Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability
Directory of Open Access Journals (Sweden)
Rózsás Árpád
2015-12-01
Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.
Estimations of parameters in Pareto reliability model in the presence of masked data
International Nuclear Information System (INIS)
Sarhan, Ammar M.
2003-01-01
Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained
Parameter estimation of component reliability models in PSA model of Krsko NPP
International Nuclear Information System (INIS)
Jordan Cizelj, R.; Vrbanic, I.
2001-01-01
In the paper, the uncertainty analysis of component reliability models for independent failures is shown. The present approach for parameter estimation of component reliability models in NPP Krsko is presented. Mathematical approaches for different types of uncertainty analyses are introduced and used in accordance with some predisposed requirements. Results of the uncertainty analyses are shown in an example for time-related components. As the most appropriate uncertainty analysis proved the Bayesian estimation with the numerical estimation of a posterior, which can be approximated with some appropriate probability distribution, in this paper with lognormal distribution.(author)
Stochastic models and reliability parameter estimation applicable to nuclear power plant safety
International Nuclear Information System (INIS)
Mitra, S.P.
1979-01-01
A set of stochastic models and related estimation schemes for reliability parameters are developed. The models are applicable for evaluating reliability of nuclear power plant systems. Reliability information is extracted from model parameters which are estimated from the type and nature of failure data that is generally available or could be compiled in nuclear power plants. Principally, two aspects of nuclear power plant reliability have been investigated: (1) The statistical treatment of inplant component and system failure data; (2) The analysis and evaluation of common mode failures. The model inputs are failure data which have been classified as either the time type of failure data or the demand type of failure data. Failures of components and systems in nuclear power plant are, in general, rare events.This gives rise to sparse failure data. Estimation schemes for treating sparse data, whenever necessary, have been considered. The following five problems have been studied: 1) Distribution of sparse failure rate component data. 2) Failure rate inference and reliability prediction from time type of failure data. 3) Analyses of demand type of failure data. 4) Common mode failure model applicable to time type of failure data. 5) Estimation of common mode failures from 'near-miss' demand type of failure data
Terry, Leann; Kelley, Ken
2012-11-01
Composite measures play an important role in psychology and related disciplines. Composite measures almost always have error. Correspondingly, it is important to understand the reliability of the scores from any particular composite measure. However, the point estimates of the reliability of composite measures are fallible and thus all such point estimates should be accompanied by a confidence interval. When confidence intervals are wide, there is much uncertainty in the population value of the reliability coefficient. Given the importance of reporting confidence intervals for estimates of reliability, coupled with the undesirability of wide confidence intervals, we develop methods that allow researchers to plan sample size in order to obtain narrow confidence intervals for population reliability coefficients. We first discuss composite reliability coefficients and then provide a discussion on confidence interval formation for the corresponding population value. Using the accuracy in parameter estimation approach, we develop two methods to obtain accurate estimates of reliability by planning sample size. The first method provides a way to plan sample size so that the expected confidence interval width for the population reliability coefficient is sufficiently narrow. The second method ensures that the confidence interval width will be sufficiently narrow with some desired degree of assurance (e.g., 99% assurance that the 95% confidence interval for the population reliability coefficient will be less than W units wide). The effectiveness of our methods was verified with Monte Carlo simulation studies. We demonstrate how to easily implement the methods with easy-to-use and freely available software. ©2011 The British Psychological Society.
Directory of Open Access Journals (Sweden)
Anupam Pathak
2014-11-01
Full Text Available Abstract: Problem Statement: The two-parameter exponentiated Rayleigh distribution has been widely used especially in the modelling of life time event data. It provides a statistical model which has a wide variety of application in many areas and the main advantage is its ability in the context of life time event among other distributions. The uniformly minimum variance unbiased and maximum likelihood estimation methods are the way to estimate the parameters of the distribution. In this study we explore and compare the performance of the uniformly minimum variance unbiased and maximum likelihood estimators of the reliability function R(t=P(X>t and P=P(X>Y for the two-parameter exponentiated Rayleigh distribution. Approach: A new technique of obtaining these parametric functions is introduced in which major role is played by the powers of the parameter(s and the functional forms of the parametric functions to be estimated are not needed. We explore the performance of these estimators numerically under varying conditions. Through the simulation study a comparison are made on the performance of these estimators with respect to the Biasness, Mean Square Error (MSE, 95% confidence length and corresponding coverage percentage. Conclusion: Based on the results of simulation study the UMVUES of R(t and ‘P’ for the two-parameter exponentiated Rayleigh distribution found to be superior than MLES of R(t and ‘P’.
Directory of Open Access Journals (Sweden)
Jiang Ge
2017-01-01
Full Text Available System degradation was usually caused by multiple-parameter degradation. The assessment result of system reliability by universal generating function was low accurate when compared with the Monte Carlo simulation. And the probability density function of the system output performance cannot be got. So the reliability assessment method based on the probability density evolution with multi-parameter was presented for complexly degraded system. Firstly, the system output function was founded according to the transitive relation between component parameters and the system output performance. Then, the probability density evolution equation based on the probability conservation principle and the system output function was established. Furthermore, probability distribution characteristics of the system output performance was obtained by solving differential equation. Finally, the reliability of the degraded system was estimated. This method did not need to discrete the performance parameters and can establish continuous probability density function of the system output performance with high calculation efficiency and low cost. Numerical example shows that this method is applicable to evaluate the reliability of multi-parameter degraded system.
DEFF Research Database (Denmark)
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian
2011-01-01
of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to generate a set......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
Reliability Estimation of Parameters of Helical Wind Turbine with Vertical Axis
Directory of Open Access Journals (Sweden)
Adela-Eliza Dumitrascu
2015-01-01
Full Text Available Due to the prolonged use of wind turbines they must be characterized by high reliability. This can be achieved through a rigorous design, appropriate simulation and testing, and proper construction. The reliability prediction and analysis of these systems will lead to identifying the critical components, increasing the operating time, minimizing failure rate, and minimizing maintenance costs. To estimate the produced energy by the wind turbine, an evaluation approach based on the Monte Carlo simulation model is developed which enables us to estimate the probability of minimum and maximum parameters. In our simulation process we used triangular distributions. The analysis of simulation results has been focused on the interpretation of the relative frequency histograms and cumulative distribution curve (ogive diagram, which indicates the probability of obtaining the daily or annual energy output depending on wind speed. The experimental researches consist in estimation of the reliability and unreliability functions and hazard rate of the helical vertical axis wind turbine designed and patented to climatic conditions for Romanian regions. Also, the variation of power produced for different wind speeds, the Weibull distribution of wind probability, and the power generated were determined. The analysis of experimental results indicates that this type of wind turbine is efficient at low wind speed.
Reliability Estimation of Parameters of Helical Wind Turbine with Vertical Axis.
Dumitrascu, Adela-Eliza; Lepadatescu, Badea; Dumitrascu, Dorin-Ion; Nedelcu, Anisor; Ciobanu, Doina Valentina
2015-01-01
Due to the prolonged use of wind turbines they must be characterized by high reliability. This can be achieved through a rigorous design, appropriate simulation and testing, and proper construction. The reliability prediction and analysis of these systems will lead to identifying the critical components, increasing the operating time, minimizing failure rate, and minimizing maintenance costs. To estimate the produced energy by the wind turbine, an evaluation approach based on the Monte Carlo simulation model is developed which enables us to estimate the probability of minimum and maximum parameters. In our simulation process we used triangular distributions. The analysis of simulation results has been focused on the interpretation of the relative frequency histograms and cumulative distribution curve (ogive diagram), which indicates the probability of obtaining the daily or annual energy output depending on wind speed. The experimental researches consist in estimation of the reliability and unreliability functions and hazard rate of the helical vertical axis wind turbine designed and patented to climatic conditions for Romanian regions. Also, the variation of power produced for different wind speeds, the Weibull distribution of wind probability, and the power generated were determined. The analysis of experimental results indicates that this type of wind turbine is efficient at low wind speed.
International Nuclear Information System (INIS)
Nyman, R.; Hegedus, D.; Tomic, B.; Lydell, B.
1997-12-01
This report summarizes results and insights from the final phase of a R and D project on piping reliability sponsored by the Swedish Nuclear Power Inspectorate (SKI). The technical scope includes the development of an analysis framework for estimating piping reliability parameters from service data. The R and D has produced a large database on the operating experience with piping systems in commercial nuclear power plants worldwide. It covers the period 1970 to the present. The scope of the work emphasized pipe failures (i.e., flaws/cracks, leaks and ruptures) in light water reactors (LWRs). Pipe failures are rare events. A data reduction format was developed to ensure that homogenous data sets are prepared from scarce service data. This data reduction format distinguishes between reliability attributes and reliability influence factors. The quantitative results of the analysis of service data are in the form of conditional probabilities of pipe rupture given failures (flaws/cracks, leaks or ruptures) and frequencies of pipe failures. Finally, the R and D by SKI produced an analysis framework in support of practical applications of service data in PSA. This, multi-purpose framework, termed 'PFCA'-Pipe Failure Cause and Attribute- defines minimum requirements on piping reliability analysis. The application of service data should reflect the requirements of an application. Together with raw data summaries, this analysis framework enables the development of a prior and a posterior pipe rupture probability distribution. The framework supports LOCA frequency estimation, steam line break frequency estimation, as well as the development of strategies for optimized in-service inspection strategies
Energy Technology Data Exchange (ETDEWEB)
Nyman, R [Swedish Nuclear Power Inspectorate, Stockholm (Sweden); Hegedus, D; Tomic, B [ENCONET Consulting GesmbH, Vienna (Austria); Lydell, B [RSA Technologies, Vista, CA (United States)
1997-12-01
This report summarizes results and insights from the final phase of a R and D project on piping reliability sponsored by the Swedish Nuclear Power Inspectorate (SKI). The technical scope includes the development of an analysis framework for estimating piping reliability parameters from service data. The R and D has produced a large database on the operating experience with piping systems in commercial nuclear power plants worldwide. It covers the period 1970 to the present. The scope of the work emphasized pipe failures (i.e., flaws/cracks, leaks and ruptures) in light water reactors (LWRs). Pipe failures are rare events. A data reduction format was developed to ensure that homogenous data sets are prepared from scarce service data. This data reduction format distinguishes between reliability attributes and reliability influence factors. The quantitative results of the analysis of service data are in the form of conditional probabilities of pipe rupture given failures (flaws/cracks, leaks or ruptures) and frequencies of pipe failures. Finally, the R and D by SKI produced an analysis framework in support of practical applications of service data in PSA. This, multi-purpose framework, termed `PFCA`-Pipe Failure Cause and Attribute- defines minimum requirements on piping reliability analysis. The application of service data should reflect the requirements of an application. Together with raw data summaries, this analysis framework enables the development of a prior and a posterior pipe rupture probability distribution. The framework supports LOCA frequency estimation, steam line break frequency estimation, as well as the development of strategies for optimized in-service inspection strategies. 63 refs, 30 tabs, 22 figs.
Directory of Open Access Journals (Sweden)
Sanjay Kumar Singh
2011-06-01
Full Text Available In this Paper we propose Bayes estimators of the parameters of Exponentiated Exponential distribution and Reliability functions under General Entropy loss function for Type II censored sample. The proposed estimators have been compared with the corresponding Bayes estimators obtained under Squared Error loss function and maximum likelihood estimators for their simulated risks (average loss over sample space.
Alaa F. Sheta; Amal Abdel-Raouf
2016-01-01
In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of...
International Nuclear Information System (INIS)
Xu, Meng; Droguett, Enrique López; Lins, Isis Didier; Chagas Moura, Márcio das
2017-01-01
The q-Weibull model is based on the Tsallis non-extensive entropy and is able to model various behaviors of the hazard rate function, including bathtub curves, by using a single set of parameters. Despite its flexibility, the q-Weibull has not been widely used in reliability applications partly because of the complicated parameters estimation. In this work, the parameters of the q-Weibull are estimated by the maximum likelihood (ML) method. Due to the intricate system of nonlinear equations, derivative-based optimization methods may fail to converge. Thus, the heuristic optimization method of artificial bee colony (ABC) is used instead. To deal with the slow convergence of ABC, it is proposed an adaptive hybrid ABC (AHABC) algorithm that dynamically combines Nelder-Mead simplex search method with ABC for the ML estimation of the q-Weibull parameters. Interval estimates for the q-Weibull parameters, including confidence intervals based on the ML asymptotic theory and on bootstrap methods, are also developed. The AHABC is validated via numerical experiments involving the q-Weibull ML for reliability applications and results show that it produces faster and more accurate convergence when compared to ABC and similar approaches. The estimation procedure is applied to real reliability failure data characterized by a bathtub-shaped hazard rate. - Highlights: • Development of an Adaptive Hybrid ABC (AHABC) algorithm for q-Weibull distribution. • AHABC combines local Nelder-Mead simplex method with ABC to enhance local search. • AHABC efficiently finds the optimal solution for the q-Weibull ML problem. • AHABC outperforms ABC and self-adaptive hybrid ABC in accuracy and convergence speed. • Useful model for reliability data with non-monotonic hazard rate.
Reliability of blood pressure parameters for dry weight estimation in hemodialysis patients.
Susantitaphong, Paweena; Laowaloet, Suthanit; Tiranathanagul, Khajohn; Chulakadabba, Adhisabandh; Katavetin, Pisut; Praditpornsilpa, Kearkiat; Tungsanga, Kriang; Eiam-Ong, Somchai
2013-02-01
Chronic volume overload resulting from interdialytic weight gain and inadequate fluid removal plays a significant role in poorly controlled high blood pressure. Although bioimpedance has been introduced as an accurate method for assessing hydration status, the instrument is not available in general hemodialysis (HEMO) centers. This study was conducted to explore the correlation between hydration status measured by bioimpedance and blood pressure parameters in chronic HEMO patients. Multifrequency bioimpedance analysis was used to determine pre- and post-dialysis hydration status in 32 stable HEMO patients. Extracellular water/total body water (ECW/TBW) determined by sum of segments from bioimpedance analysis was used as an index of hydration status. The mean age was 57.9 ± 16.4 years. The mean dry weight and body mass index were 57.7 ± 14.5 kg and 22.3 ± 4.7 kg/m(2), respectively. Pre-dialysis ECW/TBW was significantly correlated with only pulse pressure (r = 0.5, P = 0.003) whereas post-dialysis ECW/TBW had significant correlations with pulse pressure, systolic blood pressure, and diastolic blood pressure (r = 0.6, P = 0.001, r = 0.4, P = 0.04, r = -0.4, and P = 0.02, respectively). After dialysis, the mean values of ECW/TBW, systolic blood pressure, mean arterial pressure, and pulse pressure were significantly decreased. ECW/TBW was used to classify the patients into normohydration (≤ 0.4) and overhydration (>0.4) groups. Systolic blood pressure, mean arterial pressure, and pulse pressure significantly reduced after dialysis in the normohydration group but did not significantly change in the overhydration group. Pre-dialysis pulse pressure, post-dialysis pulse pressure, and post-dialysis systolic blood pressure in the overhydration group were significantly higher than normohydration group. Due to the simplicity and cost, blood pressure parameters, especially pulse pressure, might be a simple reference for clinicians to determine hydration status in HEMO
Optomechanical parameter estimation
International Nuclear Information System (INIS)
Ang, Shan Zheng; Tsang, Mankei; Harris, Glen I; Bowen, Warwick P
2013-01-01
We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cramér–Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation–maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cramér–Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry and fundamental tests of quantum mechanics. (paper)
Estimation of Bridge Reliability Distributions
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown how the so-called reliability distributions can be estimated using crude Monte Carlo simulation. The main purpose is to demonstrate the methodology. Therefor very exact data concerning reliability and deterioration are not needed. However, it is intended in the paper to ...
A Method of Nuclear Software Reliability Estimation
International Nuclear Information System (INIS)
Park, Gee Yong; Eom, Heung Seop; Cheon, Se Woo; Jang, Seung Cheol
2011-01-01
A method on estimating software reliability for nuclear safety software is proposed. This method is based on the software reliability growth model (SRGM) where the behavior of software failure is assumed to follow the non-homogeneous Poisson process. Several modeling schemes are presented in order to estimate and predict more precisely the number of software defects based on a few of software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating the software test cases into the model. It is identified that this method is capable of accurately estimating the remaining number of software defects which are on-demand type directly affecting safety trip functions. The software reliability can be estimated from a model equation and one method of obtaining the software reliability is proposed
Ranking as parameter estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav; Guy, Tatiana Valentine
2009-01-01
Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf
Improved Estimates of Thermodynamic Parameters
Lawson, D. D.
1982-01-01
Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.
Aswath Damodaran
1999-01-01
Over the last three decades, the capital asset pricing model has occupied a central and often controversial place in most corporate finance analysts’ tool chests. The model requires three inputs to compute expected returns – a riskfree rate, a beta for an asset and an expected risk premium for the market portfolio (over and above the riskfree rate). Betas are estimated, by most practitioners, by regressing returns on an asset against a stock index, with the slope of the regression being the b...
Reliability parameters of distribution networks components
Energy Technology Data Exchange (ETDEWEB)
Gono, R.; Kratky, M.; Rusek, S.; Kral, V. [Technical Univ. of Ostrava (Czech Republic)
2009-03-11
This paper presented a framework for the retrieval of parameters from various heterogenous power system databases. The framework was designed to transform the heterogenous outage data in a common relational scheme. The framework was used to retrieve outage data parameters from the Czech and Slovak republics in order to demonstrate the scalability of the framework. A reliability computation of the system was computed in 2 phases representing the retrieval of component reliability parameters and the reliability computation. Reliability rates were determined using component reliability and global reliability indices. Input data for the reliability was retrieved from data on equipment operating under similar conditions, while the probability of failure-free operations was evaluated by determining component status. Anomalies in distribution outage data were described as scheme, attribute, and term differences. Input types consisted of input relations; transformation programs; codebooks; and translation tables. The system was used to successfully retrieve data from 7 distributors in the Czech Republic and Slovak Republic between 2000-2007. The database included 301,555 records. Data were queried using SQL language. 29 refs., 2 tabs., 2 figs.
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
Directory of Open Access Journals (Sweden)
muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
Energy Technology Data Exchange (ETDEWEB)
Itagaki, H. [Yokohama National University, Yokohama (Japan). Faculty of Engineering; Asada, H.; Ito, S. [National Aerospace Laboratory, Tokyo (Japan); Shinozuka, M.
1996-12-31
Risk assessed structural positions in a pressurized fuselage of a transport-type aircraft applied with damage tolerance design are taken up as the subject of discussion. A small number of data obtained from inspections on the positions was used to discuss the Bayesian reliability analysis that can estimate also a proper non-periodic inspection schedule, while estimating proper values for uncertain factors. As a result, time period of generating fatigue cracks was determined according to procedure of detailed visual inspections. The analysis method was found capable of estimating values that are thought reasonable and the proper inspection schedule using these values, in spite of placing the fatigue crack progress expression in a very simple form and estimating both factors as the uncertain factors. Thus, the present analysis method was verified of its effectiveness. This study has discussed at the same time the structural positions, modeling of fatigue cracks generated and develop in the positions, conditions for destruction, damage factors, and capability of the inspection from different viewpoints. This reliability analysis method is thought effective also on such other structures as offshore structures. 18 refs., 8 figs., 1 tab.
Energy Technology Data Exchange (ETDEWEB)
Itagaki, H [Yokohama National University, Yokohama (Japan). Faculty of Engineering; Asada, H; Ito, S [National Aerospace Laboratory, Tokyo (Japan); Shinozuka, M
1997-12-31
Risk assessed structural positions in a pressurized fuselage of a transport-type aircraft applied with damage tolerance design are taken up as the subject of discussion. A small number of data obtained from inspections on the positions was used to discuss the Bayesian reliability analysis that can estimate also a proper non-periodic inspection schedule, while estimating proper values for uncertain factors. As a result, time period of generating fatigue cracks was determined according to procedure of detailed visual inspections. The analysis method was found capable of estimating values that are thought reasonable and the proper inspection schedule using these values, in spite of placing the fatigue crack progress expression in a very simple form and estimating both factors as the uncertain factors. Thus, the present analysis method was verified of its effectiveness. This study has discussed at the same time the structural positions, modeling of fatigue cracks generated and develop in the positions, conditions for destruction, damage factors, and capability of the inspection from different viewpoints. This reliability analysis method is thought effective also on such other structures as offshore structures. 18 refs., 8 figs., 1 tab.
Reliability Estimation Based Upon Test Plan Results
National Research Council Canada - National Science Library
Read, Robert
1997-01-01
The report contains a brief summary of aspects of the Maximus reliability point and interval estimation technique as it has been applied to the reliability of a device whose surveillance tests contain...
Dependent systems reliability estimation by structural reliability approach
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2014-01-01
Estimation of system reliability by classical system reliability methods generally assumes that the components are statistically independent, thus limiting its applicability in many practical situations. A method is proposed for estimation of the system reliability with dependent components, where...... the leading failure mechanism(s) is described by physics of failure model(s). The proposed method is based on structural reliability techniques and accounts for both statistical and failure effect correlations. It is assumed that failure of any component is due to increasing damage (fatigue phenomena...... identification. Application of the proposed method can be found in many real world systems....
Parameter estimation in plasmonic QED
Jahromi, H. Rangani
2018-03-01
We address the problem of parameter estimation in the presence of plasmonic modes manipulating emitted light via the localized surface plasmons in a plasmonic waveguide at the nanoscale. The emitter that we discuss is the nitrogen vacancy centre (NVC) in diamond modelled as a qubit. Our goal is to estimate the β factor measuring the fraction of emitted energy captured by waveguide surface plasmons. The best strategy to obtain the most accurate estimation of the parameter, in terms of the initial state of the probes and different control parameters, is investigated. In particular, for two-qubit estimation, it is found although we may achieve the best estimation at initial instants by using the maximally entangled initial states, at long times, the optimal estimation occurs when the initial state of the probes is a product one. We also find that decreasing the interqubit distance or increasing the propagation length of the plasmons improve the precision of the estimation. Moreover, decrease of spontaneous emission rate of the NVCs retards the quantum Fisher information (QFI) reduction and therefore the vanishing of the QFI, measuring the precision of the estimation, is delayed. In addition, if the phase parameter of the initial state of the two NVCs is equal to πrad, the best estimation with the two-qubit system is achieved when initially the NVCs are maximally entangled. Besides, the one-qubit estimation has been also analysed in detail. Especially, we show that, using a two-qubit probe, at any arbitrary time, enhances considerably the precision of estimation in comparison with one-qubit estimation.
Directory of Open Access Journals (Sweden)
S. Martorell
2017-01-01
Full Text Available One can find many reliability, availability, and maintainability (RAM models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD of safety equipment: (1 by demand-caused and (2 standby-related failures. It proposes a maximum likelihood estimation (MLE approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.
Directory of Open Access Journals (Sweden)
Jasbir Arora
2016-06-01
Full Text Available The indestructible nature of teeth against most of the environmental abuses makes its use in disaster victim identification (DVI. The present study has been undertaken to examine the reliability of Gustafson’s qualitative method and Kedici’s quantitative method of measuring secondary dentine for age estimation among North Western adult Indians. 196 (M = 85; F = 111 single rooted teeth were collected from the Department of Oral Health Sciences, PGIMER, Chandigarh. Ground sections were prepared and the amount of secondary dentine formed was scored qualitatively according to Gustafson’s (0–3 scoring system (method 1 and quantitatively following Kedici’s micrometric measurement method (method 2. Out of 196 teeth 180 samples (M = 80; F = 100 were found to be suitable for measuring secondary dentine following Kedici’s method. Absolute mean error of age was calculated by both methodologies. Results clearly showed that in pooled data, method 1 gave an error of ±10.4 years whereas method 2 exhibited an error of approximately ±13 years. A statistically significant difference was noted in absolute mean error of age between two methods of measuring secondary dentine for age estimation. Further, it was also revealed that teeth extracted for periodontal reasons severely decreased the accuracy of Kedici’s method however, the disease had no effect while estimating age by Gustafson’s method. No significant gender differences were noted in the absolute mean error of age by both methods which suggest that there is no need to separate data on the basis of gender.
Parameter estimation in X-ray astronomy
International Nuclear Information System (INIS)
Lampton, M.; Margon, B.; Bowyer, S.
1976-01-01
The problems of model classification and parameter estimation are examined, with the objective of establishing the statistical reliability of inferences drawn from X-ray observations. For testing the validities of classes of models, the procedure based on minimizing the chi 2 statistic is recommended; it provides a rejection criterion at any desired significance level. Once a class of models has been accepted, a related procedure based on the increase of chi 2 gives a confidence region for the values of the model's adjustable parameters. The procedure allows the confidence level to be chosen exactly, even for highly nonlinear models. Numerical experiments confirm the validity of the prescribed technique.The chi 2 /sub min/+1 error estimation method is evaluated and found unsuitable when several parameter ranges are to be derived, because it substantially underestimates their joint errors. The ratio of variances method, while formally correct, gives parameter confidence regions which are more variable than necessary
Load Estimation from Modal Parameters
DEFF Research Database (Denmark)
Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández
2007-01-01
In Natural Input Modal Analysis the modal parameters are estimated just from the responses while the loading is not recorded. However, engineers are sometimes interested in knowing some features of the loading acting on a structure. In this paper, a procedure to determine the loading from a FRF m...
Reliability Estimates for Undergraduate Grade Point Average
Westrick, Paul A.
2017-01-01
Undergraduate grade point average (GPA) is a commonly employed measure in educational research, serving as a criterion or as a predictor depending on the research question. Over the decades, researchers have used a variety of reliability coefficients to estimate the reliability of undergraduate GPA, which suggests that there has been no consensus…
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2005-01-01
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...
A SOFTWARE RELIABILITY ESTIMATION METHOD TO NUCLEAR SAFETY SOFTWARE
Directory of Open Access Journals (Sweden)
GEE-YONG PARK
2014-02-01
Full Text Available A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM, where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.
Inflation and cosmological parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Hamann, J.
2007-05-15
In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil; Alkhalifah, Tariq Ali
2015-01-01
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil
2015-08-19
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Mission Reliability Estimation for Repairable Robot Teams
Trebi-Ollennu, Ashitey; Dolan, John; Stancliff, Stephen
2010-01-01
A mission reliability estimation method has been designed to translate mission requirements into choices of robot modules in order to configure a multi-robot team to have high reliability at minimal cost. In order to build cost-effective robot teams for long-term missions, one must be able to compare alternative design paradigms in a principled way by comparing the reliability of different robot models and robot team configurations. Core modules have been created including: a probabilistic module with reliability-cost characteristics, a method for combining the characteristics of multiple modules to determine an overall reliability-cost characteristic, and a method for the generation of legitimate module combinations based on mission specifications and the selection of the best of the resulting combinations from a cost-reliability standpoint. The developed methodology can be used to predict the probability of a mission being completed, given information about the components used to build the robots, as well as information about the mission tasks. In the research for this innovation, sample robot missions were examined and compared to the performance of robot teams with different numbers of robots and different numbers of spare components. Data that a mission designer would need was factored in, such as whether it would be better to have a spare robot versus an equivalent number of spare parts, or if mission cost can be reduced while maintaining reliability using spares. This analytical model was applied to an example robot mission, examining the cost-reliability tradeoffs among different team configurations. Particularly scrutinized were teams using either redundancy (spare robots) or repairability (spare components). Using conservative estimates of the cost-reliability relationship, results show that it is possible to significantly reduce the cost of a robotic mission by using cheaper, lower-reliability components and providing spares. This suggests that the
Estimation of some stochastic models used in reliability engineering
International Nuclear Information System (INIS)
Huovinen, T.
1989-04-01
The work aims to study the estimation of some stochastic models used in reliability engineering. In reliability engineering continuous probability distributions have been used as models for the lifetime of technical components. We consider here the following distributions: exponential, 2-mixture exponential, conditional exponential, Weibull, lognormal and gamma. Maximum likelihood method is used to estimate distributions from observed data which may be either complete or censored. We consider models based on homogeneous Poisson processes such as gamma-poisson and lognormal-poisson models for analysis of failure intensity. We study also a beta-binomial model for analysis of failure probability. The estimators of the parameters for three models are estimated by the matching moments method and in the case of gamma-poisson and beta-binomial models also by maximum likelihood method. A great deal of mathematical or statistical problems that arise in reliability engineering can be solved by utilizing point processes. Here we consider the statistical analysis of non-homogeneous Poisson processes to describe the failing phenomena of a set of components with a Weibull intensity function. We use the method of maximum likelihood to estimate the parameters of the Weibull model. A common cause failure can seriously reduce the reliability of a system. We consider a binomial failure rate (BFR) model as an application of the marked point processes for modelling common cause failure in a system. The parameters of the binomial failure rate model are estimated with the maximum likelihood method
Adaptive Response Surface Techniques in Reliability Estimation
DEFF Research Database (Denmark)
Enevoldsen, I.; Faber, M. H.; Sørensen, John Dalsgaard
1993-01-01
Problems in connection with estimation of the reliability of a component modelled by a limit state function including noise or first order discontinuitics are considered. A gradient free adaptive response surface algorithm is developed. The algorithm applies second order polynomial surfaces...
Applied parameter estimation for chemical engineers
Englezos, Peter
2000-01-01
Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynam
Data Handling and Parameter Estimation
DEFF Research Database (Denmark)
Sin, Gürkan; Gernaey, Krist
2016-01-01
,engineers, and professionals. However, it is also expected that they will be useful both for graduate teaching as well as a stepping stone for academic researchers who wish to expand their theoretical interest in the subject. For the models selected to interpret the experimental data, this chapter uses available models from...... literature that are mostly based on the ActivatedSludge Model (ASM) framework and their appropriate extensions (Henze et al., 2000).The chapter presents an overview of the most commonly used methods in the estimation of parameters from experimental batch data, namely: (i) data handling and validation, (ii......Modelling is one of the key tools at the disposal of modern wastewater treatment professionals, researchers and engineers. It enables them to study and understand complex phenomena underlying the physical, chemical and biological performance of wastewater treatment plants at different temporal...
Trends in Control Area of PLC Reliability and Safety Parameters
Directory of Open Access Journals (Sweden)
Juraj Zdansky
2008-01-01
Full Text Available Extension of the PLC application possibilities is closely related to increase of reliability and safety parameters. If the requirement of reliability and safety parameters will be suitable, the PLC could by implemented to specific applications such the safety-related processes control. The goal of this article is to show the way which producers are approaching to increase PLC`s reliability and safety parameters. The second goal is to analyze these parameters for range of present choice and describe the possibility how the reliability and safety parameters can be affected.
Estimation of structural reliability under combined loads
International Nuclear Information System (INIS)
Shinozuka, M.; Kako, T.; Hwang, H.; Brown, P.; Reich, M.
1983-01-01
For the overall safety evaluation of seismic category I structures subjected to various load combinations, a quantitative measure of the structural reliability in terms of a limit state probability can be conveniently used. For this purpose, the reliability analysis method for dynamic loads, which has recently been developed by the authors, was combined with the existing standard reliability analysis procedure for static and quasi-static loads. The significant parameters that enter into the analysis are: the rate at which each load (dead load, accidental internal pressure, earthquake, etc.) will occur, its duration and intensity. All these parameters are basically random variables for most of the loads to be considered. For dynamic loads, the overall intensity is usually characterized not only by their dynamic components but also by their static components. The structure considered in the present paper is a reinforced concrete containment structure subjected to various static and dynamic loads such as dead loads, accidental pressure, earthquake acceleration, etc. Computations are performed to evaluate the limit state probabilities under each load combination separately and also under all possible combinations of such loads
Impact of staffing parameters on operational reliability
International Nuclear Information System (INIS)
Hahn, H.A.; Houghton, F.K.
1993-01-01
This paper reports on a project related to human resource management of the Department of Energy's (DOE's) High-Level Waste (HLW) Tank program. Safety and reliability of waste tank operations is impacted by several issues, including not only the design of the tanks themselves, but also how operations and operational personnel are managed. As demonstrated by management assessments performed by the Tiger Teams, DOE believes that the effective use of human resources impacts environment safety, and health concerns. For the of the current paper, human resource management activities are identified as ''Staffing'' and include the of developing the functional responsibilities and qualifications of technical and administrative personnel. This paper discusses the importance of staff plans and management in the overall view of safety and reliability. The work activities and procedures associated with the project, a review of the results of these activities, including a summary of the literature and a preliminary analysis of the data. We conclude that although identification of staffing issues and the development of staffing plans contributes to the overall reliability and safety of the HLW tanks, the relationship is not well understood and is in need of further development
Impact of staffing parameters on operational reliability
International Nuclear Information System (INIS)
Hahn, H.A.; Houghton, F.K.
1993-01-01
This paper reports on a project related to human resource management of the Department of Energy (DOEs) High-Level Waste (HLW) Tank program. Safety and reliability of waste tank operations is impacted by several issues, including not only the design of the tanks themselves, but also how operations and operational personnel are managed. As demonstrated by management assessments performed by the Tiger Teams, DOE believes that the effective use of human resources impacts environment, safety, and health concerns. For the purposes of the current paper, human resource management activities are identified as 'Staffing' and include the process of developing the functional responsibilities and qualifications of technical and administrative personnel. This paper discusses the importance of staff plans and management in the overall view of safety and reliability, the work activities and procedures associated with the project, a review of the results of these activities, including a summary of the literature and a preliminary analysis of the data. We conclude that, although identification of staffing issues and the development of staffing plans contributes to the overall reliability and safety of the HLW tanks, the relationship is not well understood and is in need of further development
Investigation of MLE in nonparametric estimation methods of reliability function
International Nuclear Information System (INIS)
Ahn, Kwang Won; Kim, Yoon Ik; Chung, Chang Hyun; Kim, Kil Yoo
2001-01-01
There have been lots of trials to estimate a reliability function. In the ESReDA 20 th seminar, a new method in nonparametric way was proposed. The major point of that paper is how to use censored data efficiently. Generally there are three kinds of approach to estimate a reliability function in nonparametric way, i.e., Reduced Sample Method, Actuarial Method and Product-Limit (PL) Method. The above three methods have some limits. So we suggest an advanced method that reflects censored information more efficiently. In many instances there will be a unique maximum likelihood estimator (MLE) of an unknown parameter, and often it may be obtained by the process of differentiation. It is well known that the three methods generally used to estimate a reliability function in nonparametric way have maximum likelihood estimators that are uniquely exist. So, MLE of the new method is derived in this study. The procedure to calculate a MLE is similar just like that of PL-estimator. The difference of the two is that in the new method, the mass (or weight) of each has an influence of the others but the mass in PL-estimator not
Estimation of structural reliability under combined loads
International Nuclear Information System (INIS)
Shinozuka, M.; Kako, T.; Hwang, H.; Brown, P.; Reich, M.
1983-01-01
For the overall safety evaluation of seismic category I structures subjected to various load combinations, a quantitative measure of the structural reliability in terms of a limit state probability can be conveniently used. For this purpose, the reliability analysis method for dynamic loads, which has recently been developed by the authors, was combined with the existing standard reliability analysis procedure for static and quasi-static loads. The significant parameters that enter into the analysis are: the rate at which each load (dead load, accidental internal pressure, earthquake, etc.) will occur, its duration and intensity. All these parameters are basically random variables for most of the loads to be considered. For dynamic loads, the overall intensity is usually characterized not only by their dynamic components but also by their static components. The structure considered in the present paper is a reinforced concrete containment structure subjected to various static and dynamic loads such as dead loads, accidental pressure, earthquake acceleration, etc. Computations are performed to evaluate the limit state probabilities under each load combination separately and also under all possible combinations of such loads. Indeed, depending on the limit state condition to be specified, these limit state probabilities can indicate which particular load combination provides the dominant contribution to the overall limit state probability. On the other hand, some of the load combinations contribute very little to the overall limit state probability. These observations provide insight into the complex problem of which load combinations must be considered for design, for which limit states and at what level of limit state probabilities. (orig.)
International Nuclear Information System (INIS)
Zhang, Yongjin; Zhao, Ming; Zhang, Shitao; Wang, Jiamei; Zhang, Yanjun
2017-01-01
Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. For certain types of products, Storage reliability may not always be 100% at the beginning of storage, unlike the operational reliability, which exist possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new integrated technique, the non-parametric measure based on the E-Bayesian estimates of current failure probabilities is combined with the parametric measure based on the exponential reliability function, is proposed to estimate and predict the storage reliability of products with possible initial failures, where the non-parametric method is used to estimate the number of failed products and the reliability at each testing time, and the parameter method is used to estimate the initial reliability and the failure rate of storage product. The proposed method has taken into consideration that, the reliability test data of storage products containing the unexamined before and during the storage process, is available for providing more accurate estimates of both the initial failure probability and the storage failure probability. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is presented in this paper. Finally, a numerical example is given to illustrate the method. Furthermore, a detailed comparison between the proposed and traditional method, for examining the rationality of assessment and prediction on the storage reliability, is investigated. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality. - Highlights:
Parameter estimation techniques for LTP system identification
Nofrarias Serra, Miquel
LISA Pathfinder (LPF) is the precursor mission of LISA (Laser Interferometer Space Antenna) and the first step towards gravitational waves detection in space. The main instrument onboard the mission is the LTP (LISA Technology Package) whose scientific goal is to test LISA's drag-free control loop by reaching a differential acceleration noise level between two masses in √ geodesic motion of 3 × 10-14 ms-2 / Hz in the milliHertz band. The mission is not only challenging in terms of technology readiness but also in terms of data analysis. As with any gravitational wave detector, attaining the instrument performance goals will require an extensive noise hunting campaign to measure all contributions with high accuracy. But, opposite to on-ground experiments, LTP characterisation will be only possible by setting parameters via telecommands and getting a selected amount of information through the available telemetry downlink. These two conditions, high accuracy and high reliability, are the main restrictions that the LTP data analysis must overcome. A dedicated object oriented Matlab Toolbox (LTPDA) has been set up by the LTP analysis team for this purpose. Among the different toolbox methods, an essential part for the mission are the parameter estimation tools that will be used for system identification during operations: Linear Least Squares, Non-linear Least Squares and Monte Carlo Markov Chain methods have been implemented as LTPDA methods. The data analysis team has been testing those methods with a series of mock data exercises with the following objectives: to cross-check parameter estimation methods and compare the achievable accuracy for each of them, and to develop the best strategies to describe the physics underlying a complex controlled experiment as the LTP. In this contribution we describe how these methods were tested with simulated LTP-like data to recover the parameters of the model and we report on the latest results of these mock data exercises.
Parameter Estimation in Continuous Time Domain
Directory of Open Access Journals (Sweden)
Gabriela M. ATANASIU
2016-12-01
Full Text Available This paper will aim to presents the applications of a continuous-time parameter estimation method for estimating structural parameters of a real bridge structure. For the purpose of illustrating this method two case studies of a bridge pile located in a highly seismic risk area are considered, for which the structural parameters for the mass, damping and stiffness are estimated. The estimation process is followed by the validation of the analytical results and comparison with them to the measurement data. Further benefits and applications for the continuous-time parameter estimation method in civil engineering are presented in the final part of this paper.
Computer Model to Estimate Reliability Engineering for Air Conditioning Systems
International Nuclear Information System (INIS)
Afrah Al-Bossly, A.; El-Berry, A.; El-Berry, A.
2012-01-01
Reliability engineering is used to predict the performance and optimize design and maintenance of air conditioning systems. Air conditioning systems are expose to a number of failures. The failures of an air conditioner such as turn on, loss of air conditioner cooling capacity, reduced air conditioning output temperatures, loss of cool air supply and loss of air flow entirely can be due to a variety of problems with one or more components of an air conditioner or air conditioning system. Forecasting for system failure rates are very important for maintenance. This paper focused on the reliability of the air conditioning systems. Statistical distributions that were commonly applied in reliability settings: the standard (2 parameter) Weibull and Gamma distributions. After distributions parameters had been estimated, reliability estimations and predictions were used for evaluations. To evaluate good operating condition in a building, the reliability of the air conditioning system that supplies conditioned air to the several The company's departments. This air conditioning system is divided into two, namely the main chilled water system and the ten air handling systems that serves the ten departments. In a chilled-water system the air conditioner cools water down to 40-45 degree F (4-7 degree C). The chilled water is distributed throughout the building in a piping system and connected to air condition cooling units wherever needed. Data analysis has been done with support a computer aided reliability software, this is due to the Weibull and Gamma distributions indicated that the reliability for the systems equal to 86.012% and 77.7% respectively. A comparison between the two important families of distribution functions, namely, the Weibull and Gamma families was studied. It was found that Weibull method performed for decision making.
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar; Allmaras, Moritz; Bangerth, Wolfgang; Tenorio, Luis
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.
2013-01-01
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
Reliability of Estimation Pile Load Capacity Methods
Directory of Open Access Journals (Sweden)
Yudhi Lastiasih
2014-04-01
Full Text Available None of numerous previous methods for predicting pile capacity is known how accurate any of them are when compared with the actual ultimate capacity of piles tested to failure. The author’s of the present paper have conducted such an analysis, based on 130 data sets of field loading tests. Out of these 130 data sets, only 44 could be analysed, of which 15 were conducted until the piles actually reached failure. The pile prediction methods used were: Brinch Hansen’s method (1963, Chin’s method (1970, Decourt’s Extrapolation Method (1999, Mazurkiewicz’s method (1972, Van der Veen’s method (1953, and the Quadratic Hyperbolic Method proposed by Lastiasih et al. (2012. It was obtained that all the above methods were sufficiently reliable when applied to data from pile loading tests that loaded to reach failure. However, when applied to data from pile loading tests that loaded without reaching failure, the methods that yielded lower values for correction factor N are more recommended. Finally, the empirical method of Reese and O’Neill (1988 was found to be reliable enough to be used to estimate the Qult of a pile foundation based on soil data only.
Modelling and estimating degradation processes with application in structural reliability
International Nuclear Information System (INIS)
Chiquet, J.
2007-06-01
The characteristic level of degradation of a given structure is modeled through a stochastic process called the degradation process. The random evolution of the degradation process is governed by a differential system with Markovian environment. We put the associated reliability framework by considering the failure of the structure once the degradation process reaches a critical threshold. A closed form solution of the reliability function is obtained thanks to Markov renewal theory. Then, we build an estimation methodology for the parameters of the stochastic processes involved. The estimation methods and the theoretical results, as well as the associated numerical algorithms, are validated on simulated data sets. Our method is applied to the modelling of a real degradation mechanism, known as crack growth, for which an experimental data set is considered. (authors)
Methodology for uranium resource estimates and reliability
International Nuclear Information System (INIS)
Blanchfield, D.M.
1980-01-01
The NURE uranium assessment method has evolved from a small group of geologists estimating resources on a few lease blocks, to a national survey involving an interdisciplinary system consisting of the following: (1) geology and geologic analogs; (2) engineering and cost modeling; (3) mathematics and probability theory, psychology and elicitation of subjective judgments; and (4) computerized calculations, computer graphics, and data base management. The evolution has been spurred primarily by two objectives; (1) quantification of uncertainty, and (2) elimination of simplifying assumptions. This has resulted in a tremendous data-gathering effort and the involvement of hundreds of technical experts, many in uranium geology, but many from other fields as well. The rationality of the methods is still largely based on the concept of an analog and the observation that the results are reasonable. The reliability, or repeatability, of the assessments is reasonably guaranteed by the series of peer and superior technical reviews which has been formalized under the current methodology. The optimism or pessimism of individual geologists who make the initial assessments is tempered by the review process, resulting in a series of assessments which are a consistent, unbiased reflection of the facts. Despite the many improvements over past methods, several objectives for future development remain, primarily to reduce subjectively in utilizing factual information in the estimation of endowment, and to improve the recognition of cost uncertainties in the assessment of economic potential. The 1980 NURE assessment methodology will undoubtly be improved, but the reader is reminded that resource estimates are and always will be a forecast for the future
Structural Reliability Using Probability Density Estimation Methods Within NESSUS
Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric
2003-01-01
A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been
JUPITER PROJECT - JOINT UNIVERSAL PARAMETER IDENTIFICATION AND EVALUATION OF RELIABILITY
The JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) project builds on the technology of two widely used codes for sensitivity analysis, data assessment, calibration, and uncertainty analysis of environmental models: PEST and UCODE.
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 form...
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
Prasad, J; Souradeep, T
2014-01-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
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 present 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 LIDAR data.
Reliability analysis of a sensitive and independent stabilometry parameter set.
Nagymáté, Gergely; Orlovits, Zsanett; Kiss, Rita M
2018-01-01
Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54-0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals.
Reliability analysis of a sensitive and independent stabilometry parameter set
Nagymáté, Gergely; Orlovits, Zsanett
2018-01-01
Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54–0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals. PMID:29664938
Lower bounds to the reliabilities of factor score estimators
Hessen, D.J.
2017-01-01
Under the general common factor model, the reliabilities of factor score estimators might be of more interest than the reliability of the total score (the unweighted sum of item scores). In this paper, lower bounds to the reliabilities of Thurstone’s factor score estimators, Bartlett’s factor score
Application of spreadsheet to estimate infiltration parameters
Directory of Open Access Journals (Sweden)
Mohammad Zakwan
2016-09-01
Full Text Available Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.
Parameter Estimation of Nonlinear Models in Forestry.
Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.
1999-01-01
Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...
Lower Bounds to the Reliabilities of Factor Score Estimators.
Hessen, David J
2016-10-06
Under the general common factor model, the reliabilities of factor score estimators might be of more interest than the reliability of the total score (the unweighted sum of item scores). In this paper, lower bounds to the reliabilities of Thurstone's factor score estimators, Bartlett's factor score estimators, and McDonald's factor score estimators are derived and conditions are given under which these lower bounds are equal. The relative performance of the derived lower bounds is studied using classic example data sets. The results show that estimates of the lower bounds to the reliabilities of Thurstone's factor score estimators are greater than or equal to the estimates of the lower bounds to the reliabilities of Bartlett's and McDonald's factor score estimators.
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.
Reionization history and CMB parameter estimation
International Nuclear Information System (INIS)
Dizgah, Azadeh Moradinezhad; Kinney, William H.; Gnedin, Nickolay Y.
2013-01-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case
Reionization history and CMB parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Dizgah, Azadeh Moradinezhad; Gnedin, Nickolay Y.; Kinney, William H.
2013-05-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.
Method for Estimating the Parameters of LFM Radar Signal
Directory of Open Access Journals (Sweden)
Tan Chuan-Zhang
2017-01-01
Full Text Available In order to obtain reliable estimate of parameters, it is very important to protect the integrality of linear frequency modulation (LFM signal. Therefore, in the practical LFM radar signal processing, the length of data frame is often greater than the pulse width (PW of signal. In this condition, estimating the parameters by fractional Fourier transform (FrFT will cause the signal to noise ratio (SNR decrease. Aiming at this problem, we multiply the data frame by a Gaussian window to improve the SNR. Besides, for a further improvement of parameters estimation precision, a novel algorithm is derived via Lagrange interpolation polynomial, and we enhance the algorithm by a logarithmic transformation. Simulation results demonstrate that the derived algorithm significantly reduces the estimation errors of chirp-rate and initial frequency.
MEASUREMENT: ACCOUNTING FOR RELIABILITY IN PERFORMANCE ESTIMATES.
Waterman, Brian; Sutter, Robert; Burroughs, Thomas; Dunagan, W Claiborne
2014-01-01
When evaluating physician performance measures, physician leaders are faced with the quandary of determining whether departures from expected physician performance measurements represent a true signal or random error. This uncertainty impedes the physician leader's ability and confidence to take appropriate performance improvement actions based on physician performance measurements. Incorporating reliability adjustment into physician performance measurement is a valuable way of reducing the impact of random error in the measurements, such as those caused by small sample sizes. Consequently, the physician executive has more confidence that the results represent true performance and is positioned to make better physician performance improvement decisions. Applying reliability adjustment to physician-level performance data is relatively new. As others have noted previously, it's important to keep in mind that reliability adjustment adds significant complexity to the production, interpretation and utilization of results. Furthermore, the methods explored in this case study only scratch the surface of the range of available Bayesian methods that can be used for reliability adjustment; further study is needed to test and compare these methods in practice and to examine important extensions for handling specialty-specific concerns (e.g., average case volumes, which have been shown to be important in cardiac surgery outcomes). Moreover, it's important to note that the provider group average as a basis for shrinkage is one of several possible choices that could be employed in practice and deserves further exploration in future research. With these caveats, our results demonstrate that incorporating reliability adjustment into physician performance measurements is feasible and can notably reduce the incidence of "real" signals relative to what one would expect to see using more traditional approaches. A physician leader who is interested in catalyzing performance improvement
Simple method for quick estimation of aquifer hydrogeological parameters
Ma, C.; Li, Y. Y.
2017-08-01
Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.
Reliability of diabetic patients' gait parameters in a challenging environment.
Allet, L; Armand, S; de Bie, R A; Golay, A; Monnin, D; Aminian, K; de Bruin, E D
2008-11-01
Activities of daily life require us to move about in challenging environments and to walk on varied surfaces. Irregular terrain has been shown to influence gait parameters, especially in a population at risk for falling. A precise portable measurement system would permit objective gait analysis under such conditions. The aims of this study are to (a) investigate the reliability of gait parameters measured with the Physilog in diabetic patients walking on different surfaces (tar, grass, and stones); (b) identify the measurement error (precision); (c) identify the minimal clinical detectable change. 16 patients with Type 2 diabetes were measured twice within 8 days. After clinical examination patients walked, equipped with a Physilog, on the three aforementioned surfaces. ICC for each surface was excellent for within-visit analyses (>0.938). Inter-visit ICC's (0.753) were excellent except for the knee range parameter (>0.503). The coefficient of variation (CV) was lower than 5% for most of the parameters. Bland and Altman Plots, SEM and SDC showed precise values, distributed around zero for all surfaces. Good reliability of Physilog measurements on different surfaces suggests that Physilog could facilitate the study of diabetic patients' gait in conditions close to real-life situations. Gait parameters during complex locomotor activities (e.g. stair-climbing, curbs, slopes) have not yet been extensively investigated. Good reliability, small measurement error and values of minimal clinical detectable change recommend the utilization of Physilog for the evaluation of gait parameters in diabetic patients.
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise levels, models, or prior knowledge. But what can we say about the validity of such estimates, and the influence of these assumptions? This paper is concerned with methods to address these questions, and for didactic purposes it is written in the context of a concrete nonlinear parameter estimation problem. We will use the results of a physical experiment conducted by Allmaras et al. at Texas A&M University [M. Allmaras et al., SIAM Rev., 55 (2013), pp. 149-167] to illustrate the importance of validation procedures for statistical parameter estimation. We describe statistical methods and data analysis tools to check the choices of likelihood and prior distributions, and provide examples of how to compare Bayesian results with those obtained by non-Bayesian methods based on different types of assumptions. We explain how different statistical methods can be used in complementary ways to improve the understanding of parameter estimates and their uncertainties.
Influences on and Limitations of Classical Test Theory Reliability Estimates.
Arnold, Margery E.
It is incorrect to say "the test is reliable" because reliability is a function not only of the test itself, but of many factors. The present paper explains how different factors affect classical reliability estimates such as test-retest, interrater, internal consistency, and equivalent forms coefficients. Furthermore, the limits of classical test…
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.
On robust parameter estimation in brain-computer interfacing
Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert
2017-12-01
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
Iterative importance sampling algorithms for parameter estimation
Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Pau, George Shu Heng; Finsterle, Stefan A.; Bell, John B.
2016-01-01
In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is ...
Bayesian parameter estimation in probabilistic risk assessment
International Nuclear Information System (INIS)
Siu, Nathan O.; Kelly, Dana L.
1998-01-01
Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics
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.
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Precision Parameter Estimation and Machine Learning
Wandelt, Benjamin D.
2008-12-01
I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.
Reliability Estimation for Digital Instrument/Control System
International Nuclear Information System (INIS)
Yang, Yaguang; Sydnor, Russell
2011-01-01
Digital instrumentation and controls (DI and C) systems are widely adopted in various industries because of their flexibility and ability to implement various functions that can be used to automatically monitor, analyze, and control complicated systems. It is anticipated that the DI and C will replace the traditional analog instrumentation and controls (AI and C) systems in all future nuclear reactor designs. There is an increasing interest for reliability and risk analyses for safety critical DI and C systems in regulatory organizations, such as The United States Nuclear Regulatory Commission. Developing reliability models and reliability estimation methods for digital reactor control and protection systems will involve every part of the DI and C system, such as sensors, signal conditioning and processing components, transmission lines and digital communication systems, D/A and A/D converters, computer system, signal processing software, control and protection software, power supply system, and actuators. Some of these components are hardware, such as sensors and actuators, their failure mechanisms are well understood, and the traditional reliability model and estimation methods can be directly applied. But many of these components are firmware which has software embedded in the hardware, and software needs special consideration because its failure mechanism is unique, and the reliability estimation method for a software system will be different from the ones used for hardware systems. In this paper, we will propose a reliability estimation method for the entire DI and C system reliability using a recently developed software reliability estimation method and a traditional hardware reliability estimation method
Reliability Estimation for Digital Instrument/Control System
Energy Technology Data Exchange (ETDEWEB)
Yang, Yaguang; Sydnor, Russell [U.S. Nuclear Regulatory Commission, Washington, D.C. (United States)
2011-08-15
Digital instrumentation and controls (DI and C) systems are widely adopted in various industries because of their flexibility and ability to implement various functions that can be used to automatically monitor, analyze, and control complicated systems. It is anticipated that the DI and C will replace the traditional analog instrumentation and controls (AI and C) systems in all future nuclear reactor designs. There is an increasing interest for reliability and risk analyses for safety critical DI and C systems in regulatory organizations, such as The United States Nuclear Regulatory Commission. Developing reliability models and reliability estimation methods for digital reactor control and protection systems will involve every part of the DI and C system, such as sensors, signal conditioning and processing components, transmission lines and digital communication systems, D/A and A/D converters, computer system, signal processing software, control and protection software, power supply system, and actuators. Some of these components are hardware, such as sensors and actuators, their failure mechanisms are well understood, and the traditional reliability model and estimation methods can be directly applied. But many of these components are firmware which has software embedded in the hardware, and software needs special consideration because its failure mechanism is unique, and the reliability estimation method for a software system will be different from the ones used for hardware systems. In this paper, we will propose a reliability estimation method for the entire DI and C system reliability using a recently developed software reliability estimation method and a traditional hardware reliability estimation method.
Estimation of the Reliability of Plastic Slabs
DEFF Research Database (Denmark)
Pirzada, G. B. : Ph.D.
In this thesis, work related to fundamental conditions has been extended to non-fundamental or the general case of probabilistic analysis. Finally, using the ss-unzipping technique a door has been opened to system reliability analysis of plastic slabs. An attempt has been made in this thesis...... to give a probabilistic treatment of plastic slabs which is parallel to the deterministic and systematic treatment of plastic slabs by Nielsen (3). The fundamental reason is that in Nielsen (3) the treatment is based on a deterministic modelling of the basic material properties for the reinforced...
Parameter estimation for an expanding universe
Directory of Open Access Journals (Sweden)
Jieci Wang
2015-03-01
Full Text Available We study the parameter estimation for excitations of Dirac fields in the expanding Robertson–Walker universe. We employ quantum metrology techniques to demonstrate the possibility for high precision estimation for the volume rate of the expanding universe. We show that the optimal precision of the estimation depends sensitively on the dimensionless mass m˜ and dimensionless momentum k˜ of the Dirac particles. The optimal precision for the ratio estimation peaks at some finite dimensionless mass m˜ and momentum k˜. We find that the precision of the estimation can be improved by choosing the probe state as an eigenvector of the hamiltonian. This occurs because the largest quantum Fisher information is obtained by performing projective measurements implemented by the projectors onto the eigenvectors of specific probe states.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.; Lombard, F.
2012-01-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal
Sensor Placement for Modal Parameter Subset Estimation
DEFF Research Database (Denmark)
Ulriksen, Martin Dalgaard; Bernal, Dionisio; Damkilde, Lars
2016-01-01
The present paper proposes an approach for deciding on sensor placements in the context of modal parameter estimation from vibration measurements. The approach is based on placing sensors, of which the amount is determined a priori, such that the minimum Fisher information that the frequency resp...
Reliability estimation of semi-Markov systems: a case study
International Nuclear Information System (INIS)
Ouhbi, Brahim; Limnios, Nikolaos
1997-01-01
In this article, we are concerned with the estimation of the reliability and the availability of a turbo-generator rotor using a set of data observed in a real engineering situation provided by Electricite De France (EDF). The rotor is modeled by a semi-Markov process, which is used to estimate the rotor's reliability and availability. To do this, we present a method for estimating the semi-Markov kernel from a censored data
Postprocessing MPEG based on estimated quantization parameters
DEFF Research Database (Denmark)
Forchhammer, Søren
2009-01-01
the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...... in the postprocessing. We focus on deringing and present a scheme which aims at suppressing ringing artifacts, while maintaining the sharpness of the texture. The goal is to improve the visual quality, so perceptual blur and ringing metrics are used in addition to PSNR evaluation. The performance of the new `pure......' postprocessing compares favorable to a reference postprocessing filter which has access to the quantization parameters not only for I-frames but also on P and B-frames....
Estimating physiological skin parameters from hyperspectral signatures
Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe
2013-05-01
We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.
Parameter estimation in stochastic differential equations
Bishwal, Jaya P N
2008-01-01
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.
Reliabilities of genomic estimated breeding values in Danish Jersey
DEFF Research Database (Denmark)
Thomasen, Jørn Rind; Guldbrandtsen, Bernt; Su, Guosheng
2012-01-01
In order to optimize the use of genomic selection in breeding plans, it is essential to have reliable estimates of the genomic breeding values. This study investigated reliabilities of direct genomic values (DGVs) in the Jersey population estimated by three different methods. The validation methods...... were (i) fivefold cross-validation and (ii) validation on the most recent 3 years of bulls. The reliability of DGV was assessed using squared correlations between DGV and deregressed proofs (DRPs). In the recent 3-year validation model, estimated reliabilities were also used to assess the reliabilities...... of DGV. The data set consisted of 1003 Danish Jersey bulls with conventional estimated breeding values (EBVs) for 14 different traits included in the Nordic selection index. The bulls were genotyped for Single-nucleotide polymorphism (SNP) markers using the Illumina 54 K chip. A Bayesian method was used...
Evaluation of Reliability Parameters in Micro-grid
Directory of Open Access Journals (Sweden)
H. Hasanzadeh Fard
2015-06-01
Full Text Available Evaluation of the reliability parameters in micro-grids based on renewable energy sources is one of the main problems that are investigated in this paper. Renewable energy sources such as solar and wind energy, battery as an energy storage system and fuel cell as a backup system are used to provide power to the electrical loads of the micro-grid. Loads in the micro-grid consist of interruptible and uninterruptible loads. In addition to the reliability parameters, Forced Outage Rate of each component and also uncertainty of wind power, PV power and demand are considered for micro-grid. In this paper, the problem is formulated as a nonlinear integer minimization problem which minimizes the sum of the total capital, operational, maintenance and replacement cost of DERs. This paper proposes PSO for solving this minimization problem.
Software Estimation: Developing an Accurate, Reliable Method
2011-08-01
based and size-based estimates is able to accurately plan, launch, and execute on schedule. Bob Sinclair, NAWCWD Chris Rickets , NAWCWD Brad Hodgins...Office by Carnegie Mellon University. SMPSP and SMTSP are service marks of Carnegie Mellon University. 1. Rickets , Chris A, “A TSP Software Maintenance...Life Cycle”, CrossTalk, March, 2005. 2. Koch, Alan S, “TSP Can Be the Building blocks for CMMI”, CrossTalk, March, 2005. 3. Hodgins, Brad, Rickets
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Basics of Bayesian reliability estimation from attribute test data
International Nuclear Information System (INIS)
Martz, H.F. Jr.; Waller, R.A.
1975-10-01
The basic notions of Bayesian reliability estimation from attribute lifetest data are presented in an introductory and expository manner. Both Bayesian point and interval estimates of the probability of surviving the lifetest, the reliability, are discussed. The necessary formulas are simply stated, and examples are given to illustrate their use. In particular, a binomial model in conjunction with a beta prior model is considered. Particular attention is given to the procedure for selecting an appropriate prior model in practice. Empirical Bayes point and interval estimates of reliability are discussed and examples are given. 7 figures, 2 tables
Estimating RASATI scores using acoustical parameters
International Nuclear Information System (INIS)
Agüero, P D; Tulli, J C; Moscardi, G; Gonzalez, E L; Uriz, A J
2011-01-01
Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.
Cosmological parameter estimation using particle swarm optimization
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
Using Genetic Algorithm to Estimate Hydraulic Parameters of Unconfined Aquifers
Directory of Open Access Journals (Sweden)
Asghar Asghari Moghaddam
2009-03-01
Full Text Available Nowadays, optimization techniques such as Genetic Algorithms (GA have attracted wide attention among scientists for solving complicated engineering problems. In this article, pumping test data are used to assess the efficiency of GA in estimating unconfined aquifer parameters and a sensitivity analysis is carried out to propose an optimal arrangement of GA. For this purpose, hydraulic parameters of three sets of pumping test data are calculated by GA and they are compared with the results of graphical methods. The results indicate that the GA technique is an efficient, reliable, and powerful method for estimating the hydraulic parameters of unconfined aquifer and, further, that in cases of deficiency in pumping test data, it has a better performance than graphical methods.
IRT-Estimated Reliability for Tests Containing Mixed Item Formats
Shu, Lianghua; Schwarz, Richard D.
2014-01-01
As a global measure of precision, item response theory (IRT) estimated reliability is derived for four coefficients (Cronbach's a, Feldt-Raju, stratified a, and marginal reliability). Models with different underlying assumptions concerning test-part similarity are discussed. A detailed computational example is presented for the targeted…
A Latent Class Approach to Estimating Test-Score Reliability
van der Ark, L. Andries; van der Palm, Daniel W.; Sijtsma, Klaas
2011-01-01
This study presents a general framework for single-administration reliability methods, such as Cronbach's alpha, Guttman's lambda-2, and method MS. This general framework was used to derive a new approach to estimating test-score reliability by means of the unrestricted latent class model. This new approach is the latent class reliability…
Processes and Procedures for Estimating Score Reliability and Precision
Bardhoshi, Gerta; Erford, Bradley T.
2017-01-01
Precision is a key facet of test development, with score reliability determined primarily according to the types of error one wants to approximate and demonstrate. This article identifies and discusses several primary forms of reliability estimation: internal consistency (i.e., split-half, KR-20, a), test-retest, alternate forms, interscorer, and…
Optimal design criteria - prediction vs. parameter estimation
Waldl, Helmut
2014-05-01
G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.
Variational estimates of point-kinetics parameters
International Nuclear Information System (INIS)
Favorite, J.A.; Stacey, W.M. Jr.
1995-01-01
Variational estimates of the effect of flux shifts on the integral reactivity parameter of the point-kinetics equations and on regional power fractions were calculated for a variety of localized perturbations in two light water reactor (LWR) model problems representing a small, tightly coupled core and a large, loosely coupled core. For the small core, the flux shifts resulting from even relatively large localized reactivity changes (∼600 pcm) were small, and the standard point-kinetics approximation estimates of reactivity were in error by only ∼10% or less, while the variational estimates were accurate to within ∼1%. For the larger core, significant (>50%) flux shifts occurred in response to local perturbations, leading to errors of the same magnitude in the standard point-kinetics approximation of the reactivity worth. For positive reactivity, the error in the variational estimate of reactivity was only a few percent in the larger core, and the resulting transient power prediction was 1 to 2 orders of magnitude more accurate than with the standard point-kinetics approximation. For a large, local negative reactivity insertion resulting in a large flux shift, the accuracy of the variational estimate broke down. The variational estimate of the effect of flux shifts on reactivity in point-kinetics calculations of transients in LWR cores was found to generally result in greatly improved accuracy, relative to the standard point-kinetics approximation, the exception being for large negative reactivity insertions with large flux shifts in large, loosely coupled cores
Pedotransfer functions estimating soil hydraulic properties using different soil parameters
DEFF Research Database (Denmark)
Børgesen, Christen Duus; Iversen, Bo Vangsø; Jacobsen, Ole Hørbye
2008-01-01
Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity...... parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic...... conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions...
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
Parameter estimation in tree graph metabolic networks
Directory of Open Access Journals (Sweden)
Laura Astola
2016-09-01
Full Text Available We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.
Parameter estimation in tree graph metabolic networks.
Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D; Groenenboom, Marian; Molenaar, Jaap J
2016-01-01
We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.
Parameter estimation for lithium ion batteries
Santhanagopalan, Shriram
With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of
Bayesian and Classical Estimation of Stress-Strength Reliability for Inverse Weibull Lifetime Models
Directory of Open Access Journals (Sweden)
Qixuan Bi
2017-06-01
Full Text Available In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.
Reliability updating based on monitoring of structural response parameters
International Nuclear Information System (INIS)
Leira, B.J.
2016-01-01
Short- and long-term aspects of measuring structural response parameters are addressed. Two specific examples of such measurements are considered for the purpose of illustration and in order to focus the discussion. These examples are taken from the petroleum industry (monitoring of riser response) and from the shipping industry (monitoring of ice-induced strains in a ship hull). Similarities and differences between the two cases are elaborated with respect to which are the most relevant mechanical limit states. Furthermore, main concerns related to reliability levels within a short-term versus long-term time horizon are highlighted. Quantifying the economic benefits of applying monitoring systems is also addressed. - Highlights: • Two examples of structural response monitoring are described. • Application of measurements is discussed in relation to updating of load and structural parameters. • Quantification of the value of response monitoring is made for both of the examples.
Composite likelihood estimation of demographic parameters
Directory of Open Access Journals (Sweden)
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
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.
User's guide to the Reliability Estimation System Testbed (REST)
Nicol, David M.; Palumbo, Daniel L.; Rifkin, Adam
1992-01-01
The Reliability Estimation System Testbed is an X-window based reliability modeling tool that was created to explore the use of the Reliability Modeling Language (RML). RML was defined to support several reliability analysis techniques including modularization, graphical representation, Failure Mode Effects Simulation (FMES), and parallel processing. These techniques are most useful in modeling large systems. Using modularization, an analyst can create reliability models for individual system components. The modules can be tested separately and then combined to compute the total system reliability. Because a one-to-one relationship can be established between system components and the reliability modules, a graphical user interface may be used to describe the system model. RML was designed to permit message passing between modules. This feature enables reliability modeling based on a run time simulation of the system wide effects of a component's failure modes. The use of failure modes effects simulation enhances the analyst's ability to correctly express system behavior when using the modularization approach to reliability modeling. To alleviate the computation bottleneck often found in large reliability models, REST was designed to take advantage of parallel processing on hypercube processors.
Preliminary Estimation of Kappa Parameter in Croatia
Stanko, Davor; Markušić, Snježana; Ivančić, Ines; Mario, Gazdek; Gülerce, Zeynep
2017-12-01
Spectral parameter kappa κ is used to describe spectral amplitude decay “crash syndrome” at high frequencies. The purpose of this research is to estimate spectral parameter kappa for the first time in Croatia based on small and moderate earthquakes. Recordings of local earthquakes with magnitudes higher than 3, epicentre distances less than 150 km, and focal depths less than 30 km from seismological stations in Croatia are used. The value of kappa was estimated from the acceleration amplitude spectrum of shear waves from the slope of the high-frequency part where the spectrum starts to decay rapidly to a noise floor. Kappa models as a function of a site and distance were derived from a standard linear regression of kappa-distance dependence. Site kappa was determined from the extrapolation of the regression line to a zero distance. The preliminary results of site kappa across Croatia are promising. In this research, these results are compared with local site condition parameters for each station, e.g. shear wave velocity in the upper 30 m from geophysical measurements and with existing global shear wave velocity - site kappa values. Spatial distribution of individual kappa’s is compared with the azimuthal distribution of earthquake epicentres. These results are significant for a couple of reasons: to extend the knowledge of the attenuation of near-surface crust layers of the Dinarides and to provide additional information on the local earthquake parameters for updating seismic hazard maps of studied area. Site kappa can be used in the re-creation, and re-calibration of attenuation of peak horizontal and/or vertical acceleration in the Dinarides area since information on the local site conditions were not included in the previous studies.
DEFF Research Database (Denmark)
Jones, Mark Nicholas; Frutiger, Jerome; Abildskov, Jens
We present a new software tool called SAFEPROPS which is able to estimate major safety-related and environmental properties for organic compounds. SAFEPROPS provides accurate, reliable and fast predictions using the Marrero-Gani group contribution (MG-GC) method. It is implemented using Python...... as the main programming language, while the necessary parameters together with their correlation matrix are obtained from a SQLite database which has been populated using off-line parameter and error estimation routines (Eq. 3-8)....
PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION
Directory of Open Access Journals (Sweden)
Samir Kamel Ashour
2010-12-01
Full Text Available Survival analysis is used in various fields for analyzing data involving the duration between two events. It is also known as event history analysis, lifetime data analysis, reliability analysis or time to event analysis. One of the difficulties which arise in this area is the presence of censored data. The lifetime of an individual is censored when it cannot be exactly measured but partial information is available. Different circumstances can produce different types of censoring. The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this paper we consider the estimation of parameters of Lomax distribution based on hybrid censored data. The parameters are estimated by the maximum likelihood and Bayesian methods. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals.
Estimation of Parameters of CCF with Staggered Testing
International Nuclear Information System (INIS)
Kim, Myung-Ki; Hong, Sung-Yull
2006-01-01
Common cause failures are extremely important in reliability analysis and would be dominant to risk contributor in a high reliable system such as a nuclear power plant. Of particular concern is common cause failure (CCF) that degrades redundancy or diversity implemented to improve a reliability of systems. Most of analyses of parameters of CCF models such as beta factor model, alpha factor model, and MGL(Multiple Greek Letters) model deal a system with a nonstaggered testing strategy. Non-staggered testing is that all components are tested at the same time (or at least the same shift) and staggered testing is that if there is a failure in the first component, all the other components are tested immediately, and if it succeeds, no more is done until the next scheduled testing time. Both of them are applied in the nuclear power plants. The strategy, however, is not explicitly described in the technical specifications, but implicitly in the periodic test procedure. For example, some redundant components particularly important to safety are being tested with staggered testing strategy. Others are being performed with non-staggered testing strategy. This paper presents the parameter estimator of CCF model such as beta factor model, MGL model, and alpha factor model with staggered testing strategy. In addition, a new CCF model, rho factor model, is proposed and its parameter is presented with staggered testing strategy
Statistical distributions applications and parameter estimates
Thomopoulos, Nick T
2017-01-01
This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for researchers in almost all disciplines. The informed researcher will select the statistical distribution that best fits the data in the study at hand. Some of the distributions are well known to the general researcher and are in use in a wide variety of ways. Other useful distributions are less understood and are not in common use. The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study. The distributions are for continuous, discrete, and bivariate random variables. In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values. In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of ...
Statistical estimation of nuclear reactor dynamic parameters
International Nuclear Information System (INIS)
Cummins, J.D.
1962-02-01
This report discusses the study of the noise in nuclear reactors and associated power plant. The report is divided into three distinct parts. In the first part parameters which influence the dynamic behaviour of some reactors will be specified and their effect on dynamic performance described. Methods of estimating dynamic parameters using statistical signals will be described in detail together with descriptions of the usefulness of the results, the accuracy and related topics. Some experiments which have been and which might be performed on nuclear reactors will be described. In the second part of the report a digital computer programme will be described. The computer programme derives the correlation functions and the spectra of signals. The programme will compute the frequency response both gain and phase for physical items of plant for which simultaneous recordings of input and output signal variations have been made. Estimations of the accuracy of the correlation functions and the spectra may be computed using the programme and the amplitude distribution of signals may also b computed. The programme is written in autocode for the Ferranti Mercury computer. In the third part of the report a practical example of the use of the method and the digital programme is presented. In order to eliminate difficulties of interpretation a very simple plant model was chosen i.e. a simple first order lag. Several interesting properties of statistical signals were measured and will be discussed. (author)
Estimation of the human error probabilities in the human reliability analysis
International Nuclear Information System (INIS)
Liu Haibin; He Xuhong; Tong Jiejuan; Shen Shifei
2006-01-01
Human error data is an important issue of human reliability analysis (HRA). Using of Bayesian parameter estimation, which can use multiple information, such as the historical data of NPP and expert judgment data to modify the human error data, could get the human error data reflecting the real situation of NPP more truly. This paper, using the numeric compute program developed by the authors, presents some typical examples to illustrate the process of the Bayesian parameter estimation in HRA and discusses the effect of different modification data on the Bayesian parameter estimation. (authors)
Parameter Estimation of Spacecraft Fuel Slosh Model
Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles
2004-01-01
Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.
Bridging the gaps between non-invasive genetic sampling and population parameter estimation
Francesca Marucco; Luigi Boitani; Daniel H. Pletscher; Michael K. Schwartz
2011-01-01
Reliable estimates of population parameters are necessary for effective management and conservation actions. The use of genetic data for captureÂrecapture (CR) analyses has become an important tool to estimate population parameters for elusive species. Strong emphasis has been placed on the genetic analysis of non-invasive samples, or on the CR analysis; however,...
Reliability of radiographic parameters in adults with hip dysplasia
Energy Technology Data Exchange (ETDEWEB)
Terjesen, Terje [Oslo University Hospital, Rikshospitalet, Department of Orthopaedics, Oslo (Norway); Gunderson, Ragnhild B. [Oslo University Hospital, Rikshospitalet, Department of Radiology, Oslo (Norway)
2012-07-15
To assess the reliability of radiographic measurements in adults previously treated for developmental dysplasia of the hip (DDH) and to clarify whether these parameters differ according to position of the patient (supine versus standing). Fifty-one patients (41 females and 10 males) with 63 affected hips were included in the study. The mean follow-up period was 45 (44-49) years in the patients who had not undergone total hip replacement (THR). Anteroposterior radiographs of the pelvis were taken with the patient in the supine and in the standing position. Measurements used for residual hip dysplasia were center-edge (CE) angle and migration percentage (MP). The joint space width (JSW) was measured at three or four locations of the upper, weight-bearing part of the joint, and the shortest distance was termed the minimum joint space width (minJSW). One radiologist and one orthopaedic surgeon, each with more than 30 years of experience, independently measured the radiographic parameters. The limits of agreement (LOA) of the CE angle (mean interobserver difference {+-} 2SD) were within the range -8 to 7 . The LOA of the MP were in the range -8 to 8% and of the minJSW -0.6 to 1.1 mm. The mean differences in CE angle between supine and standing radiographs (supine - standing) ranged from -1.1 to 0.0 and the mean differences in MP between supine and standing positions were below 1%. The mean positional differences in minJSW were below 0.1 mm and were not statistically significant. The interobserver variations with regard to CE angle, MP, and minJSW were moderate, indicating that these are reliable measurements in clinical practice. Femoral head coverage and JSW did not significantly differ between supine and weight-bearing positions. (orig.)
Reliability estimation for check valves and other components
International Nuclear Information System (INIS)
McElhaney, K.L.; Staunton, R.H.
1996-01-01
For years the nuclear industry has depended upon component operational reliability information compiled from reliability handbooks and other generic sources as well as private databases generated by recognized experts both within and outside the nuclear industry. Regrettably, these technical bases lacked the benefit of large-scale operational data and comprehensive data verification, and did not take into account the parameters and combinations of parameters that affect the determination of failure rates. This paper briefly examines the historic use of generic component reliability data, its sources, and its limitations. The concept of using a single failure rate for a particular component type is also examined. Particular emphasis is placed on check valves due to the information available on those components. The Appendix presents some of the results of the extensive analyses done by Oak Ridge National Laboratory (ORNL) on check valve performance
Analytic continuation by duality estimation of the S parameter
International Nuclear Information System (INIS)
Ignjatovic, S. R.; Wijewardhana, L. C. R.; Takeuchi, T.
2000-01-01
We investigate the reliability of the analytic continuation by duality (ACD) technique in estimating the electroweak S parameter for technicolor theories. The ACD technique, which is an application of finite energy sum rules, relates the S parameter for theories with unknown particle spectra to known OPE coefficients. We identify the sources of error inherent in the technique and evaluate them for several toy models to see if they can be controlled. The evaluation of errors is done analytically and all relevant formulas are provided in appendixes including analytical formulas for approximating the function 1/s with a polynomial in s. The use of analytical formulas protects us from introducing additional errors due to numerical integration. We find that it is very difficult to control the errors even when the momentum dependence of the OPE coefficients is known exactly. In realistic cases in which the momentum dependence of the OPE coefficients is only known perturbatively, it is impossible to obtain a reliable estimate. (c) 2000 The American Physical Society
Case Study: Zutphen : Estimates of levee system reliability
Roscoe, K.; Kothuis, Baukje; Kok, Matthijs
2017-01-01
Estimates of levee system reliability can conflict with experience and intuition. For example, a very high failure probability may be computed while no evidence of failure has been observed, or a very low failure probability when signs of failure have been detected.
Rapid estimation of high-parameter auditory-filter shapes
Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M.
2014-01-01
A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086
Multivariate phase type distributions - Applications and parameter estimation
DEFF Research Database (Denmark)
Meisch, David
The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...
Energy parameter estimation in solar powered wireless sensor networks
Mousa, Mustafa
2014-02-24
The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.
Energy parameter estimation in solar powered wireless sensor networks
Mousa, Mustafa; Claudel, Christian G.
2014-01-01
The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.
A generic method for estimating system reliability using Bayesian networks
International Nuclear Information System (INIS)
Doguc, Ozge; Ramirez-Marquez, Jose Emmanuel
2009-01-01
This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples
A generic method for estimating system reliability using Bayesian networks
Energy Technology Data Exchange (ETDEWEB)
Doguc, Ozge [Stevens Institute of Technology, Hoboken, NJ 07030 (United States); Ramirez-Marquez, Jose Emmanuel [Stevens Institute of Technology, Hoboken, NJ 07030 (United States)], E-mail: jmarquez@stevens.edu
2009-02-15
This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.
Reliability of Bluetooth Technology for Travel Time Estimation
DEFF Research Database (Denmark)
Araghi, Bahar Namaki; Olesen, Jonas Hammershøj; Krishnan, Rajesh
2015-01-01
. However, their corresponding impacts on accuracy and reliability of estimated travel time have not been evaluated. In this study, a controlled field experiment is conducted to collect both Bluetooth and GPS data for 1000 trips to be used as the basis for evaluation. Data obtained by GPS logger is used...... to calculate actual travel time, referred to as ground truth, and to geo-code the Bluetooth detection events. In this setting, reliability is defined as the percentage of devices captured per trip during the experiment. It is found that, on average, Bluetooth-enabled devices will be detected 80% of the time......-range antennae detect Bluetooth-enabled devices in a closer location to the sensor, thus providing a more accurate travel time estimate. However, the smaller the size of the detection zone, the lower the penetration rate, which could itself influence the accuracy of estimates. Therefore, there has to be a trade...
Parameter estimation in fractional diffusion models
Kubilius, Kęstutis; Ralchenko, Kostiantyn
2017-01-01
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides s...
Pollen parameters estimates of genetic variability among newly ...
African Journals Online (AJOL)
Pollen parameters estimates of genetic variability among newly selected Nigerian roselle (Hibiscus sabdariffa L.) genotypes. ... Estimates of some pollen parameters where used to assess the genetic diversity among ... HOW TO USE AJOL.
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
Estimation of light transport parameters in biological media using coherent backscattering ... backscattered light for estimating the light transport parameters of biological media has been investigated. ... Pramana – Journal of Physics | News.
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.
2006-10-01
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Reliability Estimation of the Pultrusion Process Using the First-Order Reliability Method (FORM)
DEFF Research Database (Denmark)
Baran, Ismet; Tutum, Cem Celal; Hattel, Jesper Henri
2013-01-01
In the present study the reliability estimation of the pultrusion process of a flat plate is analyzed by using the first order reliability method (FORM). The implementation of the numerical process model is validated by comparing the deterministic temperature and cure degree profiles...... with corresponding analyses in the literature. The centerline degree of cure at the exit (CDOCE) being less than a critical value and the maximum composite temperature (Tmax) during the process being greater than a critical temperature are selected as the limit state functions (LSFs) for the FORM. The cumulative...
Mathur, F. P.
1972-01-01
Description of an on-line interactive computer program called CARE (Computer-Aided Reliability Estimation) which can model self-repair and fault-tolerant organizations and perform certain other functions. Essentially CARE consists of a repository of mathematical equations defining the various basic redundancy schemes. These equations, under program control, are then interrelated to generate the desired mathematical model to fit the architecture of the system under evaluation. The mathematical model is then supplied with ground instances of its variables and is then evaluated to generate values for the reliability-theoretic functions applied to the model.
Bhattacharjya, Rajib Kumar
2018-05-01
The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.
Bayesian parameter estimation for stochastic models of biological cell migration
Dieterich, Peter; Preuss, Roland
2013-08-01
Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.
Reliability Estimation of Aero-engine Based on Mixed Weibull Distribution Model
Yuan, Zhongda; Deng, Junxiang; Wang, Dawei
2018-02-01
Aero-engine is a complex mechanical electronic system, based on analysis of reliability of mechanical electronic system, Weibull distribution model has an irreplaceable role. Till now, only two-parameter Weibull distribution model and three-parameter Weibull distribution are widely used. Due to diversity of engine failure modes, there is a big error with single Weibull distribution model. By contrast, a variety of engine failure modes can be taken into account with mixed Weibull distribution model, so it is a good statistical analysis model. Except the concept of dynamic weight coefficient, in order to make reliability estimation result more accurately, three-parameter correlation coefficient optimization method is applied to enhance Weibull distribution model, thus precision of mixed distribution reliability model is improved greatly. All of these are advantageous to popularize Weibull distribution model in engineering applications.
Application of subset simulation in reliability estimation of underground pipelines
International Nuclear Information System (INIS)
Tee, Kong Fah; Khan, Lutfor Rahman; Li, Hongshuang
2014-01-01
This paper presents a computational framework for implementing an advanced Monte Carlo simulation method, called Subset Simulation (SS) for time-dependent reliability prediction of underground flexible pipelines. The SS can provide better resolution for low failure probability level of rare failure events which are commonly encountered in pipeline engineering applications. Random samples of statistical variables are generated efficiently and used for computing probabilistic reliability model. It gains its efficiency by expressing a small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment and compared with direct Monte Carlo simulation (MCS) method. Reliability of a buried flexible steel pipe with time-dependent failure modes, namely, corrosion induced deflection, buckling, wall thrust and bending stress has been assessed in this study. The analysis indicates that corrosion induced excessive deflection is the most critical failure event whereas buckling is the least susceptible during the whole service life of the pipe. The study also shows that SS is robust method to estimate the reliability of buried pipelines and it is more efficient than MCS, especially in small failure probability prediction
Reliability estimation system: its application to the nuclear geophysical sampling of ore deposits
International Nuclear Information System (INIS)
Khaykovich, I.M.; Savosin, S.I.
1992-01-01
The reliability estimation system accepted in the Soviet Union for sampling data in nuclear geophysics is based on unique requirements in metrology and methodology. It involves estimating characteristic errors in calibration, as well as errors in measurement and interpretation. This paper describes the methods of estimating the levels of systematic and random errors at each stage of the problem. The data of nuclear geophysics sampling are considered to be reliable if there are no statistically significant, systematic differences between ore intervals determined by this method and by geological control, or by other methods of sampling; the reliability of the latter having been verified. The difference between the random errors is statistically insignificant. The system allows one to obtain information on the parameters of ore intervals with a guaranteed random error and without systematic errors. (Author)
Application of spreadsheet to estimate infiltration parameters
Zakwan, Mohammad; Muzzammil, Mohammad; Alam, Javed
2016-01-01
Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach ...
Generating human reliability estimates using expert judgment. Volume 2. Appendices
International Nuclear Information System (INIS)
Comer, M.K.; Seaver, D.A.; Stillwell, W.G.; Gaddy, C.D.
1984-11-01
The US Nuclear Regulatory Commission is conducting a research program to determine the practicality, acceptability, and usefulness of several different methods for obtaining human reliability data and estimates that can be used in nuclear power plant probabilistic risk assessments (PRA). One method, investigated as part of this overall research program, uses expert judgment to generate human error probability (HEP) estimates and associated uncertainty bounds. The project described in this document evaluated two techniques for using expert judgment: paired comparisons and direct numerical estimation. Volume 2 provides detailed procedures for using the techniques, detailed descriptions of the analyses performed to evaluate the techniques, and HEP estimates generated as part of this project. The results of the evaluation indicate that techniques using expert judgment should be given strong consideration for use in developing HEP estimates. Judgments were shown to be consistent and to provide HEP estimates with a good degree of convergent validity. Of the two techniques tested, direct numerical estimation appears to be preferable in terms of ease of application and quality of results
Estimates for the parameters of the heavy quark expansion
Energy Technology Data Exchange (ETDEWEB)
Heinonen, Johannes; Mannel, Thomas [Universitaet Siegen (Germany)
2015-07-01
We give improved estimates for the non-perturbative parameters appearing in the heavy quark expansion for inclusive decays. While the parameters appearing in low orders of this expansion can be extracted from data, the number of parameters in higher orders proliferates strongly, making a determination of these parameters from data impossible. Thus, one has to rely on theoretical estimates which may be obtained from an insertion of intermediate states. We refine this method and attempt to estimate the uncertainties of this approach.
Response-based estimation of sea state parameters - Influence of filtering
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The wave spectrum can be estimated from procedures based on measured ship responses. The paper deals with two procedures—Bayesian Modelling and Parametric Modelling...
Multi-objective optimization in quantum parameter estimation
Gong, BeiLi; Cui, Wei
2018-04-01
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data
Directory of Open Access Journals (Sweden)
Xueqin Zhou
2017-01-01
Full Text Available This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well.
A note on reliability estimation of functionally diverse systems
International Nuclear Information System (INIS)
Littlewood, B.; Popov, P.; Strigini, L.
1999-01-01
It has been argued that functional diversity might be a plausible means of claiming independence of failures between two versions of a system. We present a model of functional diversity, in the spirit of earlier models of diversity such as those of Eckhardt and Lee, and Hughes. In terms of the model, we show that the claims for independence between functionally diverse systems seem rather unrealistic. Instead, it seems likely that functionally diverse systems will exhibit positively correlated failures, and thus will be less reliable than an assumption of independence would suggest. The result does not, of course, suggest that functional diversity is not worthwhile; instead, it places upon the evaluator of such a system the onus to estimate the degree of dependence so as to evaluate the reliability of the system
Probabilistic confidence for decisions based on uncertain reliability estimates
Reid, Stuart G.
2013-05-01
Reliability assessments are commonly carried out to provide a rational basis for risk-informed decisions concerning the design or maintenance of engineering systems and structures. However, calculated reliabilities and associated probabilities of failure often have significant uncertainties associated with the possible estimation errors relative to the 'true' failure probabilities. For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail. The paper describes how the concept of probabilistic confidence can be applied to evaluate and appropriately limit the probabilities of failure attributable to particular uncertainties such as design errors that may critically affect the dependability of risk-acceptance decisions. This approach is illustrated with regard to the dependability of structural design processes based on prototype testing with uncertainties attributable to sampling variability.
Parameter estimation and testing of hypotheses
International Nuclear Information System (INIS)
Fruhwirth, R.
1996-01-01
This lecture presents the basic mathematical ideas underlying the concept of random variable and the construction and analysis of estimators and test statistics. The material presented is based mainly on four books given in the references: the general exposition of estimators and test statistics follows Kendall and Stuart which is a comprehensive review of the field; the book by Eadie et al. contains selecting topics of particular interest to experimental physicist and a host of illuminating examples from experimental high-energy physics; for the presentation of numerical procedures, the Press et al. and the Thisted books have been used. The last section deals with estimation in dynamic systems. In most books the Kalman filter is presented in a Bayesian framework, often obscured by cumbrous notation. In this lecture, the link to classical least-squares estimators and regression models is stressed with the aim of facilitating the access to this less familiar topic. References are given for specific applications to track and vertex fitting and for extended exposition of these topics. In the appendix, the link between Bayesian decision rules and feed-forward neural networks is presented. (J.S.). 10 refs., 5 figs., 1 appendix
Parameter estimation in tree graph metabolic networks
Astola, Laura; Stigter, Hans; Gomez Roldan, Maria Victoria; Eeuwijk, van Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.
2016-01-01
We study the glycosylation processes that convert initially toxic substrates to nu- tritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme
Numerical Model based Reliability Estimation of Selective Laser Melting Process
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2014-01-01
Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....
A Comparative Study of Distribution System Parameter Estimation Methods
Energy Technology Data Exchange (ETDEWEB)
Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup
2016-07-17
In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Identifying the effects of parameter uncertainty on the reliability of riverbank stability modelling
Samadi, A.; Amiri-Tokaldany, E.; Darby, S. E.
2009-05-01
Bank retreat is a key process in fluvial dynamics affecting a wide range of physical, ecological and socioeconomic issues in the fluvial environment. To predict the undesirable effects of bank retreat and to inform effective measures to prevent it, a wide range of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of driving and resisting forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the bank profile (bank height and angle), the geotechnical properties of the bank materials, as well as the hydrological status of the riverbanks. In this paper we evaluate the extent to which uncertainties in the parameterization of these controlling factors feed through to influence the reliability of the resulting bank stability estimate. This is achieved by employing a simple model of riverbank stability with respect to planar failure (which is the most common type of bank stability model) in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. These identified parameter value ranges are compared to empirically derived parameter uncertainties to determine whether they are likely to confound the reliability of the resulting bank stability calculations. Our results show that parameter uncertainties are typically high enough that the likelihood of generating unreliable predictions is typically very high (> ˜ 80% for predictions requiring a precision of < ± 15%). Because parameter uncertainties are derived primarily from the natural variability of the parameters, rather than measurement errors, much more careful attention should be paid to field sampling strategies, such that the parameter uncertainties and consequent prediction unreliabilities can be quantified more
minimum variance estimation of yield parameters of rubber tree
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... It is our opinion that Kalman filter is a robust estimator of the ... Kalman filter, parameter estimation, rubber clones, Chow failure test, autocorrelation, STAMP, data ...... Mills, T.C. Modelling Current Temperature Trends.
Availability and Reliability of FSO Links Estimated from Visibility
Directory of Open Access Journals (Sweden)
M. Tatarko
2012-06-01
Full Text Available This paper is focused on estimation availability and reliability of FSO systems. Shortcut FSO means Free Space Optics. It is a system which allows optical transmission between two steady points. We can say that it is a last mile communication system. It is an optical communication system, but the propagation media is air. This solution of last mile does not require expensive optical fiber and establishing of connection is very simple. But there are some drawbacks which have a bad influence of quality of services and availability of the link. Number of phenomena in the atmosphere such as scattering, absorption and turbulence cause a large variation of receiving optical power and laser beam attenuation. The influence of absorption and turbulence can be significantly reduced by an appropriate design of FSO link. But the visibility has the main influence on quality of the optical transmission channel. Thus, in typical continental area where rain, snow or fog occurs is important to know their values. This article gives a description of device for measuring weather conditions and information about estimation of availability and reliability of FSO links in Slovakia.
Estimation of a collision impact parameter
International Nuclear Information System (INIS)
Shmatov, S.V.; Zarubin, P.I.
2001-01-01
We demonstrate that the nuclear collision geometry (i.e. impact parameter) can be determined in an event-by-event analysis by measuring the transverse energy flow in the pseudorapidity region 3≤|η|≤5 with a minimal dependence on collision dynamics details at the LHC energy scale. Using the HIJING model we have illustrated our calculation by a simulation of events of nucleus-nucleus interactions at the c.m.s. energy from 1 up to 5.5 TeV per nucleon and various types of nuclei
Novel Method for 5G Systems NLOS Channels Parameter Estimation
Directory of Open Access Journals (Sweden)
Vladeta Milenkovic
2017-01-01
Full Text Available For the development of new 5G systems to operate in mm bands, there is a need for accurate radio propagation modelling at these bands. In this paper novel approach for NLOS channels parameter estimation will be presented. Estimation will be performed based on LCR performance measure, which will enable us to estimate propagation parameters in real time and to avoid weaknesses of ML and moment method estimation approaches.
Parameter Estimation for Improving Association Indicators in Binary Logistic Regression
Directory of Open Access Journals (Sweden)
Mahdi Bashiri
2012-02-01
Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.
Riese, H.; Vrijkotte, T.; Meijer, P.; Kluft, C.; Geus, E. de
1999-01-01
Interactions between lipoproteins and fibrinolytic parameters inducing cardiovascular disease are gradually being disclosed. The multivariate analyses often used in studies investigating this relationship implicitly assume 1) that the reliability of the parameters in a cluster is comparably high,
Estimation of gloss from rough surface parameters
Simonsen, Ingve; Larsen, Åge G.; Andreassen, Erik; Ommundsen, Espen; Nord-Varhaug, Katrin
2005-12-01
Gloss is a quantity used in the optical industry to quantify and categorize materials according to how well they scatter light specularly. With the aid of phase perturbation theory, we derive an approximate expression for this quantity for a one-dimensional randomly rough surface. It is demonstrated that gloss depends in an exponential way on two dimensionless quantities that are associated with the surface randomness: the root-mean-square roughness times the perpendicular momentum transfer for the specular direction, and a correlation function dependent factor times a lateral momentum variable associated with the collection angle. Rigorous Monte Carlo simulations are used to access the quality of this approximation, and good agreement is observed over large regions of parameter space.
A new Bayesian recursive technique for parameter estimation
Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis
2006-08-01
The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.
Control and Estimation of Distributed Parameter Systems
Kappel, F; Kunisch, K
1998-01-01
Consisting of 23 refereed contributions, this volume offers a broad and diverse view of current research in control and estimation of partial differential equations. Topics addressed include, but are not limited to - control and stability of hyperbolic systems related to elasticity, linear and nonlinear; - control and identification of nonlinear parabolic systems; - exact and approximate controllability, and observability; - Pontryagin's maximum principle and dynamic programming in PDE; and - numerics pertinent to optimal and suboptimal control problems. This volume is primarily geared toward control theorists seeking information on the latest developments in their area of expertise. It may also serve as a stimulating reader to any researcher who wants to gain an impression of activities at the forefront of a vigorously expanding area in applied mathematics.
Gravity Field Parameter Estimation Using QR Factorization
Klokocnik, J.; Wagner, C. A.; McAdoo, D.; Kostelecky, J.; Bezdek, A.; Novak, P.; Gruber, C.; Marty, J.; Bruinsma, S. L.; Gratton, S.; Balmino, G.; Baboulin, M.
2007-12-01
This study compares the accuracy of the estimated geopotential coefficients when QR factorization is used instead of the classical method applied at our institute, namely the generation of normal equations that are solved by means of Cholesky decomposition. The objective is to evaluate the gain in numerical precision, which is obtained at considerable extra cost in terms of computer resources. Therefore, a significant increase in precision must be realized in order to justify the additional cost. Numerical simulations were done in order to examine the performance of both solution methods. Reference gravity gradients were simulated, using the EIGEN-GL04C gravity field model to degree and order 300, every 3 seconds along a near-circular, polar orbit at 250 km altitude. The simulation spanned a total of 60 days. A polar orbit was selected in this simulation in order to avoid the 'polar gap' problem, which causes inaccurate estimation of the low-order spherical harmonic coefficients. Regularization is required in that case (e.g., the GOCE mission), which is not the subject of the present study. The simulated gravity gradients, to which white noise was added, were then processed with the GINS software package, applying EIGEN-CG03 as the background gravity field model, followed either by the usual normal equation computation or using the QR approach for incremental linear least squares. The accuracy assessment of the gravity field recovery consists in computing the median error degree-variance spectra, accumulated geoid errors, geoid errors due to individual coefficients, and geoid errors calculated on a global grid. The performance, in terms of memory usage, required disk space, and CPU time, of the QR versus the normal equation approach is also evaluated.
ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction
International Nuclear Information System (INIS)
Toledo, Maria Luíza Guerra de; Freitas, Marta A.; Colosimo, Enrico A.; Gilardoni, Gustavo L.
2015-01-01
An appropriate maintenance policy is essential to reduce expenses and risks related to equipment failures. A fundamental aspect to be considered when specifying such policies is to be able to predict the reliability of the systems under study, based on a well fitted model. In this paper, the classes of models Arithmetic Reduction of Age and Arithmetic Reduction of Intensity are explored. Likelihood functions for such models are derived, and a graphical method is proposed for model selection. A real data set involving failures in trucks used by a Brazilian mining is analyzed considering models with different memories. Parameters, namely, shape and scale for Power Law Process, and the efficiency of repair were estimated for the best fitted model. Estimation of model parameters allowed us to derive reliability estimators to predict the behavior of the failure process. These results are a valuable information for the mining company and can be used to support decision making regarding preventive maintenance policy. - Highlights: • Likelihood functions for imperfect repair models are derived. • A goodness-of-fit technique is proposed as a tool for model selection. • Failures in trucks owned by a Brazilian mining are modeled. • Estimation allowed deriving reliability predictors to forecast the future failure process of the trucks
Reliability of fish size estimates obtained from multibeam imaging sonar
Hightower, Joseph E.; Magowan, Kevin J.; Brown, Lori M.; Fox, Dewayne A.
2013-01-01
Multibeam imaging sonars have considerable potential for use in fisheries surveys because the video-like images are easy to interpret, and they contain information about fish size, shape, and swimming behavior, as well as characteristics of occupied habitats. We examined images obtained using a dual-frequency identification sonar (DIDSON) multibeam sonar for Atlantic sturgeon Acipenser oxyrinchus oxyrinchus, striped bass Morone saxatilis, white perch M. americana, and channel catfish Ictalurus punctatus of known size (20–141 cm) to determine the reliability of length estimates. For ranges up to 11 m, percent measurement error (sonar estimate – total length)/total length × 100 varied by species but was not related to the fish's range or aspect angle (orientation relative to the sonar beam). Least-square mean percent error was significantly different from 0.0 for Atlantic sturgeon (x̄ = −8.34, SE = 2.39) and white perch (x̄ = 14.48, SE = 3.99) but not striped bass (x̄ = 3.71, SE = 2.58) or channel catfish (x̄ = 3.97, SE = 5.16). Underestimating lengths of Atlantic sturgeon may be due to difficulty in detecting the snout or the longer dorsal lobe of the heterocercal tail. White perch was the smallest species tested, and it had the largest percent measurement errors (both positive and negative) and the lowest percentage of images classified as good or acceptable. Automated length estimates for the four species using Echoview software varied with position in the view-field. Estimates tended to be low at more extreme azimuthal angles (fish's angle off-axis within the view-field), but mean and maximum estimates were highly correlated with total length. Software estimates also were biased by fish images partially outside the view-field and when acoustic crosstalk occurred (when a fish perpendicular to the sonar and at relatively close range is detected in the side lobes of adjacent beams). These sources of
Online State Space Model Parameter Estimation in Synchronous Machines
Directory of Open Access Journals (Sweden)
Z. Gallehdari
2014-06-01
The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.
Parameter Estimates in Differential Equation Models for Chemical Kinetics
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
Estimation of ground water hydraulic parameters
Energy Technology Data Exchange (ETDEWEB)
Hvilshoej, Soeren
1998-11-01
The main objective was to assess field methods to determine ground water hydraulic parameters and to develop and apply new analysis methods to selected field techniques. A field site in Vejen, Denmark, which previously has been intensively investigated on the basis of a large amount of mini slug tests and tracer tests, was chosen for experimental application and evaluation. Particular interest was in analysing partially penetrating pumping tests and a recently proposed single-well dipole test. Three wells were constructed in which partially penetrating pumping tests and multi-level single-well dipole tests were performed. In addition, multi-level slug tests, flow meter tests, gamma-logs, and geologic characterisation of soil samples were carried out. In addition to the three Vejen analyses, data from previously published partially penetrating pumping tests were analysed assuming homogeneous anisotropic aquifer conditions. In the present study methods were developed to analyse partially penetrating pumping tests and multi-level single-well dipole tests based on an inverse numerical model. The obtained horizontal hydraulic conductivities from the partially penetrating pumping tests were in accordance with measurements obtained from multi-level slug tests and mini slug tests. Accordance was also achieved between the anisotropy ratios determined from partially penetrating pumping tests and multi-level single-well dipole tests. It was demonstrated that the partially penetrating pumping test analysed by and inverse numerical model is a very valuable technique that may provide hydraulic information on the storage terms and the vertical distribution of the horizontal and vertical hydraulic conductivity under both confined and unconfined aquifer conditions. (EG) 138 refs.
Bayesian Parameter Estimation for Heavy-Duty Vehicles
Energy Technology Data Exchange (ETDEWEB)
Miller, Eric; Konan, Arnaud; Duran, Adam
2017-03-28
Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.
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.
The reliable solution and computation time of variable parameters Logistic model
Pengfei, Wang; Xinnong, Pan
2016-01-01
The reliable computation time (RCT, marked as Tc) when applying a double precision computation of a variable parameters logistic map (VPLM) is studied. First, using the method proposed, the reliable solutions for the logistic map are obtained. Second, for a time-dependent non-stationary parameters VPLM, 10000 samples of reliable experiments are constructed, and the mean Tc is then computed. The results indicate that for each different initial value, the Tcs of the VPLM are generally different...
Parameter estimation and prediction of nonlinear biological systems: some examples
Doeswijk, T.G.; Keesman, K.J.
2006-01-01
Rearranging and reparameterizing a discrete-time nonlinear model with polynomial quotient structure in input, output and parameters (xk = f(Z, p)) leads to a model linear in its (new) parameters. As a result, the parameter estimation problem becomes a so-called errors-in-variables problem for which
Confidence Estimation of Reliability Indices of the System with Elements Duplication and Recovery
Directory of Open Access Journals (Sweden)
I. V. Pavlov
2017-01-01
Full Text Available The article considers a problem to estimate a confidence interval of the main reliability indices such as availability rate, mean time between failures, and operative availability (in the stationary state for the model of the system with duplication and independent recovery of elements.Presents a solution of the problem for a situation that often arises in practice, when there are unknown exact values of the reliability parameters of the elements, and only test data of the system or its individual parts (elements, subsystems for reliability are known. It should be noted that the problems of the confidence estimate of reliability indices of the complex systems based on the testing results of their individual elements are fairly common function in engineering practice when designing and running the various engineering systems. The available papers consider this problem, mainly, for non-recovery systems.Describes a solution of this problem for the important particular case when the system elements are duplicated by the reserved elements, and the elements that have failed in the course of system operation are recovered (regardless of the state of other elements.An approximate solution of this problem is obtained for the case of high reliability or "fast recovery" of elements on the assumption that the average recovery time of elements is small as compared to the average time between failures.
A Novel Nonlinear Parameter Estimation Method of Soft Tissues
Directory of Open Access Journals (Sweden)
Qianqian Tong
2017-12-01
Full Text Available The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
Transient analysis of intercalation electrodes for parameter estimation
Devan, Sheba
An essential part of integrating batteries as power sources in any application, be it a large scale automotive application or a small scale portable application, is an efficient Battery Management System (BMS). The combination of a battery with the microprocessor based BMS (called "smart battery") helps prolong the life of the battery by operating in the optimal regime and provides accurate information regarding the battery to the end user. The main purposes of BMS are cell protection, monitoring and control, and communication between different components. These purposes are fulfilled by tracking the change in the parameters of the intercalation electrodes in the batteries. Consequently, the functions of the BMS should be prompt, which requires the methodology of extracting the parameters to be efficient in time. The traditional transient techniques applied so far may not be suitable due to reasons such as the inability to apply these techniques when the battery is under operation, long experimental time, etc. The primary aim of this research work is to design a fast, accurate and reliable technique that can be used to extract parameter values of the intercalation electrodes. A methodology based on analysis of the short time response to a sinusoidal input perturbation, in the time domain is demonstrated using a porous electrode model for an intercalation electrode. It is shown that the parameters associated with the interfacial processes occurring in the electrode can be determined rapidly, within a few milliseconds, by measuring the response in the transient region. The short time analysis in the time domain is then extended to a single particle model that involves bulk diffusion in the solid phase in addition to interfacial processes. A systematic procedure for sequential parameter estimation using sensitivity analysis is described. Further, the short time response and the input perturbation are transformed into the frequency domain using Fast Fourier Transform
Assessment of the Maximal Split-Half Coefficient to Estimate Reliability
Thompson, Barry L.; Green, Samuel B.; Yang, Yanyun
2010-01-01
The maximal split-half coefficient is computed by calculating all possible split-half reliability estimates for a scale and then choosing the maximal value as the reliability estimate. Osburn compared the maximal split-half coefficient with 10 other internal consistency estimates of reliability and concluded that it yielded the most consistently…
Dutta, Rishabh; Jonsson, Sigurjon; Wang, Teng; Vasyura-Bathke, Hannes
2017-01-01
solutions have been neglected, making it impossible to assess the reliability of the reported solutions. We use Bayesian inference to estimate the location, geometry and slip parameters of the fault and their uncertainties using Interferometric Synthetic
Robust Parameter and Signal Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.
This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...... in nonlinear systems, have been exposed. The main objectives of this project are: - analysis and application of theories and methods for robust estimation of parameters in a model structure, obtained from knowledge of the physics of the induction motor. - analysis and application of theories and methods...... for robust estimation of the rotor speed and driving torque of the induction motor based only on measurements of stator voltages and currents. Only contimuous-time models have been used, which means that physical related signals and parameters are estimated directly and not indirectly by some discrete...
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.
Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters
Shi, L.
2015-12-01
This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.
The performance of simulated annealing in parameter estimation for vapor-liquid equilibrium modeling
Directory of Open Access Journals (Sweden)
A. Bonilla-Petriciolet
2007-03-01
Full Text Available In this paper we report the application and evaluation of the simulated annealing (SA optimization method in parameter estimation for vapor-liquid equilibrium (VLE modeling. We tested this optimization method using the classical least squares and error-in-variable approaches. The reliability and efficiency of the data-fitting procedure are also considered using different values for algorithm parameters of the SA method. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. However, in difficult problems it still can converge to local optimums of the objective function.
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.
How to fool cosmic microwave background parameter estimation
International Nuclear Information System (INIS)
Kinney, William H.
2001-01-01
With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the cosmic microwave background (CMB) have reached unprecedented precision. In this paper I show that it is possible for these estimates to be substantially biased by features in the primordial density power spectrum. I construct primordial power spectra which mimic to within cosmic variance errors the effect of changing parameters such as the baryon density and neutrino mass, meaning that even an ideal measurement would be unable to resolve the degeneracy. Complementary measurements are necessary to resolve this ambiguity in parameter estimation efforts based on CMB temperature fluctuations alone
State Estimation-based Transmission line parameter identification
Directory of Open Access Journals (Sweden)
Fredy Andrés Olarte Dussán
2010-01-01
Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.
Reliability Estimation for Single-unit Ceramic Crown Restorations
Lekesiz, H.
2014-01-01
The objective of this study was to evaluate the potential of a survival prediction method for the assessment of ceramic dental restorations. For this purpose, fast-fracture and fatigue reliabilities for 2 bilayer (metal ceramic alloy core veneered with fluorapatite leucite glass-ceramic, d.Sign/d.Sign-67, by Ivoclar; glass-infiltrated alumina core veneered with feldspathic porcelain, VM7/In-Ceram Alumina, by Vita) and 3 monolithic (leucite-reinforced glass-ceramic, Empress, and ProCAD, by Ivoclar; lithium-disilicate glass-ceramic, Empress 2, by Ivoclar) single posterior crown restorations were predicted, and fatigue predictions were compared with the long-term clinical data presented in the literature. Both perfectly bonded and completely debonded cases were analyzed for evaluation of the influence of the adhesive/restoration bonding quality on estimations. Material constants and stress distributions required for predictions were calculated from biaxial tests and finite element analysis, respectively. Based on the predictions, In-Ceram Alumina presents the best fast-fracture resistance, and ProCAD presents a comparable resistance for perfect bonding; however, ProCAD shows a significant reduction of resistance in case of complete debonding. Nevertheless, it is still better than Empress and comparable with Empress 2. In-Ceram Alumina and d.Sign have the highest long-term reliability, with almost 100% survivability even after 10 years. When compared with clinical failure rates reported in the literature, predictions show a promising match with clinical data, and this indicates the soundness of the settings used in the proposed predictions. PMID:25048249
International Nuclear Information System (INIS)
Kang, Seunghoon; Lim, Woochul; Cho, Su-gil; Park, Sanghyun; Lee, Tae Hee; Lee, Minuk; Choi, Jong-su; Hong, Sup
2015-01-01
In order to perform estimations with high reliability, it is necessary to deal with the tail part of the cumulative distribution function (CDF) in greater detail compared to an overall CDF. The use of a generalized Pareto distribution (GPD) to model the tail part of a CDF is receiving more research attention with the goal of performing estimations with high reliability. Current studies on GPDs focus on ways to determine the appropriate number of sample points and their parameters. However, even if a proper estimation is made, it can be inaccurate as a result of an incorrect threshold value. Therefore, in this paper, a GPD based on the Akaike information criterion (AIC) is proposed to improve the accuracy of the tail model. The proposed method determines an accurate threshold value using the AIC with the overall samples before estimating the GPD over the threshold. To validate the accuracy of the method, its reliability is compared with that obtained using a general GPD model with an empirical CDF
Energy Technology Data Exchange (ETDEWEB)
Kang, Seunghoon; Lim, Woochul; Cho, Su-gil; Park, Sanghyun; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Lee, Minuk; Choi, Jong-su; Hong, Sup [Korea Research Insitute of Ships and Ocean Engineering, Daejeon (Korea, Republic of)
2015-02-15
In order to perform estimations with high reliability, it is necessary to deal with the tail part of the cumulative distribution function (CDF) in greater detail compared to an overall CDF. The use of a generalized Pareto distribution (GPD) to model the tail part of a CDF is receiving more research attention with the goal of performing estimations with high reliability. Current studies on GPDs focus on ways to determine the appropriate number of sample points and their parameters. However, even if a proper estimation is made, it can be inaccurate as a result of an incorrect threshold value. Therefore, in this paper, a GPD based on the Akaike information criterion (AIC) is proposed to improve the accuracy of the tail model. The proposed method determines an accurate threshold value using the AIC with the overall samples before estimating the GPD over the threshold. To validate the accuracy of the method, its reliability is compared with that obtained using a general GPD model with an empirical CDF.
A variational approach to parameter estimation in ordinary differential equations
Directory of Open Access Journals (Sweden)
Kaschek Daniel
2012-08-01
Full Text Available Abstract Background Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. Results The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. Conclusions The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
Estimating Soil Hydraulic Parameters using Gradient Based Approach
Rai, P. K.; Tripathi, S.
2017-12-01
The conventional way of estimating parameters of a differential equation is to minimize the error between the observations and their estimates. The estimates are produced from forward solution (numerical or analytical) of differential equation assuming a set of parameters. Parameter estimation using the conventional approach requires high computational cost, setting-up of initial and boundary conditions, and formation of difference equations in case the forward solution is obtained numerically. Gaussian process based approaches like Gaussian Process Ordinary Differential Equation (GPODE) and Adaptive Gradient Matching (AGM) have been developed to estimate the parameters of Ordinary Differential Equations without explicitly solving them. Claims have been made that these approaches can straightforwardly be extended to Partial Differential Equations; however, it has been never demonstrated. This study extends AGM approach to PDEs and applies it for estimating parameters of Richards equation. Unlike the conventional approach, the AGM approach does not require setting-up of initial and boundary conditions explicitly, which is often difficult in real world application of Richards equation. The developed methodology was applied to synthetic soil moisture data. It was seen that the proposed methodology can estimate the soil hydraulic parameters correctly and can be a potential alternative to the conventional method.
A variational approach to parameter estimation in ordinary differential equations.
Kaschek, Daniel; Timmer, Jens
2012-08-14
Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
Kinetic parameter estimation from attenuated SPECT projection measurements
International Nuclear Information System (INIS)
Reutter, B.W.; Gullberg, G.T.
1998-01-01
Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters
Models for estimating photosynthesis parameters from in situ production profiles
Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana
2017-12-01
The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of
REML estimates of genetic parameters of sexual dimorphism for ...
Indian Academy of Sciences (India)
Administrator
Full and half sibs were distinguished, in contrast to usual isofemale studies in which animals ... studies. Thus, the aim of this study was to estimate genetic parameters of sexual dimorphism in isofemale lines using ..... Muscovy ducks. Genet.
A distributed approach for parameters estimation in System Biology models
International Nuclear Information System (INIS)
Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.
2009-01-01
Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.
[The impact factor--a reliable sciento-metric parameter?].
Meenen, N M
1997-08-01
with the highest impact factor. The impact front-runner from 1995 has a very low absolute number of citations. The impact factor provides limited statistical information on a journal in its special field. Using it for this purpose presupposes knowledge of rules, limitations and constraints. Its uncritical use as a general currency of science is fundamentally unscientific. In addition, this leads to the specialists in the field knowledge of the universities being disregarded in favor of a pseudo-objective parameter determined elsewhere. At all events, correction factors for the impact factor have to be applied in respect to the different disciplines. The faculties should reach agreement on relevant (also on German language) organs of publication. The impact factor is not suitable as an indicator of the research activity and the quality of a researcher or an institution. Besides careful human judgement and other classical methods of decision making, the Science Citation Index can contribute to the individual evaluation.
Kinetic parameter estimation from SPECT cone-beam projection measurements
International Nuclear Information System (INIS)
Huesman, Ronald H.; Reutter, Bryan W.; Zeng, G. Larry; Gullberg, Grant T.
1998-01-01
Kinetic parameters are commonly estimated from dynamically acquired nuclear medicine data by first reconstructing a dynamic sequence of images and subsequently fitting the parameters to time-activity curves generated from regions of interest overlaid upon the image sequence. Biased estimates can result from images reconstructed using inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system. If the SPECT data are acquired using cone-beam collimators wherein the gantry rotates so that the focal point of the collimators always remains in a plane, additional biases can arise from images reconstructed using insufficient, as well as truncated, projection samples. To overcome these problems we have investigated the estimation of kinetic parameters directly from SPECT cone-beam projection data by modelling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated chest image volume, kinetic parameters were estimated for simple one-compartment models for four myocardial regions of interest. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated cone-beam data had biases ranging between 3-26% and 0-28%, respectively. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Statistical uncertainties of parameter estimates for 10 000 000 events ranged between 0.2-9% for the uptake parameters and between 0.3-6% for the washout parameters. (author)
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Kalman filter data assimilation: Targeting observations and parameter estimation
International Nuclear Information System (INIS)
Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex
2014-01-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation
Kalman filter estimation of RLC parameters for UMP transmission line
Directory of Open Access Journals (Sweden)
Mohd Amin Siti Nur Aishah
2018-01-01
Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.
1/f noise as a reliability estimation for solar panels
Alabedra, R.; Orsal, B.
The purpose of this work is a study of the 1/f noise from a forward biased dark solar cell as a nondestructive reliability estimation of solar panels. It is shown that one cell with a given defect can be detected in a solar panel by low frequency noise measurements at obscurity. One real solar panel of 5 cells in parallel and 5 cells in series is tested by this method. The cells for space application are n(+)p monocrystalline silicon junction with an area of 8 sq cm and a base resistivity of 10 ohm/cm. In the first part of this paper the I-V, Rd=f(1) characteristics of one cell or of a panel are not modified when a small defect is introduced by a mechanical constraint. In the second part, the theoretical results on the 1/f noise in a p-n junction under forward bias are recalled. It is shown that the noise of the cell with a defect is about 10 to 15 times higher than that of a good cell. If one good cell is replaced by a cell with defect in the panel 5 x 5, this leads to an increase of about 30 percent of the noise level of the panel.
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
Parameter estimation and determinability analysis applied to Drosophila gap gene circuits
Directory of Open Access Journals (Sweden)
Jaeger Johannes
2008-09-01
Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.
Reliability-based sensitivity of mechanical components with arbitrary distribution parameters
International Nuclear Information System (INIS)
Zhang, Yi Min; Yang, Zhou; Wen, Bang Chun; He, Xiang Dong; Liu, Qiaoling
2010-01-01
This paper presents a reliability-based sensitivity method for mechanical components with arbitrary distribution parameters. Techniques from the perturbation method, the Edgeworth series, the reliability-based design theory, and the sensitivity analysis approach were employed directly to calculate the reliability-based sensitivity of mechanical components on the condition that the first four moments of the original random variables are known. The reliability-based sensitivity information of the mechanical components can be accurately and quickly obtained using a practical computer program. The effects of the design parameters on the reliability of mechanical components were studied. The method presented in this paper provides the theoretic basis for the reliability-based design of mechanical components
State and parameter estimation in biotechnical batch reactors
Keesman, K.J.
2000-01-01
In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in
On the Nature of SEM Estimates of ARMA Parameters.
Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.
2002-01-01
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
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...
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...
Estimation of genetic parameters for body weights of Kurdish sheep ...
African Journals Online (AJOL)
Genetic parameters and (co)variance components were estimated by restricted maximum likelihood (REML) procedure, using animal models of kind 1, 2, 3, 4, 5 and 6, for body weight in birth, three, six, nine and 12 months of age in a Kurdish sheep flock. Direct and maternal breeding values were estimated using the best ...
Aircraft parameter estimation ± A tool for development of ...
Indian Academy of Sciences (India)
In addition, actuator performance and controller gains may be flight condition dependent. Moreover, this approach may result in open-loop parameter estimates with low accuracy. 6. Aerodynamic databases for high fidelity flight simulators. Estimation of a comprehensive aerodynamic model suitable for a flight simulator is an.
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...... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...
The open-source, public domain JUPITER (Joint Universal Parameter IdenTification and Evaluation of Reliability) API (Application Programming Interface) provides conventions and Fortran-90 modules to develop applications (computer programs) for analyzing process models. The input ...
A Note On the Estimation of the Poisson Parameter
Directory of Open Access Journals (Sweden)
S. S. Chitgopekar
1985-01-01
distribution when there are errors in observing the zeros and ones and obtains both the maximum likelihood and moments estimates of the Poisson mean and the error probabilities. It is interesting to note that either method fails to give unique estimates of these parameters unless the error probabilities are functionally related. However, it is equally interesting to observe that the estimate of the Poisson mean does not depend on the functional relationship between the error probabilities.
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
International Nuclear Information System (INIS)
Saleh, J.H.; Marais, K.
2006-01-01
In this article, we link an engineering concept, reliability, to a financial and managerial concept, net present value, by exploring the impact of a system's reliability on its revenue generation capability. The framework here developed for non-repairable systems quantitatively captures the value of reliability from a financial standpoint. We show that traditional present value calculations of engineering systems do not account for system reliability, thus over-estimate a system's worth and can therefore lead to flawed investment decisions. It is therefore important to involve reliability engineers upfront before investment decisions are made in technical systems. In addition, the analyses here developed help designers identify the optimal level of reliability that maximizes a system's net present value-the financial value reliability provides to the system minus the cost to achieve this level of reliability. Although we recognize that there are numerous considerations driving the specification of an engineering system's reliability, we contend that the financial analysis of reliability here developed should be made available to decision-makers to support in part, or at least be factored into, the system reliability specification
Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.
Iterative methods for distributed parameter estimation in parabolic PDE
Energy Technology Data Exchange (ETDEWEB)
Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)
1994-12-31
The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.
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 in Stochastic Grey-Box Models
DEFF Research Database (Denmark)
Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay
2004-01-01
An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended...... Kalman filter and features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing observations. The software implementation is compared to an existing software tool...... and proves to have better performance both in terms of quality of estimates for nonlinear systems with significant diffusion and in terms of reproducibility. In particular, the new tool provides more accurate and more consistent estimates of the parameters of the diffusion term....
Traveltime approximations and parameter estimation for orthorhombic media
Masmoudi, Nabil
2016-05-30
Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.
Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters
Hoshino, Takahiro; Shigemasu, Kazuo
2008-01-01
The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…
Assumptions of the primordial spectrum and cosmological parameter estimation
International Nuclear Information System (INIS)
Shafieloo, Arman; Souradeep, Tarun
2011-01-01
The observables of the perturbed universe, cosmic microwave background (CMB) anisotropy and large structures depend on a set of cosmological parameters, as well as the assumed nature of primordial perturbations. In particular, the shape of the primordial power spectrum (PPS) is, at best, a well-motivated assumption. It is known that the assumed functional form of the PPS in cosmological parameter estimation can affect the best-fit-parameters and their relative confidence limits. In this paper, we demonstrate that a specific assumed form actually drives the best-fit parameters into distinct basins of likelihood in the space of cosmological parameters where the likelihood resists improvement via modifications to the PPS. The regions where considerably better likelihoods are obtained allowing free-form PPS lie outside these basins. In the absence of a preferred model of inflation, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed form of PPS. Our results strongly motivate approaches toward simultaneous estimation of the cosmological parameters and the shape of the primordial spectrum from upcoming cosmological data. It is equally important for theorists to keep an open mind towards early universe scenarios that produce features in the PPS. (paper)
Small sample GEE estimation of regression parameters for longitudinal data.
Paul, Sudhir; Zhang, Xuemao
2014-09-28
Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.
Estimation of reliability of a interleaving PFC boost converter
Directory of Open Access Journals (Sweden)
Gulam Amer Sandepudi
2010-01-01
Full Text Available Reliability plays an important role in power supplies. For other electronic equipment, a certain failure mode, at least for a part of the total system, can often be employed without serious (critical effects. However, for power supply no such condition can be accepted, since very high demands on its reliability must be achieved. At higher power levels, the continuous conduction mode (CCM boost converter is preferred topology for implementation a front end with PFC. As a result, significant efforts have been made to improve the performance of high boost converter. This paper is one of the efforts for improving the performance of the converter from the reliability point of view. In this paper, interleaving boost power factor correction converter is simulated with single switch in continuous conduction mode (CCM, discontinuous conduction mode (DCM and critical conduction mode (CRM under different output power ratings. Results of the converter are explored from reliability point of view.
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Pattern statistics on Markov chains and sensitivity to parameter estimation
Directory of Open Access Journals (Sweden)
Nuel Grégory
2006-10-01
Full Text Available Abstract Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,.... Results: In the particular case where pattern statistics (overlap counting only computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.
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.
Between-day reliability of a method for non-invasive estimation of muscle composition.
Simunič, Boštjan
2012-08-01
Tensiomyography is a method for valid and non-invasive estimation of skeletal muscle fibre type composition. The validity of selected temporal tensiomyographic measures has been well established recently; there is, however, no evidence regarding the method's between-day reliability. Therefore it is the aim of this paper to establish the between-day repeatability of tensiomyographic measures in three skeletal muscles. For three consecutive days, 10 healthy male volunteers (mean±SD: age 24.6 ± 3.0 years; height 177.9 ± 3.9 cm; weight 72.4 ± 5.2 kg) were examined in a supine position. Four temporal measures (delay, contraction, sustain, and half-relaxation time) and maximal amplitude were extracted from the displacement-time tensiomyogram. A reliability analysis was performed with calculations of bias, random error, coefficient of variation (CV), standard error of measurement, and intra-class correlation coefficient (ICC) with a 95% confidence interval. An analysis of ICC demonstrated excellent agreement (ICC were over 0.94 in 14 out of 15 tested parameters). However, lower CV was observed in half-relaxation time, presumably because of the specifics of the parameter definition itself. These data indicate that for the three muscles tested, tensiomyographic measurements were reproducible across consecutive test days. Furthermore, we indicated the most possible origin of the lowest reliability detected in half-relaxation time. Copyright © 2012 Elsevier Ltd. All rights reserved.
Improved Accuracy of Nonlinear Parameter Estimation with LAV and Interval Arithmetic Methods
Directory of Open Access Journals (Sweden)
Humberto Muñoz
2009-06-01
Full Text Available The reliable solution of nonlinear parameter es- timation problems is an important computational problem in many areas of science and engineering, including such applications as real time optimization. Its goal is to estimate accurate model parameters that provide the best ﬁt to measured data, despite small- scale noise in the data or occasional large-scale mea- surement errors (outliers. In general, the estimation techniques are based on some kind of least squares or maximum likelihood criterion, and these require the solution of a nonlinear and non-convex optimiza- tion problem. Classical solution methods for these problems are local methods, and may not be reliable for ﬁnding the global optimum, with no guarantee the best model parameters have been found. Interval arithmetic can be used to compute completely and reliably the global optimum for the nonlinear para- meter estimation problem. Finally, experimental re- sults will compare the least squares, l2, and the least absolute value, l1, estimates using interval arithmetic in a chemical engineering application.
Reliability estimation of safety-critical software-based systems using Bayesian networks
International Nuclear Information System (INIS)
Helminen, A.
2001-06-01
Due to the nature of software faults and the way they cause system failures new methods are needed for the safety and reliability evaluation of software-based safety-critical automation systems in nuclear power plants. In the research project 'Programmable automation system safety integrity assessment (PASSI)', belonging to the Finnish Nuclear Safety Research Programme (FINNUS, 1999-2002), various safety assessment methods and tools for software based systems are developed and evaluated. The project is financed together by the Radiation and Nuclear Safety Authority (STUK), the Ministry of Trade and Industry (KTM) and the Technical Research Centre of Finland (VTT). In this report the applicability of Bayesian networks to the reliability estimation of software-based systems is studied. The applicability is evaluated by building Bayesian network models for the systems of interest and performing simulations for these models. In the simulations hypothetical evidence is used for defining the parameter relations and for determining the ability to compensate disparate evidence in the models. Based on the experiences from modelling and simulations we are able to conclude that Bayesian networks provide a good method for the reliability estimation of software-based systems. (orig.)
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Energy Technology Data Exchange (ETDEWEB)
Meliopoulos, Sakis [Georgia Inst. of Technology, Atlanta, GA (United States); Cokkinides, George [Georgia Inst. of Technology, Atlanta, GA (United States); Fardanesh, Bruce [New York Power Authority, NY (United States); Hedrington, Clinton [U.S. Virgin Islands Water and Power Authority (WAPA), St. Croix (U.S. Virgin Islands)
2013-12-31
This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based
CTER—Rapid estimation of CTF parameters with error assessment
Energy Technology Data Exchange (ETDEWEB)
Penczek, Pawel A., E-mail: Pawel.A.Penczek@uth.tmc.edu [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Fang, Jia [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Li, Xueming; Cheng, Yifan [The Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158 (United States); Loerke, Justus; Spahn, Christian M.T. [Institut für Medizinische Physik und Biophysik, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin (Germany)
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. - Highlights: • We describe methodology for estimation of CTF parameters with error assessment. • Error estimates provide means for automated elimination of inferior micrographs. • High computational efficiency allows real-time monitoring of EM data quality. • Accurate CTF estimation yields structure of the 80S human ribosome at 3.85 Å.
An approach of parameter estimation for non-synchronous systems
International Nuclear Information System (INIS)
Xu Daolin; Lu Fangfang
2005-01-01
Synchronization-based parameter estimation is simple and effective but only available to synchronous systems. To come over this limitation, we propose a technique that the parameters of an unknown physical process (possibly a non-synchronous system) can be identified from a time series via a minimization procedure based on a synchronization control. The feasibility of this approach is illustrated in several chaotic systems
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... 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...
Estimation of octanol/water partition coefficients using LSER parameters
Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.
1998-01-01
The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.
Application of genetic algorithms for parameter estimation in liquid chromatography
International Nuclear Information System (INIS)
Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes
2012-01-01
In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography
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
Targeted estimation of nuisance parameters to obtain valid statistical inference.
van der Laan, Mark J
2014-01-01
In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special
Revisiting Boltzmann learning: parameter estimation in Markov random fields
DEFF Research Database (Denmark)
Hansen, Lars Kai; Andersen, Lars Nonboe; Kjems, Ulrik
1996-01-01
This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization...... and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce...
Sedaqatvand, Ramin; Nasr Esfahany, Mohsen; Behzad, Tayebeh; Mohseni, Madjid; Mardanpour, Mohammad Mahdi
2013-10-01
In this study, for the first time, the conduction-based model is extended, and then combined with Genetic Algorithm to estimate the design parameters of a MFC treating dairy wastewater. The optimized parameters are, then, validated. The estimated half-saturation potential of -0.13 V (vs. SHE) is in good agreement while the biofilm conductivity of 8.76×10(-4) mS cm(-1) is three orders of magnitude lower than that previously-reported for pure-culture biofilm. Simulations show that the ohmic and concentration overpotentials contribute almost equally in dropping cell voltage in which the concentration film and biofilm conductivity comprise the main resistances, respectively. Thus, polarization analysis and determining the controlling steps will be possible through that developed extension. This study introduces a reliable method to estimate the design parameters of a particular MFC and to characterize it. Copyright © 2013 Elsevier Ltd. All rights reserved.
SCoPE: an efficient method of Cosmological Parameter Estimation
International Nuclear Information System (INIS)
Das, Santanu; Souradeep, Tarun
2014-01-01
Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data
Estimation of Compaction Parameters Based on Soil Classification
Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.
2018-02-01
Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.
Low Complexity Parameter Estimation For Off-the-Grid Targets
Jardak, Seifallah
2015-10-05
In multiple-input multiple-output radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, a derived cost function is usually evaluated and optimized over a grid of points. The performance of such algorithms is directly affected by the size of the grid: increasing the number of points will enhance the resolution of the algorithm but exponentially increase its complexity. In this work, to estimate the parameters of a target, a reduced complexity super resolution algorithm is proposed. For off-the-grid targets, it uses a low order two dimensional fast Fourier transform to determine a suboptimal solution and then an iterative algorithm to jointly estimate the spatial location and Doppler shift. Simulation results show that the mean square estimation error of the proposed estimators achieve the Cram\\'er-Rao lower bound. © 2015 IEEE.
Estimation of object motion parameters from noisy images.
Broida, T J; Chellappa, R
1986-01-01
An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.
Revised models and genetic parameter estimates for production and ...
African Journals Online (AJOL)
Genetic parameters for production and reproduction traits in the Elsenburg Dormer sheep stud were estimated using records of 11743 lambs born between 1943 and 2002. An animal model with direct and maternal additive, maternal permanent and temporary environmental effects was fitted for traits considered traits of the ...
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten
variance and confidence ellipsoid is demonstrated. The relation is based on a new theorem on maxima of an ellipsoid. The procedure for input signal design and physical parameter estimation is tested on a number of examples, linear as well as nonlinear and simulated as well as real processes, and it appears...
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,…
Parameter extraction and estimation based on the PV panel outdoor ...
African Journals Online (AJOL)
The experimental data obtained are validated and compared with the estimated results obtained through simulation based on the manufacture's data sheet. The simulation is based on the Newton-Raphson iterative method in MATLAB environment. This approach aids the computation of the PV module's parameters at any ...
MPEG2 video parameter and no reference PSNR estimation
DEFF Research Database (Denmark)
Li, Huiying; Forchhammer, Søren
2009-01-01
MPEG coded video may be processed for quality assessment or postprocessed to reduce coding artifacts or transcoded. Utilizing information about the MPEG stream may be useful for these tasks. This paper deals with estimating MPEG parameter information from the decoded video stream without access t...
NONLINEAR PLANT PIECEWISE-CONTINUOUS MODEL MATRIX PARAMETERS ESTIMATION
Directory of Open Access Journals (Sweden)
Roman L. Leibov
2017-09-01
Full Text Available This paper presents a nonlinear plant piecewise-continuous model matrix parameters estimation technique using nonlinear model time responses and random search method. One of piecewise-continuous model application areas is defined. The results of proposed approach application for aircraft turbofan engine piecewisecontinuous model formation are presented
Estimates Of Genetic Parameters Of Body Weights Of Different ...
African Journals Online (AJOL)
four (44) farrowings were used to estimate the genetic parameters (heritability and repeatability) of body weight of pigs. Results obtained from the study showed that the heritability (h2) of birth and weaning weights were moderate (0.33±0.16 ...
Estimation of stature from facial parameters in adult Abakaliki people ...
African Journals Online (AJOL)
This study is carried out in order to estimate the height of adult Igbo people of Abakaliki ethnic group in South-Eastern Nigeria from their facial Morphology. The parameters studied include Facial Length, Bizygomatic Diameter, Bigonial Diameter, Nasal Length, and Nasal Breadth. A total of 1000 subjects comprising 669 ...
On Modal Parameter Estimates from Ambient Vibration Tests
DEFF Research Database (Denmark)
Agneni, A.; Brincker, Rune; Coppotelli, B.
2004-01-01
Modal parameter estimates from ambient vibration testing are turning into the preferred technique when one is interested in systems under actual loadings and operational conditions. Moreover, with this approach, expensive devices to excite the structure are not needed, since it can be adequately...
Measuring, calculating and estimating PEP's parasitic mode loss parameters
International Nuclear Information System (INIS)
Weaver, J.N.
1981-01-01
This note discusses various ways the parasitic mode losses from a bunched beam to a vacuum chamber can be measured, calculated or estimated. A listing of the parameter, k, for the various PEP ring components is included. A number of formulas for calculating multiple and single pass losses are discussed and evaluated for several cases. 25 refs., 1 fig., 1 tab
Visco-piezo-elastic parameter estimation in laminated plate structures
DEFF Research Database (Denmark)
Araujo, A. L.; Mota Soares, C. M.; Herskovits, J.
2009-01-01
A parameter estimation technique is presented in this article, for identification of elastic, piezoelectric and viscoelastic properties of active laminated composite plates with surface-bonded piezoelectric patches. The inverse method presented uses experimental data in the form of a set of measu...
Estimates of genetic parameters and genetic gains for growth traits ...
African Journals Online (AJOL)
Estimates of genetic parameters and genetic gains for growth traits of two Eucalyptus ... In South Africa, Eucalyptus urophylla is an important species due to its ... as hybrid parents to cross with E. grandis was 59.8% over the population mean.
Estimation of riverbank soil erodibility parameters using genetic ...
Indian Academy of Sciences (India)
Tapas Karmaker
2017-11-07
Nov 7, 2017 ... process. Therefore, this is a study to verify the applicability of inverse parameter ... successful modelling of the riverbank erosion, precise estimation of ..... For this simulation, about 40 iterations are found to attain the convergence. ..... rithm for function optimization: a Matlab implementation. NCSU-IE TR ...
estimation of shear strength parameters of lateritic soils using
African Journals Online (AJOL)
user
... a tool to estimate the. Nigerian Journal of Technology (NIJOTECH). Vol. ... modeling tools for the prediction of shear strength parameters for lateritic ... 2.2 Geotechnical Analysis of the Soils ... The back propagation learning algorithm is the most popular and ..... [10] Alsaleh, M. I., Numerical modeling for strain localization in ...
Estimation of genetic parameters for carcass traits in Japanese quail ...
African Journals Online (AJOL)
The aim of this study was to estimate genetic parameters of some carcass characteristics in the Japanese quail. For this aim, carcass weight (Cw), breast weight (Bw), leg weight (Lw), abdominal fat weight (AFw), carcass yield (CP), breast percentage (BP), leg percentage (LP) and abdominal fat percentage (AFP) were ...
Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea
Sawlan, Zaid A
2012-12-01
Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.
Dual ant colony operational modal analysis parameter estimation method
Sitarz, Piotr; Powałka, Bartosz
2018-01-01
Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
Accuracy and sensitivity analysis on seismic anisotropy parameter estimation
Yan, Fuyong; Han, De-Hua
2018-04-01
There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.
Estimation of parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-07
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
A Survey of Software Reliability Modeling and Estimation
1983-09-01
considered include: the Jelinski-Moranda Model, the ,Geometric Model,’ and Musa’s Model. A Monte -Carlo study of the behavior of the ’V"’"*least squares...ceedings Number 261, 1979, pp. 34-1, 34-11. IoelAmrit, AGieboSSukert, Alan and Goel, Ararat , "A Guidebookfor Software Reliability Assessment, 1980
Estimation of Parameters in Mean-Reverting Stochastic Systems
Directory of Open Access Journals (Sweden)
Tianhai Tian
2014-01-01
Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
International Nuclear Information System (INIS)
Imandi, Venkataramana; Chatterjee, Abhijit
2016-01-01
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Imandi, Venkataramana; Chatterjee, Abhijit, E-mail: abhijit@che.iitb.ac.in [Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076 (India)
2016-07-21
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle
Directory of Open Access Journals (Sweden)
Xiang Song
2015-01-01
Full Text Available The rear-end collision warning system requires reliable warning decision mechanism to adapt the actual driving situation. To overcome the shortcomings of existing warning methods, an adaptive strategy is proposed to address the practical aspects of the collision warning problem. The proposed strategy is based on the parameter-adaptive and variable-threshold approaches. First, several key parameter estimation algorithms are developed to provide more accurate and reliable information for subsequent warning method. They include a two-stage algorithm which contains a Kalman filter and a Luenberger observer for relative acceleration estimation, a Bayesian theory-based algorithm of estimating the road friction coefficient, and an artificial neural network for estimating the driver’s reaction time. Further, the variable-threshold warning method is designed to achieve the global warning decision. In the method, the safety distance is employed to judge the dangerous state. The calculation method of the safety distance in this paper can be adaptively adjusted according to the different driving conditions of the leading vehicle. Due to the real-time estimation of the key parameters and the adaptive calculation of the warning threshold, the strategy can adapt to various road and driving conditions. Finally, the proposed strategy is evaluated through simulation and field tests. The experimental results validate the feasibility and effectiveness of the proposed strategy.
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.
Global parameter estimation for thermodynamic models of transcriptional regulation.
Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N
2013-07-15
Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.
Estimating model parameters in nonautonomous chaotic systems using synchronization
International Nuclear Information System (INIS)
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-01-01
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
Influence of measurement errors and estimated parameters on combustion diagnosis
International Nuclear Information System (INIS)
Payri, F.; Molina, S.; Martin, J.; Armas, O.
2006-01-01
Thermodynamic diagnosis models are valuable tools for the study of Diesel combustion. Inputs required by such models comprise measured mean and instantaneous variables, together with suitable values for adjustable parameters used in different submodels. In the case of measured variables, one may estimate the uncertainty associated with measurement errors; however, the influence of errors in model parameter estimation may not be so easily established on an experimental basis. In this paper, a simulated pressure cycle has been used along with known input parameters, so that any uncertainty in the inputs is avoided. Then, the influence of errors in measured variables and geometric and heat transmission parameters on the results of a diagnosis combustion model for direct injection diesel engines have been studied. This procedure allowed to establish the relative importance of these parameters and to set limits to the maximal errors of the model, accounting for both the maximal expected errors in the input parameters and the sensitivity of the model to those errors
Evaluation and reliability of bone histological age estimation methods
African Journals Online (AJOL)
Human age estimation at death plays a vital role in forensic anthropology and bioarchaeology. Researchers used morphological and histological methods to estimate human age from their skeletal remains. This paper discussed different histological methods that used human long bones and ribs to determine age ...
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Engineer’s estimate reliability and statistical characteristics of bids
Directory of Open Access Journals (Sweden)
Fariborz M. Tehrani
2016-12-01
Full Text Available The objective of this report is to provide a methodology for examining bids and evaluating the performance of engineer’s estimates in capturing the true cost of projects. This study reviews the cost development for transportation projects in addition to two sources of uncertainties in a cost estimate, including modeling errors and inherent variability. Sample projects are highway maintenance projects with a similar scope of the work, size, and schedule. Statistical analysis of engineering estimates and bids examines the adaptability of statistical models for sample projects. Further, the variation of engineering cost estimates from inception to implementation has been presented and discussed for selected projects. Moreover, the applicability of extreme values theory is assessed for available data. The results indicate that the performance of engineer’s estimate is best evaluated based on trimmed average of bids, excluding discordant bids.
Reliability estimates for selected sensors in fusion applications
International Nuclear Information System (INIS)
Cadwallader, L.C.
1996-09-01
This report presents the results of a study to define several types of sensors in use, the qualitative reliability (failure modes) and quantitative reliability (average failure rates) for these types of process sensors. Temperature, pressure, flow, and level sensors are discussed for water coolant and for cryogenic coolants. The failure rates that have been found are useful for risk assessment and safety analysis. Repair times and calibration intervals are also given when found in the literature. All of these values can also be useful to plant operators and maintenance personnel. Designers may be able to make use of these data when planning systems. The final chapter in this report discusses failure rates for several types of personnel safety sensors, including ionizing radiation monitors, toxic and combustible gas detectors, humidity sensors, and magnetic field sensors. These data could be useful to industrial hygienists and other safety professionals when designing or auditing for personnel safety
DEFF Research Database (Denmark)
Iwankiewicz, R.; Nielsen, Søren R. K.; Skjærbæk, P. S.
The subject of the paper is the investigation of the sensitivity of structural reliability estimation by a reduced hysteretic model for a reinforced concrete frame under an earthquake excitation.......The subject of the paper is the investigation of the sensitivity of structural reliability estimation by a reduced hysteretic model for a reinforced concrete frame under an earthquake excitation....
Reliability and precision of pellet-group counts for estimating landscape-level deer density
David S. deCalesta
2013-01-01
This study provides hitherto unavailable methodology for reliably and precisely estimating deer density within forested landscapes, enabling quantitative rather than qualitative deer management. Reliability and precision of the deer pellet-group technique were evaluated in 1 small and 2 large forested landscapes. Density estimates, adjusted to reflect deer harvest and...
Saupe, Joe L.; Eimers, Mardy T.
2013-01-01
The purpose of this paper is to explore differences in the reliabilities of cumulative college grade point averages (GPAs), estimated for unweighted and weighted, one-semester, 1-year, 2-year, and 4-year GPAs. Using cumulative GPAs for a freshman class at a major university, we estimate internal consistency (coefficient alpha) reliabilities for…
The estimation of parameter compaction values for pavement subgrade stabilized with lime
Lubis, A. S.; Muis, Z. A.; Simbolon, C. A.
2018-02-01
The type of soil material, field control, maintenance and availability of funds are several factors that must be considered in compaction of the pavement subgrade. In determining the compaction parameters in laboratory desperately requires considerable materials, time and funds, and reliable laboratory operators. If the result of soil classification values can be used to estimate the compaction parameters of a subgrade material, so it would save time, energy, materials and cost on the execution of this work. This is also a clarification (cross check) of the work that has been done by technicians in the laboratory. The study aims to estimate the compaction parameter values ie. maximum dry unit weight (γdmax) and optimum water content (Wopt) of the soil subgrade that stabilized with lime. The tests that conducted in the laboratory of soil mechanics were to determine the index properties (Fines and Liquid Limit/LL) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) > 10% were made with additional 3% lime for 30 samples. By using the Goswami equation, the compaction parameter values can be estimated by equation γd max # = -0,1686 Log G + 1,8434 and Wopt # = 2,9178 log G + 17,086. From the validation calculation, there was a significant positive correlation between the compaction parameter values laboratory and the compaction parameter values estimated, with a 95% confidence interval as a strong relationship.
State and parameter estimation of the heat shock response system using Kalman and particle filters.
Liu, Xin; Niranjan, Mahesan
2012-06-01
Traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this article, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system. Here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (i) extended Kalman filter (EKF), (ii) unscented Kalman filter (UKF) and (iii) the particle filter, in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium Escherichia coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values. Software implementation of the three filters on this problem can be freely downloaded from http://users.ecs.soton.ac.uk/mn/HeatShock
Consistent Parameter and Transfer Function Estimation using Context Free Grammars
Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten
2017-04-01
This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a
On estimation of reliability for pipe lines of heat power plants under cyclic loading
International Nuclear Information System (INIS)
Verezemskij, V.G.
1986-01-01
One of the possible methods to obtain a quantitative estimate of the reliability for pipe lines of the welded heat power plants under cyclic loading due to heating-cooling and due to vibration is considered. Reliability estimate is carried out for a common case of loading by simultaneous cycles with different amplitudes and loading asymmetry. It is shown that scattering of the breaking number of cycles for the metal of welds may perceptibly decrease reliability of the welded pipe line
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Directory of Open Access Journals (Sweden)
A. Elsonbaty
2014-10-01
Full Text Available In this article, the adaptive chaos synchronization technique is implemented by an electronic circuit and applied to the hyperchaotic system proposed by Chen et al. We consider the more realistic and practical case where all the parameters of the master system are unknowns. We propose and implement an electronic circuit that performs the estimation of the unknown parameters and the updating of the parameters of the slave system automatically, and hence it achieves the synchronization. To the best of our knowledge, this is the first attempt to implement a circuit that estimates the values of the unknown parameters of chaotic system and achieves synchronization. The proposed circuit has a variety of suitable real applications related to chaos encryption and cryptography. The outputs of the implemented circuits and numerical simulation results are shown to view the performance of the synchronized system and the proposed circuit.
Parameter estimation in nonlinear models for pesticide degradation
International Nuclear Information System (INIS)
Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.
1991-01-01
A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)
Estimation of common cause failure parameters with periodic tests
Energy Technology Data Exchange (ETDEWEB)
Barros, Anne [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France)], E-mail: anne.barros@utt.fr; Grall, Antoine [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France); Vasseur, Dominique [Electricite de France, EDF R and D - Industrial Risk Management Department 1, av. du General de Gaulle- 92141 Clamart (France)
2009-04-15
In the specific case of safety systems, CCF parameters estimators for standby components depend on the periodic test schemes. Classically, the testing schemes are either staggered (alternation of tests on redundant components) or non-staggered (all components are tested at the same time). In reality, periodic tests schemes performed on safety components are more complex and combine staggered tests, when the plant is in operation, to non-staggered tests during maintenance and refueling outage periods of the installation. Moreover, the CCF parameters estimators described in the US literature are derived in a consistent way with US Technical Specifications constraints that do not apply on the French Nuclear Power Plants for staggered tests on standby components. Given these issues, the evaluation of CCF parameters from the operating feedback data available within EDF implies the development of methodologies that integrate the testing schemes specificities. This paper aims to formally propose a solution for the estimation of CCF parameters given two distinct difficulties respectively related to a mixed testing scheme and to the consistency with EDF's specific practices inducing systematic non-simultaneity of the observed failures in a staggered testing scheme.
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...... and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the same values....... Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....
Directory of Open Access Journals (Sweden)
Guo Yu
2016-01-01
Full Text Available As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.
PWR system simulation and parameter estimation with neural networks
International Nuclear Information System (INIS)
Akkurt, Hatice; Colak, Uener
2002-01-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected
PWR system simulation and parameter estimation with neural networks
Energy Technology Data Exchange (ETDEWEB)
Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr
2002-11-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.
Tracking of nuclear reactor parameters via recursive non linear estimation
International Nuclear Information System (INIS)
Pages Fita, J.; Alengrin, G.; Aguilar Martin, J.; Zwingelstein, M.
1975-01-01
The usefulness of nonlinear estimation in the supervision of nuclear reactors, as well for reactivity determination as for on-line modelisation in order to detect eventual and unwanted changes in working operation is illustrated. It is dealt with the reactivity estimation using an a priori dynamical model under the hypothesis of one group of delayed neutrons (measurements were done with an ionisation chamber). The determination of the reactivity using such measurements appears as a nonlinear estimation procedure derived from a particular form of nonlinear filter. Observed inputs being demand of power and inside temperature, and output being the reactivity balance, a recursive algorithm is derived for the estimation of the parameters that define the actual behavior of the reactor. Example of treatment of real data is given [fr
Parameter Estimation as a Problem in Statistical Thermodynamics.
Earle, Keith A; Schneider, David J
2011-03-14
In this work, we explore the connections between parameter fitting and statistical thermodynamics using the maxent principle of Jaynes as a starting point. In particular, we show how signal averaging may be described by a suitable one particle partition function, modified for the case of a variable number of particles. These modifications lead to an entropy that is extensive in the number of measurements in the average. Systematic error may be interpreted as a departure from ideal gas behavior. In addition, we show how to combine measurements from different experiments in an unbiased way in order to maximize the entropy of simultaneous parameter fitting. We suggest that fit parameters may be interpreted as generalized coordinates and the forces conjugate to them may be derived from the system partition function. From this perspective, the parameter fitting problem may be interpreted as a process where the system (spectrum) does work against internal stresses (non-optimum model parameters) to achieve a state of minimum free energy/maximum entropy. Finally, we show how the distribution function allows us to define a geometry on parameter space, building on previous work[1, 2]. This geometry has implications for error estimation and we outline a program for incorporating these geometrical insights into an automated parameter fitting algorithm.
Schroeder, J; Reer, R; Braumann, K M
2015-02-01
As reliability of raster stereography was proved only for sagittal plane parameters with repeated measures on the same day, the present study was aiming at investigating variability and reliability of back shape reconstruction for all dimensions (sagittal, frontal, transversal) and for different intervals. For a sample of 20 healthy volunteers, intra-individual variability (SEM and CV%) and reliability (ICC ± 95% CI) were proved for sagittal (thoracic kyphosis, lumbar lordosis, pelvis tilt angle, and trunk inclination), frontal (pelvis torsion, pelvis and trunk imbalance, vertebral side deviation, and scoliosis angle), transversal (vertebral rotation), and functional (hyperextension) spine shape reconstruction parameters for different test-retest intervals (on the same day, between-day, between-week) by means of video raster stereography. Reliability was high for the sagittal plane (pelvis tilt, kyphosis and lordosis angle, and trunk inclination: ICC > 0.90), and good to high for lumbar mobility (0.86 < ICC < 0.97). Apart from sagittal plane spinal alignment, there was a lack of certainty for a high reproducibility indicated by wider ICC confidence intervals. So, reliability was fair to high for vertebral side deviation and the scoliosis angle (0.71 < ICC < 0.95), and poor to good for vertebral rotation values as well as for frontal plane upper body and pelvis position parameters (0.65 < ICC < 0.92). Coefficients for the between-day and between-week interval were a little lower than for repeated measures on the same day. Variability (SEM) was less than 1.5° or 1.5 mm, except for trunk inclination. Relative variability (CV) was greater in global trunk position and pelvis parameters (35-98%) than in scoliosis (14-20%) or sagittal sway parameters (4-8 %). Although we found a lower reproducibility for the frontal plane, raster stereography is considered to be a reliable method for the non-invasive, three-dimensional assessment of spinal alignment in normal non
Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine
Directory of Open Access Journals (Sweden)
Jeremy T. Howard
2018-02-01
Full Text Available In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198 that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope. The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite was 0.15 (0.18 and 0.31 (0.40, respectively. For the parent drug (metabolite, the mean heritability across time was 0.27 (0.60 and 0.14 (0.44 for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug
Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine
Howard, Jeremy T.; Ashwell, Melissa S.; Baynes, Ronald E.; Brooks, James D.; Yeatts, James L.; Maltecca, Christian
2018-01-01
In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug
ESTIMATION OF DISTANCES TO STARS WITH STELLAR PARAMETERS FROM LAMOST
Energy Technology Data Exchange (ETDEWEB)
Carlin, Jeffrey L.; Newberg, Heidi Jo [Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 (United States); Liu, Chao; Deng, Licai; Li, Guangwei; Luo, A-Li; Wu, Yue; Yang, Ming; Zhang, Haotong [Key Lab of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Beers, Timothy C. [Department of Physics and JINA: Joint Institute for Nuclear Astrophysics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556 (United States); Chen, Li; Hou, Jinliang; Smith, Martin C. [Shanghai Astronomical Observatory, 80 Nandan Road, Shanghai 200030 (China); Guhathakurta, Puragra [UCO/Lick Observatory, Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States); Hou, Yonghui [Nanjing Institute of Astronomical Optics and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042 (China); Lépine, Sébastien [Department of Physics and Astronomy, Georgia State University, 25 Park Place, Suite 605, Atlanta, GA 30303 (United States); Yanny, Brian [Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States); Zheng, Zheng, E-mail: jeffreylcarlin@gmail.com [Department of Physics and Astronomy, University of Utah, UT 84112 (United States)
2015-07-15
We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star’s absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ∼5° diameter “plate” that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ∼20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ∼40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.
2016-11-25
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.; Litvinenko, Alexander; Rosic, Bojana V.; Zander, Elmar
2016-01-01
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
Parameter and state estimation in nonlinear dynamical systems
Creveling, Daniel R.
This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling
Estimation of Medium Voltage Cable Parameters for PD Detection
DEFF Research Database (Denmark)
Villefrance, Rasmus; Holbøll, Joachim T.; Henriksen, Mogens
1998-01-01
Medium voltage cable characteristics have been determined with respect to the parameters having influence on the evaluation of results from PD-measurements on paper/oil and XLPE-cables. In particular, parameters essential for discharge quantification and location were measured. In order to relate...... and phase constants. A method to estimate this propagation constant, based on high frequency measurements, will be presented and will be applied to different cable types under different conditions. The influence of temperature and test voltage was investigated. The relevance of the results for cable...
Estimating parameters for probabilistic linkage of privacy-preserved datasets.
Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H
2017-07-10
Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher
Estimation of economic parameters of U.S. hydropower resources
Energy Technology Data Exchange (ETDEWEB)
Hall, Douglas G. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Hunt, Richard T. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Reeves, Kelly S. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Carroll, Greg R. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL)
2003-06-01
Tools for estimating the cost of developing and operating and maintaining hydropower resources in the form of regression curves were developed based on historical plant data. Development costs that were addressed included: licensing, construction, and five types of environmental mitigation. It was found that the data for each type of cost correlated well with plant capacity. A tool for estimating the annual and monthly electric generation of hydropower resources was also developed. Additional tools were developed to estimate the cost of upgrading a turbine or a generator. The development and operation and maintenance cost estimating tools, and the generation estimating tool were applied to 2,155 U.S. hydropower sites representing a total potential capacity of 43,036 MW. The sites included totally undeveloped sites, dams without a hydroelectric plant, and hydroelectric plants that could be expanded to achieve greater capacity. Site characteristics and estimated costs and generation for each site were assembled in a database in Excel format that is also included within the EERE Library under the title, “Estimation of Economic Parameters of U.S. Hydropower Resources - INL Hydropower Resource Economics Database.”
Estimation of Spectral Exponent Parameter of 1/f Process in Additive White Background Noise
Directory of Open Access Journals (Sweden)
Semih Ergintav
2007-01-01
Full Text Available An extension to the wavelet-based method for the estimation of the spectral exponent, γ, in a 1/fγ process and in the presence of additive white noise is proposed. The approach is based on eliminating the effect of white noise by a simple difference operation constructed on the wavelet spectrum. The γ parameter is estimated as the slope of a linear function. It is shown by simulations that the proposed method gives reliable results. Global positioning system (GPS time-series noise is analyzed and the results provide experimental verification of the proposed method.
Estimating demographic parameters using a combination of known-fate and open N-mixture models.
Schmidt, Joshua H; Johnson, Devin S; Lindberg, Mark S; Adams, Layne G
2015-10-01
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package.
Approximate estimation of system reliability via fault trees
International Nuclear Information System (INIS)
Dutuit, Y.; Rauzy, A.
2005-01-01
In this article, we show how fault tree analysis, carried out by means of binary decision diagrams (BDD), is able to approximate reliability of systems made of independent repairable components with a good accuracy and a good efficiency. We consider four algorithms: the Murchland lower bound, the Barlow-Proschan lower bound, the Vesely full approximation and the Vesely asymptotic approximation. For each of these algorithms, we consider an implementation based on the classical minimal cut sets/rare events approach and another one relying on the BDD technology. We present numerical results obtained with both approaches on various examples
Directory of Open Access Journals (Sweden)
Kolosok Irina
2017-01-01
Full Text Available Reliable information on the current state parameters obtained as a result of processing the measurements from systems of the SCADA and WAMS data acquisition and processing through methods of state estimation (SE is a condition that enables to successfully manage an energy power system (EPS. SCADA and WAMS systems themselves, as any technical systems, are subject to failures and faults that lead to distortion and loss of information. The SE procedure enables to find erroneous measurements, therefore, it is a barrier for the distorted information to penetrate into control problems. At the same time, the programming and computing suite (PCS implementing the SE functions may itself provide a wrong decision due to imperfection of the software algorithms and errors. In this study, we propose to use a fault tree to analyze consequences of failures and faults in SCADA and WAMS and in the very SE procedure. Based on the analysis of the obtained measurement information and on the SE results, we determine the state estimation PCS fault tolerance level featuring its reliability.
Directory of Open Access Journals (Sweden)
Jianwei Yang
2016-06-01
Full Text Available In order to solve the reliability assessment of braking system component of high-speed electric multiple units, this article, based on two-parameter exponential distribution, provides the maximum likelihood estimation and Bayes estimation under a type-I life test. First of all, we evaluate the failure probability value according to the classical estimation method and then obtain the maximum likelihood estimation of parameters of two-parameter exponential distribution by performing and using the modified likelihood function. On the other hand, based on Bayesian theory, this article also selects the beta and gamma distributions as the prior distribution, combines with the modified maximum likelihood function, and innovatively applies a Markov chain Monte Carlo algorithm to parameters assessment based on Bayes estimation method for two-parameter exponential distribution, so that two reliability mathematical models of the electromagnetic valve are obtained. Finally, through type-I life test, the failure rates according to maximum likelihood estimation and Bayes estimation method based on Markov chain Monte Carlo algorithm are, respectively, 2.650 × 10−5 and 3.037 × 10−5. Compared with the failure rate of a electromagnetic valve 3.005 × 10−5, it proves that the Bayes method can use a Markov chain Monte Carlo algorithm to estimate reliability for two-parameter exponential distribution and Bayes estimation is more closer to the value of electromagnetic valve. So, by fully integrating multi-source, Bayes estimation method can preferably modify and precisely estimate the parameters, which can provide a certain theoretical basis for the safety operation of high-speed electric multiple units.
Unconstrained parameter estimation for assessment of dynamic cerebral autoregulation
International Nuclear Information System (INIS)
Chacón, M; Nuñez, N; Henríquez, C; Panerai, R B
2008-01-01
Measurement of dynamic cerebral autoregulation (CA), the transient response of cerebral blood flow (CBF) to changes in arterial blood pressure (ABP), has been performed with an index of autoregulation (ARI), related to the parameters of a second-order differential equation model, namely gain (K), damping factor (D) and time constant (T). Limitations of the ARI were addressed by increasing its numerical resolution and generalizing the parameter space. In 16 healthy subjects, recordings of ABP (Finapres) and CBF velocity (ultrasound Doppler) were performed at rest, before, during and after 5% CO 2 breathing, and for six repeated thigh cuff maneuvers. The unconstrained model produced lower predictive error (p < 0.001) than the original model. Unconstrained parameters (K'–D'–T') were significantly different from K–D–T but were still sensitive to different measurement conditions, such as the under-regulation induced by hypercapnia. The intra-subject variability of K' was significantly lower than that of the ARI and this parameter did not show the unexpected occurrences of zero values as observed with the ARI and the classical value of K. These results suggest that K' could be considered as a more stable and reliable index of dynamic autoregulation than ARI. Further studies are needed to validate this new index under different clinical conditions
Wan, Fubin; Tan, Yuanyuan; Jiang, Zhenhua; Chen, Xun; Wu, Yinong; Zhao, Peng
2017-12-01
Lifetime and reliability are the two performance parameters of premium importance for modern space Stirling-type pulse tube refrigerators (SPTRs), which are required to operate in excess of 10 years. Demonstration of these parameters provides a significant challenge. This paper proposes a lifetime prediction and reliability estimation method that utilizes accelerated degradation testing (ADT) for SPTRs related to gaseous contamination failure. The method was experimentally validated via three groups of gaseous contamination ADT. First, the performance degradation model based on mechanism of contamination failure and material outgassing characteristics of SPTRs was established. Next, a preliminary test was performed to determine whether the mechanism of contamination failure of the SPTRs during ADT is consistent with normal life testing. Subsequently, the experimental program of ADT was designed for SPTRs. Then, three groups of gaseous contamination ADT were performed at elevated ambient temperatures of 40 °C, 50 °C, and 60 °C, respectively and the estimated lifetimes of the SPTRs under normal condition were obtained through acceleration model (Arrhenius model). The results show good fitting of the degradation model with the experimental data. Finally, we obtained the reliability estimation of SPTRs through using the Weibull distribution. The proposed novel methodology enables us to take less than one year time to estimate the reliability of the SPTRs designed for more than 10 years.
Markov Chain Monte Carlo (MCMC) methods for parameter estimation of a novel hybrid redundant robot
International Nuclear Information System (INIS)
Wang Yongbo; Wu Huapeng; Handroos, Heikki
2011-01-01
This paper presents a statistical method for the calibration of a redundantly actuated hybrid serial-parallel robot IWR (Intersector Welding Robot). The robot under study will be used to carry out welding, machining, and remote handing for the assembly of vacuum vessel of International Thermonuclear Experimental Reactor (ITER). The robot has ten degrees of freedom (DOF), among which six DOF are contributed by the parallel mechanism and the rest are from the serial mechanism. In this paper, a kinematic error model which involves 54 unknown geometrical error parameters is developed for the proposed robot. Based on this error model, the mean values of the unknown parameters are statistically analyzed and estimated by means of Markov Chain Monte Carlo (MCMC) approach. The computer simulation is conducted by introducing random geometric errors and measurement poses which represent the corresponding real physical behaviors. The simulation results of the marginal posterior distributions of the estimated model parameters indicate that our method is reliable and robust.
Probabilistic estimation of the constitutive parameters of polymers
Directory of Open Access Journals (Sweden)
Siviour C.R.
2012-08-01
Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
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.
CosmoSIS: A System for MC Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Zuntz, Joe [Manchester U.; Paterno, Marc [Fermilab; Jennings, Elise [Chicago U., EFI; Rudd, Douglas [U. Chicago; Manzotti, Alessandro [Chicago U., Astron. Astrophys. Ctr.; Dodelson, Scott [Chicago U., Astron. Astrophys. Ctr.; Bridle, Sarah [Manchester U.; Sehrish, Saba [Fermilab; Kowalkowski, James [Fermilab
2015-01-01
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in Cosmo- SIS, including camb, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis.
Estimating parameters of chaotic systems synchronized by external driving signal
International Nuclear Information System (INIS)
Wu Xiaogang; Wang Zuxi
2007-01-01
Noise-induced synchronization (NIS) has evoked great research interests recently. Two uncoupled identical chaotic systems can achieve complete synchronization (CS) by feeding a common noise with appropriate intensity. Actually, NIS belongs to the category of external feedback control (EFC). The significance of applying EFC in secure communication lies in fact that the trajectory of chaotic systems is disturbed so strongly by external driving signal that phase space reconstruction attack fails. In this paper, however, we propose an approach that can accurately estimate the parameters of the chaotic systems synchronized by external driving signal through chaotic transmitted signal, driving signal and their derivatives. Numerical simulation indicates that this approach can estimate system parameters and external coupling strength under two driving modes in a very rapid manner, which implies that EFC is not superior to other methods in secure communication
On Using Exponential Parameter Estimators with an Adaptive Controller
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
Basic Earth's Parameters as estimated from VLBI observations
Directory of Open Access Journals (Sweden)
Ping Zhu
2017-11-01
Full Text Available The global Very Long Baseline Interferometry observation for measuring the Earth rotation's parameters was launched around 1970s. Since then the precision of the measurements is continuously improving by taking into account various instrumental and environmental effects. The MHB2000 nutation model was introduced in 2002, which is constructed based on a revised nutation series derived from 20 years VLBI observations (1980–1999. In this work, we firstly estimated the amplitudes of all nutation terms from the IERS-EOP-C04 VLBI global solutions w.r.t. IAU1980, then we further inferred the BEPs (Basic Earth's Parameters by fitting the major nutation terms. Meanwhile, the BEPs were obtained from the same nutation time series using a BI (Bayesian Inversion. The corrections to the precession rate and the estimated BEPs are in an agreement, independent of which methods have been applied.
Directory of Open Access Journals (Sweden)
Krutitskiy M.N.
2016-03-01
Full Text Available The method of statistical tests examines the impact of the correlation of the parameters of fatigue-such as the durability of the shaft mechanism of an overhead traveling crane for General use is under consideration in this article. It is be-lieved that the normal and shear stresses together affect the overall durability of the shaft. There may be a correlation between endurance limits and coefficients of block similarity of loading. To calculate resource used corrected linear theory of fatigue damage accumulation. Parameters on the reliability are computed after building the function, the reli-ability function directly or through private functions the reliability function for each type of stress.
Model calibration and parameter estimation for environmental and water resource systems
Sun, Ne-Zheng
2015-01-01
This three-part book provides a comprehensive and systematic introduction to the development of useful models for complex systems. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get famili...
Estimation of parameters of interior permanent magnet synchronous motors
International Nuclear Information System (INIS)
Hwang, C.C.; Chang, S.M.; Pan, C.T.; Chang, T.Y.
2002-01-01
This paper presents a magnetic circuit model to the estimation of machine parameters of an interior permanent magnet synchronous machine. It extends the earlier work of Hwang and Cho that focused mainly on the magnetic aspects of motor design. The proposed model used to calculate EMF, d- and q-axis reactances. These calculations are compared to those from finite element analysis and measurement with good agreement
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens
2016-01-01
be used directly for accurate full-scale transient simulations. The model was validated against full-scale data with an engine following the European Transient Cycle. The validation showed that the predictive capability for nitrogen oxides (NOx) was satisfactory. After re-estimation of the adsorption...... and desorption parameters with full-scale transient data, the fit for both NOx and NH3-slip was satisfactory....
Fundamental limits of radio interferometers: calibration and source parameter estimation
Trott, Cathryn M.; Wayth, Randall B.; Tingay, Steven J.
2012-01-01
We use information theory to derive fundamental limits on the capacity to calibrate next-generation radio interferometers, and measure parameters of point sources for instrument calibration, point source subtraction, and data deconvolution. We demonstrate the implications of these fundamental limits, with particular reference to estimation of the 21cm Epoch of Reionization power spectrum with next-generation low-frequency instruments (e.g., the Murchison Widefield Array -- MWA, Precision Arra...
Robust estimation of track parameters in wire chambers
International Nuclear Information System (INIS)
Bogdanova, N.B.; Bourilkov, D.T.
1988-01-01
The aim of this paper is to compare numerically the possibilities of the least square fit (LSF) and robust methods for modelled and real track data to determine the linear regression parameters of charged particles in wire chambers. It is shown, that Tukey robust estimate is superior to more standard (versions of LSF) methods. The efficiency of the method is illustrated by tables and figures for some important physical characteristics
Factorized Estimation of Partially Shared Parameters in Diffusion Networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2017-01-01
Roč. 65, č. 19 (2017), s. 5153-5163 ISSN 1053-587X R&D Projects: GA ČR(CZ) GP14-06678P; GA ČR GA16-09848S Institutional support: RVO:67985556 Keywords : Diffusion network * Diffusion estimation * Heterogeneous parameters * Multitask networks Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 4.300, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf
Statistical methods of parameter estimation for deterministically chaotic time series
Pisarenko, V. F.; Sornette, D.
2004-03-01
We discuss the possibility of applying some standard statistical methods (the least-square method, the maximum likelihood method, and the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A “segmentation fitting” maximum likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value x1 considered as an additional unknown parameter. The segmentation fitting method, called “piece-wise” ML, is similar in spirit but simpler and has smaller bias than the “multiple shooting” previously proposed. Comparisons with different previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size N and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically).
Estimation of parameters of interior permanent magnet synchronous motors
Hwang, C C; Pan, C T; Chang, T Y
2002-01-01
This paper presents a magnetic circuit model to the estimation of machine parameters of an interior permanent magnet synchronous machine. It extends the earlier work of Hwang and Cho that focused mainly on the magnetic aspects of motor design. The proposed model used to calculate EMF, d- and q-axis reactances. These calculations are compared to those from finite element analysis and measurement with good agreement.
CTER-rapid estimation of CTF parameters with error assessment.
Penczek, Pawel A; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M T
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03Å without, and 3.85Å with, inclusion of astigmatism parameters. Copyright © 2014 Elsevier B.V. All rights reserved.
Estimation of solid earth tidal parameters and FCN with VLBI
International Nuclear Information System (INIS)
Krásná, H.
2012-01-01
Measurements of a space-geodetic technique VLBI (Very Long Baseline Interferometry) are influenced by a variety of processes which have to be modelled and put as a priori information into the analysis of the space-geodetic data. The increasing accuracy of the VLBI measurements allows access to these parameters and provides possibilities to validate them directly from the measured data. The gravitational attraction of the Moon and the Sun causes deformation of the Earth's surface which can reach several decimetres in radial direction during a day. The displacement is a function of the so-called Love and Shida numbers. Due to the present accuracy of the VLBI measurements the parameters have to be specified as complex numbers, where the imaginary parts describe the anelasticity of the Earth's mantle. Moreover, it is necessary to distinguish between the single tides within the various frequency bands. In this thesis, complex Love and Shida numbers of twelve diurnal and five long-period tides included in the solid Earth tidal displacement modelling are estimated directly from the 27 years of VLBI measurements (1984.0 - 2011.0). In this work, the period of the Free Core Nutation (FCN) is estimated which shows up in the frequency dependent solid Earth tidal displacement as well as in a nutation model describing the motion of the Earth's axis in space. The FCN period in both models is treated as a single parameter and it is estimated in a rigorous global adjustment of the VLBI data. The obtained value of -431.18 ± 0.10 sidereal days differs slightly from the conventional value -431.39 sidereal days given in IERS Conventions 2010. An empirical FCN model based on variable amplitude and phase is determined, whose parameters are estimated in yearly steps directly within VLBI global solutions. (author) [de
Directory of Open Access Journals (Sweden)
Akatsuki eKimura
2015-03-01
Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.
Model parameters estimation and sensitivity by genetic algorithms
International Nuclear Information System (INIS)
Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca
2003-01-01
In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The
2011-01-01
Background A clinical study was conducted to determine the intra and inter-rater reliability of digital scanning and the neutral suspension casting technique to measure six foot parameters. The neutral suspension casting technique is a commonly utilised method for obtaining a negative impression of the foot prior to orthotic fabrication. Digital scanning offers an alternative to the traditional plaster of Paris techniques. Methods Twenty one healthy participants volunteered to take part in the study. Six casts and six digital scans were obtained from each participant by two raters of differing clinical experience. The foot parameters chosen for investigation were cast length (mm), forefoot width (mm), rearfoot width (mm), medial arch height (mm), lateral arch height (mm) and forefoot to rearfoot alignment (degrees). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) were calculated to determine the intra and inter-rater reliability. Measurement error was assessed through the calculation of the standard error of the measurement (SEM) and smallest real difference (SRD). Results ICC values for all foot parameters using digital scanning ranged between 0.81-0.99 for both intra and inter-rater reliability. For neutral suspension casting technique inter-rater reliability values ranged from 0.57-0.99 and intra-rater reliability values ranging from 0.36-0.99 for rater 1 and 0.49-0.99 for rater 2. Conclusions The findings of this study indicate that digital scanning is a reliable technique, irrespective of clinical experience, with reduced measurement variability in all foot parameters investigated when compared to neutral suspension casting. PMID:21375757
Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim
2016-01-01
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
Ait-El-Fquih, Boujemaa
2016-08-12
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
Estimates of selection parameters in protein mutants of spring barley
International Nuclear Information System (INIS)
Gaul, H.; Walther, H.; Seibold, K.H.; Brunner, H.; Mikaelsen, K.
1976-01-01
Detailed studies have been made with induced protein mutants regarding a possible genetic advance in selection including the estimation of the genetic variation and heritability coefficients. Estimates were obtained for protein content and protein yield. The variation of mutant lines in different environments was found to be many times as large as the variation of the line means. The detection of improved protein mutants seems therefore possible only in trials with more than one environment. The heritability of protein content and protein yield was estimated in different sets of environments and was found to be low. However, higher values were found with an increasing number of environments. At least four environments seem to be necessary to obtain reliable heritability estimates. The geneticall component of the variation between lines was significant for protein content in all environmental combinations. For protein yield some environmental combinations only showed significant differences. The expected genetic advance with one selection step was small for both protein traits. Genetically significant differences between protein micromutants give, however, a first indication that selection among protein mutants with small differences seems also possible. (author)
Applicability of genetic algorithms to parameter estimation of economic models
Directory of Open Access Journals (Sweden)
Marcel Ševela
2004-01-01
Full Text Available The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.
Comparison of sampling techniques for Bayesian parameter estimation
Allison, Rupert; Dunkley, Joanna
2014-02-01
The posterior probability distribution for a set of model parameters encodes all that the data have to tell us in the context of a given model; it is the fundamental quantity for Bayesian parameter estimation. In order to infer the posterior probability distribution we have to decide how to explore parameter space. Here we compare three prescriptions for how parameter space is navigated, discussing their relative merits. We consider Metropolis-Hasting sampling, nested sampling and affine-invariant ensemble Markov chain Monte Carlo (MCMC) sampling. We focus on their performance on toy-model Gaussian likelihoods and on a real-world cosmological data set. We outline the sampling algorithms themselves and elaborate on performance diagnostics such as convergence time, scope for parallelization, dimensional scaling, requisite tunings and suitability for non-Gaussian distributions. We find that nested sampling delivers high-fidelity estimates for posterior statistics at low computational cost, and should be adopted in favour of Metropolis-Hastings in many cases. Affine-invariant MCMC is competitive when computing clusters can be utilized for massive parallelization. Affine-invariant MCMC and existing extensions to nested sampling naturally probe multimodal and curving distributions.
Automatic estimation of elasticity parameters in breast tissue
Skerl, Katrin; Cochran, Sandy; Evans, Andrew
2014-03-01
Shear wave elastography (SWE), a novel ultrasound imaging technique, can provide unique information about cancerous tissue. To estimate elasticity parameters, a region of interest (ROI) is manually positioned over the stiffest part of the shear wave image (SWI). The aim of this work is to estimate the elasticity parameters i.e. mean elasticity, maximal elasticity and standard deviation, fully automatically. Ultrasonic SWI of a breast elastography phantom and breast tissue in vivo were acquired using the Aixplorer system (SuperSonic Imagine, Aix-en-Provence, France). First, the SWI within the ultrasonic B-mode image was detected using MATLAB then the elasticity values were extracted. The ROI was automatically positioned over the stiffest part of the SWI and the elasticity parameters were calculated. Finally all values were saved in a spreadsheet which also contains the patient's study ID. This spreadsheet is easily available for physicians and clinical staff for further evaluation and so increase efficiency. Therewith the efficiency is increased. This algorithm simplifies the handling, especially for the performance and evaluation of clinical trials. The SWE processing method allows physicians easy access to the elasticity parameters of the examinations from their own and other institutions. This reduces clinical time and effort and simplifies evaluation of data in clinical trials. Furthermore, reproducibility will be improved.
International Nuclear Information System (INIS)
Zhang, L.F.; Xie, M.; Tang, L.C.
2006-01-01
Estimation of the Weibull shape parameter is important in reliability engineering. However, commonly used methods such as the maximum likelihood estimation (MLE) and the least squares estimation (LSE) are known to be biased. Bias correction methods for MLE have been studied in the literature. This paper investigates the methods for bias correction when model parameters are estimated with LSE based on probability plot. Weibull probability plot is very simple and commonly used by practitioners and hence such a study is useful. The bias of the LS shape parameter estimator for multiple censored data is also examined. It is found that the bias can be modeled as the function of the sample size and the censoring level, and is mainly dependent on the latter. A simple bias function is introduced and bias correcting formulas are proposed for both complete and censored data. Simulation results are also presented. The bias correction methods proposed are very easy to use and they can typically reduce the bias of the LSE of the shape parameter to less than half percent
Estimating the reliability of eyewitness identifications from police lineups.
Wixted, John T; Mickes, Laura; Dunn, John C; Clark, Steven E; Wells, William
2016-01-12
Laboratory-based mock crime studies have often been interpreted to mean that (i) eyewitness confidence in an identification made from a lineup is a weak indicator of accuracy and (ii) sequential lineups are diagnostically superior to traditional simultaneous lineups. Largely as a result, juries are increasingly encouraged to disregard eyewitness confidence, and up to 30% of law enforcement agencies in the United States have adopted the sequential procedure. We conducted a field study of actual eyewitnesses who were assigned to simultaneous or sequential photo lineups in the Houston Police Department over a 1-y period. Identifications were made using a three-point confidence scale, and a signal detection model was used to analyze and interpret the results. Our findings suggest that (i) confidence in an eyewitness identification from a fair lineup is a highly reliable indicator of accuracy and (ii) if there is any difference in diagnostic accuracy between the two lineup formats, it likely favors the simultaneous procedure.
Basic MR sequence parameters systematically bias automated brain volume estimation
International Nuclear Information System (INIS)
Haller, Sven; Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte; Meuli, Reto; Thiran, Jean-Philippe; Krueger, Gunnar; Lovblad, Karl-Olof; Kober, Tobias
2016-01-01
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)
Impact of relativistic effects on cosmological parameter estimation
Lorenz, Christiane S.; Alonso, David; Ferreira, Pedro G.
2018-01-01
Future surveys will access large volumes of space and hence very long wavelength fluctuations of the matter density and gravitational field. It has been argued that the set of secondary effects that affect the galaxy distribution, relativistic in nature, will bring new, complementary cosmological constraints. We study this claim in detail by focusing on a subset of wide-area future surveys: Stage-4 cosmic microwave background experiments and photometric redshift surveys. In particular, we look at the magnification lensing contribution to galaxy clustering and general-relativistic corrections to all observables. We quantify the amount of information encoded in these effects in terms of the tightening of the final cosmological constraints as well as the potential bias in inferred parameters associated with neglecting them. We do so for a wide range of cosmological parameters, covering neutrino masses, standard dark-energy parametrizations and scalar-tensor gravity theories. Our results show that, while the effect of lensing magnification to number counts does not contain a significant amount of information when galaxy clustering is combined with cosmic shear measurements, this contribution does play a significant role in biasing estimates on a host of parameter families if unaccounted for. Since the amplitude of the magnification term is controlled by the slope of the source number counts with apparent magnitude, s (z ), we also estimate the accuracy to which this quantity must be known to avoid systematic parameter biases, finding that future surveys will need to determine s (z ) to the ˜5 %- 10 % level. On the contrary, large-scale general-relativistic corrections are irrelevant both in terms of information content and parameter bias for most cosmological parameters but significant for the level of primordial non-Gaussianity.
Basic MR sequence parameters systematically bias automated brain volume estimation
Energy Technology Data Exchange (ETDEWEB)
Haller, Sven [University of Geneva, Faculty of Medicine, Geneva (Switzerland); Affidea Centre de Diagnostique Radiologique de Carouge CDRC, Geneva (Switzerland); Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Meuli, Reto [University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Thiran, Jean-Philippe [LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland); Krueger, Gunnar [Siemens Medical Solutions USA, Inc., Boston, MA (United States); Lovblad, Karl-Olof [University of Geneva, Faculty of Medicine, Geneva (Switzerland); University Hospitals of Geneva, Geneva (Switzerland); Kober, Tobias [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland)
2016-11-15
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)
Chloramine demand estimation using surrogate chemical and microbiological parameters.
Moradi, Sina; Liu, Sanly; Chow, Christopher W K; van Leeuwen, John; Cook, David; Drikas, Mary; Amal, Rose
2017-07-01
A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F m ) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection. Copyright © 2017. Published by Elsevier B.V.
Estimation of Snow Parameters from Dual-Wavelength Airborne Radar
Liao, Liang; Meneghini, Robert; Iguchi, Toshio; Detwiler, Andrew
1997-01-01
Estimation of snow characteristics from airborne radar measurements would complement In-situ measurements. While In-situ data provide more detailed information than radar, they are limited in their space-time sampling. In the absence of significant cloud water contents, dual-wavelength radar data can be used to estimate 2 parameters of a drop size distribution if the snow density is assumed. To estimate, rather than assume, a snow density is difficult, however, and represents a major limitation in the radar retrieval. There are a number of ways that this problem can be investigated: direct comparisons with in-situ measurements, examination of the large scale characteristics of the retrievals and their comparison to cloud model outputs, use of LDR measurements, and comparisons to the theoretical results of Passarelli(1978) and others. In this paper we address the first approach and, in part, the second.
A parameter tree approach to estimating system sensitivities to parameter sets
International Nuclear Information System (INIS)
Jarzemba, M.S.; Sagar, B.
2000-01-01
A post-processing technique for determining relative system sensitivity to groups of parameters and system components is presented. It is assumed that an appropriate parametric model is used to simulate system behavior using Monte Carlo techniques and that a set of realizations of system output(s) is available. The objective of our technique is to analyze the input vectors and the corresponding output vectors (that is, post-process the results) to estimate the relative sensitivity of the output to input parameters (taken singly and as a group) and thereby rank them. This technique is different from the design of experimental techniques in that a partitioning of the parameter space is not required before the simulation. A tree structure (which looks similar to an event tree) is developed to better explain the technique. Each limb of the tree represents a particular combination of parameters or a combination of system components. For convenience and to distinguish it from the event tree, we call it the parameter tree. To construct the parameter tree, the samples of input parameter values are treated as either a '+' or a '-' based on whether or not the sampled parameter value is greater than or less than a specified branching criterion (e.g., mean, median, percentile of the population). The corresponding system outputs are also segregated into similar bins. Partitioning the first parameter into a '+' or a '-' bin creates the first level of the tree containing two branches. At the next level, realizations associated with each first-level branch are further partitioned into two bins using the branching criteria on the second parameter and so on until the tree is fully populated. Relative sensitivities are then inferred from the number of samples associated with each branch of the tree. The parameter tree approach is illustrated by applying it to a number of preliminary simulations of the proposed high-level radioactive waste repository at Yucca Mountain, NV. Using a
Modeling Parameters of Reliability of Technological Processes of Hydrocarbon Pipeline Transportation
Directory of Open Access Journals (Sweden)
Shalay Viktor
2016-01-01
Full Text Available On the basis of methods of system analysis and parametric reliability theory, the mathematical modeling of processes of oil and gas equipment operation in reliability monitoring was conducted according to dispatching data. To check the quality of empiric distribution coordination , an algorithm and mathematical methods of analysis are worked out in the on-line mode in a changing operating conditions. An analysis of physical cause-and-effect relations mechanism between the key factors and changing parameters of technical systems of oil and gas facilities is made, the basic types of technical distribution parameters are defined. Evaluation of the adequacy the analyzed parameters of the type of distribution is provided by using a criterion A.Kolmogorov, as the most universal, accurate and adequate to verify the distribution of continuous processes of complex multiple-technical systems. Methods of calculation are provided for supervising by independent bodies for risk assessment and safety facilities.
Empirical Study of Travel Time Estimation and Reliability
Li, Ruimin; Chai, Huajun; Tang, Jin
2013-01-01
This paper explores the travel time distribution of different types of urban roads, the link and path average travel time, and variance estimation methods by analyzing the large-scale travel time dataset detected from automatic number plate readers installed throughout Beijing. The results show that the best-fitting travel time distribution for different road links in 15 min time intervals differs for different traffic congestion levels. The average travel time for all links on all days can b...
Estimating unknown parameters in haemophilia using expert judgement elicitation.
Fischer, K; Lewandowski, D; Janssen, M P
2013-09-01
The increasing attention to healthcare costs and treatment efficiency has led to an increasing demand for quantitative data concerning patient and treatment characteristics in haemophilia. However, most of these data are difficult to obtain. The aim of this study was to use expert judgement elicitation (EJE) to estimate currently unavailable key parameters for treatment models in severe haemophilia A. Using a formal expert elicitation procedure, 19 international experts provided information on (i) natural bleeding frequency according to age and onset of bleeding, (ii) treatment of bleeds, (iii) time needed to control bleeding after starting secondary prophylaxis, (iv) dose requirements for secondary prophylaxis according to onset of bleeding, and (v) life-expectancy. For each parameter experts provided their quantitative estimates (median, P10, P90), which were combined using a graphical method. In addition, information was obtained concerning key decision parameters of haemophilia treatment. There was most agreement between experts regarding bleeding frequencies for patients treated on demand with an average onset of joint bleeding (1.7 years): median 12 joint bleeds per year (95% confidence interval 0.9-36) for patients ≤ 18, and 11 (0.8-61) for adult patients. Less agreement was observed concerning estimated effective dose for secondary prophylaxis in adults: median 2000 IU every other day The majority (63%) of experts expected that a single minor joint bleed could cause irreversible damage, and would accept up to three minor joint bleeds or one trauma related joint bleed annually on prophylaxis. Expert judgement elicitation allowed structured capturing of quantitative expert estimates. It generated novel data to be used in computer modelling, clinical care, and trial design. © 2013 John Wiley & Sons Ltd.
Estimation of real-time runway surface contamination using flight data recorder parameters
Curry, Donovan
Within this research effort, the development of an analytic process for friction coefficient estimation is presented. Under static equilibrium, the sum of forces and moments acting on the aircraft, in the aircraft body coordinate system, while on the ground at any instant is equal to zero. Under this premise the longitudinal, lateral and normal forces due to landing are calculated along with the individual deceleration components existent when an aircraft comes to a rest during ground roll. In order to validate this hypothesis a six degree of freedom aircraft model had to be created and landing tests had to be simulated on different surfaces. The simulated aircraft model includes a high fidelity aerodynamic model, thrust model, landing gear model, friction model and antiskid model. Three main surfaces were defined in the friction model; dry, wet and snow/ice. Only the parameters recorded by an FDR are used directly from the aircraft model all others are estimated or known a priori. The estimation of unknown parameters is also presented in the research effort. With all needed parameters a comparison and validation with simulated and estimated data, under different runway conditions, is performed. Finally, this report presents results of a sensitivity analysis in order to provide a measure of reliability of the analytic estimation process. Linear and non-linear sensitivity analysis has been performed in order to quantify the level of uncertainty implicit in modeling estimated parameters and how they can affect the calculation of the instantaneous coefficient of friction. Using the approach of force and moment equilibrium about the CG at landing to reconstruct the instantaneous coefficient of friction appears to be a reasonably accurate estimate when compared to the simulated friction coefficient. This is also true when the FDR and estimated parameters are introduced to white noise and when crosswind is introduced to the simulation. After the linear analysis the
Fleischer, Christian; Waag, Wladislaw; Heyn, Hans-Martin; Sauer, Dirk Uwe
2014-09-01
Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored. These include: battery state of charge (SoC), battery state of health (capacity fade determination, SoH), and state of function (power fade determination, SoF). The second paper concludes the series by presenting a multi-stage online parameter identification technique based on a weighted recursive least quadratic squares parameter estimator to determine the parameters of the proposed battery model from the first paper during operation. A novel mutation based algorithm is developed to determine the nonlinear current dependency of the charge-transfer resistance. The influence of diffusion is determined by an on-line identification technique and verified on several batteries at different operation conditions. This method guarantees a short response time and, together with its fully recursive structure, assures a long-term stable monitoring of the battery parameters. The relative dynamic voltage prediction error of the algorithm is reduced to 2%. The changes of parameters are used to determine the states of the battery. The algorithm is real-time capable and can be implemented on embedded systems.
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Tuffner, Francis K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Williams, Tess L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Schneider, Kevin P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Elizondo, Marcelo A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sun, Yannan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Liu, Chen-Ching [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Xu, Yin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gourisetti, Sri Nikhil Gup [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
NEWBOX: A computer program for parameter estimation in diffusion problems
International Nuclear Information System (INIS)
Nestor, C.W. Jr.; Godbee, H.W.; Joy, D.S.
1989-01-01
In the analysis of experiments to determine amounts of material transferred form 1 medium to another (e.g., the escape of chemically hazardous and radioactive materials from solids), there are at least 3 important considerations. These are (1) is the transport amenable to treatment by established mass transport theory; (2) do methods exist to find estimates of the parameters which will give a best fit, in some sense, to the experimental data; and (3) what computational procedures are available for evaluating the theoretical expressions. The authors have made the assumption that established mass transport theory is an adequate model for the situations under study. Since the solutions of the diffusion equation are usually nonlinear in some parameters (diffusion coefficient, reaction rate constants, etc.), use of a method of parameter adjustment involving first partial derivatives can be complicated and prone to errors in the computation of the derivatives. In addition, the parameters must satisfy certain constraints; for example, the diffusion coefficient must remain positive. For these reasons, a variant of the constrained simplex method of M. J. Box has been used to estimate parameters. It is similar, but not identical, to the downhill simplex method of Nelder and Mead. In general, they calculate the fraction of material transferred as a function of time from expressions obtained by the inversion of the Laplace transform of the fraction transferred, rather than by taking derivatives of a calculated concentration profile. With the above approaches to the 3 considerations listed at the outset, they developed a computer program NEWBOX, usable on a personal computer, to calculate the fractional release of material from 4 different geometrical shapes (semi-infinite medium, finite slab, finite circular cylinder, and sphere), accounting for several different boundary conditions
Lehnert, Teresa; Timme, Sandra; Pollmächer, Johannes; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo
2015-01-01
Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely
Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model
Energy Technology Data Exchange (ETDEWEB)
Marseguerra, M.; Zio, E.; Canetta, R. [Polytechnic of Milan, Dept. of Nuclear Engineering, Milano (Italy)
2005-07-01
The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)
Multi-objective genetic algorithm parameter estimation in a reduced nuclear reactor model
International Nuclear Information System (INIS)
Marseguerra, M.; Zio, E.; Canetta, R.
2005-01-01
The fast increase in computing power has rendered, and will continue to render, more and more feasible the incorporation of dynamics in the safety and reliability models of complex engineering systems. In particular, the Monte Carlo simulation framework offers a natural environment for estimating the reliability of systems with dynamic features. However, the time-integration of the dynamic processes may render the Monte Carlo simulation quite burdensome so that it becomes mandatory to resort to validated, simplified models of process evolution. Such models are typically based on lumped effective parameters whose values need to be suitably estimated so as to best fit to the available plant data. In this paper we propose a multi-objective genetic algorithm approach for the estimation of the effective parameters of a simplified model of nuclear reactor dynamics. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest to the actual evolution profiles. A case study is reported in which the real reactor is simulated by the QUAndry based Reactor Kinetics (Quark) code available from the Nuclear Energy Agency and the simplified model is based on the point kinetics approximation to describe the neutron balance in the core and on thermal equilibrium relations to describe the energy exchange between the different loops. (authors)
International Nuclear Information System (INIS)
Larbi, M.; Besnier, P.; Pecqueux, B.
2014-01-01
This paper deals with the risk analysis of an EMC default using a statistical approach. It is based on reliability methods from probabilistic engineering mechanics. A computation of probability of failure (i.e. probability of exceeding a threshold) of an induced current by crosstalk is established by taking into account uncertainties on input parameters influencing levels of interference in the context of transmission lines. The study has allowed us to evaluate the probability of failure of the induced current by using reliability methods having a relative low computational cost compared to Monte Carlo simulation. (authors)
Statistical estimation Monte Carlo for unreliability evaluation of highly reliable system
International Nuclear Information System (INIS)
Xiao Gang; Su Guanghui; Jia Dounan; Li Tianduo
2000-01-01
Based on analog Monte Carlo simulation, statistical Monte Carlo methods for unreliable evaluation of highly reliable system are constructed, including direct statistical estimation Monte Carlo method and weighted statistical estimation Monte Carlo method. The basal element is given, and the statistical estimation Monte Carlo estimators are derived. Direct Monte Carlo simulation method, bounding-sampling method, forced transitions Monte Carlo method, direct statistical estimation Monte Carlo and weighted statistical estimation Monte Carlo are used to evaluate unreliability of a same system. By comparing, weighted statistical estimation Monte Carlo estimator has smallest variance, and has highest calculating efficiency
Directory of Open Access Journals (Sweden)
Cameron Alyse FM
2009-11-01
Full Text Available Abstract Background Diagnostic ultrasound provides a method of analysing soft tissue structures of the musculoskeletal system effectively and reliably. The aim of this study was to evaluate within and between session reliability of measuring muscle dorso-plantar thickness, medio-lateral length and cross-sectional area, of the abductor hallucis muscle using two different ultrasound machines, a higher end Philips HD11 Ultrasound machine and clinically orientated Chison 8300 Deluxe Digital Portable Ultrasound System. Methods The abductor hallucis muscle of both the left and right feet of thirty asymptomatic participants was imaged and then measured using both ultrasound machines. Interclass correlation coefficients (ICC with 95% confidence intervals (CI were used to calculate both within and between session intra-tester reliability. Standard error of the measurement (SEM calculations were undertaken to assess difference between the actual measured score across trials and the smallest real difference (SRD was calculated from the SEM to indicate the degree of change that would exceed the expected trial to trial variability. Results The ICCs, SEM and SRD for dorso-plantar thickness and medial-lateral length were shown to have excellent to high within and between-session reliability for both ultrasound machines. The between-session reliability indices for cross-sectional area were acceptable for both ultrasound machines. Conclusion The results of the current study suggest that regardless of the type ultrasound machine, intra-tester reliability for the measurement the abductor hallucis muscle parameters is very high.
Statistical estimation of ultrasonic propagation path parameters for aberration correction.
Waag, Robert C; Astheimer, Jeffrey P
2005-05-01
Parameters in a linear filter model for ultrasonic propagation are found using statistical estimation. The model uses an inhomogeneous-medium Green's function that is decomposed into a homogeneous-transmission term and a path-dependent aberration term. Power and cross-power spectra of random-medium scattering are estimated over the frequency band of the transmit-receive system by using closely situated scattering volumes. The frequency-domain magnitude of the aberration is obtained from a normalization of the power spectrum. The corresponding phase is reconstructed from cross-power spectra of subaperture signals at adjacent receive positions by a recursion. The subapertures constrain the receive sensitivity pattern to eliminate measurement system phase contributions. The recursion uses a Laplacian-based algorithm to obtain phase from phase differences. Pulse-echo waveforms were acquired from a point reflector and a tissue-like scattering phantom through a tissue-mimicking aberration path from neighboring volumes having essentially the same aberration path. Propagation path aberration parameters calculated from the measurements of random scattering through the aberration phantom agree with corresponding parameters calculated for the same aberrator and array position by using echoes from the point reflector. The results indicate the approach describes, in addition to time shifts, waveform amplitude and shape changes produced by propagation through distributed aberration under realistic conditions.
PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
S. Kalaivani
2012-07-01
Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Sensitivity and parameter-estimation precision for alternate LISA configurations
International Nuclear Information System (INIS)
Vallisneri, Michele; Crowder, Jeff; Tinto, Massimo
2008-01-01
We describe a simple framework to assess the LISA scientific performance (more specifically, its sensitivity and expected parameter-estimation precision for prescribed gravitational-wave signals) under the assumption of failure of one or two inter-spacecraft laser measurements (links) and of one to four intra-spacecraft laser measurements. We apply the framework to the simple case of measuring the LISA sensitivity to monochromatic circular binaries, and the LISA parameter-estimation precision for the gravitational-wave polarization angle of these systems. Compared to the six-link baseline configuration, the five-link case is characterized by a small loss in signal-to-noise ratio (SNR) in the high-frequency section of the LISA band; the four-link case shows a reduction by a factor of √2 at low frequencies, and by up to ∼2 at high frequencies. The uncertainty in the estimate of polarization, as computed in the Fisher-matrix formalism, also worsens when moving from six to five, and then to four links: this can be explained by the reduced SNR available in those configurations (except for observations shorter than three months, where five and six links do better than four even with the same SNR). In addition, we prove (for generic signals) that the SNR and Fisher matrix are invariant with respect to the choice of a basis of TDI observables; rather, they depend only on which inter-spacecraft and intra-spacecraft measurements are available
Temporal Parameters Estimation for Wheelchair Propulsion Using Wearable Sensors
Directory of Open Access Journals (Sweden)
Manoela Ojeda
2014-01-01
Full Text Available Due to lower limb paralysis, individuals with spinal cord injury (SCI rely on their upper limbs for mobility. The prevalence of upper extremity pain and injury is high among this population. We evaluated the performance of three triaxis accelerometers placed on the upper arm, wrist, and under the wheelchair, to estimate temporal parameters of wheelchair propulsion. Twenty-six participants with SCI were asked to push their wheelchair equipped with a SMARTWheel. The estimated stroke number was compared with the criterion from video observations and the estimated push frequency was compared with the criterion from the SMARTWheel. Mean absolute errors (MAE and mean absolute percentage of error (MAPE were calculated. Intraclass correlation coefficients and Bland-Altman plots were used to assess the agreement. Results showed reasonable accuracies especially using the accelerometer placed on the upper arm where the MAPE was 8.0% for stroke number and 12.9% for push frequency. The ICC was 0.994 for stroke number and 0.916 for push frequency. The wrist and seat accelerometer showed lower accuracy with a MAPE for the stroke number of 10.8% and 13.4% and ICC of 0.990 and 0.984, respectively. Results suggested that accelerometers could be an option for monitoring temporal parameters of wheelchair propulsion.
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.
Condon, David; Revelle, William
2017-01-01
Separating the signal in a test from the irrelevant noise is a challenge for all measurement. Low test reliability limits test validity, attenuates important relationships, and can lead to regression artifacts. Multiple approaches to the assessment and improvement of reliability are discussed. The advantages and disadvantages of several different approaches to reliability are considered. Practical advice on how to assess reliability using open source software is provided.
Dai, Yu; Xue, Yuan; Zhang, Jianxun
2016-01-01
Impulsive noise caused by some random events has the main character of short rise-time and wide frequency spectrum range, so it has the potential to degrade the performance and reliability of the harmonic estimation. This paper focuses on the harmonic estimation procedure based on continuous wavelet transform (CWT) when the analyzed signal is corrupted by the impulsive noise. The digital CWT of both the time-varying sinusoidal signal and the impulsive noise are analyzed, and there are two cross ridges in the time-frequency plane of CWT, which are generated by the signal and the noise separately. In consideration of the amplitude of the noise and the number of the spike event, two inequalities are derived to provide limitations on the wavelet parameters. Based on the amplitude distribution of the noise, the optimal wavelet parameters determined by solving these inequalities are used to suppress the contamination of the noise, as well as increase the amplitude of the ridge corresponding to the signal, so the parameters of each harmonic component can be estimated accurately. The proposed procedure is applied to a numerical simulation and a bone vibration signal test giving satisfactory results of stationary and time-varying harmonic parameter estimation.
A method for model identification and parameter estimation
International Nuclear Information System (INIS)
Bambach, M; Heinkenschloss, M; Herty, M
2013-01-01
We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)
Bozorgzadeh, Nezam; Yanagimura, Yoko; Harrison, John P.
2017-12-01
The Hoek-Brown empirical strength criterion for intact rock is widely used as the basis for estimating the strength of rock masses. Estimations of the intact rock H-B parameters, namely the empirical constant m and the uniaxial compressive strength σc, are commonly obtained by fitting the criterion to triaxial strength data sets of small sample size. This paper investigates how such small sample sizes affect the uncertainty associated with the H-B parameter estimations. We use Monte Carlo (MC) simulation to generate data sets of different sizes and different combinations of H-B parameters, and then investigate the uncertainty in H-B parameters estimated from these limited data sets. We show that the uncertainties depend not only on the level of variability but also on the particular combination of parameters being investigated. As particular combinations of H-B parameters can informally be considered to represent specific rock types, we discuss that as the minimum number of required samples depends on rock type it should correspond to some acceptable level of uncertainty in the estimations. Also, a comparison of the results from our analysis with actual rock strength data shows that the probability of obtaining reliable strength parameter estimations using small samples may be very low. We further discuss the impact of this on ongoing implementation of reliability-based design protocols and conclude with suggestions for improvements in this respect.
Alkan, Hilal; Balkaya, Çağlayan
2018-02-01
We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram. The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems.
Transport parameter estimation from lymph measurements and the Patlak equation.
Watson, P D; Wolf, M B
1992-01-01
Two methods of estimating protein transport parameters for plasma-to-lymph transport data are presented. Both use IBM-compatible computers to obtain least-squares parameters for the solvent drag reflection coefficient and the permeability-surface area product using the Patlak equation. A matrix search approach is described, and the speed and convenience of this are compared with a commercially available gradient method. The results from both of these methods were different from those of a method reported by Reed, Townsley, and Taylor [Am. J. Physiol. 257 (Heart Circ. Physiol. 26): H1037-H1041, 1989]. It is shown that the Reed et al. method contains a systematic error. It is also shown that diffusion always plays an important role for transmembrane transport at the exit end of a membrane channel under all conditions of lymph flow rate and that the statement that diffusion becomes zero at high lymph flow rate depends on a mathematical definition of diffusion.
Averaging models: parameters estimation with the R-Average procedure
Directory of Open Access Journals (Sweden)
S. Noventa
2010-01-01
Full Text Available The Functional Measurement approach, proposed within the theoretical framework of Information Integration Theory (Anderson, 1981, 1982, can be a useful multi-attribute analysis tool. Compared to the majority of statistical models, the averaging model can account for interaction effects without adding complexity. The R-Average method (Vidotto & Vicentini, 2007 can be used to estimate the parameters of these models. By the use of multiple information criteria in the model selection procedure, R-Average allows for the identification of the best subset of parameters that account for the data. After a review of the general method, we present an implementation of the procedure in the framework of R-project, followed by some experiments using a Monte Carlo method.
Synchronization and parameter estimations of an uncertain Rikitake system
International Nuclear Information System (INIS)
Aguilar-Ibanez, Carlos; Martinez-Guerra, Rafael; Aguilar-Lopez, Ricardo; Mata-Machuca, Juan L.
2010-01-01
In this Letter we address the synchronization and parameter estimation of the uncertain Rikitake system, under the assumption the state is partially known. To this end we use the master/slave scheme in conjunction with the adaptive control technique. Our control approach consists of proposing a slave system which has to follow asymptotically the uncertain Rikitake system, refereed as the master system. The gains of the slave system are adjusted continually according to a convenient adaptation control law, until the measurable output errors converge to zero. The convergence analysis is carried out by using the Barbalat's Lemma. Under this context, uncertainty means that although the system structure is known, only a partial knowledge of the corresponding parameter values is available.
Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data
Klein, Vladislav; Murphy, Patrick C.
1998-01-01
Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.
Optimization-based particle filter for state and parameter estimation
Institute of Scientific and Technical Information of China (English)
Li Fu; Qi Fei; Shi Guangming; Zhang Li
2009-01-01
In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.
Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong
2016-05-30
Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.
Estimation of modal parameters using bilinear joint time frequency distributions
Roshan-Ghias, A.; Shamsollahi, M. B.; Mobed, M.; Behzad, M.
2007-07-01
In this paper, a new method is proposed for modal parameter estimation using time-frequency representations. Smoothed Pseudo Wigner-Ville distribution which is a member of the Cohen's class distributions is used to decouple vibration modes completely in order to study each mode separately. This distribution reduces cross-terms which are troublesome in Wigner-Ville distribution and retains the resolution as well. The method was applied to highly damped systems, and results were superior to those obtained via other conventional methods.
Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver
Kang, Ling; Zhou, Liwei
2018-02-01
Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.
Estimating the parameters of a generalized lambda distribution
International Nuclear Information System (INIS)
Fournier, B.; Rupin, N.; Najjar, D.; Iost, A.; Rupin, N.; Bigerelle, M.; Wilcox, R.; Fournier, B.
2007-01-01
The method of moments is a popular technique for estimating the parameters of a generalized lambda distribution (GLD), but published results suggest that the percentile method gives superior results. However, the percentile method cannot be implemented in an automatic fashion, and automatic methods, like the starship method, can lead to prohibitive execution time with large sample sizes. A new estimation method is proposed that is automatic (it does not require the use of special tables or graphs), and it reduces the computational time. Based partly on the usual percentile method, this new method also requires choosing which quantile u to use when fitting a GLD to data. The choice for u is studied and it is found that the best choice depends on the final goal of the modeling process. The sampling distribution of the new estimator is studied and compared to the sampling distribution of estimators that have been proposed. Naturally, all estimators are biased and here it is found that the bias becomes negligible with sample sizes n ≥ 2 * 10(3). The.025 and.975 quantiles of the sampling distribution are investigated, and the difference between these quantiles is found to decrease proportionally to 1/root n.. The same results hold for the moment and percentile estimates. Finally, the influence of the sample size is studied when a normal distribution is modeled by a GLD. Both bounded and unbounded GLDs are used and the bounded GLD turns out to be the most accurate. Indeed it is shown that, up to n = 10(6), bounded GLD modeling cannot be rejected by usual goodness-of-fit tests. (authors)
International Nuclear Information System (INIS)
Wattanapongskorn, Naruemon; Coit, David W.
2007-01-01
In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed
Energy Technology Data Exchange (ETDEWEB)
Sung, Baek Ju; Kim, Do Sik [Korea Institute of Machinery and Materials, Daejeon (Korea, Republic of)
2014-05-15
The precision hydraulic valve is widely used in various industrial field like aircraft, automobile, and general machinery. Servo actuator is the most important device for driving the precise hydraulic valve. The reliable operation of servo actuator effects on the overall hydraulic system. The performance of servo actuator relies on frequency response and step response according to arbitrary input signal. In this paper, we performed the analysis for the components of servo actuator to satisfy the reliable operation and response characteristics through the reliability analysis, and also induced the design parameters to realize the reliable operation and fast response characteristics of servo actuator for hydraulic valve operation through the empirical knowledge of experts and electromagnetic theories. We suggested the design equations to determine the values of design parameters of servo actuator as like bobbin size, length of yoke and plunger and turn number of coil, and verified the achieved design values through FEM analysis and performance tests using some prototypes of servo actuators adapted in hydraulic valve.
Directory of Open Access Journals (Sweden)
Fenglei Qi
2016-01-01
Full Text Available Enzymatic hydrolysis is an integral step in the conversion of lignocellulosic biomass to ethanol. The conversion of cellulose to fermentable sugars in the presence of inhibitors is a complex kinetic problem. In this study, we describe a novel approach to estimating the kinetic parameters underlying this process. This study employs experimental data measuring substrate and enzyme loadings, sugar and acid inhibitions for the production of glucose. Multiple objectives to minimize the difference between model predictions and experimental observations are developed and optimized by adopting multi-objective particle swarm optimization method. Model reliability is assessed by exploring likelihood profile in each parameter space. Compared to previous studies, this approach improved the prediction of sugar yields by reducing the mean squared errors by 34% for glucose and 2.7% for cellobiose, suggesting improved agreement between model predictions and the experimental data. Furthermore, kinetic parameters such as K2IG2, K1IG, K2IG, K1IA, and K3IA are identified as contributors to the model non-identifiability and wide parameter confidence intervals. Model reliability analysis indicates possible ways to reduce model non-identifiability and tighten parameter confidence intervals. These results could help improve the design of lignocellulosic biorefineries by providing higher fidelity predictions of fermentable sugars under inhibitory conditions.
Wagner, Brian J.; Harvey, Judson W.
1997-01-01
Tracer experiments are valuable tools for analyzing the transport characteristics of streams and their interactions with shallow groundwater. The focus of this work is the design of tracer studies in high-gradient stream systems subject to advection, dispersion, groundwater inflow, and exchange between the active channel and zones in surface or subsurface water where flow is stagnant or slow moving. We present a methodology for (1) evaluating and comparing alternative stream tracer experiment designs and (2) identifying those combinations of stream transport properties that pose limitations to parameter estimation and therefore a challenge to tracer test design. The methodology uses the concept of global parameter uncertainty analysis, which couples solute transport simulation with parameter uncertainty analysis in a Monte Carlo framework. Two general conclusions resulted from this work. First, the solute injection and sampling strategy has an important effect on the reliability of transport parameter estimates. We found that constant injection with sampling through concentration rise, plateau, and fall provided considerably more reliable parameter estimates than a pulse injection across the spectrum of transport scenarios likely encountered in high-gradient streams. Second, for a given tracer test design, the uncertainties in mass transfer and storage-zone parameter estimates are strongly dependent on the experimental Damkohler number, DaI, which is a dimensionless combination of the rates of exchange between the stream and storage zones, the stream-water velocity, and the stream reach length of the experiment. Parameter uncertainties are lowest at DaI values on the order of 1.0. When DaI values are much less than 1.0 (owing to high velocity, long exchange timescale, and/or short reach length), parameter uncertainties are high because only a small amount of tracer interacts with storage zones in the reach. For the opposite conditions (DaI ≫ 1.0), solute
LENUS (Irish Health Repository)
Malone, Ailish
2012-02-01
The aims of this study were to validate a computerised method to detect muscle activity from surface electromyography (SEMG) signals in gait in patients with cervical spondylotic myelopathy (CSM), and to evaluate the test-retest reliability of the activation times designated by this method. SEMG signals were recorded from rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius (MG), during gait in 12 participants with CSM on two separate test days. Four computerised activity detection methods, based on the Teager-Kaiser Energy Operator (TKEO), were applied to a subset of signals and compared to visual interpretation of muscle activation. The most accurate method was then applied to all signals for evaluation of test-retest reliability. A detection method based on a combined slope and amplitude threshold showed the highest agreement (87.5%) with visual interpretation. With respect to reliability, the standard error of measurement (SEM) of the timing of RF, TA and MG between test days was 5.5% stride duration or less, while the SEM of BF was 9.4%. The timing parameters of RF, TA and MG designated by this method were considered sufficiently reliable for use in clinical practice, however the reliability of BF was questionable.
The reliable solution and computation time of variable parameters logistic model
Wang, Pengfei; Pan, Xinnong
2018-05-01
The study investigates the reliable computation time (RCT, termed as T c) by applying a double-precision computation of a variable parameters logistic map (VPLM). Firstly, by using the proposed method, we obtain the reliable solutions for the logistic map. Secondly, we construct 10,000 samples of reliable experiments from a time-dependent non-stationary parameters VPLM and then calculate the mean T c. The results indicate that, for each different initial value, the T cs of the VPLM are generally different. However, the mean T c trends to a constant value when the sample number is large enough. The maximum, minimum, and probable distribution functions of T c are also obtained, which can help us to identify the robustness of applying a nonlinear time series theory to forecasting by using the VPLM output. In addition, the T c of the fixed parameter experiments of the logistic map is obtained, and the results suggest that this T c matches the theoretical formula-predicted value.
International Nuclear Information System (INIS)
Wang, Guodong; He, Zhen; Xue, Li; Cui, Qingan; Lv, Shanshan; Zhou, Panpan
2017-01-01
Factors which significantly affect product reliability are of great interest to reliability practitioners. This paper proposes a bootstrap-based methodology for identifying significant factors when both location and scale parameters of the smallest extreme value distribution vary over experimental factors. An industrial thermostat experiment is presented, analyzed, and discussed as an illustrative example. The analysis results show that 1) the misspecification of a constant scale parameter may lead to misidentify spurious effects; 2) the important factors identified by different bootstrap methods (i.e., percentile bootstrapping, bias-corrected percentile bootstrapping, and bias-corrected and accelerated percentile bootstrapping) are different; 3) the number of factors affecting 10th percentile lifetime significantly is less than the number of important factors identified at 63.21th percentile. - Highlights: • Product reliability is improved by design of experiments under both scale and location parameters of smallest extreme value distribution vary with experimental factors. • A bootstrap-based methodology is proposed to identify important factors which affect 100pth lifetime percentile significantly. • Bootstrapping confidence intervals associating experimental factors are obtained by using three bootstrap methods (i.e., percentile bootstrapping, bias-corrected percentile bootstrapping, and bias-corrected and accelerated percentile bootstrapping). • The important factors identified by different bootstrap methods are different. • The number of factors affecting 10th percentile significantly is less than the number of important factors identified at 63.21th percentile.
Reliability of bounce drop jump parameters within elite male rugby players.
Costley, Lisa; Wallace, Eric; Johnston, Michael; Kennedy, Rodney
2017-07-25
The aims of the study were to investigate the number of familiarisation sessions required to establish reliability of the bounce drop jump (BDJ) and subsequent reliability once familiarisation is achieved. Seventeen trained male athletes completed 4 BDJs in 4 separate testing sessions. Force-time data from a 20 cm BDJ was obtained using two force plates (ensuring ground contact < 250 ms). Subjects were instructed to 'jump for maximal height and minimal contact time' while the best and average of four jumps were compared. A series of performance variables were assessed in both eccentric and concentric phases including jump height, contact time, flight time, reactive strength index (RSI), peak power, rate of force development (RFD) and actual dropping height (ADH). Reliability was assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV) while familiarisation was assessed using a repeated measures analysis of variance (ANOVA). The majority of DJ parameters exhibited excellent reliability with no systematic bias evident, while the average of 4 trials provided greater reliability. With the exception of vertical stiffness (CV: 12.0 %) and RFD (CV: 16.2 %) all variables demonstrated low within subject variation (CV range: 3.1 - 8.9 %). Relative reliability was very poor for ADH, with heights ranging from 14.87 - 29.85 cm. High levels of reliability can be obtained from the BDJ with the exception of vertical stiffness and RFD, however, extreme caution must be taken when comparing DJ results between individuals and squads due to large discrepancies between actual drop height and platform height.
A robust methodology for modal parameters estimation applied to SHM
Cardoso, Rharã; Cury, Alexandre; Barbosa, Flávio
2017-10-01
The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis.
Parameter estimation in space systems using recurrent neural networks
Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.
1991-01-01
The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
Parameter estimation and hypothesis testing in linear models
Koch, Karl-Rudolf
1999-01-01
The necessity to publish the second edition of this book arose when its third German edition had just been published. This second English edition is there fore a translation of the third German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1997. It differs from the first English edition by the addition of a new chapter on robust estimation of parameters and the deletion of the section on discriminant analysis, which has been more completely dealt with by the author in the book Bayesian In ference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, 1990. Smaller additions and deletions have been incorporated, to im prove the text, to point out new developments or to eliminate errors which became apparent. A few examples have been also added. I thank Springer-Verlag for publishing this second edition and for the assistance in checking the translation, although the responsibility of errors remains with the author. I also want to express my thanks...
Periodic orbits of hybrid systems and parameter estimation via AD
International Nuclear Information System (INIS)
Guckenheimer, John; Phipps, Eric Todd; Casey, Richard
2004-01-01
Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical models of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method (GM00, Phi03). Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...
Reliability/Cost Evaluation on Power System connected with Wind Power for the Reserve Estimation
DEFF Research Database (Denmark)
Lee, Go-Eun; Cha, Seung-Tae; Shin, Je-Seok
2012-01-01
Wind power is ideally a renewable energy with no fuel cost, but has a risk to reduce reliability of the whole system because of uncertainty of the output. If the reserve of the system is increased, the reliability of the system may be improved. However, the cost would be increased. Therefore...... the reserve needs to be estimated considering the trade-off between reliability and economic aspects. This paper suggests a methodology to estimate the appropriate reserve, when wind power is connected to the power system. As a case study, when wind power is connected to power system of Korea, the effects...
Thermodynamic criteria for estimating the kinetic parameters of catalytic reactions
Mitrichev, I. I.; Zhensa, A. V.; Kol'tsova, E. M.
2017-01-01
Kinetic parameters are estimated using two criteria in addition to the traditional criterion that considers the consistency between experimental and modeled conversion data: thermodynamic consistency and the consistency with entropy production (i.e., the absolute rate of the change in entropy due to exchange with the environment is consistent with the rate of entropy production in the steady state). A special procedure is developed and executed on a computer to achieve the thermodynamic consistency of a set of kinetic parameters with respect to both the standard entropy of a reaction and the standard enthalpy of a reaction. A problem of multi-criterion optimization, reduced to a single-criterion problem by summing weighted values of the three criteria listed above, is solved. Using the reaction of NO reduction with CO on a platinum catalyst as an example, it is shown that the set of parameters proposed by D.B. Mantri and P. Aghalayam gives much worse agreement with experimental values than the set obtained on the basis of three criteria: the sum of the squares of deviations for conversion, the thermodynamic consistency, and the consistency with entropy production.
Estimating negative binomial parameters from occurrence data with detection times.
Hwang, Wen-Han; Huggins, Richard; Stoklosa, Jakub
2016-11-01
The negative binomial distribution is a common model for the analysis of count data in biology and ecology. In many applications, we may not observe the complete frequency count in a quadrat but only that a species occurred in the quadrat. If only occurrence data are available then the two parameters of the negative binomial distribution, the aggregation index and the mean, are not identifiable. This can be overcome by data augmentation or through modeling the dependence between quadrat occupancies. Here, we propose to record the (first) detection time while collecting occurrence data in a quadrat. We show that under what we call proportionate sampling, where the time to survey a region is proportional to the area of the region, that both negative binomial parameters are estimable. When the mean parameter is larger than two, our proposed approach is more efficient than the data augmentation method developed by Solow and Smith (, Am. Nat. 176, 96-98), and in general is cheaper to conduct. We also investigate the effect of misidentification when collecting negative binomially distributed data, and conclude that, in general, the effect can be simply adjusted for provided that the mean and variance of misidentification probabilities are known. The results are demonstrated in a simulation study and illustrated in several real examples. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Estimation Parameters And Modelling Zero Inflated Negative Binomial
Directory of Open Access Journals (Sweden)
Cindy Cahyaning Astuti
2016-11-01
Full Text Available Regression analysis is used to determine relationship between one or several response variable (Y with one or several predictor variables (X. Regression model between predictor variables and the Poisson distributed response variable is called Poisson Regression Model. Since, Poisson Regression requires an equality between mean and variance, it is not appropriate to apply this model on overdispersion (variance is higher than mean. Poisson regression model is commonly used to analyze the count data. On the count data type, it is often to encounteredd some observations that have zero value with large proportion of zero value on the response variable (zero Inflation. Poisson regression can be used to analyze count data but it has not been able to solve problem of excess zero value on the response variable. An alternative model which is more suitable for overdispersion data and can solve the problem of excess zero value on the response variable is Zero Inflated Negative Binomial (ZINB. In this research, ZINB is applied on the case of Tetanus Neonatorum in East Java. The aim of this research is to examine the likelihood function and to form an algorithm to estimate the parameter of ZINB and also applying ZINB model in the case of Tetanus Neonatorum in East Java. Maximum Likelihood Estimation (MLE method is used to estimate the parameter on ZINB and the likelihood function is maximized using Expectation Maximization (EM algorithm. Test results of ZINB regression model showed that the predictor variable have a partial significant effect at negative binomial model is the percentage of pregnant women visits and the percentage of maternal health personnel assisted, while the predictor variables that have a partial significant effect at zero inflation model is the percentage of neonatus visits.
Automated modal parameter estimation using correlation analysis and bootstrap sampling
Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.
2018-02-01
The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to
Pasma, J.H.; Kordelaar, J. van; Kam, D. de; Weerdesteyn, V.G.M.; Schouten, A.C.; Kooij, H. van der
2017-01-01
BACKGROUND: Closed loop system identification (CLSIT) is a method to disentangle the contribution of underlying systems in standing balance. We investigated whether taking into account lower leg muscle activation in CLSIT could improve the reliability and accuracy of estimated parameters identifying
Pasma, J. H.; Van Kordelaar, J.; de Kam, D.; Weerdesteyn, V.; Schouten, A. C.; Van Der Kooij, H.
2017-01-01
Background: Closed loop system identification (CLSIT) is a method to disentangle the contribution of underlying systems in standing balance. We investigated whether taking into account lower leg muscle activation in CLSIT could improve the reliability and accuracy of estimated parameters identifying
Pasma, J.H.; van Kordelaar, J.; de Kam, D.; Weerdesteyn, V.; Schouten, A.C.; van der Kooij, H.
2017-01-01
Background: Closed loop system identification (CLSIT) is a method to disentangle the contribution of underlying systems in standing balance. We investigated whether taking into account lower leg muscle activation in CLSIT could improve the reliability and accuracy of estimated parameters
Learn-as-you-go acceleration of cosmological parameter estimates
International Nuclear Information System (INIS)
Aslanyan, Grigor; Easther, Richard; Price, Layne C.
2015-01-01
Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly
An adaptive neuro fuzzy model for estimating the reliability of component-based software systems
Directory of Open Access Journals (Sweden)
Kirti Tyagi
2014-01-01
Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.
Liinamo, A E; Karjalainen, L; Ojala, M; Vilva, V
1997-03-01
Data from field trials of Finnish Hounds between 1988 and 1992 in Finland were used to estimate genetic parameters and environmental effects for measures of hunting performance using REML procedures and an animal model. The original data set included 28,791 field trial records from 5,666 dogs. Males and females had equal hunting performance, whereas experience acquired by age improved trial results compared with results for young dogs (P Hounds with respect to their hunting ability should be based on animal model BLUP methods instead of mere performance testing. The evaluation system of field trials should also be revised for more reliability.
Melching, C.S.; Marquardt, J.S.
1997-01-01
had multiple correlation coefficients of 0.873, 0.961, 0.968, and 0.963 for TC, R, UL, and TL, respectively, and the estimation equations utilizing main channel length had multiple correlation coefficients of 0.845, 0.957, 0.961, and 0.963 for TC, R, UL, and TL, respectively. Simulation of the measured hydrographs for the verification storms utilizing TC and R obtained from the estimation equations yielded good results without calibration. The peak discharge for 8 of the 11 storms was estimated within 25 percent and the time-to-peak discharge for 10 of the 11 storms was estimated within 20 percent. Thus, application of the estimation equations to determine synthetic unit-hydrograph parameters for design-storm simulation may result in reliable design hydrographs; as long as the physical characteristics of the watersheds under consideration are within the range of those for the watersheds in this study (area: 0.06-37 mi2, main channel length: 0.33-16.6 miles, main channel slope: 3.13-55.3 feet per mile, and percentage of impervious cover: 7.32-40.6 percent). The estimation equations are most reliable when applied to watersheds with areas less than 25 mi2.
Reliance on and Reliability of the Engineer’s Estimate in Heavy Civil Projects
Directory of Open Access Journals (Sweden)
George Okere
2017-06-01
Full Text Available To the contractor, the engineer’s estimate is the target number to aim for, and the basis for a contractor to evaluate the accuracy of their estimate. To the owner, the engineer’s estimate is the basis for funding, evaluation of bids, and for predicting project costs. As such the engineer’s estimate is the benchmark. This research sought to investigate the reliance on, and the reliability of the engineer’s estimate in heavy civil cost estimate. The research objective was to characterize the engineer’s estimate and allow owners and contractors re-evaluate or affirm their reliance on the engineer’s estimate. A literature review was conducted to understand the reliance on the engineer’s estimate, and secondary data from Washington State Department of Transportation was used to investigate the reliability of the engineer’s estimate. The findings show the need for practitioners to re-evaluate their reliance on the engineer’s estimate. The empirical data showed that, within various contexts, the engineer’s estimate fell outside the expected accuracy range of the low bids or the cost to complete projects. The study recommends direct tracking of costs by project owners while projects are under construction, the use of a second estimate to improve the accuracy of their estimates, and use of the cost estimating practices found in highly reputable construction companies.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
Directory of Open Access Journals (Sweden)
Afnizanfaizal Abdullah
Full Text Available The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V
2013-01-01
The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.
International Nuclear Information System (INIS)
Ferretti, M.; Brambilla, E.; Brunialti, G.; Fornasier, F.; Mazzali, C.; Giordani, P.; Nimis, P.L.
2004-01-01
Sampling requirements related to lichen biomonitoring include optimal sampling density for obtaining precise and unbiased estimates of population parameters and maps of known reliability. Two available datasets on a sub-national scale in Italy were used to determine a cost-effective sampling density to be adopted in medium-to-large-scale biomonitoring studies. As expected, the relative error in the mean Lichen Biodiversity (Italian acronym: BL) values and the error associated with the interpolation of BL values for (unmeasured) grid cells increased as the sampling density decreased. However, the increase in size of the error was not linear and even a considerable reduction (up to 50%) in the original sampling effort led to a far smaller increase in errors in the mean estimates (<6%) and in mapping (<18%) as compared with the original sampling densities. A reduction in the sampling effort can result in considerable savings of resources, which can then be used for a more detailed investigation of potentially problematic areas. It is, however, necessary to decide the acceptable level of precision at the design stage of the investigation, so as to select the proper sampling density. - An acceptable level of precision must be decided before determining a sampling design
Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang
2017-05-01
The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE 1 and MLE 2 , respectively), and Greenwood approximation (MLE gw ) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE 1 , the MLE 2 and MLE gw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE 2 and MLE gw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE 2 and MLE gw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.
Reliance on and Reliability of the Engineer’s Estimate in Heavy Civil Projects
Okere, George
2017-01-01
To the contractor, the engineer’s estimate is the target number to aim for, and the basis for a contractor to evaluate the accuracy of their estimate. To the owner, the engineer’s estimate is the basis for funding, evaluation of bids, and for predicting project costs. As such the engineer’s estimate is the benchmark. This research sought to investigate the reliance on, and the reliability of the engineer’s estimate in heavy civil cost estimate. The research objective was to characterize the e...
An Energy-Based Limit State Function for Estimation of Structural Reliability in Shock Environments
Directory of Open Access Journals (Sweden)
Michael A. Guthrie
2013-01-01
Full Text Available limit state function is developed for the estimation of structural reliability in shock environments. This limit state function uses peak modal strain energies to characterize environmental severity and modal strain energies at failure to characterize the structural capacity. The Hasofer-Lind reliability index is briefly reviewed and its computation for the energy-based limit state function is discussed. Applications to two degree of freedom mass-spring systems and to a simple finite element model are considered. For these examples, computation of the reliability index requires little effort beyond a modal analysis, but still accounts for relevant uncertainties in both the structure and environment. For both examples, the reliability index is observed to agree well with the results of Monte Carlo analysis. In situations where fast, qualitative comparison of several candidate designs is required, the reliability index based on the proposed limit state function provides an attractive metric which can be used to compare and control reliability.
Automated parameter estimation for biological models using Bayesian statistical model checking.
Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K
2015-01-01
Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.
Colocated MIMO Radar: Beamforming, Waveform design, and Target Parameter Estimation
Jardak, Seifallah
2014-04-01
Thanks to its improved capabilities, the Multiple Input Multiple Output (MIMO) radar is attracting the attention of researchers and practitioners alike. Because it transmits orthogonal or partially correlated waveforms, this emerging technology outperformed the phased array radar by providing better parametric identifiability, achieving higher spatial resolution, and designing complex beampatterns. To avoid jamming and enhance the signal to noise ratio, it is often interesting to maximize the transmitted power in a given region of interest and minimize it elsewhere. This problem is known as the transmit beampattern design and is usually tackled as a two-step process: a transmit covariance matrix is firstly designed by minimizing a convex optimization problem, which is then used to generate practical waveforms. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method maps easily generated Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability density function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. The second part of this thesis covers the topic of target parameter estimation. To determine the reflection coefficient, spatial location, and Doppler shift of a target, maximum likelihood estimation yields the best performance. However, it requires a two dimensional search problem. Therefore, its computational complexity is prohibitively high. So, we proposed a reduced complexity and optimum performance algorithm which allows the two dimensional fast Fourier transform to jointly estimate the spatial location
Estimation of the Alpha Factor Parameters Using the ICDE Database
Energy Technology Data Exchange (ETDEWEB)
Kang, Dae Il; Hwang, M. J.; Han, S. H
2007-04-15
Detailed common cause failure (CCF) analysis generally need for the data for CCF events of other nuclear power plants because the CCF events rarely occur. KAERI has been participated at the international common cause failure data exchange (ICDE) project to get the data for the CCF events. The operation office of the ICDE project sent the CCF event data for EDG to the KAERI at December 2006. As a pilot study, we performed the detailed CCF analysis of EDGs for Yonggwang Units 3 and 4 and Ulchin Units 3 and 4 using the ICDE database. There are two onsite EDGs for each NPP. When an offsite power and the two onsite EDGs are not available, one alternate AC (AAC) diesel generator (hereafter AAC) is provided. Two onsite EDGs and the AAC are manufactured by the same company, but they are designed differently. We estimated the Alpha Factor and the CCF probability for the cases where three EDGs were assumed to be identically designed, and for those were assumed to be not identically designed. For the cases where three EDGs were assumed to be identically designed, double CCF probabilities of Yonggwang Units 3/4 and Ulchin Units 3/4 for 'fails to start' were estimated as 2.20E-4 and 2.10E-4, respectively. Triple CCF probabilities of those were estimated as 2.39E-4 and 2.42E-4, respectively. As each NPP has no experience for 'fails to run', Yonggwang Units 3/4 and Ulchin Units 3/4 have the same CCF probability. The estimated double and triple CCF probabilities for 'fails to run' are 4.21E-4 and 4.61E-4, respectively. Quantification results show that the system unavailability for the cases where the three EDGs are identical is higher than that where the three EDGs are different. The estimated system unavailability of the former case was increased by 3.4% comparing with that of the latter. As a future study, a computerization work for the estimations of the CCF parameters will be performed.
A probabilistic approach for the estimation of earthquake source parameters from spectral inversion
Supino, M.; Festa, G.; Zollo, A.
2017-12-01
The amplitude spectrum of a seismic signal related to an earthquake source carries information about the size of the rupture, moment, stress and energy release. Furthermore, it can be used to characterize the Green's function of the medium crossed by the seismic waves. We describe the earthquake amplitude spectrum assuming a generalized Brune's (1970) source model, and direct P- and S-waves propagating in a layered velocity model, characterized by a frequency-independent Q attenuation factor. The observed displacement spectrum depends indeed on three source parameters, the seismic moment (through the low-frequency spectral level), the corner frequency (that is a proxy of the fault length) and the high-frequency decay parameter. These parameters are strongly correlated each other and with the quality factor Q; a rigorous estimation of the associated uncertainties and parameter resolution is thus needed to obtain reliable estimations.In this work, the uncertainties are characterized adopting a probabilistic approach for the parameter estimation. Assuming an L2-norm based misfit function, we perform a global exploration of the parameter space to find the absolute minimum of the cost function and then we explore the cost-function associated joint a-posteriori probability density function around such a minimum, to extract the correlation matrix of the parameters. The global exploration relies on building a Markov chain in the parameter space and on combining a deterministic minimization with a random exploration of the space (basin-hopping technique). The joint pdf is built from the misfit function using the maximum likelihood principle and assuming a Gaussian-like distribution of the parameters. It is then computed on a grid centered at the global minimum of the cost-function. The numerical integration of the pdf finally provides mean, variance and correlation matrix associated with the set of best-fit parameters describing the model. Synthetic tests are performed to
DEFF Research Database (Denmark)
Hu, Teng; Sørensen, Peter; Wahlström, Ellen Margrethe
2018-01-01
and management factors may affect this allometric relationship making such estimates uncertain and biased. Therefore, we aimed to explore how root biomass for typical cereal crops, catch crops and weeds could most reliably be estimated. Published and unpublished data on aboveground and root biomass (corrected...
Estimation of genetic parameters for reproductive traits in Shall sheep.
Amou Posht-e-Masari, Hesam; Shadparvar, Abdol Ahad; Ghavi Hossein-Zadeh, Navid; Hadi Tavatori, Mohammad Hossein
2013-06-01
The objective of this study was to estimate genetic parameters for reproductive traits in Shall sheep. Data included 1,316 records on reproductive performances of 395 Shall ewes from 41 sires and 136 dams which were collected from 2001 to 2007 in Shall breeding station in Qazvin province at the Northwest of Iran. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Test of significance to include fixed effects in the statistical model was performed using the general linear model procedure of SAS. The effects of lambing year and ewe age at lambing were significant (Psheep.
Multiphase flow parameter estimation based on laser scattering
Vendruscolo, Tiago P.; Fischer, Robert; Martelli, Cicero; Rodrigues, Rômulo L. P.; Morales, Rigoberto E. M.; da Silva, Marco J.
2015-07-01
The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time.
Estimating Phenomenological Parameters in Multi-Assets Markets
Raffaelli, Giacomo; Marsili, Matteo
Financial correlations exhibit a non-trivial dynamic behavior. This is reproduced by a simple phenomenological model of a multi-asset financial market, which takes into account the impact of portfolio investment on price dynamics. This captures the fact that correlations determine the optimal portfolio but are affected by investment based on it. Such a feedback on correlations gives rise to an instability when the volume of investment exceeds a critical value. Close to the critical point the model exhibits dynamical correlations very similar to those observed in real markets. We discuss how the model's parameter can be estimated in real market data with a maximum likelihood principle. This confirms the main conclusion that real markets operate close to a dynamically unstable point.
Dynamic systems models new methods of parameter and state estimation
2016-01-01
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...
Cosmological Parameter Estimation with Large Scale Structure Observations
Di Dio, Enea; Durrer, Ruth; Lesgourgues, Julien
2014-01-01
We estimate the sensitivity of future galaxy surveys to cosmological parameters, using the redshift dependent angular power spectra of galaxy number counts, $C_\\ell(z_1,z_2)$, calculated with all relativistic corrections at first order in perturbation theory. We pay special attention to the redshift dependence of the non-linearity scale and present Fisher matrix forecasts for Euclid-like and DES-like galaxy surveys. We compare the standard $P(k)$ analysis with the new $C_\\ell(z_1,z_2)$ method. We show that for surveys with photometric redshifts the new analysis performs significantly better than the $P(k)$ analysis. For spectroscopic redshifts, however, the large number of redshift bins which would be needed to fully profit from the redshift information, is severely limited by shot noise. We also identify surveys which can measure the lensing contribution and we study the monopole, $C_0(z_1,z_2)$.
Multiphase flow parameter estimation based on laser scattering
International Nuclear Information System (INIS)
Vendruscolo, Tiago P; Fischer, Robert; Martelli, Cicero; Da Silva, Marco J; Rodrigues, Rômulo L P; Morales, Rigoberto E M
2015-01-01
The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time. (paper)
Review of methods for level density estimation from resonance parameters
International Nuclear Information System (INIS)
Froehner, F.H.
1983-01-01
A number of methods are available for statistical analysis of resonance parameter sets, i.e. for estimation of level densities and average widths with account of missing levels. The main categories are (i) methods based on theories of level spacings (orthogonal-ensemble theory, Dyson-Mehta statistics), (ii) methods based on comparison with simulated cross section curves (Monte Carlo simulation, Garrison's autocorrelation method), (iii) methods exploiting the observed neutron width distribution by means of Bayesian or more approximate procedures such as maximum-likelihood, least-squares or moment methods, with various recipes for the treatment of detection thresholds and resolution effects. The present review will concentrate on (iii) with the aim of clarifying the basic mathematical concepts and the relationship between the various techniques. Recent theoretical progress in the treatment of resolution effects, detectability thresholds and p-wave admixture is described. (Auth.)
MANOVA, LDA, and FA criteria in clusters parameter estimation
Directory of Open Access Journals (Sweden)
Stan Lipovetsky
2015-12-01
Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.
State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications
Phanomchoeng, Gridsada
presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.
How Many Sleep Diary Entries Are Needed to Reliably Estimate Adolescent Sleep?
Arora, Teresa; Gradisar, Michael; Taheri, Shahrad; Carskadon, Mary A.
2017-01-01
Abstract Study Objectives: To investigate (1) how many nights of sleep diary entries are required for reliable estimates of five sleep-related outcomes (bedtime, wake time, sleep onset latency [SOL], sleep duration, and wake after sleep onset [WASO]) and (2) the test–retest reliability of sleep diary estimates of school night sleep across 12 weeks. Methods: Data were drawn from four adolescent samples (Australia [n = 385], Qatar [n = 245], United Kingdom [n = 770], and United States [n = 366]), who provided 1766 eligible sleep diary weeks for reliability analyses. We performed reliability analyses for each cohort using complete data (7 days), one to five school nights, and one to two weekend nights. We also performed test–retest reliability analyses on 12-week sleep diary data available from a subgroup of 55 US adolescents. Results: Intraclass correlation coefficients for bedtime, SOL, and sleep duration indicated good-to-excellent reliability from five weekday nights of sleep diary entries across all adolescent cohorts. Four school nights was sufficient for wake times in the Australian and UK samples, but not the US or Qatari samples. Only Australian adolescents showed good reliability for two weekend nights of bedtime reports; estimates of SOL were adequate for UK adolescents based on two weekend nights. WASO was not reliably estimated using 1 week of sleep diaries. We observed excellent test–rest reliability across 12 weeks of sleep diary data in a subsample of US adolescents. Conclusion: We recommend at least five weekday nights of sleep dairy entries to be made when studying adolescent bedtimes, SOL, and sleep duration. Adolescent sleep patterns were stable across 12 consecutive school weeks. PMID:28199718
Smoothing of, and parameter estimation from, noisy biophysical recordings.
Directory of Open Access Journals (Sweden)
Quentin J M Huys
2009-05-01
Full Text Available Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering" methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.
Project Parameter Estimation on the Basis of an Erp Database
Directory of Open Access Journals (Sweden)
Relich Marcin
2013-12-01
Full Text Available Nowadays, more and more enterprises are using Enterprise Resource Planning (EPR systems that can also be used to plan and control the development of new products. In order to obtain a project schedule, certain parameters (e.g. duration have to be specified in an ERP system. These parameters can be defined by the employees according to their knowledge, or can be estimated on the basis of data from previously completed projects. This paper investigates using an ERP database to identify those variables that have a significant influence on the duration of a project phase. In the paper, a model of knowledge discovery from an ERP database is proposed. The presented method contains four stages of the knowledge discovery process such as data selection, data transformation, data mining and interpretation of patterns in the context of new product development. Among data mining techniques, a fuzzy neural system is chosen to seek relationships on the basis of data from completed projects stored in an ERP system.
On the Reliability of Source Time Functions Estimated Using Empirical Green's Function Methods
Gallegos, A. C.; Xie, J.; Suarez Salas, L.
2017-12-01
The Empirical Green's Function (EGF) method (Hartzell, 1978) has been widely used to extract source time functions (STFs). In this method, seismograms generated by collocated events with different magnitudes are deconvolved. Under a fundamental assumption that the STF of the small event is a delta function, the deconvolved Relative Source Time Function (RSTF) yields the large event's STF. While this assumption can be empirically justified by examination of differences in event size and frequency content of the seismograms, there can be a lack of rigorous justification of the assumption. In practice, a small event might have a finite duration when the RSTF is retrieved and interpreted as the large event STF with a bias. In this study, we rigorously analyze this bias using synthetic waveforms generated by convolving a realistic Green's function waveform with pairs of finite-duration triangular or parabolic STFs. The RSTFs are found using a time-domain based matrix deconvolution. We find when the STFs of smaller events are finite, the RSTFs are a series of narrow non-physical spikes. Interpreting these RSTFs as a series of high-frequency source radiations would be very misleading. The only reliable and unambiguous information we can retrieve from these RSTFs is the difference in durations and the moment ratio of the two STFs. We can apply a Tikhonov smoothing to obtain a single-pulse RSTF, but its duration is dependent on the choice of weighting, which may be subjective. We then test the Multi-Channel Deconvolution (MCD) method (Plourde & Bostock, 2017) which assumes that both STFs have finite durations to be solved for. A concern about the MCD method is that the number of unknown parameters is larger, which would tend to make the problem rank-deficient. Because the kernel matrix is dependent on the STFs to be solved for under a positivity constraint, we can only estimate the rank-deficiency with a semi-empirical approach. Based on the results so far, we find that the
Estimation of fracture parameters using elastic full-waveform inversion
Zhang, Zhendong
2017-08-17
Current methodologies to characterize fractures at the reservoir scale have serious limitations in spatial resolution and suffer from uncertainties in the inverted parameters. Here, we propose to estimate the spatial distribution and physical properties of fractures using full-waveform inversion (FWI) of multicomponent surface seismic data. An effective orthorhombic medium with five clusters of vertical fractures distributed in a checkboard fashion is used to test the algorithm. A shape regularization term is added to the objective function to improve the estimation of the fracture azimuth, which is otherwise poorly constrained. The cracks are assumed to be penny-shaped to reduce the nonuniqueness in the inverted fracture weaknesses and achieve a faster convergence. To better understand the inversion results, we analyze the radiation patterns induced by the perturbations in the fracture weaknesses and orientation. Due to the high-resolution potential of elastic FWI, the developed algorithm can recover the spatial fracture distribution and identify localized “sweet spots” of intense fracturing. However, the fracture azimuth can be resolved only using long-offset data.
A Data-Driven Reliability Estimation Approach for Phased-Mission Systems
Directory of Open Access Journals (Sweden)
Hua-Feng He
2014-01-01
Full Text Available We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example.
Estimation of genetic parameters for reproductive traits in alpacas.
Cruz, A; Cervantes, I; Burgos, A; Morante, R; Gutiérrez, J P
2015-12-01
One of the main deficiencies affecting animal breeding programs in Peruvian alpacas is the low reproductive performance leading to low number of animals available to select from, decreasing strongly the selection intensity. Some reproductive traits could be improved by artificial selection, but very few information about genetic parameters exists for these traits in this specie. The aim of this study was to estimate genetic parameters for six reproductive traits in alpacas both in Suri (SU) and Huacaya (HU) ecotypes, as well as their genetic relationship with fiber and morphological traits. Dataset belonging to Pacomarca experimental farm collected between 2000 and 2014 was used. Number of records for age at first service (AFS), age at first calving (AFC), copulation time (CT), pregnancy diagnosis (PD), gestation length (GL), and calving interval (CI) were, respectively, 1704, 854, 19,770, 5874, 4290 and 934. Pedigree consisted of 7742 animals. Regarding reproductive traits, model of analysis included additive and residual random effects for all traits, and also permanent environmental effect for CT, PD, GL and CI traits, with color and year of recording as fixed effects for all the reproductive traits and also age at mating and sex of calf for GL trait. Estimated heritabilities, respectively for HU and SU were 0.19 and 0.09 for AFS, 0.45 and 0.59 for AFC, 0.04 and 0.05 for CT, 0.07 and 0.05 for PD, 0.12 and 0.20 for GL, and 0.14 and 0.09 for CI. Genetic correlations between them ranged from -0.96 to 0.70. No important genetic correlations were found between reproductive traits and fiber or morphological traits in HU. However, some moderate favorable genetic correlations were found between reproductive and either fiber and morphological traits in SU. According to estimated genetic correlations, some reproductive traits might be included as additional selection criteria in HU. Copyright © 2015 Elsevier B.V. All rights reserved.
Burkatovskaya, Yuliya Borisovna; Kabanova, T.; Khaustov, Pavel Aleksandrovich
2016-01-01
CUSUM algorithm for controlling chain state switching in the Markov modulated Poissonprocess was investigated via simulation. Recommendations concerning the parameter choice were givensubject to characteristics of the process. Procedure of the process parameter estimation was described.
Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph
2011-12-01
The reliability of biokinetic models is essential for the assessment of internal doses and a radiation risk analysis for the public and occupational workers exposed to radionuclides. In the present study, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. In the first part of the paper, the parameter uncertainty was analyzed for two biokinetic models of zirconium (Zr); one was reported by the International Commission on Radiological Protection (ICRP), and one was developed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU). In the second part of the paper, the parameter uncertainties and distributions of the Zr biokinetic models evaluated in Part I are used as the model inputs for identifying the most influential parameters in the models. Furthermore, the most influential model parameter on the integral of the radioactivity of Zr over 50 y in source organs after ingestion was identified. The results of the systemic HMGU Zr model showed that over the first 10 d, the parameters of transfer rates between blood and other soft tissues have the largest influence on the content of Zr in the blood and the daily urinary excretion; however, after day 1,000, the transfer rate from bone to blood becomes dominant. For the retention in bone, the transfer rate from blood to bone surfaces has the most influence out to the endpoint of the simulation; the transfer rate from blood to the upper larger intestine contributes a lot in the later days; i.e., after day 300. The alimentary tract absorption factor (fA) influences mostly the integral of radioactivity of Zr in most source organs after ingestion.
Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph
2011-12-01
The reliability of biokinetic models is essential in internal dose assessments and radiation risk analysis for the public, occupational workers, and patients exposed to radionuclides. In this paper, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. The paper is divided into two parts. In the first part of the study published here, the uncertainty sources of the model parameters for zirconium (Zr), developed by the International Commission on Radiological Protection (ICRP), were identified and analyzed. Furthermore, the uncertainty of the biokinetic experimental measurement performed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU) for developing a new biokinetic model of Zr was analyzed according to the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. The confidence interval and distribution of model parameters of the ICRP and HMGU Zr biokinetic models were evaluated. As a result of computer biokinetic modelings, the mean, standard uncertainty, and confidence interval of model prediction calculated based on the model parameter uncertainty were presented and compared to the plasma clearance and urinary excretion measured after intravenous administration. It was shown that for the most important compartment, the plasma, the uncertainty evaluated for the HMGU model was much smaller than that for the ICRP model; that phenomenon was observed for other organs and tissues as well. The uncertainty of the integral of the radioactivity of Zr up to 50 y calculated by the HMGU model after ingestion by adult members of the public was shown to be smaller by a factor of two than that of the ICRP model. It was also shown that the distribution type of the model parameter strongly influences the model prediction, and the correlation of the model input parameters affects the model prediction to a
Estimating Between-Person and Within-Person Subscore Reliability with Profile Analysis.
Bulut, Okan; Davison, Mark L; Rodriguez, Michael C
2017-01-01
Subscores are of increasing interest in educational and psychological testing due to their diagnostic function for evaluating examinees' strengths and weaknesses within particular domains of knowledge. Previous studies about the utility of subscores have mostly focused on the overall reliability of individual subscores and ignored the fact that subscores should be distinct and have added value over the total score. This study introduces a profile reliability approach that partitions the overall subscore reliability into within-person and between-person subscore reliability. The estimation of between-person reliability and within-person reliability coefficients is demonstrated using subscores from number-correct scoring, unidimensional and multidimensional item response theory scoring, and augmented scoring approaches via a simulation study and a real data study. The effects of various testing conditions, such as subtest length, correlations among subscores, and the number of subtests, are examined. Results indicate that there is a substantial trade-off between within-person and between-person reliability of subscores. Profile reliability coefficients can be useful in determining the extent to which subscores provide distinct and reliable information under various testing conditions.
Integrated Reliability Estimation of a Nuclear Maintenance Robot including a Software
Energy Technology Data Exchange (ETDEWEB)
Eom, Heung Seop; Kim, Jae Hee; Jeong, Kyung Min [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2011-10-15
Conventional reliability estimation techniques such as Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Markov Model, and Event Tree Analysis (ETA) have been used widely and approved in some industries. Then there are some limitations when we use them for a complicate robot systems including software such as intelligent reactor inspection robots. Therefore an expert's judgment plays an important role in estimating the reliability of a complicate system in practice, because experts can deal with diverse evidence related to the reliability and then perform an inference based on them. The proposed method in this paper combines qualitative and quantitative evidences and performs an inference like experts. Furthermore, it does the work in a formal and in a quantitative way unlike human experts, by the benefits of Bayesian Nets (BNs)
Directory of Open Access Journals (Sweden)
Yu. A. Ezrokhi
2017-01-01
Full Text Available The paper considers methodological approaches to the mathematical models (MM of various levels, dedicated to estimate an impact of the entrance flow heterogeneity on the main parameters and performances of the aviation GTE and it units. By an example of calculation of a twin-shaft turbofan engine in cruiser mode, demonstrates engineering mathematical model capabilities to define the impact of the total pressure field distortion on engine trust and air flow parameters, and also gas dynamic stability margin of the both compressors.It is shown that the presented first level mathematical model allows us to estimate sufficiently the impact of entrance total pressure heterogeneity on the engine parameters. Here reliability of calculations is proved to be true by their comparison with the results, obtained owing to well fulfilled 2D & 3D mathematical models of the engine, which have been repeatedly identified by the results of experiments.It is shown that received results including those on decreasing values of stability margin of both compressors can be used for tentative estimates when choosing a desirable stability margin, providing steady operation of compressors and engine in an entire range of its operating modes. Carrying out a definitive testing calculation using the specialized engine MM of a higher level will not only confirm the results obtained, but also reduce their expected error with regard to the real values reached as a result of tests.
When celibacy matters: incorporating non-breeders improves demographic parameter estimates.
Pardo, Deborah; Weimerskirch, Henri; Barbraud, Christophe
2013-01-01
In long-lived species only a fraction of a population breeds at a given time. Non-breeders can represent more than half of adult individuals, calling in doubt the relevance of estimating demographic parameters from the sole breeders. Here we demonstrate the importance of considering observable non-breeders to estimate reliable demographic traits: survival, return, breeding, hatching and fledging probabilities. We study the long-lived quasi-biennial breeding wandering albatross (Diomedea exulans). In this species, the breeding cycle lasts almost a year and birds that succeed a given year tend to skip the next breeding occasion while birds that fail tend to breed again the following year. Most non-breeders remain unobservable at sea, but still a substantial number of observable non-breeders (ONB) was identified on breeding sites. Using multi-state capture-mark-recapture analyses, we used several measures to compare the performance of demographic estimates between models incorporating or ignoring ONB: bias (difference in mean), precision (difference is standard deviation) and accuracy (both differences in mean and standard deviation). Our results highlight that ignoring ONB leads to bias and loss of accuracy on breeding probability and survival estimates. These effects are even stronger when studied in an age-dependent framework. Biases on breeding probabilities and survival increased with age leading to overestimation of survival at old age and thus actuarial senescence and underestimation of reproductive senescence. We believe our study sheds new light on the difficulties of estimating demographic parameters in species/taxa where a significant part of the population does not breed every year. Taking into account ONB appeared important to improve demographic parameter estimates, models of population dynamics and evolutionary conclusions regarding senescence within and across taxa.
An Empirical Study of Parameter Estimation for Stated Preference Experimental Design
Directory of Open Access Journals (Sweden)
Fei Yang
2014-01-01
Full Text Available The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.
Genetic parameter estimation of reproductive traits of Litopenaeus vannamei
Tan, Jian; Kong, Jie; Cao, Baoxiang; Luo, Kun; Liu, Ning; Meng, Xianhong; Xu, Shengyu; Guo, Zhaojia; Chen, Guoliang; Luan, Sheng
2017-02-01
In this study, the heritability, repeatability, phenotypic correlation, and genetic correlation of the reproductive and growth traits of L. vannamei were investigated and estimated. Eight traits of 385 shrimps from forty-two families, including the number of eggs (EN), number of nauplii (NN), egg diameter (ED), spawning frequency (SF), spawning success (SS), female body weight (BW) and body length (BL) at insemination, and condition factor (K), were measured,. A total of 519 spawning records including multiple spawning and 91 no spawning records were collected. The genetic parameters were estimated using an animal model, a multinomial logit model (for SF), and a sire-dam and probit model (for SS). Because there were repeated records, permanent environmental effects were included in the models. The heritability estimates for BW, BL, EN, NN, ED, SF, SS, and K were 0.49 ± 0.14, 0.51 ± 0.14, 0.12 ± 0.08, 0, 0.01 ± 0.04, 0.06 ± 0.06, 0.18 ± 0.07, and 0.10 ± 0.06, respectively. The genetic correlation was 0.99 ± 0.01 between BW and BL, 0.90 ± 0.19 between BW and EN, 0.22 ± 0.97 between BW and ED, -0.77 ± 1.14 between EN and ED, and -0.27 ± 0.36 between BW and K. The heritability of EN estimated without a covariate was 0.12 ± 0.08, and the genetic correlation was 0.90 ± 0.19 between BW and EN, indicating that improving BW may be used in selection programs to genetically improve the reproductive output of L. vannamei during the breeding. For EN, the data were also analyzed using body weight as a covariate (EN-2). The heritability of EN-2 was 0.03 ± 0.05, indicating that it is difficult to improve the reproductive output by genetic improvement. Furthermore, excessive pursuit of this selection is often at the expense of growth speed. Therefore, the selection of high-performance spawners using BW and SS may be an important strategy to improve nauplii production.
Steven E. Stemler
2004-01-01
This article argues that the general practice of describing interrater reliability as a single, unified concept is..at best imprecise, and at worst potentially misleading. Rather than representing a single concept, different..statistical methods for computing interrater reliability can be more accurately classified into one of three..categories based upon the underlying goals of analysis. The three general categories introduced and..described in this paper are: 1) consensus estimates, 2) cons...
Reliability estimation for multiunit nuclear and fossil-fired industrial energy systems
International Nuclear Information System (INIS)
Sullivan, W.G.; Wilson, J.V.; Klepper, O.H.
1977-01-01
As petroleum-based fuels grow increasingly scarce and costly, nuclear energy may become an important alternative source of industrial energy. Initial applications would most likely include a mix of fossil-fired and nuclear sources of process energy. A means for determining the overall reliability of these mixed systems is a fundamental aspect of demonstrating their feasibility to potential industrial users. Reliability data from nuclear and fossil-fired plants are presented, and several methods of applying these data for calculating the reliability of reasonably complex industrial energy supply systems are given. Reliability estimates made under a number of simplifying assumptions indicate that multiple nuclear units or a combination of nuclear and fossil-fired plants could provide adequate reliability to meet industrial requirements for continuity of service
The relationship between cost estimates reliability and BIM adoption: SEM analysis
Ismail, N. A. A.; Idris, N. H.; Ramli, H.; Rooshdi, R. R. Raja Muhammad; Sahamir, S. R.
2018-02-01
This paper presents the usage of Structural Equation Modelling (SEM) approach in analysing the effects of Building Information Modelling (BIM) technology adoption in improving the reliability of cost estimates. Based on the questionnaire survey results, SEM analysis using SPSS-AMOS application examined the relationships between BIM-improved information and cost estimates reliability factors, leading to BIM technology adoption. Six hypotheses were established prior to SEM analysis employing two types of SEM models, namely the Confirmatory Factor Analysis (CFA) model and full structural model. The SEM models were then validated through the assessment on their uni-dimensionality, validity, reliability, and fitness index, in line with the hypotheses tested. The final SEM model fit measures are: P-value=0.000, RMSEA=0.0790.90, TLI=0.956>0.90, NFI=0.935>0.90 and ChiSq/df=2.259; indicating that the overall index values achieved the required level of model fitness. The model supports all the hypotheses evaluated, confirming that all relationship exists amongst the constructs are positive and significant. Ultimately, the analysis verified that most of the respondents foresee better understanding of project input information through BIM visualization, its reliable database and coordinated data, in developing more reliable cost estimates. They also perceive to accelerate their cost estimating task through BIM adoption.
On estimation of reliability of a nuclear power plant with tokamak reactor
International Nuclear Information System (INIS)
Klemin, A.I.; Smetannikov, V.P.; Shiverskij, E.A.
1982-01-01
The results of the analysis of INTOR plant reliability are presented. The first stage of the analysis consists in the calculation of the INTOR plant structural reliability factors (15 ibs main systems have been considered). For each system the failure flow parameter (W(1/h)) and operational readiness Ksub(r) have been determined which for the plant as a whole besides these factors-technological utilization coefficient Ksub(TU) and mean-cycles-between failures Tsub(o). The second stage of the reliability analysis consists in investigating methods of improving its reliability factors reratively to the one calculated at the first level stage. It is shown that the reliability of the whole plant to the most essential extent is determined by the power supply system reliability. The following as to the influence extent on the INTOR plant reliability is the cryogenic system. Calculations of the INTOR plant reliability factors have given the following values: W=4,5x10 -3 1/h. Tsub(o)=152 h, Ksub(r)=0,71, Ksub(TU)=o,4 g
International Nuclear Information System (INIS)
Pross, G.
Possibilities of optimizing focus machines with a given energy content in the sense of high neutron yield and high reliability of the discharges are investigated experimentally. For this purpose, a focus machine of the Mather type with an energy content of 12 kJ was constructed. The following experimental parameters were varied: the material of the insulator in the ignition zone, the structure of the outside electrode, the length of the inside electrode, the filling pressure and the amount and polarity of the battery voltage. An important part of the diagnostic program consists of measurements of the azimuthal and axial current distribution in the accelerator, correlated with short-term photographs of the luminous front as a function of time. The results are given. A functional schematic has been drafted for focus discharge as an aid in extensive optimization of focus machines, combining findings from theory and experiments. The schematic takes into account the multiparameter character of the discharge and clarifies relationships between the experimental parameters and the target variables neutron yield and reliability
Kiuchi, R.; Mori, J. J.
2015-12-01
As a way to understand the characteristics of the earthquake source, studies of source parameters (such as radiated energy and stress drop) and their scaling are important. In order to estimate source parameters reliably, often we must use appropriate source spectrum models and the omega-square model is most frequently used. In this model, the spectrum is flat in lower frequencies and the falloff is proportional to the angular frequency squared. However, Some studies (e.g. Allmann and Shearer, 2009; Yagi et al., 2012) reported that the exponent of the high frequency falloff is other than -2. Therefore, in this study we estimate the source parameters using a spectral model for which the falloff exponent is not fixed. We analyze the mainshock and larger aftershocks of the 2008 Iwate-Miyagi Nairiku earthquake. Firstly, we calculate the P wave and SH wave spectra using empirical Green functions (EGF) to remove the path effect (such as attenuation) and site effect. For the EGF event, we select a smaller earthquake that is highly-correlated with the target event. In order to obtain the stable results, we calculate the spectral ratios using a multitaper spectrum analysis (Prieto et al., 2009). Then we take a geometric mean from multiple stations. Finally, using the obtained spectra ratios, we perform a grid search to determine the high frequency falloffs, as well as corner frequency of both of events. Our results indicate the high frequency falloff exponent is often less than 2.0. We do not observe any regional, focal mechanism, or depth dependencies for the falloff exponent. In addition, our estimated corner frequencies and falloff exponents are consistent between the P wave and SH wave analysis. In our presentation, we show differences in estimated source parameters using a fixed omega-square model and a model allowing variable high-frequency falloff.
AUTOMATIC ESTIMATION OF SIZE PARAMETERS USING VERIFIED COMPUTERIZED STEREOANALYSIS
Directory of Open Access Journals (Sweden)
Peter R Mouton
2011-05-01
Full Text Available State-of-the-art computerized stereology systems combine high-resolution video microscopy and hardwaresoftware integration with stereological methods to assist users in quantifying multidimensional parameters of importance to biomedical research, including volume, surface area, length, number, their variation and spatial distribution. The requirement for constant interactions between a trained, non-expert user and the targeted features of interest currently limits the throughput efficiency of these systems. To address this issue we developed a novel approach for automatic stereological analysis of 2-D images, Verified Computerized Stereoanalysis (VCS. The VCS approach minimizes the need for user interactions with high contrast [high signal-to-noise ratio (S:N] biological objects of interest. Performance testing of the VCS approach confirmed dramatic increases in the efficiency of total object volume (size estimation, without a loss of accuracy or precision compared to conventional computerized stereology. The broad application of high efficiency VCS to high-contrast biological objects on tissue sections could reduce labor costs, enhance hypothesis testing, and accelerate the progress of biomedical research focused on improvements in health and the management of disease.
Directory of Open Access Journals (Sweden)
Kwan-Shik Shim
2017-04-01
Full Text Available This paper describes a multiple time interval (“multi-interval” parameter estimation method. The multi-interval parameter estimation method estimates a parameter from a new multi-interval prediction error polynomial that can simultaneously consider multiple time intervals. The root of the multi-interval prediction error polynomial includes the effect on each time interval, and the important mode can be estimated by solving one polynomial for multiple time intervals or signals. The algorithm of the multi-interval parameter estimation method proposed in this paper is applied to the test function and the data measured from a PMU (phasor measurement unit installed in the KEPCO (Korea Electric Power Corporation system. The results confirm that the proposed multi-interval parameter estimation method accurately and reliably estimates important parameters.
Estimated Value of Service Reliability for Electric Utility Customers in the United States
Energy Technology Data Exchange (ETDEWEB)
Sullivan, M.J.; Mercurio, Matthew; Schellenberg, Josh
2009-06-01
Information on the value of reliable electricity service can be used to assess the economic efficiency of investments in generation, transmission and distribution systems, to strategically target investments to customer segments that receive the most benefit from system improvements, and to numerically quantify the risk associated with different operating, planning and investment strategies. This paper summarizes research designed to provide estimates of the value of service reliability for electricity customers in the US. These estimates were obtained by analyzing the results from 28 customer value of service reliability studies conducted by 10 major US electric utilities over the 16 year period from 1989 to 2005. Because these studies used nearly identical interruption cost estimation or willingness-to-pay/accept methods it was possible to integrate their results into a single meta-database describing the value of electric service reliability observed in all of them. Once the datasets from the various studies were combined, a two-part regression model was used to estimate customer damage functions that can be generally applied to calculate customer interruption costs per event by season, time of day, day of week, and geographical regions within the US for industrial, commercial, and residential customers. Estimated interruption costs for different types of customers and of different duration are provided. Finally, additional research and development designed to expand the usefulness of this powerful database and analysis are suggested.
Energy Technology Data Exchange (ETDEWEB)
Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
International Nuclear Information System (INIS)
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-01-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm
Xiao, Ning-Cong; Li, Yan-Feng; Wang, Zhonglai; Peng, Weiwen; Huang, Hong-Zhong
2013-01-01
In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to cal...
Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates
Todorovic, Andrijana; Plavsic, Jasna
2015-04-01
A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters
Taylor, Jennifer C; Sutter, Carolyn; Ontai, Lenna L; Nishina, Adrienne; Zidenberg-Cherr, Sheri
2018-01-01
Although increasing attention is placed on the quality of foods in children's packed lunches, few studies have examined the capacity of observational methods to reliably determine both what is selected and consumed from these lunches. The objective of this project was to assess the feasibility and inter-rater reliability of digital imaging for determining selection and consumption from students' packed lunches, by adapting approaches previously applied to school lunches. Study 1 assessed feasibility and reliability of data collection among a sample of packed lunches (n = 155), while Study 2 further examined reliability in a larger sample of packed (n = 386) as well as school (n = 583) lunches. Based on the results from Study 1, it was feasible to collect and code most items in packed lunch images; missing data were most commonly attributed to packaging that limited visibility of contents. Across both studies, there was satisfactory reliability for determining food types selected, quantities selected, and quantities consumed in the eight food categories examined (weighted kappa coefficients 0.68-0.97 for packed lunches, 0.74-0.97 for school lunches), with lowest reliability for estimating condiments and meats/meat alternatives in packed lunches. In extending methods predominately applied to school lunches, these findings demonstrate the capacity of digital imaging for the objective estimation of selection and consumption from both school and packed lunches. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Donald D. Anderson
2012-01-01
Full Text Available Recent findings suggest that contact stress is a potent predictor of subsequent symptomatic osteoarthritis development in the knee. However, much larger numbers of knees (likely on the order of hundreds, if not thousands need to be reliably analyzed to achieve the statistical power necessary to clarify this relationship. This study assessed the reliability of new semiautomated computational methods for estimating contact stress in knees from large population-based cohorts. Ten knees of subjects from the Multicenter Osteoarthritis Study were included. Bone surfaces were manually segmented from sequential 1.0 Tesla magnetic resonance imaging slices by three individuals on two nonconsecutive days. Four individuals then registered the resulting bone surfaces to corresponding bone edges on weight-bearing radiographs, using a semi-automated algorithm. Discrete element analysis methods were used to estimate contact stress distributions for each knee. Segmentation and registration reliabilities (day-to-day and interrater for peak and mean medial and lateral tibiofemoral contact stress were assessed with Shrout-Fleiss intraclass correlation coefficients (ICCs. The segmentation and registration steps of the modeling approach were found to have excellent day-to-day (ICC 0.93–0.99 and good inter-rater reliability (0.84–0.97. This approach for estimating compartment-specific tibiofemoral contact stress appears to be sufficiently reliable for use in large population-based cohorts.
Directory of Open Access Journals (Sweden)
N. A. Siddiqui
2011-06-01
Full Text Available Underground concrete barriers are frequently used to protect strategic structures like Nuclear power plants (NPP, deep under the soil against any possible high velocity missile impact. For a given range and type of missile (or projectile it is of paramount importance to examine the reliability of underground concrete barriers under expected uncertainties involved in the missile, concrete, and soil parameters. In this paper, a simple procedure for the reliability assessment of underground concrete barriers against normal missile impact has been presented using the First Order Reliability Method (FORM. The presented procedure is illustrated by applying it to a concrete barrier that lies at a certain depth in the soil. Some parametric studies are also conducted to obtain the design values which make the barrier as reliable as desired.
Automated Modal Parameter Estimation of Civil Engineering Structures
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Goursat, Maurice
In this paper the problems of doing automatic modal parameter extraction of ambient excited civil engineering structures is considered. Two different approaches for obtaining the modal parameters automatically are presented: The Frequency Domain Decomposition (FDD) technique and a correlation...
ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS
Directory of Open Access Journals (Sweden)
Z.-G. Zhou
2016-06-01
Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.
Estimation of uranium migration parameters in sandstone aquifers.
Malov, A I
2016-03-01
The chemical composition and isotopes of carbon and uranium were investigated in groundwater samples that were collected from 16 wells and 2 sources in the Northern Dvina Basin, Northwest Russia. Across the dataset, the temperatures in the groundwater ranged from 3.6 to 6.9 °C, the pH ranged from 7.6 to 9.0, the Eh ranged from -137 to +128 mV, the total dissolved solids (TDS) ranged from 209 to 22,000 mg L(-1), and the dissolved oxygen (DO) ranged from 0 to 9.9 ppm. The (14)C activity ranged from 0 to 69.96 ± 0.69 percent modern carbon (pmC). The uranium content in the groundwater ranged from 0.006 to 16 ppb, and the (234)U:(238)U activity ratio ranged from 1.35 ± 0.21 to 8.61 ± 1.35. The uranium concentration and (234)U:(238)U activity ratio increased from the recharge area to the redox barrier; behind the barrier, the uranium content is minimal. The results were systematized by creating a conceptual model of the Northern Dvina Basin's hydrogeological system. The use of uranium isotope dating in conjunction with radiocarbon dating allowed the determination of important water-rock interaction parameters, such as the dissolution rate:recoil loss factor ratio Rd:p (a(-1)) and the uranium retardation factor:recoil loss factor ratio R:p in the aquifer. The (14)C age of the water was estimated to be between modern and >35,000 years. The (234)U-(238)U age of the water was estimated to be between 260 and 582,000 years. The Rd:p ratio decreases with increasing groundwater residence time in the aquifer from n × 10(-5) to n × 10(-7) a(-1). This finding is observed because the TDS increases in that direction from 0.2 to 9 g L(-1), and accordingly, the mineral saturation indices increase. Relatively high values of R:p (200-1000) characterize aquifers in sandy-clayey sediments from the Late Pleistocene and the deepest parts of the Vendian strata. In samples from the sandstones of the upper part of the Vendian strata, the R:p value is ∼ 24, i.e., sorption processes are
Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs
Directory of Open Access Journals (Sweden)
Bo Jiang
2017-03-01
Full Text Available The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs. It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.
Anderson, Christian Carl
characterization of anisotropy. A novel piecewise linear model for the cyclic variation of ultrasonic backscatter from myocardium was proposed. Models of cyclic variation for 100 type 2 diabetes patients and 43 normal control subjects were constructed using Bayesian parameter estimation. Parameters determined from the model, specifically rise time and slew rate, were found to be more reliable in differentiating between subject groups than the previously employed magnitude parameter.
Estimating crop net primary production using inventory data and MODIS-derived parameters
Energy Technology Data Exchange (ETDEWEB)
Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.; Izaurralde, Roberto C.
2013-06-03
National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.
Huang, Po-Hsien; Weng, Li-Jen
2012-01-01
A procedure for estimating the reliability of test scores in the context of ecological momentary assessment (EMA) was proposed to take into account the characteristics of EMA measures. Two commonly used test scores in EMA were considered: the aggregated score (AGGS) and the within-person centered score (WPCS). Conceptually, AGGS and WPCS represent…
Nonparametric Estimation of Interval Reliability for Discrete-Time Semi-Markov Systems
DEFF Research Database (Denmark)
Georgiadis, Stylianos; Limnios, Nikolaos
2016-01-01
In this article, we consider a repairable discrete-time semi-Markov system with finite state space. The measure of the interval reliability is given as the probability of the system being operational over a given finite-length time interval. A nonparametric estimator is proposed for the interval...
Uncertainty in reliability estimation : when do we know everything we know?
Houben, M.J.H.A.; Sonnemans, P.J.M.; Newby, M.J.; Bris, R.; Guedes Soares, C.; Martorell, S.
2009-01-01
In this paperwe demonstrate the use of an adapted GroundedTheory approach through interviews and their analysis to determine explicit uncertainty (known unknowns) for reliability estimation in the early phases of product development.We have applied the adapted Grounded Theory approach in a case
Boermans, M.A.; Kattenberg, M.A.C.
2011-01-01
We show how to estimate a Cronbach's alpha reliability coefficient in Stata after running a principal component or factor analysis. Alpha evaluates to what extent items measure the same underlying content when the items are combined into a scale or used for latent variable. Stata allows for testing
Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation?
R. L. Czaplewski
2003-01-01
Tucker and Townshend (2000) conclude that wall-to-wall coverage is needed to avoid gross errors in estimations of deforestation rates' because tropical deforestation is concentrated along roads and rivers. They specifically question the reliability of the 10% sample of Landsat sensor scenes used in the global remote sensing survey conducted by the Food and...
Perceptual and Acoustic Reliability Estimates for the Speech Disorders Classification System (SDCS)
Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.
2010-01-01
A companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). The SDCS uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice. The present paper provides reliability estimates for…
Vandenplas, J.; Colinet, F.G.; Glorieux, G.; Bertozzi, C.; Gengler, N.
2015-01-01
Based on a Bayesian view of linear mixed models, several studies showed the possibilities to integrate estimated breeding values (EBV) and associated reliabilities (REL) provided by genetic evaluations performed outside a given evaluation system into this genetic evaluation. Hereafter, the term
Energy Technology Data Exchange (ETDEWEB)
Bibilashvili, Yu K; Malachenko, L L; Medvedev, A V; Solyany, V I; Sukhanov, G I; Tonkov, V Yu
1987-05-01
Present approach to requirements for reference parameters and properties of materials for WWER-1000 fuel elements is presented as well as evaluation of their effects on fuel reliability. Some results of investigations with the aim of improving fuel element reliability in operational NPP conditions are discussed. 4 references, 7 figures, 3 tables.
International Nuclear Information System (INIS)
Bibilashvili, Yu.K.; Malachenko, L.L.; Medvedev, A.V.; Solyany, V.I.; Sukhanov, G.I.; Tonkov, V.Yu.
1987-01-01
Present approach to requirements for reference parameters and properties of materials for WWER-1000 fuel elements is presented as well as evaluation of their effects on fuel reliability. Some results of investigations with the aim of improving fuel element reliability in operational NPP conditions are discussed. (author)
DEFF Research Database (Denmark)
Sørensen, Joan Solgaard; Kjaer, Per; Jensen, Tue Secher
2006-01-01
PURPOSE: To determine the intra- and interobserver reliability in grading disc and muscle parameters using low-field magnetic resonance imaging (MRI). MATERIAL AND METHODS: MRI scans of 100 subjects representative of the general population were evaluated blindly by two radiologists. Criteria......: Convincing reliability was found in the evaluation of disc- and muscle-related MRI variables....
Kamiaka, Shoya; Benomar, Othman; Suto, Yasushi
2018-05-01
Advances in asteroseismology of solar-like stars, now provide a unique method to estimate the stellar inclination i⋆. This enables to evaluate the spin-orbit angle of transiting planetary systems, in a complementary fashion to the Rossiter-McLaughlineffect, a well-established method to estimate the projected spin-orbit angle λ. Although the asteroseismic method has been broadly applied to the Kepler data, its reliability has yet to be assessed intensively. In this work, we evaluate the accuracy of i⋆ from asteroseismology of solar-like stars using 3000 simulated power spectra. We find that the low signal-to-noise ratio of the power spectra induces a systematic under-estimate (over-estimate) bias for stars with high (low) inclinations. We derive analytical criteria for the reliable asteroseismic estimate, which indicates that reliable measurements are possible in the range of 20° ≲ i⋆ ≲ 80° only for stars with high signal-to-noise ratio. We also analyse and measure the stellar inclination of 94 Kepler main-sequence solar-like stars, among which 33 are planetary hosts. According to our reliability criteria, a third of them (9 with planets, 22 without) have accurate stellar inclination. Comparison of our asteroseismic estimate of vsin i⋆ against spectroscopic measurements indicates that the latter suffers from a large uncertainty possibly due to the modeling of macro-turbulence, especially for stars with projected rotation speed vsin i⋆ ≲ 5km/s. This reinforces earlier claims, and the stellar inclination estimated from the combination of measurements from spectroscopy and photometric variation for slowly rotating stars needs to be interpreted with caution.
International Nuclear Information System (INIS)
He Jie; Zhang Binbin
2013-01-01
In the probabilistic safety assessment (PSA) of nuclear power plants, there are few historical records on some initiating event frequencies or component failures in industry. In order to determine the noninformative priors of such reliability parameters and initiating event frequencies, the Jeffreys method in Bayesian statistics was employed. The mathematical mechanism of the Jeffreys prior and the simplified constrained noninformative distribution (SCNID) were elaborated in this paper. The Jeffreys noninformative formulas and the credible intervals of the Gamma-Poisson and Beta-Binomial models were introduced. As an example, the small break loss-of-coolant accident (SLOCA) was employed to show the application of the Jeffreys prior in determining an initiating event frequency. The result shows that the Jeffreys method is an effective method for noninformative prior calculation. (authors)
Impact of power plant reliability on the choice of operating parameter values
International Nuclear Information System (INIS)
Kramer, R.A.
1985-01-01
In this thesis, the basic structure for the development of a methodology to evaluate the effect of operating parameters on plant availability and generating system economic dispatch optimization is described. Plant availability is determined by a fault free model. In this model historic, time dependent, component induced forced outage data is utilized as the basis for the calculation of projected plant forced outage rates. The influence of a particular fuel-cycle length at a specific generating station on the operational planning of a multi unit generating system is considered. The basis of the dispatch of units in this analysis is optimal economic operation, i.e., the minimization of the cost of reliability supplying electricity to the system's customers. As a result of the utilization of this technique, a simplified example that considers the choice between a 12- and 18-month fuel cycle length is evaluated in terms of its impact on plant availability, fuel cycle economics and overall optimal generating system economic dispatch. The reliability portion of this methodology is applied to a simplified representation of the recirculation system of a pressurized water reactor nuclear power plant to illustrate the analytic techniques
Kim, Sooyeon; Livingston, Samuel A.
2017-01-01
The purpose of this simulation study was to assess the accuracy of a classical test theory (CTT)-based procedure for estimating the alternate-forms reliability of scores on a multistage test (MST) having 3 stages. We generated item difficulty and discrimination parameters for 10 parallel, nonoverlapping forms of the complete 3-stage test and…
α-Decomposition for estimating parameters in common cause failure modeling based on causal inference
International Nuclear Information System (INIS)
Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi
2013-01-01
The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system
Reliability analysis based on a novel density estimation method for structures with correlations
Directory of Open Access Journals (Sweden)
Baoyu LI
2017-06-01
Full Text Available Estimating the Probability Density Function (PDF of the performance function is a direct way for structural reliability analysis, and the failure probability can be easily obtained by integration in the failure domain. However, efficiently estimating the PDF is still an urgent problem to be solved. The existing fractional moment based maximum entropy has provided a very advanced method for the PDF estimation, whereas the main shortcoming is that it limits the application of the reliability analysis method only to structures with independent inputs. While in fact, structures with correlated inputs always exist in engineering, thus this paper improves the maximum entropy method, and applies the Unscented Transformation (UT technique to compute the fractional moments of the performance function for structures with correlations, which is a very efficient moment estimation method for models with any inputs. The proposed method can precisely estimate the probability distributions of performance functions for structures with correlations. Besides, the number of function evaluations of the proposed method in reliability analysis, which is determined by UT, is really small. Several examples are employed to illustrate the accuracy and advantages of the proposed method.
Recursive Parameter Identification for Estimating and Displaying Maneuvering Vessel Path
National Research Council Canada - National Science Library
Pullard, Stephen
2003-01-01
...). The extended least squares (ELS) parameter identification approach allows the system to be installed on most platforms without prior knowledge of system dynamics provided vessel states are available...
Marginal likelihood estimation of negative binomial parameters with applications to RNA-seq data.
León-Novelo, Luis; Fuentes, Claudio; Emerson, Sarah
2017-10-01
RNA-Seq data characteristically exhibits large variances, which need to be appropriately accounted for in any proposed model. We first explore the effects of this variability on the maximum likelihood estimator (MLE) of the dispersion parameter of the negative binomial distribution, and propose instead to use an estimator obtained via maximization of the marginal likelihood in a conjugate Bayesian framework. We show, via simulation studies, that the marginal MLE can better control this variation and produce a more stable and reliable estimator. We then formulate a conjugate Bayesian hierarchical model, and use this new estimator to propose a Bayesian hypothesis test to detect differentially expressed genes in RNA-Seq data. We use numerical studies to show that our much simpler approach is competitive with other negative binomial based procedures, and we use a real data set to illustrate the implementation and flexibility of the procedure. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
An automated method for estimating reliability of grid systems using Bayesian networks
International Nuclear Information System (INIS)
Doguc, Ozge; Emmanuel Ramirez-Marquez, Jose
2012-01-01
Grid computing has become relevant due to its applications to large-scale resource sharing, wide-area information transfer, and multi-institutional collaborating. In general, in grid computing a service requests the use of a set of resources, available in a grid, to complete certain tasks. Although analysis tools and techniques for these types of systems have been studied, grid reliability analysis is generally computation-intensive to obtain due to the complexity of the system. Moreover, conventional reliability models have some common assumptions that cannot be applied to the grid systems. Therefore, new analytical methods are needed for effective and accurate assessment of grid reliability. This study presents a new method for estimating grid service reliability, which does not require prior knowledge about the grid system structure unlike the previous studies. Moreover, the proposed method does not rely on any assumptions about the link and node failure rates. This approach is based on a data-mining algorithm, the K2, to discover the grid system structure from raw historical system data, that allows to find minimum resource spanning trees (MRST) within the grid then, uses Bayesian networks (BN) to model the MRST and estimate grid service reliability.
Multiple-hit parameter estimation in monolithic detectors.
Hunter, William C J; Barrett, Harrison H; Lewellen, Tom K; Miyaoka, Robert S
2013-02-01
We examine a maximum-a-posteriori method for estimating the primary interaction position of gamma rays with multiple interaction sites (hits) in a monolithic detector. In assessing the performance of a multiple-hit estimator over that of a conventional one-hit estimator, we consider a few different detector and readout configurations of a 50-mm-wide square cerium-doped lutetium oxyorthosilicate block. For this study, we use simulated data from SCOUT, a Monte-Carlo tool for photon tracking and modeling scintillation- camera output. With this tool, we determine estimate bias and variance for a multiple-hit estimator and compare these with similar metrics for a one-hit maximum-likelihood estimator, which assumes full energy deposition in one hit. We also examine the effect of event filtering on these metrics; for this purpose, we use a likelihood threshold to reject signals that are not likely to have been produced under the assumed likelihood model. Depending on detector design, we observe a 1%-12% improvement of intrinsic resolution for a 1-or-2-hit estimator as compared with a 1-hit estimator. We also observe improved differentiation of photopeak events using a 1-or-2-hit estimator as compared with the 1-hit estimator; more than 6% of photopeak events that were rejected by likelihood filtering for the 1-hit estimator were accurately identified as photopeak events and positioned without loss of resolution by a 1-or-2-hit estimator; for PET, this equates to at least a 12% improvement in coincidence-detection efficiency with likelihood filtering applied.
Single-Channel Blind Estimation of Reverberation Parameters
DEFF Research Database (Denmark)
Doire, C.S.J.; Brookes, M. D.; Naylor, P. A.
2015-01-01
The reverberation of an acoustic channel can be characterised by two frequency-dependent parameters: the reverberation time and the direct-to-reverberant energy ratio. This paper presents an algorithm for blindly determining these parameters from a single-channel speech signal. The algorithm uses...
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...