorism attacks. © 2011 Society for Risk Analysis.
Albert, Carlo; Ulzega, Simone; Stoop, Ruedi
2016-04-01
Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact, and very efficient approach for generating posterior parameter distributions for stochastic differential equation models calibrated to measured time series. The algorithm is inspired by reinterpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for one-dimensional problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.
Failure analysis of parameter-induced simulation crashes in climate models
Directory of Open Access Journals (Sweden)
D. D. Lucas
2013-01-01
Full Text Available Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2 component of the Community Climate System Model (CCSM4. About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We apply support vector machine (SVM classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicts model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC curve metric (AUC > 0.96. The causes of the simulation failures are determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations are the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.
Failure analysis of parameter-induced simulation crashes in climate models
Directory of Open Access Journals (Sweden)
D. D. Lucas
2013-08-01
Full Text Available Simulations using IPCC (Intergovernmental Panel on Climate Change-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2 component of the Community Climate System Model (CCSM4. About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC curve metric (AUC > 0.96. The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... conditions near an inflow boundary where data is lacking and which exhibit apparent significant nonlinear behavior. It is shown that inclusion of Tikhonov regularization can stabilize and speed up the parameter estimation process. A method of linearized model analysis of predictive uncertainty...... nonlinear functions. Recommendations concerning the use of pilot points and singular value decomposition in real-world groundwater model calibration are finally given. (c) 2008 Elsevier Ltd. All rights reserved....
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jakob Laigaard; Brincker, Rune; Rytter, Anders
In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the param......In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty...... by a simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been chosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore...
Institute of Scientific and Technical Information of China (English)
2008-01-01
Based on the empirical electron theory of solids and molecules (EET), the statisti- cal values of valence electron structure parameters SnA and SEA which can char- acterize the properties of alloy phases are calculated, and influences of alloying elements (e.g., V, Nb, Mo, Hf, Zr, Fe, Mn, Co, Cr, Si, and so on) on the phase transi- tion temperature and eutectoid reaction of titanium alloy are discussed with the statistical values of valence electron structure parameters. The research results agree well with real situations.
Amirkhanov, I V; Sarker, N R; Sarhadov, I
2004-01-01
In this work the solutions of different boundary-value problems are retrieved analytically and numerically for the equation of high order with small parameter at a higher derivative. The analysis of these solutions is given. It is found that for some variants of symmetric boundary conditions the solutions of a boundary-value problem for the equations of the 4th, 6th, $\\ldots$ orders transfer into the solution of a Schrödinger equation at $\\varepsilon \\to 0$ ($\\varepsilon $ is small parameter). The retrieved solutions with different knots are orthogonal among themselves. The results of numerical calculations are given.
Parameter identification of ZnO surge arrester models based on genetic algorithms
Energy Technology Data Exchange (ETDEWEB)
Bayadi, Abdelhafid [Laboratoire d' Automatique de Setif, Departement d' Electrotechnique, Faculte des Sciences de l' Ingenieur, Universite Ferhat ABBAS de Setif, Route de Bejaia Setif 19000 (Algeria)
2008-07-15
The correct and adequate modelling of ZnO surge arresters characteristics is very important for insulation coordination studies and systems reliability. In this context many researchers addressed considerable efforts to the development of surge arresters models to reproduce the dynamic characteristics observed in their behaviour when subjected to fast front impulse currents. The difficulties with these models reside essentially in the calculation and the adjustment of their parameters. This paper proposes a new technique based on genetic algorithm to obtain the best possible series of parameter values of ZnO surge arresters models. The validity of the predicted parameters is then checked by comparing the predicted results with the experimental results available in the literature. Using the ATP-EMTP package, an application of the arrester model on network system studies is presented and discussed. (author)
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Liu, Fei; Heiner, Monika; Yang, Ming
2016-01-01
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
Self-Service Banking: Value Creation Models and Information Exchange
Directory of Open Access Journals (Sweden)
Ragnvald Sannes
2001-01-01
Full Text Available This paper argues that most banks have failed to exploit the potential of self-service banking because they base their service design on an incomplete business model for self-service. A framework for evaluation of self-service banking concepts is developed on the basis of Stabell and Fjeldstad's three value configurations. The value network and the value shop are consistent with self-service banking while the value chain is inappropriate. The impact of the value configurations on information exchange and self-service functionality is discussed, and a framework for design of such services proposed. Current self-service banking practices are compared to the framework, and it is concluded that current practice matches the concept of a value network and not the value shop. However, current practices are only a partial implementation of a value network-based self-service banking concept.
Estimation of growth parameters using a nonlinear mixed Gompertz model.
Wang, Z; Zuidhof, M J
2004-06-01
In order to maximize the utility of simulation models for decision making, accurate estimation of growth parameters and associated variances is crucial. A mixed Gompertz growth model was used to account for between-bird variation and heterogeneous variance. The mixed model had several advantages over the fixed effects model. The mixed model partitioned BW variation into between- and within-bird variation, and the covariance structure assumed with the random effect accounted for part of the BW correlation across ages in the same individual. The amount of residual variance decreased by over 55% with the mixed model. The mixed model reduced estimation biases that resulted from selective sampling. For analysis of longitudinal growth data, the mixed effects growth model is recommended.
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes
Yang, Xin-She; Luan, Tao; Koziel, Slawomir
2014-01-01
Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
The influences of model parameters on the characteristics of memristors
Institute of Scientific and Technical Information of China (English)
Zhou Jing; Huang Da
2012-01-01
As the fourth passive circuit component,a memristor is a nonlinear resistor that can "remember" the amount of charge passing through it.The characteristic of "remembering" the charge and non-volatility makes memristors great potential candidates in many fields.Nowadays,only a few groups have the ability to fabricate memristors,and most researchers study them by theoretic analysis and simulation.In this paper,we first analyse the theoretical base and characteristics of memristors,then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics.Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.
Directory of Open Access Journals (Sweden)
Rutao Luo
Full Text Available Mathematical models based on ordinary differential equations (ODE have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3-5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.
The sensitivity of flowline models of tidewater glaciers to parameter uncertainty
Directory of Open Access Journals (Sweden)
E. M. Enderlin
2013-10-01
Full Text Available Depth-integrated (1-D flowline models have been widely used to simulate fast-flowing tidewater glaciers and predict change because the continuous grounding line tracking, high horizontal resolution, and physically based calving criterion that are essential to realistic modeling of tidewater glaciers can easily be incorporated into the models while maintaining high computational efficiency. As with all models, the values for parameters describing ice rheology and basal friction must be assumed and/or tuned based on observations. For prognostic studies, these parameters are typically tuned so that the glacier matches observed thickness and speeds at an initial state, to which a perturbation is applied. While it is well know that ice flow models are sensitive to these parameters, the sensitivity of tidewater glacier models has not been systematically investigated. Here we investigate the sensitivity of such flowline models of outlet glacier dynamics to uncertainty in three key parameters that influence a glacier's resistive stress components. We find that, within typical observational uncertainty, similar initial (i.e., steady-state glacier configurations can be produced with substantially different combinations of parameter values, leading to differing transient responses after a perturbation is applied. In cases where the glacier is initially grounded near flotation across a basal over-deepening, as typically observed for rapidly changing glaciers, these differences can be dramatic owing to the threshold of stability imposed by the flotation criterion. The simulated transient response is particularly sensitive to the parameterization of ice rheology: differences in ice temperature of ~ 2 °C can determine whether the glaciers thin to flotation and retreat unstably or remain grounded on a marine shoal. Due to the highly non-linear dependence of tidewater glaciers on model parameters, we recommend that their predictions are accompanied by
Using a scalar parameter to trace dislocation evolution in atomistic modeling
Energy Technology Data Exchange (ETDEWEB)
Yang, Jinbo [ORNL; Zhang, Z F [Shenyang National Laboratory for Materials Science; Osetskiy, Yury N [ORNL; Stoller, Roger E [ORNL
2015-01-01
A scalar gamma-parameter is proposed from the Nye tensor. Its maximum value occurs along a dislocation line, either straight or curved, when the coordinate system is purposely chosen. This parameter can be easily obtained from the Nye tensor calculated at each atom in atomistic modeling. Using the gamma-parameter, a fully automated approach is developed to determine core atoms and the Burgers vectors of dislocations simultaneously. The approach is validated by revealing the smallest dislocation loop and by tracing the whole formation process of complicated dislocation networks on the fly.
Calculation of Thermodynamic Parameters for Freundlich and Temkin Isotherm Models
Institute of Scientific and Technical Information of China (English)
ZHANGZENGQIANG; ZHANGYIPING; 等
1999-01-01
Derivation of the Freundlich and Temkin isotherm models from the kinetic adsorption/desorption equations was carried out to calculate their thermodynamic equilibrium constants.The calculation formulase of three thermodynamic parameters,the standard molar Gibbs free energy change,the standard molar enthalpy change and the standard molar entropy change,of isothermal adsorption processes for Freundlich and Temkin isotherm models were deduced according to the relationship between the thermodynamic equilibrium constants and the temperature.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
2002-01-01
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Parabolic problems with parameters arising in evolution model for phytromediation
Sahmurova, Aida; Shakhmurov, Veli
2012-12-01
The past few decades, efforts have been made to clean sites polluted by heavy metals as chromium. One of the new innovative methods of eradicating metals from soil is phytoremediation. This uses plants to pull metals from the soil through the roots. This work develops a system of differential equations with parameters to model the plant metal interaction of phytoremediation (see [1]).
Lumped-parameter Model of a Bucket Foundation
DEFF Research Database (Denmark)
Andersen, Lars; Ibsen, Lars Bo; Liingaard, Morten
2009-01-01
As an alternative to gravity footings or pile foundations, offshore wind turbines at shallow water can be placed on a bucket foundation. The present analysis concerns the development of consistent lumped-parameter models for this type of foundation. The aim is to formulate a computationally effic...
Improved parameter estimation for hydrological models using weighted object functions
Stein, A.; Zaadnoordijk, W.J.
1999-01-01
This paper discusses the sensitivity of calibration of hydrological model parameters to different objective functions. Several functions are defined with weights depending upon the hydrological background. These are compared with an objective function based upon kriging. Calibration is applied to pi
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
Institute of Scientific and Technical Information of China (English)
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Modeling and simulation of HTS cables for scattering parameter analysis
Bang, Su Sik; Lee, Geon Seok; Kwon, Gu-Young; Lee, Yeong Ho; Chang, Seung Jin; Lee, Chun-Kwon; Sohn, Songho; Park, Kijun; Shin, Yong-June
2016-11-01
Most of modeling and simulation of high temperature superconducting (HTS) cables are inadequate for high frequency analysis since focus of the simulation's frequency is fundamental frequency of the power grid, which does not reflect transient characteristic. However, high frequency analysis is essential process to research the HTS cables transient for protection and diagnosis of the HTS cables. Thus, this paper proposes a new approach for modeling and simulation of HTS cables to derive the scattering parameter (S-parameter), an effective high frequency analysis, for transient wave propagation characteristics in high frequency range. The parameters sweeping method is used to validate the simulation results to the measured data given by a network analyzer (NA). This paper also presents the effects of the cable-to-NA connector in order to minimize the error between the simulated and the measured data under ambient and superconductive conditions. Based on the proposed modeling and simulation technique, S-parameters of long-distance HTS cables can be accurately derived in wide range of frequency. The results of proposed modeling and simulation can yield the characteristics of the HTS cables and will contribute to analyze the HTS cables.
Diagnosing $\\Lambda$HDE model with statefinder hierarchy and fractional growth parameter
Zhou, Lanjun
2016-01-01
Recently, a new dark energy model called $\\Lambda$HDE was proposed. In this model, dark energy consists of two parts: cosmological constant $\\Lambda$ and holographic dark energy (HDE). Two key parameters of this model are the fractional density of cosmological constant $\\Omega_{\\Lambda0}$, and the dimensionless HDE parameter $c$. Since these two parameters determine the dynamical properties of DE and the destiny of universe, it is important to study the impacts of different values of $\\Omega_{\\Lambda0}$ and $c$ on the $\\Lambda$HDE model. In this paper, we apply various DE diagnostic tools to diagnose $\\Lambda$HDE models with different values of $\\Omega_{\\Lambda0}$ and $c$; these tools include statefinder hierarchy \\{$S_3^{(1)}, S_4^{(1)}$\\}, fractional growth parameter $\\epsilon$, and composite null diagnostic (CND), which is a combination of \\{$S_3^{(1)}, S_4^{(1)}$\\} and $\\epsilon$. We find that: (1) adopting different values of $\\Omega_{\\Lambda0}$ only has quantitative impacts on the evolution of the $\\Lam...
Energy Technology Data Exchange (ETDEWEB)
Koyama, Hisanobu, E-mail: hkoyama@med.kobe-u.ac.jp [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe (Japan); Ohno, Yoshiharu [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe (Japan); Seki, Shinichiro [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe (Japan); Nishio, Mizuho; Yoshikawa, Takeshi; Matsumoto, Sumiaki [Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe (Japan); Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe (Japan); Maniwa, Yoshimasa [Department of General Thoracic Surgery, Kobe University Graduate School of Medicine, Kobe (Japan); Itoh, Tomoo [Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe (Japan); Nishimura, Yoshihiro [Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe (Japan); Sugimura, Kazuro [Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe (Japan)
2015-03-15
Highlights: •Signal–intensity ratio evaluation between lesion and spinal cord is practical method. •Apparent diffusion coefficients may not contribute to the diagnosis of malignant. •True diffusion coefficients may have low potential for the differentiation. •Perfusion fractions may be less specific parameter of diagnosis of pulmonary nodule. •Choice of b values shows little impact for differentiation of pulmonary nodules. -- Abstract: Objectives: To determine the appropriate parameters and evaluation method for characterizing solitary pulmonary nodules (SPNs) using quantitative parameters of diffusion-weighted imaging (DWI). Methods: Thirty-two subjects with 36 SPNs underwent DWI with seven different b values (0, 50, 100, 150, 300, 500, and 1000 s/mm{sup 2}). Five quantitative parameters were obtained from the region of interest drawn over each SPN: apparent diffusion coefficients (ADCs), true diffusion coefficients (DCs), and perfusion fractions (PFs), and signal–intensity ratios between lesion and spinal cord from DWI (b values: 1000 [LSR{sub 1000}] and 500 [LSR{sub 500})]). All quantitative parameters and the diagnostic capabilities were statistically compared. Results: SPNs were diagnosed as follow: malignant (n = 27) and benign (n = 9). Parameter comparisons for malignant and benign showed both LSRs differed significantly (p < 0.05). Applying feasible threshold values showed LSR{sub 500} specificity (88.9% [8/9]) and accuracy (77.8% [28/36]) were significantly higher than ADC, DC, and PF specificity and accuracy (p < 0.05). LSR{sub 1000} accuracy (72.2% [26/36]) was significantly higher than DC accuracy, and its specificity (88.9% [8/9]) was significantly higher than ADC, DC, and PF specificities (p < 0.05). Conclusions: For quantitative differentiation of SPNs, LSR evaluation was more useful and practical than ADC, DC, and PF, and choice of b values showed little impact for the differentiation.
Mean Value Modelling of a Turbocharged SI Engine
DEFF Research Database (Denmark)
Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.
1998-01-01
An important paradigm for the modelling of naturallly aspirated (NA) spark ignition (SI) engines for control purposes is the Mean Value Engine Model (MVEM). Such models have a time resolution which is just sufficient to capture the main details of the dynamic performance of NA SI engines but not ...
Directory of Open Access Journals (Sweden)
Rudiati Evi Masithoh
2013-03-01
Full Text Available Artificial neural networks (ANN was used to predict the quality parameters of tomato, i.e. Brix, citric acid, total carotene, and vitamin C. ANN was developed from Red Green Blue (RGB image data of tomatoes measured using a developed computer vision system (CVS. Qualitative analysis of tomato compositions were obtained from laboratory experiments. ANN model was based on a feedforward backpropagation network with different training functions, namely gradient descent (traingd, gradient descent with the resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab and Shanno (BFGS quasi-Newton (trainbfg, as well as Levenberg Marquardt (trainlm. The network structure using logsig and linear (purelin activation function at the hidden and output layer, respectively, and using the trainlm as a training function resulted in the best performance. Correlation coefficient (r of training and validation process were 0.97 - 0.99 and 0.92 - 0.99, whereas the MAE values ranged from 0.01 to 0.23 and 0.03 to 0.59, respectively. Keywords: Artificial neural network, trainlm, tomato, RGB Jaringan syaraf tiruan (JST digunakan untuk memprediksi parameter kualitas tomat, yaitu Brix, asam sitrat, karoten total, dan vitamin C. JST dikembangkan dari data Red Green Blue (RGB citra tomat yang diukur menggunakan computer vision system. Data kualitas tomat diperoleh dari analisis di laboratorium. Struktur model JST didasarkan pada jaringan feedforward backpropagation dengan berbagai fungsi pelatihan, yaitu gradient descent (traingd, gradient descent dengan resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab dan Shanno (BFGS quasi-Newton (trainbfg, serta Levenberg Marquardt (trainlm. Fungsi pelatihan yang terbaik adalah menggunakan trainlm, serta pada struktur jaringan digunakan fungsi aktivasi logsig pada lapisan tersembunyi dan linier (purelin pada lapisan keluaran. dengan 1000 epoch. Nilai koefisien korelasi (r pada tahap pelatihan dan validasi
Agricultural and Environmental Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rasmuson; K. Rautenstrauch
2004-09-14
This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters.
López-Cuevas, Armando; Castillo-Toledo, Bernardino; Medina-Ceja, Laura; Ventura-Mejía, Consuelo
2015-06-01
Status epilepticus is an emergency condition in patients with prolonged seizure or recurrent seizures without full recovery between them. The pathophysiological mechanisms of status epilepticus are not well established. With this argument, we use a computational modeling approach combined with in vivo electrophysiological data obtained from an experimental model of status epilepticus to infer about changes that may lead to a seizure. Special emphasis is done to analyze parameter changes during or after pilocarpine administration. A cubature Kalman filter is utilized to estimate parameters and states of the model in real time from the observed electrophysiological signals. It was observed that during basal activity (before pilocarpine administration) the parameters presented a standard deviation below 30% of the mean value, while during SE activity, the parameters presented variations larger than 200% of the mean value with respect to basal state. The ratio of excitation-inhibition, increased during SE activity by 80% with respect to the transition state, and reaches the lowest value during cessation. In addition, a progression between low and fast inhibitions before or during this condition was found. This method can be implemented in real time, which is particularly important for the design of stimulation devices that attempt to stop seizures. These changes in the parameters analyzed during seizure activity can lead to better understanding of the mechanisms of epilepsy and to improve its treatments.
A termination criterion for parameter estimation in stochastic models in systems biology.
Zimmer, Christoph; Sahle, Sven
2015-11-01
Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model.
National Research Council Canada - National Science Library
Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli
2013-01-01
.... The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value...
Multiscale Parameter Regionalization for consistent global water resources modelling
Wanders, Niko; Wood, Eric; Pan, Ming; Samaniego, Luis; Thober, Stephan; Kumar, Rohini; Sutanudjaja, Edwin; van Beek, Rens; Bierkens, Marc F. P.
2017-04-01
Due to an increasing demand for high- and hyper-resolution water resources information, it has become increasingly important to ensure consistency in model simulations across scales. This consistency can be ensured by scale independent parameterization of the land surface processes, even after calibration of the water resource model. Here, we use the Multiscale Parameter Regionalization technique (MPR, Samaniego et al. 2010, WRR) to allow for a novel, spatially consistent, scale independent parameterization of the global water resource model PCR-GLOBWB. The implementation of MPR in PCR-GLOBWB allows for calibration at coarse resolutions and subsequent parameter transfer to the hyper-resolution. In this study, the model was calibrated at 50 km resolution over Europe and validation carried out at resolutions of 50 km, 10 km and 1 km. MPR allows for a direct transfer of the calibrated transfer function parameters across scales and we find that we can maintain consistent land-atmosphere fluxes across scales. Here we focus on the 2003 European drought and show that the new parameterization allows for high-resolution calibrated simulations of water resources during the drought. For example, we find a reduction from 29% to 9.4% in the percentile difference in the annual evaporative flux across scales when compared against default simulations. Soil moisture errors are reduced from 25% to 6.9%, clearly indicating the benefits of the MPR implementation. This new parameterization allows us to show more spatial detail in water resources simulations that are consistent across scales and also allow validation of discharge for smaller catchments, even with calibrations at a coarse 50 km resolution. The implementation of MPR allows for novel high-resolution calibrated simulations of a global water resources model, providing calibrated high-resolution model simulations with transferred parameter sets from coarse resolutions. The applied methodology can be transferred to other
Directory of Open Access Journals (Sweden)
Petras Rupšys
2015-01-01
Full Text Available A stochastic modeling approach based on the Bertalanffy law gained interest due to its ability to produce more accurate results than the deterministic approaches. We examine tree crown width dynamic with the Bertalanffy type stochastic differential equation (SDE and mixed-effects parameters. In this study, we demonstrate how this simple model can be used to calculate predictions of crown width. We propose a parameter estimation method and computational guidelines. The primary goal of the study was to estimate the parameters by considering discrete sampling of the diameter at breast height and crown width and by using maximum likelihood procedure. Performance statistics for the crown width equation include statistical indexes and analysis of residuals. We use data provided by the Lithuanian National Forest Inventory from Scots pine trees to illustrate issues of our modeling technique. Comparison of the predicted crown width values of mixed-effects parameters model with those obtained using fixed-effects parameters model demonstrates the predictive power of the stochastic differential equations model with mixed-effects parameters. All results were implemented in a symbolic algebra system MAPLE.
Trust Model for Social Network using Singular Value Decomposition
Davis Bundi Ntwiga; Patrick Weke; Michael Kiura Kirumbu
2016-01-01
For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the...
Model and parameter uncertainty in IDF relationships under climate change
Chandra, Rupa; Saha, Ujjwal; Mujumdar, P. P.
2015-05-01
Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.
Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates
Todorovic, Andrijana; Plavsic, Jasna
2015-04-01
. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.