Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

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.

Modal Parameter Identification from Responses of General Unknown Random Inputs

DEFF Research Database (Denmark)

Ibrahim, S. R.; Asmussen, J. C.; Brincker, Rune

1996-01-01

Modal parameter identification from ambient responses due to a general unknown random inputs is investigated. Existing identification techniques which are based on assumptions of white noise and or stationary random inputs are utilized even though the inputs conditions are not satisfied....... This is accomplished via adding. In cascade. A force cascade conversion to the structures system under consideration. The input to the force conversion system is white noise and the output of which is the actual force(s) applied to the structure. The white noise input(s) and the structures responses are then used...

On Optimal Input Design and Model Selection for Communication Channels

Energy Technology Data Exchange (ETDEWEB)

Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

2013-01-01

In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

Adaptive distributed parameter and input estimation in linear parabolic PDEs

KAUST Repository

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.

Inhalation Exposure Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

M. A. Wasiolek

2003-01-01

This analysis is one of the nine reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2003a) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents a set of input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for a Yucca Mountain repository. This report, ''Inhalation Exposure Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003b). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available at that time. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this analysis report. This analysis report defines and justifies values of mass loading, which is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Measurements of mass loading are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air surrounding crops and concentrations in air inhaled by a receptor. Concentrations in air to which the

Inhalation Exposure Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. Wasiolek

2006-06-05

This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This

Inhalation Exposure Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

M. Wasiolek

2006-01-01

This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This report is concerned primarily with the

A summary of the sources of input parameter values for the Waste Isolation Pilot Plant final porosity surface calculations

International Nuclear Information System (INIS)

Butcher, B.M.

1997-08-01

A summary of the input parameter values used in final predictions of closure and waste densification in the Waste Isolation Pilot Plant disposal room is presented, along with supporting references. These predictions are referred to as the final porosity surface data and will be used for WIPP performance calculations supporting the Compliance Certification Application to be submitted to the U.S. Environmental Protection Agency. The report includes tables and list all of the input parameter values, references citing their source, and in some cases references to more complete descriptions of considerations leading to the selection of values

A summary of the sources of input parameter values for the Waste Isolation Pilot Plant final porosity surface calculations

Energy Technology Data Exchange (ETDEWEB)

Butcher, B.M.

1997-08-01

A summary of the input parameter values used in final predictions of closure and waste densification in the Waste Isolation Pilot Plant disposal room is presented, along with supporting references. These predictions are referred to as the final porosity surface data and will be used for WIPP performance calculations supporting the Compliance Certification Application to be submitted to the U.S. Environmental Protection Agency. The report includes tables and list all of the input parameter values, references citing their source, and in some cases references to more complete descriptions of considerations leading to the selection of values.

Disaggregated seismic hazard and the elastic input energy spectrum: An approach to design earthquake selection

Science.gov (United States)

Chapman, Martin Colby

1998-12-01

The design earthquake selection problem is fundamentally probabilistic. Disaggregation of a probabilistic model of the seismic hazard offers a rational and objective approach that can identify the most likely earthquake scenario(s) contributing to hazard. An ensemble of time series can be selected on the basis of the modal earthquakes derived from the disaggregation. This gives a useful time-domain realization of the seismic hazard, to the extent that a single motion parameter captures the important time-domain characteristics. A possible limitation to this approach arises because most currently available motion prediction models for peak ground motion or oscillator response are essentially independent of duration, and modal events derived using the peak motions for the analysis may not represent the optimal characterization of the hazard. The elastic input energy spectrum is an alternative to the elastic response spectrum for these types of analyses. The input energy combines the elements of amplitude and duration into a single parameter description of the ground motion that can be readily incorporated into standard probabilistic seismic hazard analysis methodology. This use of the elastic input energy spectrum is examined. Regression analysis is performed using strong motion data from Western North America and consistent data processing procedures for both the absolute input energy equivalent velocity, (Vsbea), and the elastic pseudo-relative velocity response (PSV) in the frequency range 0.5 to 10 Hz. The results show that the two parameters can be successfully fit with identical functional forms. The dependence of Vsbea and PSV upon (NEHRP) site classification is virtually identical. The variance of Vsbea is uniformly less than that of PSV, indicating that Vsbea can be predicted with slightly less uncertainty as a function of magnitude, distance and site classification. The effects of site class are important at frequencies less than a few Hertz. The regression

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

Science.gov (United States)

Morelli, Eugene A.; Klein, Vladislav

1990-01-01

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Harmonize input selection for sediment transport prediction

Science.gov (United States)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Inhalation Exposure Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. A. Wasiolek

2003-09-24

This analysis is one of the nine reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2003a) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents a set of input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for a Yucca Mountain repository. This report, ''Inhalation Exposure Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003b). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available at that time. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this analysis report. This analysis report defines and justifies values of mass loading, which is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Measurements of mass loading are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air surrounding crops and concentrations in air

Statistical Analysis of Input Parameters Impact on the Modelling of Underground Structures

Directory of Open Access Journals (Sweden)

M. Hilar

2008-01-01

Full Text Available The behaviour of a geomechanical model and its final results are strongly affected by the input parameters. As the inherent variability of rock mass is difficult to model, engineers are frequently forced to face the question “Which input values should be used for analyses?” The correct answer to such a question requires a probabilistic approach, considering the uncertainty of site investigations and variation in the ground. This paper describes the statistical analysis of input parameters for FEM calculations of traffic tunnels in the city of Prague. At the beginning of the paper, the inaccuracy in the geotechnical modelling is discussed. In the following part the Fuzzy techniques are summarized, including information about an application of the Fuzzy arithmetic on the shotcrete parameters. The next part of the paper is focused on the stochastic simulation – Monte Carlo Simulation is briefly described, Latin Hypercubes method is described more in details. At the end several practical examples are described: statistical analysis of the input parameters on the numerical modelling of the completed Mrázovka tunnel (profile West Tunnel Tube km 5.160 and modelling of the constructed tunnel Špejchar – Pelc Tyrolka. 

ECOS - analysis of sensitivity to database and input parameters

International Nuclear Information System (INIS)

Sumerling, T.J.; Jones, C.H.

1986-06-01

The sensitivity of doses calculated by the generic biosphere code ECOS to parameter changes has been investigated by the authors for the Department of the Environment as part of its radioactive waste management research programme. The sensitivity of results to radionuclide dependent parameters has been tested by specifying reasonable parameter ranges and performing code runs for best estimate, upper-bound and lower-bound parameter values. The work indicates that doses are most sensitive to scenario parameters: geosphere input fractions, area of contaminated land, land use and diet, flux of contaminated waters and water use. Recommendations are made based on the results of sensitivity. (author)

An input feature selection method applied to fuzzy neural networks for signal esitmation

International Nuclear Information System (INIS)

Na, Man Gyun; Sim, Young Rok

2001-01-01

It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

Soil-Related Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Smith, A. J.

2004-01-01

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This

On the relationship between input parameters in two-mass vocal-fold model with acoustical coupling an signal parameters of the glottal flow

NARCIS (Netherlands)

van Hirtum, Annemie; Lopez, Ines; Hirschberg, Abraham; Pelorson, Xavier

2003-01-01

In this paper the sensitivity of the two-mass model with acoustical coupling to the model input-parameters is assessed. The model-output or the glottal volume air flow is characterised by signal-parameters in the time-domain. The influence of changing input-parameters on the signal-parameters is

A Novel Coupled State/Input/Parameter Identification Method for Linear Structural Systems

Directory of Open Access Journals (Sweden)

Zhimin Wan

2018-01-01

Full Text Available In many engineering applications, unknown states, inputs, and parameters exist in the structures. However, most methods require one or two of these variables to be known in order to identify the other(s. Recently, the authors have proposed a method called EGDF for coupled state/input/parameter identification for nonlinear system in state space. However, the EGDF method based solely on acceleration measurements is found to be unstable, which can cause the drift of the identified inputs and displacements. Although some regularization methods can be adopted for solving the problem, they are not suitable for joint input-state identification in real time. In this paper, a strategy of data fusion of displacement and acceleration measurements is used to avoid the low-frequency drift in the identified inputs and structural displacements for linear structural systems. Two numerical examples about a plane truss and a single-stage isolation system are conducted to verify the effectiveness of the proposed modified EGDF algorithm.

Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

Science.gov (United States)

Quilty, J.; Adamowski, J. F.

2015-12-01

Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

Soil-related Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

A. J. Smith

2003-01-01

This analysis is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the geologic repository at Yucca Mountain. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN biosphere model is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003 [163602]). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. ''The Biosphere Model Report'' (BSC 2003 [160699]) describes in detail the conceptual model as well as the mathematical model and its input parameters. The purpose of this analysis was to develop the biosphere model parameters needed to evaluate doses from pathways associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation and ash

Soil-Related Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

A. J. Smith

2004-09-09

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure

Key processes and input parameters for environmental tritium models

International Nuclear Information System (INIS)

Bunnenberg, C.; Taschner, M.; Ogram, G.L.

1994-01-01

The primary objective of the work reported here is to define key processes and input parameters for mathematical models of environmental tritium behaviour adequate for use in safety analysis and licensing of fusion devices like NET and associated tritium handling facilities. (author). 45 refs., 3 figs

Key processes and input parameters for environmental tritium models

Energy Technology Data Exchange (ETDEWEB)

Bunnenberg, C; Taschner, M [Niedersaechsisches Inst. fuer Radiooekologie, Hannover (Germany); Ogram, G L [Ontario Hydro, Toronto, ON (Canada)

1994-12-31

The primary objective of the work reported here is to define key processes and input parameters for mathematical models of environmental tritium behaviour adequate for use in safety analysis and licensing of fusion devices like NET and associated tritium handling facilities. (author). 45 refs., 3 figs.

On the relationship between input parameters in the two-mass vocal-fold model with acoustical coupling and signal parameters of the glottal flow

NARCIS (Netherlands)

Hirtum, van A.; Lopez Arteaga, I.; Hirschberg, A.; Pelorson, X.

2003-01-01

In this paper the sensitivity of the two-mass model with acoustical coupling to the model input-parameters is assessed. The model-output or the glottal volume air flow is characterised by signal-parameters in the time-domain. The influence of changing input-parameters on the signal-parameters is

Agricultural and Environmental Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

K. Rasmuson; K. Rautenstrauch

2004-09-14

This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters.

Agricultural and Environmental Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

K. Rasmuson; K. Rautenstrauch

2004-01-01

This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters

Input parameters to codes which analyze LMFBR wire-wrapped bundles

International Nuclear Information System (INIS)

Hawley, J.T.; Chan, Y.N.; Todreas, N.E.

1980-12-01

This report provides a current summary of recommended values of key input parameters required by ENERGY code analysis of LMFBR wire wrapped bundles. This data is based on the interpretation of experimental results from the MIT and other available laboratory programs

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

International Nuclear Information System (INIS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-01-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

Abnormal externally guided movement preparation in recent-onset schizophrenia is associated with impaired selective attention to external input.

Science.gov (United States)

Smid, Henderikus G O M; Westenbroek, Joanna M; Bruggeman, Richard; Knegtering, Henderikus; Van den Bosch, Robert J

2009-11-30

Several theories propose that the primary cognitive impairment in schizophrenia concerns a deficit in the processing of external input information. There is also evidence, however, for impaired motor preparation in schizophrenia. This provokes the question whether the impaired motor preparation in schizophrenia is a secondary consequence of disturbed (selective) processing of the input needed for that preparation, or an independent primary deficit. The aim of the present study was to discriminate between these hypotheses, by investigating externally guided movement preparation in relation to selective stimulus processing. The sample comprised 16 recent-onset schizophrenia patients and 16 controls who performed a movement-precuing task. In this task, a precue delivered information about one, two or no parameters of a movement summoned by a subsequent stimulus. Performance measures and measures derived from the electroencephalogram showed that patients yielded smaller benefits from the precues and showed less cue-based preparatory activity in advance of the imperative stimulus than the controls, suggesting a response preparation deficit. However, patients also showed less activity reflecting selective attention to the precue. We therefore conclude that the existing evidence for an impairment of externally guided motor preparation in schizophrenia is most likely due to a deficit in selective attention to the external input, which lends support to theories proposing that the primary cognitive deficit in schizophrenia concerns the processing of input information.

Adaptive control of a quadrotor aerial vehicle with input constraints and uncertain parameters

Science.gov (United States)

Tran, Trong-Toan; Ge, Shuzhi Sam; He, Wei

2018-05-01

In this paper, we address the problem of adaptive bounded control for the trajectory tracking of a Quadrotor Aerial Vehicle (QAV) while the input saturations and uncertain parameters with the known bounds are simultaneously taken into account. First, to deal with the underactuated property of the QAV model, we decouple and construct the QAV model as a cascaded structure which consists of two fully actuated subsystems. Second, to handle the input constraints and uncertain parameters, we use a combination of the smooth saturation function and smooth projection operator in the control design. Third, to ensure the stability of the overall system of the QAV, we develop the technique for the cascaded system in the presence of both the input constraints and uncertain parameters. Finally, the region of stability of the closed-loop system is constructed explicitly, and our design ensures the asymptotic convergence of the tracking errors to the origin. The simulation results are provided to illustrate the effectiveness of the proposed method.

An improved robust model predictive control for linear parameter-varying input-output models

NARCIS (Netherlands)

Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

2018-01-01

This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

Distribution Development for STORM Ingestion Input Parameters

Energy Technology Data Exchange (ETDEWEB)

Fulton, John [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-07-01

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

Investigating gaze-controlled input in a cognitive selection test

OpenAIRE

Gayraud, Katja; Hasse, Catrin; Eißfeldt, Hinnerk; Pannasch, Sebastian

2017-01-01

In the field of aviation, there is a growing interest in developing more natural forms of interaction between operators and systems to enhance safety and efficiency. These efforts also include eye gaze as an input channel for human-machine interaction. The present study investigates the application of gaze-controlled input in a cognitive selection test called Eye Movement Conflict Detection Test. The test enables eye movements to be studied as an indicator for psychological test performance a...

A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model

Energy Technology Data Exchange (ETDEWEB)

Covey, C; Brandon, S; Bremer, P T; Domyancis, D; Garaizar, X; Johannesson, G; Klein, R; Klein, S A; Lucas, D D; Tannahill, J; Zhang, Y

2011-10-27

Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.

Evaluation of selected parameters on exposure rates in Westinghouse designed nuclear power plants

International Nuclear Information System (INIS)

Bergmann, C.A.

1989-01-01

During the past ten years, Westinghouse under EPRI contract and independently, has performed research and evaluation of plant data to define the trends of ex-core component exposure rates and the effects of various parameters on the exposure rates. The effects of the parameters were evaluated using comparative analyses or empirical techniques. This paper updates the information presented at the Fourth Bournemouth Conference and the conclusions obtained from the effects of selected parameters namely, coolant chemistry, physical changes, use of enriched boric acid, and cobalt input on plant exposure rates. The trends of exposure rates and relationship to doses is also presented. (author)

Assigning probability distributions to input parameters of performance assessment models

Energy Technology Data Exchange (ETDEWEB)

Mishra, Srikanta [INTERA Inc., Austin, TX (United States)

2002-02-01

This study presents an overview of various approaches for assigning probability distributions to input parameters and/or future states of performance assessment models. Specifically,three broad approaches are discussed for developing input distributions: (a) fitting continuous distributions to data, (b) subjective assessment of probabilities, and (c) Bayesian updating of prior knowledge based on new information. The report begins with a summary of the nature of data and distributions, followed by a discussion of several common theoretical parametric models for characterizing distributions. Next, various techniques are presented for fitting continuous distributions to data. These include probability plotting, method of moments, maximum likelihood estimation and nonlinear least squares analysis. The techniques are demonstrated using data from a recent performance assessment study for the Yucca Mountain project. Goodness of fit techniques are also discussed, followed by an overview of how distribution fitting is accomplished in commercial software packages. The issue of subjective assessment of probabilities is dealt with in terms of the maximum entropy distribution selection approach, as well as some common rules for codifying informal expert judgment. Formal expert elicitation protocols are discussed next, and are based primarily on the guidance provided by the US NRC. The Bayesian framework for updating prior distributions (beliefs) when new information becomes available is discussed. A simple numerical approach is presented for facilitating practical applications of the Bayes theorem. Finally, a systematic framework for assigning distributions is presented: (a) for the situation where enough data are available to define an empirical CDF or fit a parametric model to the data, and (b) to deal with the situation where only a limited amount of information is available.

Assigning probability distributions to input parameters of performance assessment models

International Nuclear Information System (INIS)

Mishra, Srikanta

2002-02-01

This study presents an overview of various approaches for assigning probability distributions to input parameters and/or future states of performance assessment models. Specifically,three broad approaches are discussed for developing input distributions: (a) fitting continuous distributions to data, (b) subjective assessment of probabilities, and (c) Bayesian updating of prior knowledge based on new information. The report begins with a summary of the nature of data and distributions, followed by a discussion of several common theoretical parametric models for characterizing distributions. Next, various techniques are presented for fitting continuous distributions to data. These include probability plotting, method of moments, maximum likelihood estimation and nonlinear least squares analysis. The techniques are demonstrated using data from a recent performance assessment study for the Yucca Mountain project. Goodness of fit techniques are also discussed, followed by an overview of how distribution fitting is accomplished in commercial software packages. The issue of subjective assessment of probabilities is dealt with in terms of the maximum entropy distribution selection approach, as well as some common rules for codifying informal expert judgment. Formal expert elicitation protocols are discussed next, and are based primarily on the guidance provided by the US NRC. The Bayesian framework for updating prior distributions (beliefs) when new information becomes available is discussed. A simple numerical approach is presented for facilitating practical applications of the Bayes theorem. Finally, a systematic framework for assigning distributions is presented: (a) for the situation where enough data are available to define an empirical CDF or fit a parametric model to the data, and (b) to deal with the situation where only a limited amount of information is available

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Directory of Open Access Journals (Sweden)

Hui Wang

2017-10-01

Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

Science.gov (United States)

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

A statistical survey of heat input parameters into the cusp thermosphere

Science.gov (United States)

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

2017-12-01

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

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

Science.gov (United States)

Chowdhury, S.; Sharma, A.

2005-12-01

Hydrological model inputs are often derived from measurements at point locations taken at discrete time steps. The nature of uncertainty associated with such inputs is thus a function of the quality and number of measurements available in time. A change in these characteristics (such as a change in the number of rain-gauge inputs used to derive spatially averaged rainfall) results in inhomogeneity in the associated distributional profile. Ignoring such uncertainty can lead to models that aim to simulate based on the observed input variable instead of the true measurement, resulting in a biased representation of the underlying system dynamics as well as an increase in both bias and the predictive uncertainty in simulations. This is especially true of cases where the nature of uncertainty likely in the future is significantly different to that in the past. Possible examples include situations where the accuracy of the catchment averaged rainfall has increased substantially due to an increase in the rain-gauge density, or accuracy of climatic observations (such as sea surface temperatures) increased due to the use of more accurate remote sensing technologies. We introduce here a method to ascertain the true value of parameters in the presence of additive uncertainty in model inputs. This method, known as SIMulation EXtrapolation (SIMEX, [Cook, 1994]) operates on the basis of an empirical relationship between parameters and the level of additive input noise (or uncertainty). The method starts with generating a series of alternate realisations of model inputs by artificially adding white noise in increasing multiples of the known error variance. The alternate realisations lead to alternate sets of parameters that are increasingly biased with respect to the truth due to the increased variability in the inputs. Once several such realisations have been drawn, one is able to formulate an empirical relationship between the parameter values and the level of additive noise

Assessment of input function distortions on kinetic model parameters in simulated dynamic 82Rb PET perfusion studies

International Nuclear Information System (INIS)

Meyer, Carsten; Peligrad, Dragos-Nicolae; Weibrecht, Martin

2007-01-01

Cardiac 82 rubidium dynamic PET studies allow quantifying absolute myocardial perfusion by using tracer kinetic modeling. Here, the accurate measurement of the input function, i.e. the tracer concentration in blood plasma, is a major challenge. This measurement is deteriorated by inappropriate temporal sampling, spillover, etc. Such effects may influence the measured input peak value and the measured blood pool clearance. The aim of our study is to evaluate the effect of input function distortions on the myocardial perfusion as estimated by the model. To this end, we simulate noise-free myocardium time activity curves (TACs) with a two-compartment kinetic model. The input function to the model is a generic analytical function. Distortions of this function have been introduced by varying its parameters. Using the distorted input function, the compartment model has been fitted to the simulated myocardium TAC. This analysis has been performed for various sets of model parameters covering a physiologically relevant range. The evaluation shows that ±10% error in the input peak value can easily lead to ±10-25% error in the model parameter K 1 , which relates to myocardial perfusion. Variations in the input function tail are generally less relevant. We conclude that an accurate estimation especially of the plasma input peak is crucial for a reliable kinetic analysis and blood flow estimation

Consultants’ Meeting on Recommended Input Parameters for Fission Cross-Section Calculations. Summary Report

International Nuclear Information System (INIS)

Capote Noy, Roberto; Simakov, Stanislav; Goriely, Stephane; Hilaire, Stephane; Iwamoto, Osamu; Kawano, Toshihiko; Koning, Arjan

2014-12-01

A Consultants’ Meeting on “Recommended Input Parameters for Fission Cross-Section Calculations” was held at IAEA Headquarters, Vienna, Austria to define the scope, deliverables and appropriate work programme of a possible Coordinated Research Project (CRP) on the subject. Presentations are available online at https://www-nds.iaea.org/indexmeeting-crp/CM-RIPL-fission/. A new CRP was endorsed to recommend a comprehensive set of fission input parameters needed for the modelling of fission cross sections. Special attention will be given to the modelling of photon and nucleon induced reactions on actinides with emphasis on incident energies below 30 MeV. The goals and detailed deliverables of the planned CRP were proposed. A Hauser-Feshbach code intercomparison was recommended. (author)

A Method to Select Software Test Cases in Consideration of Past Input Sequence

International Nuclear Information System (INIS)

Kim, Hee Eun; Kim, Bo Gyung; Kang, Hyun Gook

2015-01-01

In the Korea Nuclear I and C Systems (KNICS) project, the software for the fully-digitalized reactor protection system (RPS) was developed under a strict procedure. Even though the behavior of the software is deterministic, the randomness of input sequence produces probabilistic behavior of software. A software failure occurs when some inputs to the software occur and interact with the internal state of the digital system to trigger a fault that was introduced into the software during the software lifecycle. In this paper, the method to select test set for software failure probability estimation is suggested. This test set reflects past input sequence of software, which covers all possible cases. In this study, the method to select test cases for software failure probability quantification was suggested. To obtain profile of paired state variables, relationships of the variables need to be considered. The effect of input from human operator also have to be considered. As an example, test set of PZR-PR-Lo-Trip logic was examined. This method provides framework for selecting test cases of safety-critical software

The TESS Input Catalog and Selection of Targets for the TESS Transit Search

Science.gov (United States)

Pepper, Joshua; Stassun, Keivan G.; Paegert, Martin; Oelkers, Ryan; De Lee, Nathan Michael; Torres, Guillermo; TESS Target Selection Working Group

2018-01-01

The TESS mission will photometrically survey millions of the brightest stars over almost the entire the sky to detect transiting exoplanets. A key step to enable that search is the creation of the TESS Input Catalog (TIC), a compiled catalog of 700 million stars and galaxies with observed and calculated parameters. From the TIC we derive the Candidate Target List (CTL) to identify target stars for the 2-minute TESS postage stamps. The CTL is designed to identify the best stars for the detection of small planets, which includes all bright cool dwarf stars in the sky. I will describe the target selection strategy, the distribution of stars in the current CTL, and how both the TIC and CTL will expand and improve going forward.

Day-ahead load forecast using random forest and expert input selection

International Nuclear Information System (INIS)

Lahouar, A.; Ben Hadj Slama, J.

2015-01-01

Highlights: • A model based on random forests for short term load forecast is proposed. • An expert feature selection is added to refine inputs. • Special attention is paid to customers behavior, load profile and special holidays. • The model is flexible and able to handle complex load signal. • A technical comparison is performed to assess the forecast accuracy. - Abstract: The electrical load forecast is getting more and more important in recent years due to the electricity market deregulation and integration of renewable resources. To overcome the incoming challenges and ensure accurate power prediction for different time horizons, sophisticated intelligent methods are elaborated. Utilization of intelligent forecast algorithms is among main characteristics of smart grids, and is an efficient tool to face uncertainty. Several crucial tasks of power operators such as load dispatch rely on the short term forecast, thus it should be as accurate as possible. To this end, this paper proposes a short term load predictor, able to forecast the next 24 h of load. Using random forest, characterized by immunity to parameter variations and internal cross validation, the model is constructed following an online learning process. The inputs are refined by expert feature selection using a set of if–then rules, in order to include the own user specifications about the country weather or market, and to generalize the forecast ability. The proposed approach is tested through a real historical set from the Tunisian Power Company, and the simulation shows accurate and satisfactory results for one day in advance, with an average error exceeding rarely 2.3%. The model is validated for regular working days and weekends, and special attention is paid to moving holidays, following non Gregorian calendar

Sensitivity of traffic input parameters on rutting performance of a flexible pavement using Mechanistic Empirical Pavement Design Guide

Directory of Open Access Journals (Sweden)

Nur Hossain

2016-11-01

Full Text Available The traffic input parameters in the Mechanistic Empirical Pavement Design Guide (MEPDG are: (a general traffic inputs, (b traffic volume adjustment factors, and (c axle load spectra (ALS. Of these three traffic inputs, the traffic volume adjustment factors specifically monthly adjustment factor (MAF and the ALS are widely considered to be important and sensitive factors, which can significantly affect design of and prediction of distress in flexible pavements. Therefore, the present study was undertaken to assess the sensitivity of ALS and MAF traffic inputs on rutting distress of a flexible pavement. The traffic data of four years (from 2008 to 2012 were collected from an instrumented test section on I-35 in Oklahoma. Site specific traffic input parameters were developed. It was observed that significant differences exist between the MEPDG default and developed site-specific traffic input values. However, the differences in the yearly ALS and MAF data, developed for these four years, were not found to be as significant when compared to one another. In addition, quarterly field rut data were measured on the test section and compared with the MEPDG predicted rut values using the default and developed traffic input values for different years. It was found that significant differences exist between the measured rut and the MEPDG (AASHTOWare-ME predicted rut when default values were used. Keywords: MEPDG, Rut, Level 1 inputs, Axle load spectra, Traffic input parameters, Sensitivity

Selection Input Output by Restriction Using DEA Models Based on a Fuzzy Delphi Approach and Expert Information

Science.gov (United States)

Arsad, Roslah; Nasir Abdullah, Mohammad; Alias, Suriana; Isa, Zaidi

2017-09-01

Stock evaluation has always been an interesting problem for investors. In this paper, a comparison regarding the efficiency stocks of listed companies in Bursa Malaysia were made through the application of estimation method of Data Envelopment Analysis (DEA). One of the interesting research subjects in DEA is the selection of appropriate input and output parameter. In this study, DEA was used to measure efficiency of stocks of listed companies in Bursa Malaysia in terms of the financial ratio to evaluate performance of stocks. Based on previous studies and Fuzzy Delphi Method (FDM), the most important financial ratio was selected. The results indicated that return on equity, return on assets, net profit margin, operating profit margin, earnings per share, price to earnings and debt to equity were the most important ratios. Using expert information, all the parameter were clarified as inputs and outputs. The main objectives were to identify most critical financial ratio, clarify them based on expert information and compute the relative efficiency scores of stocks as well as rank them in the construction industry and material completely. The methods of analysis using Alirezaee and Afsharian’s model were employed in this study, where the originality of Charnes, Cooper and Rhodes (CCR) with the assumption of Constant Return to Scale (CSR) still holds. This method of ranking relative efficiency of decision making units (DMUs) was value-added by the Balance Index. The interested data was made for year 2015 and the population of the research includes accepted companies in stock markets in the construction industry and material (63 companies). According to the ranking, the proposed model can rank completely for 63 companies using selected financial ratio.

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

Science.gov (United States)

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

2011-09-01

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

Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

Science.gov (United States)

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

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

Science.gov (United States)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is

Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations.

Science.gov (United States)

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

2015-03-01

Biological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eurostat; however, BOD data at the national level is only available for 28 of 35 listed European countries for the period prior to 2008, among which 46% of data is missing. This paper describes the development of an artificial neural network model for the forecasting of annual BOD values at the national level, using widely available sustainability and economical/industrial parameters as inputs. The initial general regression neural network (GRNN) model was trained, validated and tested utilizing 20 inputs. The number of inputs was reduced to 15 using the Monte Carlo simulation technique as the input selection method. The best results were achieved with the GRNN model utilizing 25% less inputs than the initial model and a comparison with a multiple linear regression model trained and tested using the same input variables using multiple statistical performance indicators confirmed the advantage of the GRNN model. Sensitivity analysis has shown that inputs with the greatest effect on the GRNN model were (in descending order) precipitation, rural population with access to improved water sources, treatment capacity of wastewater treatment plants (urban) and treatment of municipal waste, with the last two having an equal effect. Finally, it was concluded that the developed GRNN model can be useful as a tool to support the decision-making process on sustainable development at a regional, national and international level.

Comparisons of CAP88PC version 2.0 default parameters to site specific inputs

International Nuclear Information System (INIS)

Lehto, M. A.; Courtney, J. C.; Charter, N.; Egan, T.

2000-01-01

The effects of varying the input for the CAP88PC Version 2.0 program on the total effective dose equivalents (TEDEs) were determined for hypothetical releases from the Hot Fuel Examination Facility (HFEF) located at the Argonne National Laboratory site on the Idaho National Engineering and Environmental Laboratory (INEEL). Values for site specific meteorological conditions and agricultural production parameters were determined for the 80 km radius surrounding the HFEF. Four nuclides, 3 H, 85 Kr, 129 I, and 137 Cs (with its short lived progeny, 137m Ba) were selected for this study; these are the radioactive materials most likely to be released from HFEF under normal or abnormal operating conditions. Use of site specific meteorological parameters of annual precipitation, average temperature, and the height of the inversion layer decreased the TEDE from 137 Cs- 137m Ba up to 36%; reductions for other nuclides were less than 3%. Use of the site specific agricultural parameters reduced TEDE values between 7% and 49%, depending on the nuclide. Reductions are associated with decreased committed effective dose equivalents (CEDEs) from the ingestion pathway. This is not surprising since the HFEF is located well within the INEEL exclusion area, and the surrounding area closest to the release point is a high desert with limited agricultural diversity. Livestock and milk production are important in some counties at distances greater than 30 km from the HFEF

Selection of input devices and controls for modern process control consoles

International Nuclear Information System (INIS)

Hasenfuss, O.; Zimmermann, R.

1975-06-01

In modern process control consoles man-machine communication is realized more and more by computer driven CRT displays, the most efficient communication system today. This paper describes the most important input devices and controls for such control consoles. A certain number of facts are given, which should be considered during the selection. The aptitude of the described devices for special tasks is discussed and recommendations are given for carrying out a selection. (orig.) [de

Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

International Nuclear Information System (INIS)

Khalaquzzaman, M.; Lee, Seung Jun; Cho, Jaehyun; Jung, Wondea

2016-01-01

Recently, the input-profile-based testing for safety critical software has been proposed for determining the number of test cases and quantifying the failure probability of the software. Input-profile of a reactor protection system (RPS) software is the input which causes activation of the system for emergency shutdown of a reactor. This paper presents a method to determine the input-profile of a RPS software which considers concurrent events/transients. A deviation of a process parameter value begins through an event and increases owing to the concurrent multi-events depending on the correlation of process parameters and severity of incidents. A case of reactor trip caused by feedwater loss and main steam line break is simulated and analyzed to determine the RPS software input-profile and estimate the number of test cases. The different sizes of the main steam line breaks (e.g., small, medium, large break) with total loss of feedwater supply are considered in constructing the input-profile. The uncertainties of the simulation related to the input-profile-based software testing are also included. Our study is expected to provide an option to determine test cases and quantification of RPS software failure probability. (author)

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

KAUST Repository

Zhang, Xuesong

2011-11-01

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. © 2011 Elsevier B.V.

Monte Carlo Simulation of Influence of Input Parameters Uncertainty on Output Data

International Nuclear Information System (INIS)

Sobek, Lukas

2010-01-01

Input parameters of a complex system in the probabilistic simulation are treated by means of probability density function (PDF). The result of the simulation have also probabilistic character. Monte Carlo simulation is widely used to obtain predictions concerning the probability of the risk. The Monte Carlo method was performed to calculate histograms of PDF for release rate given by uncertainty in distribution coefficient of radionuclides 135 Cs and 235 U.

Probabilistic leak-before-break analysis with correlated input parameters

International Nuclear Information System (INIS)

Qian Guian; Niffenegger, Markus; Karanki, Durga Rao; Li Shuxin

2013-01-01

Highlights: ► The correlation of crack growth has the most significant impact on LBB behavior. ► The correlation impact increases with the correlation coefficients. ► The correlation impact increases with the number of cracks. ► Independent assumption may lead to nonconservative result. - Abstract: The paper presents a probabilistic methodology considering the correlations between the input variables for the analysis of leak-before-break (LBB) behavior of a pressure tube. A computer program based on Monte Carlo (MC) simulation with Nataf transformation has been developed to allow the proposed methodology to calculate both the time from the first leakage to unstable fracture and the time from leakage detection to unstable fracture. The results show that the correlation of the crack growth rates between different cracks has the most significant impact on the LBB behavior of the pressure tube. The impact of the parameters correlation on LBB behavior increases with the crack numbers. If the correlations between different parameters for an individual crack are not considered, the predicted results are nonconservative when the cumulative probability is below 50% and conservative when it is above 50%.

The Sensitivity of the Input Impedance Parameters of Track Circuits to Changes in the Parameters of the Track

Directory of Open Access Journals (Sweden)

Lubomir Ivanek

2017-01-01

Full Text Available This paper deals with the sensitivity of the input impedance of an open track circuit in the event that the parameters of the track are changed. Weather conditions and the state of pollution are the most common reasons for parameter changes. The results were obtained from the measured values of the parameters R (resistance, G (conductance, L (inductance, and C (capacitance of a rail superstructure depending on the frequency. Measurements were performed on a railway siding in Orlova. The results are used to design a predictor of occupancy of a track section. In particular, we were interested in the frequencies of 75 and 275 Hz for this purpose. Many parameter values of track substructures have already been solved in different works in literature. At first, we had planned to use the parameter values from these sources when we designed the predictor. Deviations between them, however, are large and often differ by three orders of magnitude (see Tab.8. From this perspective, this article presents data that have been updated using modern measurement devices and computer technology. And above all, it shows a transmission (cascade matrix used to determine the parameters.

Control rod drive WWER 1000 – tuning of input parameters

Directory of Open Access Journals (Sweden)

Markov P.

2007-10-01

Full Text Available The article picks up on the contributions presented at the conferences Computational Mechanics 2005 and 2006, in which a calculational model of an upgraded control rod linear stepping drive for the reactors WWER 1000 (LKP-M/3 was described and results of analysis of dynamical response of its individual parts when moving up- and downwards were included. The contribution deals with the tuning of input parameters of the 3rd generation drive with the objective of reaching its running as smooth as possible so as to get a minimum wear of its parts as a result and hence to achieve maximum life-time.

Deep convolutional neural network based antenna selection in multiple-input multiple-output system

Science.gov (United States)

Cai, Jiaxin; Li, Yan; Hu, Ying

2018-03-01

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

Sensitivity Analysis of Input Parameters for the Dose Assessment from Gaseous Effluents due to the Normal Operation of Jordan Research and Training Reactor

International Nuclear Information System (INIS)

Kim, Sukhoon; Lee, Seunghee; Kim, Juyoul; Kim, Juyub; Han, Moonhee

2015-01-01

In this study, therefore, the sensitivity analysis of input variables for the dose assessment was performed for reviewing the effect of each parameter on the result after determining the type and range of parameters that could affect the exposure dose of the public. (Since JRTR will be operated by the concept of 'no liquid discharge,' the input parameters used for calculation of dose due to liquid effluents are not considered in the sensitivity analysis.) In this paper, the sensitivity analysis of input parameters for the dose assessment in the vicinity of the site boundary due to gaseous effluents was performed for a total of thirty-five (35) cases. And, detailed results for the input variables that have an significant effect are shown in Figures 1 through 7, respectively. For preparing a R-ER for the operating license of the JRTR, these results will be updated by the additional information and could be applied to predicting the variation trend of the exposure dose in the process of updating the input parameters for the dose assessment reflecting the characteristics of the JRTR site

The scaling of edge parameters in jet with plasma input power

International Nuclear Information System (INIS)

Erents, S.K.; McCracken, G.M.; Harbour, P.J.; Clement, S.; Summers, D.D.R.; Tagle, J.A.; Kock, L. de

1989-01-01

The scaling of edge parameters of density and temperature with central density and ohmic power in JET has been presented previously for the discrete limiter geometry and more recently for the new belt limiter configuration. However, the scaling with plasma current (I p ) is difficult to interpret because varying I p does not only change the input power but also the safety factor qs and consequently the SOL thickness. The use of additional heating at constant current allows more direct observation of the effects of changing heating power. In this paper we present data in which the plasma input power is increased by ICRH, (Pt<20MW), using a 3MA target plasma, and compare data for different plasma currents using discrete and belt limiter geometries. Edge data is presented from Langmuir probes in tiles at the top of the torus, when the tokamak is operated in single null magnetic separatrix (divertor) mode, as well as for probes in the main plasma boundary to contrast these data with limiter data. (author) 3 refs., 4 figs

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

Science.gov (United States)

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

2018-04-01

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

Orientation selectivity of synaptic input to neurons in mouse and cat primary visual cortex.

Science.gov (United States)

Tan, Andrew Y Y; Brown, Brandon D; Scholl, Benjamin; Mohanty, Deepankar; Priebe, Nicholas J

2011-08-24

Primary visual cortex (V1) is the site at which orientation selectivity emerges in mammals: visual thalamus afferents to V1 respond equally to all stimulus orientations, whereas their target V1 neurons respond selectively to stimulus orientation. The emergence of orientation selectivity in V1 has long served as a model for investigating cortical computation. Recent evidence for orientation selectivity in mouse V1 opens cortical computation to dissection by genetic and imaging tools, but also raises two essential questions: (1) How does orientation selectivity in mouse V1 neurons compare with that in previously described species? (2) What is the synaptic basis for orientation selectivity in mouse V1? A comparison of orientation selectivity in mouse and in cat, where such measures have traditionally been made, reveals that orientation selectivity in mouse V1 is weaker than in cat V1, but that spike threshold plays a similar role in narrowing selectivity between membrane potential and spike rate. To uncover the synaptic basis for orientation selectivity, we made whole-cell recordings in vivo from mouse V1 neurons, comparing neuronal input selectivity-based on membrane potential, synaptic excitation, and synaptic inhibition-to output selectivity based on spiking. We found that a neuron's excitatory and inhibitory inputs are selective for the same stimulus orientations as is its membrane potential response, and that inhibitory selectivity is not broader than excitatory selectivity. Inhibition has different dynamics than excitation, adapting more rapidly. In neurons with temporally modulated responses, the timing of excitation and inhibition was different in mice and cats.

Overview of input parameters for calculation of the probability of a brittle fracture of the reactor pressure vessel

International Nuclear Information System (INIS)

Horacek, L.

1994-12-01

The parameters are summarized for a calculation of the probability of brittle fracture of the WWER-440 reactor pressure vessel (RPV). The parameters were selected for 2 basic approaches, viz., one based on the Monte Carlo method and the other on the FORM and SORM methods (First and Second Order Reliability Methods). The approaches were represented by US computer codes VISA-II and OCA-P and by the German ZERBERUS code. The philosophy of the deterministic and probabilistic aspects of the VISA-II code is outlined, and the differences between the US and Czech PWR's are discussed in this context. Briefly described is the partial approach to the evaluation of the WWER type RPV's based on the assessment of their resistance to brittle fracture by fracture mechanics tools and by using the FORM and SORM methods. Attention is paid to the input data for the WWER modification of the VISA-II code. The data are categorized with respect to randomness, i.e. to the stochastic or deterministic nature of their behavior. 18 tabs., 14 refs

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity.

Science.gov (United States)

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2012-12-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Selection and verification of safety parameters in safety parameter display system for nuclear power plants

International Nuclear Information System (INIS)

Zhang Yuangfang

1992-02-01

The method and results for safety parameter selection and its verification in safety parameter display system of nuclear power plants are introduced. According to safety analysis, the overall safety is divided into six critical safety functions, and a certain amount of safety parameters which can represent the integrity degree of each function and the causes of change are strictly selected. The verification of safety parameter selection is carried out from the view of applying the plant emergency procedures and in the accident man oeuvres on a full scale nuclear power plant simulator

Parameters for calculation of nuclear reactions of relevance to non-energy nuclear applications (Reference Input Parameter Library: Phase III). Summary report of the first research coordination meeting

International Nuclear Information System (INIS)

Capote Noy, R.

2004-08-01

A summary is given of the First Research Coordination Meeting on Parameters for Calculation of Nuclear Reactions of Relevance to Non-Energy Nuclear Applications (Reference Input Parameter Library: Phase III), including a critical review of the RIPL-2 file. The new library should serve as input for theoretical calculations of nuclear reaction data at incident energies up to 200 MeV, as needed for energy and non-energy modern applications of nuclear data. Technical discussions and the resulting work plan of the Coordinated Research Programme are summarized, along with actions and deadlines. Participants' contributions to the RCM are also attached. (author)

On Input Vector Representation for the SVR model of Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

Determination and optimization of reactor core loading pattern is an important factor in nuclear power plant operation. The goal is to minimize the amount of enriched uranium (fresh fuel) and burnable absorbers placed in the core, while maintaining nuclear power plant operational and safety characteristics. The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. Recently, we proposed a new method for fast loading pattern evaluation based on general robust regression model relying on the state of the art research in the field of machine learning. We employed Support Vector Regression (SVR) technique. SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem. The preliminary tests revealed a good potential of the SVR method application for fast and accurate reactor core loading pattern evaluation. However, some aspects of model development are still unresolved. The main objective of the work reported in this paper was to conduct additional tests and analyses required for full clarification of the SVR applicability for loading pattern evaluation. We focused our attention on the parameters defining input vector, primarily its structure and complexity, and parameters defining kernel functions. All the tests were conducted on the NPP Krsko reactor core, using MCRAC code for the calculation of reactor core loading pattern critical parameters. The tested input vector structures did not influence the accuracy of the models suggesting that the initially tested input vector, consisted of the number of IFBAs and the k-inf at the beginning of the cycle, is adequate. The influence of kernel function specific parameters (σ for RBF kernel

Effect of stimulation on the input parameters of stochastic leaky integrate-and-fire neuronal model

Czech Academy of Sciences Publication Activity Database

Lánský, Petr; Šanda, Pavel; He, J.

2010-01-01

Roč. 104, 3-4 (2010), s. 160-166 ISSN 0928-4257 R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z50110509 Keywords : membrane depolarization * input parameters * diffusion Subject RIV: BO - Biophysics Impact factor: 3.030, year: 2010

Multi-Objective Parameter Selection for Classifers

Directory of Open Access Journals (Sweden)

Christoph Mussel

2012-01-01

Full Text Available Setting the free parameters of classifiers to different values can have a profound impact on their performance. For some methods, specialized tuning algorithms have been developed. These approaches mostly tune parameters according to a single criterion, such as the cross-validation error. However, it is sometimes desirable to obtain parameter values that optimize several concurrent - often conflicting - criteria. The TunePareto package provides a general and highly customizable framework to select optimal parameters for classifiers according to multiple objectives. Several strategies for sampling andoptimizing parameters are supplied. The algorithm determines a set of Pareto-optimal parameter configuration and leaves the ultimate decision on the weighting of objectives to the researcher. Decision support is provided by novel visualization techniques.

Inference of directional selection and mutation parameters assuming equilibrium.

Science.gov (United States)

Vogl, Claus; Bergman, Juraj

2015-12-01

In a classical study, Wright (1931) proposed a model for the evolution of a biallelic locus under the influence of mutation, directional selection and drift. He derived the equilibrium distribution of the allelic proportion conditional on the scaled mutation rate, the mutation bias and the scaled strength of directional selection. The equilibrium distribution can be used for inference of these parameters with genome-wide datasets of "site frequency spectra" (SFS). Assuming that the scaled mutation rate is low, Wright's model can be approximated by a boundary-mutation model, where mutations are introduced into the population exclusively from sites fixed for the preferred or unpreferred allelic states. With the boundary-mutation model, inference can be partitioned: (i) the shape of the SFS distribution within the polymorphic region is determined by random drift and directional selection, but not by the mutation parameters, such that inference of the selection parameter relies exclusively on the polymorphic sites in the SFS; (ii) the mutation parameters can be inferred from the amount of polymorphic and monomorphic preferred and unpreferred alleles, conditional on the selection parameter. Herein, we derive maximum likelihood estimators for the mutation and selection parameters in equilibrium and apply the method to simulated SFS data as well as empirical data from a Madagascar population of Drosophila simulans. Copyright © 2015 Elsevier Inc. All rights reserved.

TART input manual

International Nuclear Information System (INIS)

Kimlinger, J.R.; Plechaty, E.F.

1982-01-01

The TART code is a Monte Carlo neutron/photon transport code that is only on the CRAY computer. All the input cards for the TART code are listed, and definitions for all input parameters are given. The execution and limitations of the code are described, and input for two sample problems are given

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

Science.gov (United States)

Dill, Vanderson; Klein, Pedro Costa; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2018-04-01

Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains. To increase the segmentation precision in these cases, without any manual intervention, multiple atlases can be used. However, registration to many templates leads to a high computational cost. Researchers have proposed to use an atlas pre-selection technique based on meta-information followed by the selection of an atlas based on image similarity. Unfortunately, this method also presents a high computational cost due to the image-similarity process. Thus, it is desirable to pre-select a smaller number of atlases as long as this does not impact on the segmentation quality. To pick out an atlas that provides the best registration, we evaluate the use of three meta-information parameters (medical condition, age range, and gender) to choose the atlas. In this work, 24 atlases were defined and each is based on the combination of the three meta-information parameters. These atlases were used to segment 352 vol from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Hippocampus segmentation with each of these atlases was evaluated and compared to reference segmentations of the hippocampus, which are available from ADNI. The use of atlas selection by meta-information led to a significant gain in the Dice similarity coefficient, which reached 0.68 ± 0.11, compared to 0.62 ± 0.12 when using only the standard MNI152 atlas. Statistical analysis showed that the three meta-information parameters provided a significant improvement in the segmentation accuracy. Copyright © 2018 Elsevier Ltd

Statistical MOSFET Parameter Extraction with Parameter Selection for Minimal Point Measurement

Directory of Open Access Journals (Sweden)

Marga Alisjahbana

2013-11-01

Full Text Available A method to statistically extract MOSFET model parameters from a minimal number of transistor I(V characteristic curve measurements, taken during fabrication process monitoring. It includes a sensitivity analysis of the model, test/measurement point selection, and a parameter extraction experiment on the process data. The actual extraction is based on a linear error model, the sensitivity of the MOSFET model with respect to the parameters, and Newton-Raphson iterations. Simulated results showed good accuracy of parameter extraction and I(V curve fit for parameter deviations of up 20% from nominal values, including for a process shift of 10% from nominal.

Reducing uncertainty at minimal cost: a method to identify important input parameters and prioritize data collection

NARCIS (Netherlands)

Uwizeye, U.A.; Groen, E.A.; Gerber, P.J.; Schulte, Rogier P.O.; Boer, de I.J.M.

2016-01-01

The study aims to illustrate a method to identify important input parameters that explain most of the output variance ofenvironmental assessment models. The method is tested for the computation of life-cycle nitrogen (N) use efficiencyindicators among mixed dairy production systems in Rwanda. We

Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs.

Directory of Open Access Journals (Sweden)

Robert R Kerr

Full Text Available Learning rules, such as spike-timing-dependent plasticity (STDP, change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.

Input data preprocessing method for exchange rate forecasting via neural network

Directory of Open Access Journals (Sweden)

Antić Dragan S.

2014-01-01

Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006

Study on Parameters Modeling of Wind Turbines Using SCADA Data

Directory of Open Access Journals (Sweden)

Yonglong YAN

2014-08-01

Full Text Available Taking the advantage of the current massive monitoring data from Supervisory Control and Data Acquisition (SCADA system of wind farm, it is of important significance for anomaly detection, early warning and fault diagnosis to build the data model of state parameters of wind turbines (WTs. The operational conditions and the relationships between the state parameters of wind turbines are complex. It is difficult to establish the model of state parameter accurately, and the modeling method of state parameters of wind turbines considering parameter selection is proposed. Firstly, by analyzing the characteristic of SCADA data, a reasonable range of data and monitoring parameters are chosen. Secondly, neural network algorithm is adapted, and the selection method of input parameters in the model is presented. Generator bearing temperature and cooling air temperature are regarded as target parameters, and the two models are built and input parameters of the models are selected, respectively. Finally, the parameter selection method in this paper and the method using genetic algorithm-partial least square (GA-PLS are analyzed comparatively, and the results show that the proposed methods are correct and effective. Furthermore, the modeling of two parameters illustrate that the method in this paper can applied to other state parameters of wind turbines.

IAEA nuclear data for applications: Cross section standards and the reference input parameter library (RIPL)

International Nuclear Information System (INIS)

Capote Noy, Roberto; Nichols, Alan L.; Pronyaev, Vladimir G.

2003-01-01

develop a library of validated nuclear-model input parameters, referred to as the Reference Input Parameter Library (RIPL). The first stage of this work was initiated in 1994 and the second step began in 1998, both as IAEA CRPs. A consistent library of recommended nuclear theoretical input parameters is now available (RIPL-2) that includes a large amount of theoretical information suitable for nuclear reaction calculations, along with a number of computer codes for parameter retrieval and related calculations. A third further phase of this project has been recently initiated in order to extend the applicability of the RIPL library to cross sections for reactions on nuclei far from the line of stability, incident energies up to 200 MeV, and reactions induced by charged particles. (authors)

Visual Input Enhances Selective Speech Envelope Tracking in Auditory Cortex at a ‘Cocktail Party’

Science.gov (United States)

Golumbic, Elana Zion; Cogan, Gregory B.; Schroeder, Charles E.; Poeppel, David

2013-01-01

Our ability to selectively attend to one auditory signal amidst competing input streams, epitomized by the ‘Cocktail Party’ problem, continues to stimulate research from various approaches. How this demanding perceptual feat is achieved from a neural systems perspective remains unclear and controversial. It is well established that neural responses to attended stimuli are enhanced compared to responses to ignored ones, but responses to ignored stimuli are nonetheless highly significant, leading to interference in performance. We investigated whether congruent visual input of an attended speaker enhances cortical selectivity in auditory cortex, leading to diminished representation of ignored stimuli. We recorded magnetoencephalographic (MEG) signals from human participants as they attended to segments of natural continuous speech. Using two complementary methods of quantifying the neural response to speech, we found that viewing a speaker’s face enhances the capacity of auditory cortex to track the temporal speech envelope of that speaker. This mechanism was most effective in a ‘Cocktail Party’ setting, promoting preferential tracking of the attended speaker, whereas without visual input no significant attentional modulation was observed. These neurophysiological results underscore the importance of visual input in resolving perceptual ambiguity in a noisy environment. Since visual cues in speech precede the associated auditory signals, they likely serve a predictive role in facilitating auditory processing of speech, perhaps by directing attentional resources to appropriate points in time when to-be-attended acoustic input is expected to arrive. PMID:23345218

Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system

KAUST Repository

Belkhatir, Zehor

2016-08-05

This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain and the objective is to characterize brain regions using functional Magnetic Resonance Imaging (fMRI) data. For this reason, we propose an adaptive estimator and prove the asymptotic convergence of the state, the unknown input and the unknown parameters. The proof is based on a Lyapunov approach combined with a priori identifiability assumptions. The performance of the proposed observer is illustrated through some simulation results.

Blind Deconvolution for Distributed Parameter Systems with Unbounded Input and Output and Determining Blood Alcohol Concentration from Transdermal Biosensor Data.

Science.gov (United States)

Rosen, I G; Luczak, Susan E; Weiss, Jordan

2014-03-15

We develop a blind deconvolution scheme for input-output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.

Modelling pesticide leaching under climate change: parameter vs. climate input uncertainty

Directory of Open Access Journals (Sweden)

K. Steffens

2014-02-01

Full Text Available Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in south-western Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM, greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set (1970–1999 for an important agricultural production area in south-western Sweden based on monthly change factors for 2070–2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

Directory of Open Access Journals (Sweden)

Rupert Faltermeier

2015-01-01

Full Text Available Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP and intracranial pressure (ICP. Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP, with the outcome of the patients represented by the Glasgow Outcome Scale (GOS. For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology.

Science.gov (United States)

Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

RIP Input Tables From WAPDEG for LA Design Selection: Continuous Pre-Closure Ventilation

International Nuclear Information System (INIS)

K.G. Mon

1999-01-01

The purpose of this calculation is to document the creation of .tables for input into Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) from Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. ''Software Routine Report for WAPDEG'' (Version 3.09)) simulations. This calculation details the creation of the RIP input tables (representing waste package corrosion degradation over time) for the License Application Design Selection (LADS) analysis of the effects of continuous pre-closure ventilation. Ventilation during the operational phase of the repository could remove considerable water from the system, as well as reduce temperatures. Pre-closure ventilation is LADS Design Feature 7

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

Science.gov (United States)

Krenn, Julia; Zangerl, Christian; Mergili, Martin

2017-04-01

r.randomwalk is a GIS-based, multi-functional, conceptual open source model application for forward and backward analyses of the propagation of mass flows. It relies on a set of empirically derived, uncertain input parameters. In contrast to many other tools, r.randomwalk accepts input parameter ranges (or, in case of two or more parameters, spaces) in order to directly account for these uncertainties. Parameter spaces represent a possibility to withdraw from discrete input values which in most cases are likely to be off target. r.randomwalk automatically performs multiple calculations with various parameter combinations in a given parameter space, resulting in the impact indicator index (III) which denotes the fraction of parameter value combinations predicting an impact on a given pixel. Still, there is a need to constrain the parameter space used for a certain process type or magnitude prior to performing forward calculations. This can be done by optimizing the parameter space in terms of bringing the model results in line with well-documented past events. As most existing parameter optimization algorithms are designed for discrete values rather than for ranges or spaces, the necessity for a new and innovative technique arises. The present study aims at developing such a technique and at applying it to derive guiding parameter spaces for the forward calculation of rock avalanches through back-calculation of multiple events. In order to automatize the work flow we have designed r.ranger, an optimization and sensitivity analysis tool for parameter spaces which can be directly coupled to r.randomwalk. With r.ranger we apply a nested approach where the total value range of each parameter is divided into various levels of subranges. All possible combinations of subranges of all parameters are tested for the performance of the associated pattern of III. Performance indicators are the area under the ROC curve (AUROC) and the factor of conservativeness (FoC). This

Automation of RELAP5 input calibration and code validation using genetic algorithm

International Nuclear Information System (INIS)

Phung, Viet-Anh; Kööp, Kaspar; Grishchenko, Dmitry; Vorobyev, Yury; Kudinov, Pavel

2016-01-01

Highlights: • Automated input calibration and code validation using genetic algorithm is presented. • Predictions generally overlap experiments for individual system response quantities (SRQs). • It was not possible to predict simultaneously experimental maximum flow rate and oscillation period. • Simultaneous consideration of multiple SRQs is important for code validation. - Abstract: Validation of system thermal-hydraulic codes is an important step in application of the codes to reactor safety analysis. The goal of the validation process is to determine how well a code can represent physical reality. This is achieved by comparing predicted and experimental system response quantities (SRQs) taking into account experimental and modelling uncertainties. Parameters which are required for the code input but not measured directly in the experiment can become an important source of uncertainty in the code validation process. Quantification of such parameters is often called input calibration. Calibration and uncertainty quantification may become challenging tasks when the number of calibrated input parameters and SRQs is large and dependencies between them are complex. If only engineering judgment is employed in the process, the outcome can be prone to so called “user effects”. The goal of this work is to develop an automated approach to input calibration and RELAP5 code validation against data on two-phase natural circulation flow instability. Multiple SRQs are used in both calibration and validation. In the input calibration, we used genetic algorithm (GA), a heuristic global optimization method, in order to minimize the discrepancy between experimental and simulation data by identifying optimal combinations of uncertain input parameters in the calibration process. We demonstrate the importance of the proper selection of SRQs and respective normalization and weighting factors in the fitness function. In the code validation, we used maximum flow rate as the

Automation of RELAP5 input calibration and code validation using genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Phung, Viet-Anh, E-mail: vaphung@kth.se [Division of Nuclear Power Safety, Royal Institute of Technology, Roslagstullsbacken 21, 10691 Stockholm (Sweden); Kööp, Kaspar, E-mail: kaspar@safety.sci.kth.se [Division of Nuclear Power Safety, Royal Institute of Technology, Roslagstullsbacken 21, 10691 Stockholm (Sweden); Grishchenko, Dmitry, E-mail: dmitry@safety.sci.kth.se [Division of Nuclear Power Safety, Royal Institute of Technology, Roslagstullsbacken 21, 10691 Stockholm (Sweden); Vorobyev, Yury, E-mail: yura3510@gmail.com [National Research Center “Kurchatov Institute”, Kurchatov square 1, Moscow 123182 (Russian Federation); Kudinov, Pavel, E-mail: pavel@safety.sci.kth.se [Division of Nuclear Power Safety, Royal Institute of Technology, Roslagstullsbacken 21, 10691 Stockholm (Sweden)

2016-04-15

Highlights: • Automated input calibration and code validation using genetic algorithm is presented. • Predictions generally overlap experiments for individual system response quantities (SRQs). • It was not possible to predict simultaneously experimental maximum flow rate and oscillation period. • Simultaneous consideration of multiple SRQs is important for code validation. - Abstract: Validation of system thermal-hydraulic codes is an important step in application of the codes to reactor safety analysis. The goal of the validation process is to determine how well a code can represent physical reality. This is achieved by comparing predicted and experimental system response quantities (SRQs) taking into account experimental and modelling uncertainties. Parameters which are required for the code input but not measured directly in the experiment can become an important source of uncertainty in the code validation process. Quantification of such parameters is often called input calibration. Calibration and uncertainty quantification may become challenging tasks when the number of calibrated input parameters and SRQs is large and dependencies between them are complex. If only engineering judgment is employed in the process, the outcome can be prone to so called “user effects”. The goal of this work is to develop an automated approach to input calibration and RELAP5 code validation against data on two-phase natural circulation flow instability. Multiple SRQs are used in both calibration and validation. In the input calibration, we used genetic algorithm (GA), a heuristic global optimization method, in order to minimize the discrepancy between experimental and simulation data by identifying optimal combinations of uncertain input parameters in the calibration process. We demonstrate the importance of the proper selection of SRQs and respective normalization and weighting factors in the fitness function. In the code validation, we used maximum flow rate as the

Radiation safety assessment and development of environmental radiation monitoring technology; standardization of input parameters for the calculation of annual dose from routine releases from commercial reactor effluents

Energy Technology Data Exchange (ETDEWEB)

Rhee, I. H.; Cho, D.; Youn, S. H.; Kim, H. S.; Lee, S. J.; Ahn, H. K. [Soonchunhyang University, Ahsan (Korea)

2002-04-01

This research is to develop a standard methodology for determining the input parameters that impose a substantial impact on radiation doses of residential individuals in the vicinity of four nuclear power plants in Korea. We have selected critical nuclei, pathways and organs related to the human exposure via simulated estimation with K-DOSE 60 based on the updated ICRP-60 and sensitivity analyses. From the results, we found that 1) the critical nuclides were found to be {sup 3}H, {sup 133}Xe, {sup 60}Co for Kori plants and {sup 14}C, {sup 41}Ar for Wolsong plants. The most critical pathway was 'vegetable intake' for adults and 'milk intake' for infants. However, there was no preference in the effective organs, and 2) sensitivity analyses showed that the chemical composition in a nuclide much more influenced upon the radiation dose than any other input parameters such as food intake, radiation discharge, and transfer/concentration coefficients by more than 102 factor. The effect of transfer/concentration coefficients on the radiation dose was negligible. All input parameters showed highly estimated correlation with the radiation dose, approximated to 1.0, except for food intake in Wolsong power plant (partial correlation coefficient (PCC)=0.877). Consequently, we suggest that a prediction model or scenarios for food intake reflecting the current living trend and a formal publications including details of chemical components in the critical nuclei from each plant are needed. Also, standardized domestic values of the parameters used in the calculation must replace the values of the existed or default-set imported factors via properly designed experiments and/or modelling such as transport of liquid discharge in waters nearby the plants, exposure tests on corps and plants so on. 4 figs., 576 tabs. (Author)

MPC for LPV Systems Based on Parameter-Dependent Lyapunov Function with Perturbation on Control Input Strategy

Directory of Open Access Journals (Sweden)

Pornchai Bumroongsri

2012-04-01

Full Text Available In this paper, the model predictive control (MPC algorithm for linear parameter varying (LPV systems is proposed. The proposed algorithm consists of two steps. The first step is derived by using parameter-dependent Lyapunov function and the second step is derived by using the perturbation on control input strategy. In order to achieve good control performance, the bounds on the rate of variation of the parameters are taken into account in the controller synthesis. An overall algorithm is proved to guarantee robust stability. The controller design is illustrated with two case studies of continuous stirred-tank reactors. Comparisons with other MPC algorithms for LPV systems have been undertaken. The results show that the proposed algorithm can achieve better control performance.

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.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

Science.gov (United States)

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

Directory of Open Access Journals (Sweden)

Ming-Yuan Cho

2017-01-01

Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Optimizing Input/Output Using Adaptive File System Policies

Science.gov (United States)

Madhyastha, Tara M.; Elford, Christopher L.; Reed, Daniel A.

1996-01-01

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

Investigations of the sensitivity of a coronal mass ejection model (ENLIL) to solar input parameters

DEFF Research Database (Denmark)

Falkenberg, Thea Vilstrup; Vršnak, B.; Taktakishvili, A.

2010-01-01

Understanding space weather is not only important for satellite operations and human exploration of the solar system but also to phenomena here on Earth that may potentially disturb and disrupt electrical signals. Some of the most violent space weather effects are caused by coronal mass ejections...... (CMEs), but in order to predict the caused effects, we need to be able to model their propagation from their origin in the solar corona to the point of interest, e.g., Earth. Many such models exist, but to understand the models in detail we must understand the primary input parameters. Here we...... investigate the parameter space of the ENLILv2.5b model using the CME event of 25 July 2004. ENLIL is a time‐dependent 3‐D MHD model that can simulate the propagation of cone‐shaped interplanetary coronal mass ejections (ICMEs) through the solar system. Excepting the cone parameters (radius, position...

F-18 High Alpha Research Vehicle (HARV) parameter identification flight test maneuvers for optimal input design validation and lateral control effectiveness

Science.gov (United States)

Morelli, Eugene A.

1995-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for open loop parameter identification purposes, specifically for optimal input design validation at 5 degrees angle of attack, identification of individual strake effectiveness at 40 and 50 degrees angle of attack, and study of lateral dynamics and lateral control effectiveness at 40 and 50 degrees angle of attack. Each maneuver is to be realized by applying square wave inputs to specific control effectors using the On-Board Excitation System (OBES). Maneuver descriptions and complete specifications of the time/amplitude points define each input are included, along with plots of the input time histories.

A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

Science.gov (United States)

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Directory of Open Access Journals (Sweden)

Peng Li

2016-01-01

Full Text Available The optimal performance of the ant colony algorithm (ACA mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA, considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA and a particle swarm optimization (PSO, and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

Phenotype and genetic parameters for body measurements, reproductive traits and gut lenght of Nile tilapia (Oreochromis niloticus) selected for growth in low-input earthen ponds

NARCIS (Netherlands)

Charo-Karisa, H.; Bovenhuis, H.; Rezk, M.A.; Ponzoni, R.W.; Arendonk, van J.A.M.; Komen, J.

2007-01-01

In this study we present estimates of phenotypic and genetic parameters for body size measurements, reproductive traits, and gut length for Nile tilapia (Oreochromis niloticus) selected for growth in fertilized earthen ponds for two generations. Throughout the experiment, ponds were fertilized daily

Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings

International Nuclear Information System (INIS)

Dodoo, Ambrose; Tettey, Uniben Yao Ayikoe; Gustavsson, Leif

2017-01-01

In this study, we modelled the influence of different simulation assumptions on energy balances of two variants of a residential building, comprising the building in its existing state and with energy-efficient improvements. We explored how selected parameter combinations and variations affect the energy balances of the building configurations. The selected parameters encompass outdoor microclimate, building thermal envelope and household electrical equipment including technical installations. Our modelling takes into account hourly as well as seasonal profiles of different internal heat gains. The results suggest that the impact of parameter interactions on calculated space heating of buildings is somewhat small and relatively more noticeable for an energy-efficient building in contrast to a conventional building. We find that the influence of parameters combinations is more apparent as more individual parameters are varied. The simulations show that a building's calculated space heating demand is significantly influenced by how heat gains from electrical equipment are modelled. For the analyzed building versions, calculated final energy for space heating differs by 9–14 kWh/m"2 depending on the assumed energy efficiency level for electrical equipment. The influence of electrical equipment on calculated final space heating is proportionally more significant for an energy-efficient building compared to a conventional building. This study shows the influence of different simulation assumptions and parameter combinations when varied simultaneously. - Highlights: • Energy balances are modelled for conventional and efficient variants of a building. • Influence of assumptions and parameter combinations and variations are explored. • Parameter interactions influence is apparent as more single parameters are varied. • Calculated space heating demand is notably affected by how heat gains are modelled.

Research on filter’s parameter selection based on PROMETHEE method

Science.gov (United States)

Zhu, Hui-min; Wang, Hang-yu; Sun, Shi-yan

2018-03-01

The selection of filter’s parameters in target recognition was studied in this paper. The PROMETHEE method was applied to the optimization problem of Gabor filter parameters decision, the correspondence model of the elemental relation between two methods was established. The author took the identification of military target as an example, problem about the filter’s parameter decision was simulated and calculated by PROMETHEE. The result showed that using PROMETHEE method for the selection of filter’s parameters was more scientific. The human disturbance caused by the experts method and empirical method could be avoided by this way. The method can provide reference for the parameter configuration scheme decision of the filter.

RIP INPUT TABLES FROM WAPDEG FOR LA DESIGN SELECTION: ENHANCED DESIGN ALTERNATIVE V

International Nuclear Information System (INIS)

K. Mon

1999-01-01

The purpose of this calculation is to document (1) the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b, Software Routine Report for WAPDEG (Version 3.09)) simulations used to analyze degradation and failure of 2-cm thick titanium grade 7 corrosion resistant material (CRM) drip shields (that are placed over waste packages composed of a 2-cm thick Alloy 22 corrosion resistant material (CRM) as the outer barrier and an unspecified material to provide structural support as the inner barrier) as well as degradation and failure of the waste packages themselves, and (2) post-processing of these results into tables of drip shield/waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) computer code. Performance credit of the inner barrier material is not taken in this calculation. This calculation supports Performance Assessment analysis of the License Application Design Selection (LADS) Enhanced Design Alternative V. Additional details concerning the Enhanced Design Alternative V are provided in a Design Input Request (CRWMS M and O 1999e, Design Input Request for LADS Phase II EDA Evaluations, Item 3)

Inverse Tasks In The Tsunami Problem: Nonlinear Regression With Inaccurate Input Data

Science.gov (United States)

Lavrentiev, M.; Shchemel, A.; Simonov, K.

A variant of modified training functional that allows considering inaccurate input data is suggested. A limiting case when a part of input data is completely undefined, and, therefore, a problem of reconstruction of hidden parameters should be solved, is also considered. Some numerical experiments are presented. It is assumed that a dependence of known output variables on known input ones should be found is the classic problem definition, which is widely used in the majority of neural nets algorithms. The quality of approximation is evaluated as a performance function. Often the error of the task is evaluated as squared distance between known input data and predicted data multiplied by weighed coefficients. These coefficients may be named "precision coefficients". When inputs are not known exactly, natural generalization of performance function is adding member that responsible for distance between known inputs and shifted inputs, which lessen model's error. It is desirable that the set of variable parameters is compact for training to be con- verging. In the above problem it is possible to choose variants of demands of a priori compactness, which allow meaningful interpretation in the smoothness of the model dependence. Two kinds of regularization was used, first limited squares of coefficients responsible for nonlinearity and second limited multiplication of the above coeffi- cients and linear coefficients. Asymptotic universality of neural net ability to approxi- mate various smooth functions with any accuracy by increase of the number of tunable parameters is often the base for selecting a type of neural net approximation. It is pos- sible to show that used neural net will approach to Fourier integral transform, which approximate abilities are known, with increasing of the number of tunable parameters. In the limiting case, when input data is set with zero precision, the problem of recon- struction of hidden parameters with observed output data appears. The

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.

Selection of noise parameters for Kalman filter

Institute of Scientific and Technical Information of China (English)

Ka-Veng Yuen; Ka-In Hoi; Kai-Meng Mok

2007-01-01

The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.

Effects of Input Data Content on the Uncertainty of Simulating Water Resources

Directory of Open Access Journals (Sweden)

Carla Camargos

2018-05-01

Full Text Available The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT requires a large amount of spatial input data, such as a digital elevation model (DEM, land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis–Hasting Markov Chain Monte Carlo (MCMC algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45 for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Science.gov (United States)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

International Nuclear Information System (INIS)

Zhang, Chu; Zhou, Jianzhong; Li, Chaoshun; Fu, Wenlong; Peng, Tian

2017-01-01

Highlights: • A novel hybrid approach is proposed for wind speed forecasting. • The variational mode decomposition (VMD) is optimized to decompose the original wind speed series. • The input matrix and parameters of ELM are optimized simultaneously by using a hybrid BSA. • Results show that OVMD-HBSA-ELM achieves better performance in terms of prediction accuracy. - Abstract: Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the

Guidelines for the Selection of Near-Earth Thermal Environment Parameters for Spacecraft Design

Science.gov (United States)

Anderson, B. J.; Justus, C. G.; Batts, G. W.

2001-01-01

Thermal analysis and design of Earth orbiting systems requires specification of three environmental thermal parameters: the direct solar irradiance, Earth's local albedo, and outgoing longwave radiance (OLR). In the early 1990s data sets from the Earth Radiation Budget Experiment were analyzed on behalf of the Space Station Program to provide an accurate description of these parameters as a function of averaging time along the orbital path. This information, documented in SSP 30425 and, in more generic form in NASA/TM-4527, enabled the specification of the proper thermal parameters for systems of various thermal response time constants. However, working with the engineering community and SSP-30425 and TM-4527 products over a number of years revealed difficulties in interpretation and application of this material. For this reason it was decided to develop this guidelines document to help resolve these issues of practical application. In the process, the data were extensively reprocessed and a new computer code, the Simple Thermal Environment Model (STEM) was developed to simplify the process of selecting the parameters for input into extreme hot and cold thermal analyses and design specifications. In the process, greatly improved values for the cold case OLR values for high inclination orbits were derived. Thermal parameters for satellites in low, medium, and high inclination low-Earth orbit and with various system thermal time constraints are recommended for analysis of extreme hot and cold conditions. Practical information as to the interpretation and application of the information and an introduction to the STEM are included. Complete documentation for STEM is found in the user's manual, in preparation.

Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system

KAUST Repository

Belkhatir, Zehor; Mechhoud, Sarra; Laleg-Kirati, Taous-Meriem

2016-01-01

This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain

Selection of regularization parameter for l1-regularized damage detection

Science.gov (United States)

Hou, Rongrong; Xia, Yong; Bao, Yuequan; Zhou, Xiaoqing

2018-06-01

The l1 regularization technique has been developed for structural health monitoring and damage detection through employing the sparsity condition of structural damage. The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution. However, the l1 regularization problem has no closed-form solution, and the regularization parameter is usually selected by experience. This study proposes two strategies of selecting the regularization parameter for the l1-regularized damage detection problem. The first method utilizes the residual and solution norms of the optimization problem and ensures that they are both small. The other method is based on the discrepancy principle, which requires that the variance of the discrepancy between the calculated and measured responses is close to the variance of the measurement noise. The two methods are applied to a cantilever beam and a three-story frame. A range of the regularization parameter, rather than one single value, can be determined. When the regularization parameter in this range is selected, the damage can be accurately identified even for multiple damage scenarios. This range also indicates the sensitivity degree of the damage identification problem to the regularization parameter.

Basic Thermal Parameters of Selected Foods and Food Raw Materials

OpenAIRE

Monika Božiková; Ľubomír Híreš; Michal Valach; Martin Malínek; Jan Mareček

2017-01-01

In general, processing and manipulation with foods and food raw materials have significant influence on their physical properties. The article is focused on thermophysical parameters measurement of selected foods and food raw materials. There were examined thermal conductivity and thermal diffusivity of selected materials. For detection of thermal parameters was used instrument Isomet 2104, which principle of measurement is based on transient methods. In text are presented summary results of ...

Production and Application of Domestic Input Data for Safety Assessment of Disposal

International Nuclear Information System (INIS)

Park, Chung Kyun; Lee, Jae Kwang; Baik, Min Hoon; Lee, Youn Myoung; Ko, Nak Youl; Jeong, Jong Tae

2012-01-01

To provide domestic values of input parameters in a safety assessment of radioactive waste disposal under domestic deep underground environments, various kinds of experiments have been carried out under KURT (KAERI Underground Research Tunnel) conditions. The input parameters were classified, and some of them were selected for this study by the criteria of importance. The domestic experimental data under KURT environments were given top priority in the data review process. Foreign data under similar conditions to KURT were also gathered. The collected data were arranged and the statistical calculations were processed. The properties and distribution of the data were explained and compared to foreign values in view of their validity. The following parameters were analysed: failure time and early time failure rate of a container, solubility of nuclides, porosity and density of the buffer, and distribution coefficients of nuclides in the geomedia, hydraulic conductivity, diffusion depth of nuclides, groundwater flow rate, fracture aperture, length of internal fracture, and width of faulted rock mass in the host rock.

Nuclear model parameter testing for nuclear data evaluation (Reference Input Parameter Library: Phase II). Summary report of the third research co-ordination meeting

International Nuclear Information System (INIS)

Herman, M.

2002-04-01

This report summarises the results and recommendations of the third Research Co-ordination Meeting on improving and testing the Reference Input Parameter Library: Phase II. A primary aim of the meeting was to review the achievements of the CRP, to assess the testing of the library and to approve the final contents. Actions were approved that will result in completion of the file and a draft report by the end of February 2002. Full release of the library is scheduled for July 2002. (author)

Nuclear model parameter testing for nuclear data evaluation (Reference Input Parameter Library: Phase II). Summary report of the second research co-ordination meeting

International Nuclear Information System (INIS)

Herman, M.

2000-09-01

This report summarizes the results and recommendations of the Second Research Coordination Meeting on Testing and Improvement of the Reference Input Parameter Library: Phase II. A primary aim of this meeting was to review progress in the CRP work, to review results of testing the library, to establish the RIPL-2 format and to decide on the contents of the library. The actions were agreed with an aim to complete the project by the end of 2001. Separate abstracts were prepared for 10 individual papers

The Effect of Vocabulary Self-Selection Strategy and Input Enhancement Strategy on the Vocabulary Knowledge of Iranian EFL Learners

Science.gov (United States)

Masoudi, Golfam

2017-01-01

The present study was designed to investigate empirically the effect of Vocabulary Self-Selection strategy and Input Enhancement strategy on the vocabulary knowledge of Iranian EFL Learners. After taking a diagnostic pretest, both experimental groups enrolled in two classes. Learners who practiced Vocabulary Self-Selection were allowed to…

WORM: A general-purpose input deck specification language

International Nuclear Information System (INIS)

Jones, T.

1999-01-01

Using computer codes to perform criticality safety calculations has become common practice in the industry. The vast majority of these codes use simple text-based input decks to represent the geometry, materials, and other parameters that describe the problem. However, the data specified in input files are usually processed results themselves. For example, input decks tend to require the geometry specification in linear dimensions and materials in atom or weight fractions, while the parameter of interest might be mass or concentration. The calculations needed to convert from the item of interest to the required parameter in the input deck are usually performed separately and then incorporated into the input deck. This process of calculating, editing, and renaming files to perform a simple parameter study is tedious at best. In addition, most computer codes require dimensions to be specified in centimeters, while drawings or other materials used to create the input decks might be in other units. This also requires additional calculation or conversion prior to composition of the input deck. These additional calculations, while extremely simple, introduce a source for error in both the calculations and transcriptions. To overcome these difficulties, WORM (Write One, Run Many) was created. It is an easy-to-use programming language to describe input decks and can be used with any computer code that uses standard text files for input. WORM is available, via the Internet, at worm.lanl.gov. A user's guide, tutorials, example models, and other WORM-related materials are also available at this Web site. Questions regarding WORM should be directed to wormatlanl.gov

Plant Friendly Input Design for Parameter Estimation in an Inertial System with Respect to D-Efficiency Constraints

Directory of Open Access Journals (Sweden)

Wiktor Jakowluk

2014-11-01

Full Text Available System identification, in practice, is carried out by perturbing processes or plants under operation. That is why in many industrial applications a plant-friendly input signal would be preferred for system identification. The goal of the study is to design the optimal input signal which is then employed in the identification experiment and to examine the relationships between the index of friendliness of this input signal and the accuracy of parameter estimation when the measured output signal is significantly affected by noise. In this case, the objective function was formulated through maximisation of the Fisher information matrix determinant (D-optimality expressed in conventional Bolza form. As setting such conditions of the identification experiment we can only talk about the D-suboptimality, we quantify the plant trajectories using the D-efficiency measure. An additional constraint, imposed on D-efficiency of the solution, should allow one to attain the most adequate information content  from the plant which operating point is perturbed in the least invasive (most friendly way. A simple numerical example, which clearly demonstrates the idea presented in the paper, is included and discussed.

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

Science.gov (United States)

Majumder, Himadri; Maity, Kalipada

2018-03-01

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

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

Science.gov (United States)

Shoaib, Syed Abu; Marshall, Lucy; Sharma, Ashish

2018-06-01

Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

Selective laser melting of Ni-rich NiTi: selection of process parameters and the superelastic response

Science.gov (United States)

Shayesteh Moghaddam, Narges; Saedi, Soheil; Amerinatanzi, Amirhesam; Saghaian, Ehsan; Jahadakbar, Ahmadreza; Karaca, Haluk; Elahinia, Mohammad

2018-03-01

Material and mechanical properties of NiTi shape memory alloys strongly depend on the fabrication process parameters and the resulting microstructure. In selective laser melting, the combination of parameters such as laser power, scanning speed, and hatch spacing determine the microstructural defects, grain size and texture. Therefore, processing parameters can be adjusted to tailor the microstructure and mechanical response of the alloy. In this work, NiTi samples were fabricated using Ni50.8Ti (at.%) powder via SLM PXM by Phenix/3D Systems and the effects of processing parameters were systematically studied. The relationship between the processing parameters and superelastic properties were investigated thoroughly. It will be shown that energy density is not the only parameter that governs the material response. It will be shown that hatch spacing is the dominant factor to tailor the superelastic response. It will be revealed that with the selection of right process parameters, perfect superelasticity with recoverable strains of up to 5.6% can be observed in the as-fabricated condition.

An Interoperability Consideration in Selecting Domain Parameters for Elliptic Curve Cryptography

Science.gov (United States)

Ivancic, Will (Technical Monitor); Eddy, Wesley M.

2005-01-01

Elliptic curve cryptography (ECC) will be an important technology for electronic privacy and authentication in the near future. There are many published specifications for elliptic curve cryptosystems, most of which contain detailed descriptions of the process for the selection of domain parameters. Selecting strong domain parameters ensures that the cryptosystem is robust to attacks. Due to a limitation in several published algorithms for doubling points on elliptic curves, some ECC implementations may produce incorrect, inconsistent, and incompatible results if domain parameters are not carefully chosen under a criterion that we describe. Few documents specify the addition or doubling of points in such a manner as to avoid this problematic situation. The safety criterion we present is not listed in any ECC specification we are aware of, although several other guidelines for domain selection are discussed in the literature. We provide a simple example of how a set of domain parameters not meeting this criterion can produce catastrophic results, and outline a simple means of testing curve parameters for interoperable safety over doubling.

Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning.

Science.gov (United States)

Iwata, Kazunori

2016-05-11

Softmax selection is one of the most popular methods for action selection in reinforcement learning. Although various recently proposed methods may be more effective with full parameter tuning, implementing a complicated method that requires the tuning of many parameters can be difficult. Thus, softmax selection is still worth revisiting, considering the cost savings of its implementation and tuning. In fact, this method works adequately in practice with only one parameter appropriately set for the environment. The aim of this paper is to improve the variable setting of this method to extend the bandwidth of good parameters, thereby reducing the cost of implementation and parameter tuning. To achieve this, we take advantage of the asymptotic equipartition property in a Markov decision process to extend the peak bandwidth of softmax selection. Using a variety of episodic tasks, we show that our setting is effective in extending the bandwidth and that it yields a better policy in terms of stability. The bandwidth is quantitatively assessed in a series of statistical tests.

Modeling and generating input processes

Energy Technology Data Exchange (ETDEWEB)

Johnson, M.E.

1987-01-01

This tutorial paper provides information relevant to the selection and generation of stochastic inputs to simulation studies. The primary area considered is multivariate but much of the philosophy at least is relevant to univariate inputs as well. 14 refs.

The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis

Directory of Open Access Journals (Sweden)

He Yan

2016-01-01

Full Text Available The particle swarm optimization (PSO is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO.

Experiments for practical education in process parameter optimization for selective laser sintering to increase workpiece quality

Science.gov (United States)

Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas

2016-04-01

Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.

An analysis of selected parameters for the BIOPATH-program

International Nuclear Information System (INIS)

Bergstroem, U.; Wilkens, A.B.

1983-06-01

The radioecological model BIOPATH (82) has been used to calculate the radiological contributions to man from leakage of groundwaterborne nuclides from a repository. BIOPATH is a computer code based upon compartment theory. A revision of the most necessary input data for the BIOPATH code has been performed. Both nuclide independent and nuclide dependent parameters have been treated. Especially for the uptake in food chains statistical properties are given to enable sensitivity analyses of the results. The present diet and food consumption habits are reviewed where average intake values are given both for Sweden and for different parts of the world. (authors)

Selected parameters of moulding sands for designing quality control systems

Directory of Open Access Journals (Sweden)

J. Jakubski

2010-07-01

Full Text Available One of the modern methods of production optimisation are artificial neural networks. Neural networks owe their popularity to the fact thatthey are convenient tools, which can be utilised in a wide scope of problems. They are capable of reflecting complex functions. Especiallytheir non-linearity should be emphasised. They are gaining wider and wider application in the foundry industry, among others, to controlmelting processes in cupolas and arc furnaces, designing castings and supply systems, control of moulding sands treatments, prediction ofproperties of cast alloys as well as selecting die casting.An attempt of the application neural networks to the quality control of moulding sands with bentonite is presented in the paper. This isa method of assessing the suitability of moulding sands by finding correlations in between individual parameters, by means of artificialneural network systems. The presented investigations were performed with the application of the Statistica 8.0 program.The investigations were aimed at the selection of the proper kind of a neural network for prediction a sand moistness on the bases ofcertain moulding sand properties such as: permeability, compactibility and friability. These parameters – determined as sand moistness functions - were introduced as initial parameters.Application of the Statistica program allowed for an automatic selection of the most suitable network for the reflection of dependencies and interactions existing among the proposed parameters. The best results were obtained for unidirectional multi-layer perception network (MLP. The neural network sensitivity to individual moulding sand parameters was determined, which allowed to reject not important parameters when constructing the network.

Selection and collection of multi parameter physiological data for cardiac rhythm diagnostic algorithm development

Energy Technology Data Exchange (ETDEWEB)

Bostock, J.; Weller, P. [School of Informatics, City University London, London EC1V 0HB (United Kingdom); Cooklin, M., E-mail: jbostock1@msn.co [Cardiovascular Directorate, Guy' s and St. Thomas' NHS Foundation Trust, London, SE1 7EH (United Kingdom)

2010-07-01

Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.

Selection and collection of multi parameter physiological data for cardiac rhythm diagnostic algorithm development

International Nuclear Information System (INIS)

Bostock, J.; Weller, P.; Cooklin, M.

2010-01-01

Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions.

Science.gov (United States)

Blank, Idan A; Fedorenko, Evelina

2017-10-11

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people ( n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

Science.gov (United States)

Ramadan, Ahmed; Boss, Connor; Choi, Jongeun; Peter Reeves, N; Cholewicki, Jacek; Popovich, John M; Radcliffe, Clark J

2018-07-01

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

CSIR Research Space (South Africa)

Oosthuizen, Nadia

2017-07-01

Full Text Available frica Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin Nadia Oosthuizen1,2, Denis A. Hughes2, Evison Kapangaziwiri1, Jean-Marc Mwenge Kahinda1, and Vuyelwa Mvandaba1,2 1...

Optimal Parameter Selection of Power System Stabilizer using Genetic Algorithm

Energy Technology Data Exchange (ETDEWEB)

Chung, Hyeng Hwan; Chung, Dong Il; Chung, Mun Kyu [Dong-AUniversity (Korea); Wang, Yong Peel [Canterbury Univeristy (New Zealand)

1999-06-01

In this paper, it is suggested that the selection method of optimal parameter of power system stabilizer (PSS) with robustness in low frequency oscillation for power system using real variable elitism genetic algorithm (RVEGA). The optimal parameters were selected in the case of power system stabilizer with one lead compensator, and two lead compensator. Also, the frequency responses characteristics of PSS, the system eigenvalues criterion and the dynamic characteristics were considered in the normal load and the heavy load, which proved usefulness of RVEGA compare with Yu's compensator design theory. (author). 20 refs., 15 figs., 8 tabs.

Star Classification for the Kepler Input Catalog: From Images to Stellar Parameters

Science.gov (United States)

Brown, T. M.; Everett, M.; Latham, D. W.; Monet, D. G.

2005-12-01

The Stellar Classification Project is a ground-based effort to screen stars within the Kepler field of view, to allow removal of stars with large radii (and small potential transit signals) from the target list. Important components of this process are: (1) An automated photometry pipeline estimates observed magnitudes both for target stars and for stars in several calibration fields. (2) Data from calibration fields yield extinction-corrected AB magnitudes (with g, r, i, z magnitudes transformed to the SDSS system). We merge these with 2MASS J, H, K magnitudes. (3) The Basel grid of stellar atmosphere models yields synthetic colors, which are transformed to our photometric system by calibration against observations of stars in M67. (4) We combine the r magnitude and stellar galactic latitude with a simple model of interstellar extinction to derive a relation connecting {Teff, luminosity} to distance and reddening. For models satisfying this relation, we compute a chi-squared statistic describing the match between each model and the observed colors. (5) We create a merit function based on the chi-squared statistic, and on a Bayesian prior probability distribution which gives probability as a function of Teff, luminosity, log(Z), and height above the galactic plane. The stellar parameters ascribed to a star are those of the model that maximizes this merit function. (6) Parameter estimates are merged with positional and other information from extant catalogs to yield the Kepler Input Catalog, from which targets will be chosen. Testing and validation of this procedure are underway, with encouraging initial results.

Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

Science.gov (United States)

Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

2015-04-01

This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

Basic Thermal Parameters of Selected Foods and Food Raw Materials

Directory of Open Access Journals (Sweden)

Monika Božiková

2017-01-01

Full Text Available In general, processing and manipulation with foods and food raw materials have significant influence on their physical properties. The article is focused on thermophysical parameters measurement of selected foods and food raw materials. There were examined thermal conductivity and thermal diffusivity of selected materials. For detection of thermal parameters was used instrument Isomet 2104, which principle of measurement is based on transient methods. In text are presented summary results of thermal parameters measurement for various foods and food raw materials as: granular materials – corn flour and wheat flour; fruits, vegetables and fruit products – grated apple, dried apple and apple juice; liquid materials – milk, beer etc. Measurements were performed in two temperature ranges according to the character of examined material. From graphical relations of thermophysical parameter is evident, that thermal conductivity and diffusivity increases with temperature and moisture content linearly, only for granular materials were obtained non‑linear dependencies. Results shows, that foods and food raw materials have different thermal properties, which are influenced by their type, structure, chemical and physical properties. From presented results is evident, that basic thermal parameters are important for material quality detection in food industry.

Evaluation of selected sewage sludge gasification technological parameters

Science.gov (United States)

Gałko, Grzegorz; Król, Danuta

2018-02-01

Evaluation of selected sewage sludge gasification technological parameters was shown in this paper. Degree of carbon conducted in combustible substance and syngas efficiency (technological readiness coefficient) in accordance with equations were calculated. Enthalpy of individual compounds formation and energy balance were calculated in accordance with rule of Hess.

Modelling Technical and Economic Parameters in Selection of Manufacturing Devices

Directory of Open Access Journals (Sweden)

Naqib Daneshjo

2017-11-01

Full Text Available Sustainable science and technology development is also conditioned by continuous development of means of production which have a key role in structure of each production system. Mechanical nature of the means of production is complemented by controlling and electronic devices in context of intelligent industry. A selection of production machines for a technological process or technological project has so far been practically resolved, often only intuitively. With regard to increasing intelligence, the number of variable parameters that have to be considered when choosing a production device is also increasing. It is necessary to use computing techniques and decision making methods according to heuristic methods and more precise methodological procedures during the selection. The authors present an innovative model for optimization of technical and economic parameters in the selection of manufacturing devices for industry 4.0.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin, E-mail: dengbin@tju.edu.cn; Chan, Wai-lok [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2016-06-15

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

Incorporating uncertainty in RADTRAN 6.0 input files.

Energy Technology Data Exchange (ETDEWEB)

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John (Alion Science and Technology)

2010-02-01

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine is required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.

Parameter selection for the SSC trade-offs and optimization

International Nuclear Information System (INIS)

Edwards, D.A.; Syphers, M.J.

1991-01-01

In November of 1988, a site was selected in the state of Texas for the SSC. In January of 1989, the SSC Laboratory was established in Texas to adapt the design of the collider to the site and to manage the construction of the project. This paper describes the evolution of the SSC design since site selection, notes the increased concentration on the injector system, and addresses the rationale for choice of parameters

ANALYSIS THE DIURNAL VARIATIONS ON SELECTED PHYSICAL AND PHYSIOLOGICAL PARAMETERS

Directory of Open Access Journals (Sweden)

A. MAHABOOBJAN

2010-12-01

Full Text Available The purpose of the study was to analyze the diurnal variations on selected physical and physiological parameters such as speed, explosive power, resting heart rate and breath holding time among college students. To achieve the purpose of this study, a total of twenty players (n=20 from Government Arts College, Salem were selected as subjects To study the diurnal variation of the players on selected physiological and performance variables, the data were collected 4 times a day with every four hours in between the times it from 6.00 to 18.00 hours were selected as another categorical variable. One way repeated measures (ANOVA was used to analyze the data. If the obtained F-ratio was significant, Seheffe’s post-hoc test was used to find out the significant difference if anyamong the paired means. The level of significance was fixed at.05 level. It has concluded that both physical and physiological parameters were significantly deferred with reference to change of temperature in a day

A Dual-Bridge LLC Resonant Converter with Fixed-Frequency PWM Control for Wide Input Applications

DEFF Research Database (Denmark)

Xiaofeng, Sun; Li, Xiaohua; Shen, Yanfeng

2017-01-01

This paper proposes a dual-bridge (DB) LLC resonant converter for wide input applications. The topology is an integration of a half-bridge (HB) LLC circuit and a full-bridge (FB) LLC circuit. The fixed-frequency PWM control is employed and a range of twice the minimum input voltage can be covered....... Compared with the traditional pulse frequency modulation (PFM) controlled HB/FB LLC resonant converter, the voltage gain range is independent of the quality factor and the magnetizing inductor has little influence on the voltage gain, which can simplify the parameter selection process and benefit...

Working fluid selection for organic Rankine cycles - Impact of uncertainty of fluid properties

DEFF Research Database (Denmark)

Frutiger, Jerome; Andreasen, Jesper Graa; Liu, Wei

2016-01-01

of processmodels and constraints 2) selection of property models, i.e. Penge Robinson equation of state 3)screening of 1965 possible working fluid candidates including identification of optimal process parametersbased on Monte Carlo sampling 4) propagating uncertainty of fluid parameters to the ORC netpower output......This study presents a generic methodology to select working fluids for ORC (Organic Rankine Cycles)taking into account property uncertainties of the working fluids. A Monte Carlo procedure is described as a tool to propagate the influence of the input uncertainty of the fluid parameters on the ORC....... The net power outputs of all the feasible working fluids were ranked including their uncertainties. The method could propagate and quantify the input property uncertainty of the fluidproperty parameters to the ORC model, giving an additional dimension to the fluid selection process. In the given analysis...

Parameters in selective laser melting for processing metallic powders

Science.gov (United States)

Kurzynowski, Tomasz; Chlebus, Edward; Kuźnicka, Bogumiła; Reiner, Jacek

2012-03-01

The paper presents results of studies on Selective Laser Melting. SLM is an additive manufacturing technology which may be used to process almost all metallic materials in the form of powder. Types of energy emission sources, mainly fiber lasers and/or Nd:YAG laser with similar characteristics and the wavelength of 1,06 - 1,08 microns, are provided primarily for processing metallic powder materials with high absorption of laser radiation. The paper presents results of selected variable parameters (laser power, scanning time, scanning strategy) and fixed parameters such as the protective atmosphere (argon, nitrogen, helium), temperature, type and shape of the powder material. The thematic scope is very broad, so the work was focused on optimizing the process of selective laser micrometallurgy for producing fully dense parts. The density is closely linked with other two conditions: discontinuity of the microstructure (microcracks) and stability (repeatability) of the process. Materials used for the research were stainless steel 316L (AISI), tool steel H13 (AISI), and titanium alloy Ti6Al7Nb (ISO 5832-11). Studies were performed with a scanning electron microscope, a light microscopes, a confocal microscope and a μCT scanner.

Input variable selection for interpolating high-resolution climate ...

African Journals Online (AJOL)

Although the primary input data of climate interpolations are usually meteorological data, other related (independent) variables are frequently incorporated in the interpolation process. One such variable is elevation, which is known to have a strong influence on climate. This research investigates the potential of 4 additional ...

A Parameter Selection Method for Wind Turbine Health Management through SCADA Data

Directory of Open Access Journals (Sweden)

Mian Du

2017-02-01

Full Text Available Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copulabased mutual information estimation method (ECMI is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

Transportation radiological risk assessment for the programmatic environmental impact statement: An overview of methodologies, assumptions, and input parameters

International Nuclear Information System (INIS)

Monette, F.; Biwer, B.; LePoire, D.; Chen, S.Y.

1994-01-01

The U.S. Department of Energy is considering a broad range of alternatives for the future configuration of radioactive waste management at its network of facilities. Because the transportation of radioactive waste is an integral component of the management alternatives being considered, the estimated human health risks associated with both routine and accident transportation conditions must be assessed to allow a complete appraisal of the alternatives. This paper provides an overview of the technical approach being used to assess the radiological risks from the transportation of radioactive wastes. The approach presented employs the RADTRAN 4 computer code to estimate the collective population risk during routine and accident transportation conditions. Supplemental analyses are conducted using the RISKIND computer code to address areas of specific concern to individuals or population subgroups. RISKIND is used for estimating routine doses to maximally exposed individuals and for assessing the consequences of the most severe credible transportation accidents. The transportation risk assessment is designed to ensure -- through uniform and judicious selection of models, data, and assumptions -- that relative comparisons of risk among the various alternatives are meaningful. This is accomplished by uniformly applying common input parameters and assumptions to each waste type for all alternatives. The approach presented can be applied to all radioactive waste types and provides a consistent and comprehensive evaluation of transportation-related risk

Algorithms of control parameters selection for automation of FDM 3D printing process

Directory of Open Access Journals (Sweden)

Kogut Paweł

2017-01-01

Full Text Available The paper presents algorithms of control parameters selection of the Fused Deposition Modelling (FDM technology in case of an open printing solutions environment and 3DGence ONE printer. The following parameters were distinguished: model mesh density, material flow speed, cooling performance, retraction and printing speeds. These parameters are independent in principle printing system, but in fact to a certain degree that results from the selected printing equipment features. This is the first step for automation of the 3D printing process in FDM technology.

Better temperature predictions in geothermal modelling by improved quality of input parameters: a regional case study from the Danish-German border region

Science.gov (United States)

Fuchs, Sven; Bording, Thue S.; Balling, Niels

2015-04-01

Thermal modelling is used to examine the subsurface temperature field and geothermal conditions at various scales (e.g. sedimentary basins, deep crust) and in the framework of different problem settings (e.g. scientific or industrial use). In such models, knowledge of rock thermal properties is prerequisites for the parameterisation of boundary conditions and layer properties. In contrast to hydrogeological ground-water models, where parameterization of the major rock property (i.e. hydraulic conductivity) is generally conducted considering lateral variations within geological layers, parameterization of thermal models (in particular regarding thermal conductivity but also radiogenic heat production and specific heat capacity) in most cases is conducted using constant parameters for each modelled layer. For such constant thermal parameter values, moreover, initial values are normally obtained from rare core measurements and/or literature values, which raise questions for their representativeness. Some few studies have considered lithological composition or well log information, but still keeping the layer values constant. In the present thermal-modelling scenario analysis, we demonstrate how the use of different parameter input type (from literature, well logs and lithology) and parameter input style (constant or laterally varying layer values) affects the temperature model prediction in sedimentary basins. For this purpose, rock thermal properties are deduced from standard petrophysical well logs and lithological descriptions for several wells in a project area. Statistical values of thermal properties (mean, standard deviation, moments, etc.) are calculated at each borehole location for each geological formation and, moreover, for the entire dataset. Our case study is located at the Danish-German border region (model dimension: 135 x115 km, depth: 20 km). Results clearly show that (i) the use of location-specific well-log derived rock thermal properties and (i

Selective enhancement of main olfactory input to the medial amygdala by GnRH.

Science.gov (United States)

Blake, Camille Bond; Meredith, Michael

2010-03-04

In male hamsters mating behavior is dependent on chemosensory input from the main olfactory and vomeronasal systems, whose central pathways contain cell bodies and fibers of gonadotropin-releasing hormone (GnRH) neurons. In sexually naive males, vomeronasal organ removal (VNX), but not main olfactory lesions, impairs mating behavior. Intracerebroventricular (i.c.v.)-GnRH restores mating in sexually naive VNX males and enhances medial amygdala (Me) immediate-early gene activation by chemosensory stimulation. In sexually experienced males, VNX does not impair mating and i.c.v.-GnRH suppresses Me activation. Thus, the main olfactory system is sufficient for mating in experienced-VNX males, but not in naive-VNX males. We investigated the possibility that GnRH enhances main olfactory input to the amygdala in naive-VNX males using i.c.v.-GnRH and pharmacological stimulation (bicuculline/D,L-homocysteic acid mixture) of the main olfactory bulb (MOB). In sexually naive intact males there was a robust increase of Fos protein expression in the anteroventral medial amygdala (MeAv) with MOB stimulation, but no effect of GnRH. There was no effect of stimulation or GnRH in posterodorsal medial amygdala (MePd). In naive-VNX animals, GnRH increased Fos in MeAv and MePv. Only combined MOB stimulation and i.c.v.-GnRH produced a significant increase in Fos in the dorsal (reproduction-related) portion of MeP (MePd). When the animals were sexually experienced before VNX, a condition in which GnRH does not enhance mating, i.c.v.-GnRH combined with MOB stimulation suppressed Fos expression in MePd. This suggests a more selective effect of GnRH on olfactory input in MePd than elsewhere in medial amygdala of VNX males. 2009 Elsevier B.V. All rights reserved.

Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Selective inhibition of distracting input.

Science.gov (United States)

Noonan, MaryAnn P; Crittenden, Ben M; Jensen, Ole; Stokes, Mark G

2017-10-16

We review a series of studies exploring distractor suppression. It is often assumed that preparatory distractor suppression is controlled via top-down mechanisms of attention akin to those that prepare brain areas for target enhancement. Here, we consider two alternative mechanisms: secondary inhibition and expectation suppression within a predictive coding framework. We draw on behavioural studies, evidence from neuroimaging and some animal studies. We conclude that there is very limited evidence for selective top-down control of preparatory inhibition. By contrast, we argue that distractor suppression often relies secondary inhibition of non-target items (relatively non-selective inhibition) and on statistical regularities of the environment, learned through direct experience. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters

International Nuclear Information System (INIS)

Stepanova, Marina; Antonova, Elizavieta; Munos-Uribe, F A; Gordo, S L Gomez; Torres-Sanchez, M V

2008-01-01

Different kinds of neural networks have established themselves as an effective tool in the prediction of different geomagnetic indices, including the Dst being the most important constituent for determination of the impact of Space Weather on the human life. Feed-forward networks with one hidden layer are used to forecast the Dst variation, using separately the solar wind paramenters, polar cap index, and auroral electrojet index as input parameters. It was found that in all three cases the storm-time intervals were predicted much more precisely as quite time intervals. The majority of cross-correlation coefficients between predicted and observed Dst of strong geomagnetic storms are situated between 0.8 and 0.9. Changes in the neural network architecture, including the number of nodes in the input and hidden layers and the transfer functions between them lead to an improvement of a network performance up to 10%.

Optimization of Storage Parameters of Selected Fruits in Passive ...

African Journals Online (AJOL)

This study was carried out to determine the optimum storage parameters of selected fruit using three sets of four types of passive evaporative cooling structures made of two different materials clay and aluminium. One set consisted of four separate cooling chambers. Two cooling chambers were made with aluminium ...

Effect of Thermo-extrusion Process Parameters on Selected Quality ...

African Journals Online (AJOL)

Effect of Thermo-extrusion Process Parameters on Selected Quality Attributes of Meat Analogue from Mucuna Bean Seed Flour. ... Nigerian Food Journal ... The product functional responses with coefficients of determination (R2) ranging between 0.658 and 0.894 were most affected by changes in barrel temperature and ...

Selected fishery and population parameters of eight shore-angling ...

African Journals Online (AJOL)

Selected fishery and population parameters of eight shore-angling species in the ... Five different estimates of natural mortality (M), and the coefficients of ... for the most abundant species, blacktail Diplodus capensis, with a mean CPUE of 0.252 ... Keywords: catch per unit effort; fisheries management; marine protected area; ...

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling.

Science.gov (United States)

Li, Xin; Cai, Yu; Moloney, Brendan; Chen, Yiyi; Huang, Wei; Woods, Mark; Coakley, Fergus V; Rooney, William D; Garzotto, Mark G; Springer, Charles S

2016-08-01

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K(trans)) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging

Visual exploration of parameter influence on phylogenetic trees.

Science.gov (United States)

Hess, Martin; Bremm, Sebastian; Weissgraeber, Stephanie; Hamacher, Kay; Goesele, Michael; Wiemeyer, Josef; von Landesberger, Tatiana

2014-01-01

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Modeling and Analysis of CNC Milling Process Parameters on Al3030 based Composite

Science.gov (United States)

Gupta, Anand; Soni, P. K.; Krishna, C. M.

2018-04-01

The machining of Al3030 based composites on Computer Numerical Control (CNC) high speed milling machine have assumed importance because of their wide application in aerospace industries, marine industries and automotive industries etc. Industries mainly focus on surface irregularities; material removal rate (MRR) and tool wear rate (TWR) which usually depends on input process parameters namely cutting speed, feed in mm/min, depth of cut and step over ratio. Many researchers have carried out researches in this area but very few have taken step over ratio or radial depth of cut also as one of the input variables. In this research work, the study of characteristics of Al3030 is carried out at high speed CNC milling machine over the speed range of 3000 to 5000 r.p.m. Step over ratio, depth of cut and feed rate are other input variables taken into consideration in this research work. A total nine experiments are conducted according to Taguchi L9 orthogonal array. The machining is carried out on high speed CNC milling machine using flat end mill of diameter 10mm. Flatness, MRR and TWR are taken as output parameters. Flatness has been measured using portable Coordinate Measuring Machine (CMM). Linear regression models have been developed using Minitab 18 software and result are validated by conducting selected additional set of experiments. Selection of input process parameters in order to get best machining outputs is the key contributions of this research work.

RIP INPUT TABLES FROM WAPDEG FOR LA DESIGN SELECTION: HIGHER THERMAL LOADING

International Nuclear Information System (INIS)

K. Mon

1999-01-01

The purpose of this calculation is to document (1) the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. Software Routine Report for WAPDEG (Version 3.09)) simulations used to analyze waste package degradation and failure under the repository exposure conditions characterized by the higher thermal loading repository design feature and, (2) post-processing of these results into tables of waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems version 5.19.01 (RIP) computer program (Golder Associates 1998). Specifically, the WAPDEG simulations discussed in this calculation correspond to waste package emplacement conditions (repository environment and design) defined in the Total System Performance Assessment-Viability Assessment (TSPA-VA), with the exception that the higher thermal loading Design Feature (Design Feature 26) of the License Application Design Selection (LADS) analysis was analyzed. Higher thermal loading would keep the drift temperature above the boiling point of water for a longer period of time, thereby minimizing moisture around the waste packages during a longer post-closure period. The higher thermal loading would also affect the surrounding rock, which may have adverse effects. The only failure mechanism of this feature would be if the effects on the surrounding rock were determined to be unacceptable. As a result of the change in waste package placement relative to the TSPA-VA base-case design, different temperature and relative humidity time histories at the waste package surface are calculated (input to the WAPDEG simulations), and consequently different waste package failure histories (as calculated by WAPDEG) result

Sensitivity of seismic design parameters to input variables

International Nuclear Information System (INIS)

Wium, D.J.W.

1987-01-01

The probabilistic method introduced by Cornell (1968) has been used to a large extent for this purpose. Due to its probabilistic approach, this technique provides a sound basis for studying the influence of the dominant parameters in such a model. Although the Southern African region is not well known for its seismicity, a number of events in the recent past has focussed the attention on some seismically active areas where special attention may be needed in defining the correct design parameters. The relatively sparse historical seismic data has been used to develop a mathematical model which represents this region. This paper briefly discusses this model, and uses it as a basis for evaluating the influence of the uncertainty in each of the principal parameters, being the seismicity of the region, the attenuation of seismic waves after an event, and models that can be used to arrive at engineering design values. (orig./HP)

Selection method and device for reactor core performance calculation input indication

International Nuclear Information System (INIS)

Yuto, Yoshihiro.

1994-01-01

The position of a reactor core component on a reactor core map, which is previously designated and optionally changeable, is displayed by different colors on a CRT screen by using data of a data file incorporating results of a calculation for reactor core performance, such as incore thermal limit values. That is, an operator specifies the kind of the incore component to be sampled on a menu screen, to display the position of the incore component which satisfies a predetermined condition on the CRT screen by different colors in the form of a reactor core map. The position for the reactor core component displayed on the CRT screen by different colors is selected and designated on the screen by a touch panel, a mouse or a light pen, thereby automatically outputting detailed data of evaluation for the reactor core performance of the reactor core component at the indicated position. Retrieval of coordinates of fuel assemblies to be data sampled and input of the coordinates and demand for data sampling can be conducted at once by one menu screen. (N.H.)

Selecting of key safety parameters in reactor nuclear safety supervision

International Nuclear Information System (INIS)

He Fan; Yu Hong

2014-01-01

The safety parameters indicate the operational states and safety of research reactor are the basis of nuclear safety supervision institution to carry out effective supervision to nuclear facilities. In this paper, the selecting of key safety parameters presented by the research reactor operating unit to National Nuclear Safety Administration that can express the research reactor operational states and safety when operational occurrence or nuclear accident happens, and the interrelationship between them are discussed. Analysis shows that, the key parameters to nuclear safety supervision of research reactor including design limits, operational limits and conditions, safety system settings, safety limits, acceptable limits and emergency action level etc. (authors)

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Science.gov (United States)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Input reduction for long-term morphodynamic simulations

NARCIS (Netherlands)

Walstra, D.J.R.; Ruessink, G.; Hoekstra, R.; Tonnon, P.K.

2013-01-01

Input reduction is imperative to long-term (> years) morphodynamic simulations to avoid excessive computation times. Here, we discuss the input-reduction framework for wave-dominated coastal settings introduced by Walstra et al. (2013). The framework comprised 4 steps, viz. (1) the selection of the

Methods, Devices and Computer Program Products Providing for Establishing a Model for Emulating a Physical Quantity Which Depends on at Least One Input Parameter, and Use Thereof

DEFF Research Database (Denmark)

2014-01-01

The present invention proposes methods, devices and computer program products. To this extent, there is defined a set X including N distinct parameter values x_i for at least one input parameter x, N being an integer greater than or equal to 1, first measured the physical quantity Pm1 for each...

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

Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer.

Science.gov (United States)

Chen, Xiaoliang; Xu, Yanyan; Duan, Jianghui; Li, Chuandong; Sun, Hongliang; Wang, Wu

2017-07-01

To investigate the potential relationship between perfusion parameters from first-pass dual-input perfusion computed tomography (DI-PCT) and iodine uptake levels estimated from dual-energy CT (DE-CT).The pre-experimental part of this study included a dynamic DE-CT protocol in 15 patients to evaluate peak arterial enhancement of lung cancer based on time-attenuation curves, and the scan time of DE-CT was determined. In the prospective part of the study, 28 lung cancer patients underwent whole-volume perfusion CT and single-source DE-CT using 320-row CT. Pulmonary flow (PF, mL/min/100 mL), aortic flow (AF, mL/min/100 mL), and a perfusion index (PI = PF/[PF + AF]) were automatically generated by in-house commercial software using the dual-input maximum slope method for DI-PCT. For the dual-energy CT data, iodine uptake was estimated by the difference (λ) and the slope (λHU). λ was defined as the difference of CT values between 40 and 70 KeV monochromatic images in lung lesions. λHU was calculated by the following equation: λHU = |λ/(70 - 40)|. The DI-PCT and DE-CT parameters were analyzed by Pearson/Spearman correlation analysis, respectively.All subjects were pathologically proved as lung cancer patients (including 16 squamous cell carcinoma, 8 adenocarcinoma, and 4 small cell lung cancer) by surgery or CT-guided biopsy. Interobserver reproducibility in DI-PCT (PF, AF, PI) and DE-CT (λ, λHU) were relatively good to excellent (intraclass correlation coefficient [ICC]Inter = 0.8726-0.9255, ICCInter = 0.8179-0.8842; ICCInter = 0.8881-0.9177, ICCInter = 0.9820-0.9970, ICCInter = 0.9780-0.9971, respectively). Correlation coefficient between λ and AF, and PF were as follows: 0.589 (P input CT perfusion analysis method can be applied to assess blood supply of lung cancer patients. Preliminary results demonstrated that the iodine uptake relevant parameters derived from DE-CT significantly correlated with perfusion

Parameter Selection and Performance Analysis of Mobile Terminal Models Based on Unity3D

Institute of Scientific and Technical Information of China (English)

KONG Li-feng; ZHAO Hai-ying; XU Guang-mei

2014-01-01

Mobile platform is now widely seen as a promising multimedia service with a favorable user group and market prospect. To study the influence of mobile terminal models on the quality of scene roaming, a parameter setting platform of mobile terminal models is established to select the parameter selection and performance index on different mobile platforms in this paper. This test platform is established based on model optimality principle, analyzing the performance curve of mobile terminals in different scene models and then deducing the external parameter of model establishment. Simulation results prove that the established test platform is able to analyze the parameter and performance matching list of a mobile terminal model.

READDATA: a FORTRAN 77 codeword input package

International Nuclear Information System (INIS)

Lander, P.A.

1983-07-01

A new codeword input package has been produced as a result of the incompatibility between different dialects of FORTRAN, especially when character variables are passed as parameters. This report is for those who wish to use a codeword input package with FORTRAN 77. The package, called ''Readdata'', attempts to combine the best features of its predecessors such as BINPUT and pseudo-BINPUT. (author)

Cooperative Technique Based on Sensor Selection in Wireless Sensor Network

Directory of Open Access Journals (Sweden)

ISLAM, M. R.

2009-02-01

Full Text Available An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs are used to form a Multiple Input Single Output (MISO structure wirelessly connected with a Network Capable Application Processor (NCAP. Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for evaluating derived parameters. The results show that the selected MISO structure outperforms the unselected MISO structure and it shows energy efficient performance than SISO structure after a certain distance.

PARAMETER ESTIMATION AND MODEL SELECTION FOR INDOOR ENVIRONMENTS BASED ON SPARSE OBSERVATIONS

Directory of Open Access Journals (Sweden)

Y. Dehbi

2017-09-01

Full Text Available This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

Science.gov (United States)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Application of Firefly Algorithm for Parameter Estimation of Damped Compound Pendulum

Directory of Open Access Journals (Sweden)

Saad Mohd Sazli

2016-01-01

Full Text Available This paper presents an investigation into the parameter estimation of the damped compound pendulum using Firefly algorithm method. In estimating the damped compound pendulum, the system necessarily needs a good model. Therefore, the aim of the work described in this paper is to obtain a dynamic model of the damped compound pendulum. By considering a discrete time form for the system, an autoregressive with exogenous input (ARX model structures was selected. In order to collect input-output data from the experiment, the PRBS signal is used to be input signal to regulate the motor speed. Where, the output signal is taken from position sensor. Firefly algorithm (FA algorithm is used to estimate the model parameters based on model 2nd orders. The model validation was done by comparing the measured output against the predicted output in terms of the closeness of both outputs via mean square error (MSE value. The performance of FA is measured in terms of mean square error (MSE.

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Science.gov (United States)

Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

2017-08-01

Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii

The influence of selected teacher inputs on students’ academic achievement in the junior secondary school certificate mathematics in Namibia

Directory of Open Access Journals (Sweden)

Simon E. Akpo

2013-12-01

Full Text Available This study explored the link between teachers’ inputs and students’ academic achievement in the JSC Mathematics for the period 2006 to 2010. One hundred and fifty secondary schools selected from 573 secondary schools in the country constituted the target population. One hundred and sixty-four JSC mathematics teachers from the 150 secondary schools participated in the study, with the final JSC Mathematics results of the students serving as the dependent variable of the study. Mathematics teachers’ input data (academic qualifications, teaching experience and subject specialisation were collected from a questionnaire developed by the researchers. Standard multiple regression was used to analyse the link between teachers’ inputs and students’ academic achievement in JSC Mathematics at P < 0.05and P < 0.10 respectively. The study found that teachers’ academic qualifications and subject specialisation had a significant and positive relationship with students’ academic achievement in JSC Mathematics. Teachers’ gender, however, was not significantly related to students’ academic achievement in JSC Mathematics. This is the first time within the Namibian context that we have empirically demonstrated the link between teachers’ inputs and students’ academic achievement in JSC Mathematics. The study therefore provides support for the policy initiatives that seek to link teachers’ academic qualifications, subject specialisation and teaching experience to employment and classroom allocation.

Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning.

Directory of Open Access Journals (Sweden)

Samat Moldakarimov

2014-08-01

Full Text Available Perceptual learning has been used to probe the mechanisms of cortical plasticity in the adult brain. Feedback projections are ubiquitous in the cortex, but little is known about their role in cortical plasticity. Here we explore the hypothesis that learning visual orientation discrimination involves learning-dependent plasticity of top-down feedback inputs from higher cortical areas, serving a different function from plasticity due to changes in recurrent connections within a cortical area. In a Hodgkin-Huxley-based spiking neural network model of visual cortex, we show that modulation of feedback inputs to V1 from higher cortical areas results in shunting inhibition in V1 neurons, which changes the response properties of V1 neurons. The orientation selectivity of V1 neurons is enhanced without changing orientation preference, preserving the topographic organizations in V1. These results provide new insights to the mechanisms of plasticity in the adult brain, reconciling apparently inconsistent experiments and providing a new hypothesis for a functional role of the feedback connections.

Content dependent selection of image enhancement parameters for mobile displays

Science.gov (United States)

Lee, Yoon-Gyoo; Kang, Yoo-Jin; Kim, Han-Eol; Kim, Ka-Hee; Kim, Choon-Woo

2011-01-01

Mobile devices such as cellular phones and portable multimedia player with capability of playing terrestrial digital multimedia broadcasting (T-DMB) contents have been introduced into consumer market. In this paper, content dependent image quality enhancement method for sharpness and colorfulness and noise reduction is presented to improve perceived image quality on mobile displays. Human visual experiments are performed to analyze viewers' preference. Relationship between the objective measures and the optimal values of image control parameters are modeled by simple lookup tables based on the results of human visual experiments. Content dependent values of image control parameters are determined based on the calculated measures and predetermined lookup tables. Experimental results indicate that dynamic selection of image control parameters yields better image quality.

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Directory of Open Access Journals (Sweden)

Hailun Wang

2017-01-01

Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

Adaptive Control for Revolute Joints Robot Manipulator with Uncertain/Unknown Dynamic Parameters and in Presence of Disturbance in Control Input

DEFF Research Database (Denmark)

Seyed Sakha, Masoud; Shaker, Hamid Reza

2017-01-01

This paper presents an effective adaptive controller for revolute joints robot manipulator where the control input is accompanied with a random disturbance (with unknown PSD). It is clear that, disturbance can compromise the overall performance of the system. To cope with this problem, a control...... technique is proposed which uses the concept of exponential practical stability. Unlike other counterparts, the proposed method does not need information such as the physical parameters of robot and gravitational acceleration. The results show that the proposed controller achieves an excellent performance...

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

Science.gov (United States)

Tsaur, Ruey-Chyn

2015-02-01

In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

Auto type-selection of constant supporting in nuclear power stations

International Nuclear Information System (INIS)

Liu Hu; Wang Fujun; Liu Wei; Li Zhaoqing

2013-01-01

To solve the type-selection of constant supporting in nuclear power stations, combining the characteristics of constant supporting which can adjust in the certain scope and the rules of load-displacement, the requirements and process for the type-selection of constant supporting is proposed, and the process of type-selection is optimized by Visual Basic. After inputting of the known parameters, the process can automatically select the most economical and reasonable constant supporting by array and function. (authors)

RIP Input Tables from WAPDEG for LA Design Selection: Enhanced Design Alternative II-3

International Nuclear Information System (INIS)

A.M. Monib

1999-01-01

The purpose of this calculation is to document (1) the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. ''Software Routine Report for WAPDEG'' (Version 3.09)) simulations used to analyze degradation and failure of 2-cm thick titanium grade 7 corrosion resistant material (CRM) drip shields (that are placed over waste packages composed of a 2-cm thick Alloy 22 corrosion resistant material (CRM) as the outer barrier and an unspecified material to provide structural support as the inner barrier) as well as degradation and failure of the waste packages themselves, and (2) post-processing of these results into tables of drip shield/waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) computer code. This calculation supports Performance Assessment analysis of the License Application Design Selection (LADS) Enhanced Design Alternative (EDA) II-3. The aging period in the EDA II design (CRWMS M and O 1999f. ''Design Input Request for LADS Phase II EDA Evaluations'', Item 1 Row 9 Column 3) was replaced in the case of EDA II-3 with 25 years preclosure ventilation, leading to a total of 50 years preclosure ventilation. The waste packages are line loaded in the repository and no backfill is used

Response to family selection and genetic parameters in Japanese quail selected for four week breast weight

DEFF Research Database (Denmark)

Khaldari, Majid; Yeganeh, Hassan Mehrabani; Pakdel, Abbas

2011-01-01

An experiment was conducted to investigate the effect of short-term selection for 4 week breast weight (4wk BRW), and to estimate genetic parameters of body weight, and carcass traits. A selection (S) line and control (C) line was randomly selected from a base population. Data were collected over...... was 0.35±0.06. There were a significant difference for BW, and carcass weights but not for carcass percent components between lines (Pcarcass and leg weights were 0.46, 0.41 and 0.47, and 13.2, 16.2, 4.4 %, respectively....... The genetic correlations of BRW with BW, carcass, leg, and back weights were 0.85, 0.88 and 0.72, respectively. Selection for 4 wk BRW improved feed conversion ratio (FCR) about 0.19 units over the selection period. Inbreeding caused an insignificant decline of the mean of some traits. Results from...

Statistical identification of effective input variables

International Nuclear Information System (INIS)

Vaurio, J.K.

1982-09-01

A statistical sensitivity analysis procedure has been developed for ranking the input data of large computer codes in the order of sensitivity-importance. The method is economical for large codes with many input variables, since it uses a relatively small number of computer runs. No prior judgemental elimination of input variables is needed. The sceening method is based on stagewise correlation and extensive regression analysis of output values calculated with selected input value combinations. The regression process deals with multivariate nonlinear functions, and statistical tests are also available for identifying input variables that contribute to threshold effects, i.e., discontinuities in the output variables. A computer code SCREEN has been developed for implementing the screening techniques. The efficiency has been demonstrated by several examples and applied to a fast reactor safety analysis code (Venus-II). However, the methods and the coding are general and not limited to such applications

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

Science.gov (United States)

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

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

Science.gov (United States)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

Science.gov (United States)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

Robust input design for nonlinear dynamic modeling of AUV.

Science.gov (United States)

Nouri, Nowrouz Mohammad; Valadi, Mehrdad

2017-09-01

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The effect of selected parameters of the honing process on cylinder liner surface topography

International Nuclear Information System (INIS)

Pawlus, P; Dzierwa, A; Michalski, J; Reizer, R; Wieczorowski, M; Majchrowski, R

2014-01-01

Many truck cylinder liners made from gray cast iron were machined. Ceramic and diamond honing stones were used in the last stages of operation: coarse honing and plateau honing. The effect of honing parameters on the cylinder liner surface topography was studied. Selected surface topography parameters were response variables. It was found that parameters from the Sq group were sensitive to honing parameter change. When plateau honing time varied, the Smq parameter increased, while the other parameters, Spq and Svq, were stable. (papers)

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

Science.gov (United States)

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters

Science.gov (United States)

Galea, Joseph M.; Ruge, Diane; Buijink, Arthur; Bestmann, Sven; Rothwell, John C.

2013-01-01

Action selection describes the high-level process which selects between competing movements. In animals, behavioural variability is critical for the motor exploration required to select the action which optimizes reward and minimizes cost/punishment, and is guided by dopamine (DA). The aim of this study was to test in humans whether low-level movement parameters are affected by punishment and reward in ways similar to high-level action selection. Moreover, we addressed the proposed dependence of behavioural and neurophysiological variability on DA, and whether this may underpin the exploration of kinematic parameters. Participants performed an out-and-back index finger movement and were instructed that monetary reward and punishment were based on its maximal acceleration (MA). In fact, the feedback was not contingent on the participant’s behaviour but pre-determined. Blocks highly-biased towards punishment were associated with increased MA variability relative to blocks with either reward or without feedback. This increase in behavioural variability was positively correlated with neurophysiological variability, as measured by changes in cortico-spinal excitability with transcranial magnetic stimulation over the primary motor cortex. Following the administration of a DA-antagonist, the variability associated with punishment diminished and the correlation between behavioural and neurophysiological variability no longer existed. Similar changes in variability were not observed when participants executed a pre-determined MA, nor did DA influence resting neurophysiological variability. Thus, under conditions of punishment, DA-dependent processes influence the selection of low-level movement parameters. We propose that the enhanced behavioural variability reflects the exploration of kinematic parameters for less punishing, or conversely more rewarding, outcomes. PMID:23447607

Energetical optimization and parameters selection for a fixed faceted mirror concentrator

International Nuclear Information System (INIS)

Nicolas, R.O.; Duran, J.C.; Dawidowski, L.E.

1990-01-01

A method which allows to select the parameters of a cylindrical solar collector by means of an energetical optimization is presented. In particular, the energy collected by the operating fluid and the collection efficiency of a Fixed Faceted Mirror Concentrator (FFMC) are obtained and compared for different sets of parameters. To this end, the two-dimensional optical analysis for non-perfect cylindrical concentrators presented previously is used. Some graphs analyzing the variations of the yearly efficiency of the FFMC as a function of those parameters are given. Finally, the possibility of using a second concentrator in the receiver plane of the FFMC in order to improve the whole efficiency of the prototype is also analyzed. (Author)

Love, Truth, Peace and Death as Extolled by Selected Literary Philosophers: Inputs in Understanding Spirituality as a Transformative Agent of Society

Directory of Open Access Journals (Sweden)

Dr. Maria Luisa A. Valdez

2015-11-01

Full Text Available This qualitative study analyzed the selected works of the Christian mystics St. Augustine, St. Anselm, and William Blake as well as those of the Oriental mystics Confucius, and Rabindranath Tagore pointing out events and situations on how mysticism is reflected in their works. Likewise, this study tried to present how these identified literary philosophers extolled the meaning of love, truth, peace and death which serve as inputs in understanding spirituality as a transformative agent of the society. The selected writings consider the direct union of the human soul with the Divine through contemplation, meditation, prayer and love as the end of these mystics’ philosophy. For in these selected prose and poetry manifest their mystical attitude and the spiritual truth that the meaning of human existence is the mindful and enlightened manifestation of love as the core of human life and the divine supreme law that guides humanity. The spiritual manifestations of human existence find their noblest expressions and exemplifications on their lives and works. There surfaces a unifying thread interwoven in all their works which centers on the constant and balanced yearning of men to be united with the Divine. These inputs serve as a new paradigm in understanding spirituality as a transformative agent of society.

RIP Input Tables From Wapdeg For LA Design Selection: Enhanced Design Alternative IIIb

International Nuclear Information System (INIS)

K.G. Mon; K.G. Mast; J.H. Lee

1999-01-01

The purpose of this calculation is to document the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. 'Software Routine Report for WAPDEG' (Version 3.09)) simulations used to analyze degradation and failure of 2-cm thick titanium grade 7 corrosion resistant material (CRM) drip shields as well as degradation and failure of the waste packages over which they are placed. The waste packages are composed of two corrosion resistant materials (CRM) barriers. The outer barrier is composed of 2 cm of Alloy 22 and the inner barrier is composed of 1.5 cm of titanium grade 7. The WAPDEG simulation results are post-processed into tables of drip shield/waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) computer code. This calculation supports Performance Assessment analysis of the License Application Design Selection (LADS) Enhanced Design Alternative IIIb

Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.

Science.gov (United States)

Rigby, Robert A; Stasinopoulos, Dimitrios M

2014-08-01

A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

CBM first-level event selector input interface

Energy Technology Data Exchange (ETDEWEB)

Hutter, Dirk [Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt (Germany); Collaboration: CBM-Collaboration

2016-07-01

The CBM First-level Event Selector (FLES) is the central event selection system of the upcoming CBM experiment at FAIR. Designed as a high-performance computing cluster, its task is an online analysis of the physics data at a total data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES performs timeslice building, which combines the data from all given input links to self-contained, overlapping processing intervals and distributes them to compute nodes. Partitioning the input data streams into specialized containers allows to perform this task very efficiently. The FLES Input Interface defines the linkage between FEE and FLES data transport framework. Utilizing a custom FPGA board, it receives data via optical links, prepares them for subsequent timeslice building, and transfers the data via DMA to the PC's memory. An accompanying HDL module implements the front-end logic interface and FLES link protocol in the front-end FPGAs. Prototypes of all Input Interface components have been implemented and integrated into the FLES framework. In contrast to earlier prototypes, which included components to work without a FPGA layer between FLES and FEE, the structure matches the foreseen final setup. This allows the implementation and evaluation of the final CBM read-out chain. An overview of the FLES Input Interface as well as studies on system integration and system start-up are presented.

Simulation of the selective oxidation process of semiconductors

International Nuclear Information System (INIS)

Chahoud, M.

2012-01-01

A new approach to simulate the selective oxidation of semiconductors is presented. This approach is based on the so-called b lack box simulation method . This method is usually used to simulate complex processes. The chemical and physical details within the process are not considered. Only the input and output data of the process are relevant for the simulation. A virtual function linking the input and output data has to be found. In the case of selective oxidation the input data are the mask geometry and the oxidation duration whereas the output data are the oxidation thickness distribution. The virtual function is determined as four virtual diffusion processes between the masked und non-masked areas. Each process delivers one part of the oxidation profile. The method is applied successfully on the oxidation system silicon-silicon nitride (Si-Si 3 N 4 ). The fitting parameters are determined through comparison of experimental and simulation results two-dimensionally.(author)

Heat input control in coke ovens battery using artificial intelligence

Energy Technology Data Exchange (ETDEWEB)

Kumar, R.; Kannan, C.; Sistla, S.; Kumar, D. [Tata Steel, Jamshedpur (India)

2005-07-01

Controlled heating is very essential for producing coke with certain desired properties. Controlled heating involves controlling the heat input into the battery dynamically depending on the various process parameters like current battery temperature, the set point of battery temperature, moisture in coal, ambient temperature, coal fineness, cake breakage etc. An artificial intelligence (AI) based heat input control has been developed in which currently some of the above mentioned process parameters are considered and used for calculating the pause time which is applied between reversal during the heating process. The AI based model currently considers 3 input variables, temperature deviation history, current deviation of the battery temperature from the target temperature and the actual heat input into the battery. Work is in progress to control the standard deviation of coke end temperature using this model. The new system which has been developed in-house has replaced Hoogovens supplied model. 7 figs.

Selection of the optimal Box-Cox transformation parameter for modelling and forecasting age-specific fertility

OpenAIRE

Shang, Han Lin

2015-01-01

The Box-Cox transformation can sometimes yield noticeable improvements in model simplicity, variance homogeneity and precision of estimation, such as in modelling and forecasting age-specific fertility. Despite its importance, there have been few studies focusing on the optimal selection of Box-Cox transformation parameters in demographic forecasting. A simple method is proposed for selecting the optimal Box-Cox transformation parameter, along with an algorithm based on an in-sample forecast ...

Input parameters for the statistical seismic hazard assessment in central part of Romania territory using crustal earthquakes

International Nuclear Information System (INIS)

Moldovan, A.I.; Bazacliu, O.; Popescu, E.

2004-01-01

The seismic hazard assessment in dense-populated geographical regions and subsequently the design of the strategic objectives (dams, nuclear power plants, etc.) are based on the knowledge of the seismicity parameters of the seismic sources which can generate ground motion amplitudes above the minimum level considered risky at the specific site and the way the seismic waves propagate between the focus and the site. The purpose of this paper is to provide a set of information required for a probabilistic assessment of the seismic hazard in the central Romanian territory relative to the following seismic sources: Fagaras zone (FC), Campulung zone (CP), and Transilvania zone (TD) all of them in the crust domain. Extremely vulnerable objectives are present in the central part of Romania, including cities of Pitesti and Sibiu and the 'Vidraru' dam. The analysis that we propose implies: (1) geometrical definition of the seismic sources, (2) estimation of the maximum possible magnitude, (3) estimation of the frequency - magnitude relationship and (4) estimation of the attenuation laws. As an example, the obtained input parameters are used to evaluate the seismic hazard distribution due to the crustal earthquakes applying the McGuire's procedure (1976). These preliminary results are in good agreement with the previous research based on deterministic approach (Radulian et al., 2000). (authors)

Input design for linear dynamic systems using maxmin criteria

DEFF Research Database (Denmark)

Sadegh, Payman; Hansen, Lars H.; Madsen, Henrik

1998-01-01

This paper considers the problem of input design for maximizing the smallest eigenvalue of the information matrix for linear dynamic systems. The optimization of the smallest eigenvalue is of interest in parameter estimation and parameter change detection problems. We describe a simple cutting...

Selection of the battery pack parameters for an electric vehicle based on performance requirements

Science.gov (United States)

Koniak, M.; Czerepicki, A.

2017-06-01

Each type of vehicle has specific power requirements. Some require a rapid charging, other make long distances between charges, but a common feature is the longest battery life time. Additionally, the battery is influenced by factors such as temperature, depth of discharge and the operation current. The article contain the parameters of chemical cells that should be taken into account during the design of the battery for a specific application. This is particularly important because the batteries are not properly matched and can wear prematurely and cause an additional costs. The method of selecting the correct cell type should take previously discussed features and operating characteristics of the vehicle into account. The authors present methods of obtaining such characteristics along with their assessment and examples. Also there has been described an example of the battery parameters selection based on design assumptions of the vehicle and the expected performance characteristics. Selecting proper battery operating parameters is important due to its impact on the economic result of investments in electric vehicles. For example, for some Li-Ion technologies, the earlier worn out of batteries in a fleet of cruise boats or buses having estimated lifetime of 10 years is not acceptable, because this will cause substantial financial losses for the owner of the rolling stock. The presented method of choosing the right cell technology in the selected application, can be the basis for making the decision on future battery technical parameters.

Spacecraft reorientation control in presence of attitude constraint considering input saturation and stochastic disturbance

Science.gov (United States)

Cheng, Yu; Ye, Dong; Sun, Zhaowei; Zhang, Shijie

2018-03-01

This paper proposes a novel feedback control law for spacecraft to deal with attitude constraint, input saturation, and stochastic disturbance during the attitude reorientation maneuver process. Applying the parameter selection method to improving the existence conditions for the repulsive potential function, the universality of the potential-function-based algorithm is enhanced. Moreover, utilizing the auxiliary system driven by the difference between saturated torque and command torque, a backstepping control law, which satisfies the input saturation constraint and guarantees the spacecraft stability, is presented. Unlike some methods that passively rely on the inherent characteristic of the existing controller to stabilize the adverse effects of external stochastic disturbance, this paper puts forward a nonlinear disturbance observer to compensate the disturbance in real-time, which achieves a better performance of robustness. The simulation results validate the effectiveness, reliability, and universality of the proposed control law.

Dual ant colony operational modal analysis parameter estimation method

Science.gov (United States)

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.

Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

DEFF Research Database (Denmark)

Heide, J; Zhang, Qi; Fitzek, F H P

2013-01-01

This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...

Exact Repetition as Input Enhancement in Second Language Acquisition.

Science.gov (United States)

Jensen, Eva Dam; Vinther, Thora

2003-01-01

Reports on two studies on input enhancement used to support learners' selection of focus of attention in Spanish second language listening material. Input consisted of video recordings of dialogues between native speakers. Exact repetition and speech rate reduction were examined for effect on comprehension, acquisition of decoding strategies, and…

Selection of physiological parameters for optoelectronic system supporting behavioral therapy of autistic children

Science.gov (United States)

Landowska, A.; Karpienko, K.; Wróbel, M.; Jedrzejewska-Szczerska, M.

2014-11-01

In this article the procedure of selection of physiological parameters for optoelectronic system supporting behavioral therapy of autistic children is proposed. Authors designed and conducted an experiment in which a group of 30 health volunteers (16 females and 14 males) were examined. Under controlled conditions people were exposed to a stressful situation caused by the picture or sound (1kHz constant sound, which was gradually silenced and finished with a shot sound). For each of volunteers, a set of physiological parameters were recorded, including: skin conductance, heart rate, peripheral temperature, respiration rate and electromyography. The selected characteristics were measured in different locations in order to choose the most suitable one for the designed therapy supporting system. The bio-statistical analysis allowed us to discern the proper physiological parameters that are most associated to changes due to emotional state of a patient, such as: skin conductance, temperatures and respiration rate. This allowed us to design optoelectronic sensors network for supporting behavioral therapy of children with autism.

On how to avoid input and structural uncertainties corrupt the inference of hydrological parameters using a Bayesian framework

Science.gov (United States)

Hernández, Mario R.; Francés, Félix

2015-04-01

One phase of the hydrological models implementation process, significantly contributing to the hydrological predictions uncertainty, is the calibration phase in which values of the unknown model parameters are tuned by optimizing an objective function. An unsuitable error model (e.g. Standard Least Squares or SLS) introduces noise into the estimation of the parameters. The main sources of this noise are the input errors and the hydrological model structural deficiencies. Thus, the biased calibrated parameters cause the divergence model phenomenon, where the errors variance of the (spatially and temporally) forecasted flows far exceeds the errors variance in the fitting period, and provoke the loss of part or all of the physical meaning of the modeled processes. In other words, yielding a calibrated hydrological model which works well, but not for the right reasons. Besides, an unsuitable error model yields a non-reliable predictive uncertainty assessment. Hence, with the aim of prevent all these undesirable effects, this research focuses on the Bayesian joint inference (BJI) of both the hydrological and error model parameters, considering a general additive (GA) error model that allows for correlation, non-stationarity (in variance and bias) and non-normality of model residuals. As hydrological model, it has been used a conceptual distributed model called TETIS, with a particular split structure of the effective model parameters. Bayesian inference has been performed with the aid of a Markov Chain Monte Carlo (MCMC) algorithm called Dream-ZS. MCMC algorithm quantifies the uncertainty of the hydrological and error model parameters by getting the joint posterior probability distribution, conditioned on the observed flows. The BJI methodology is a very powerful and reliable tool, but it must be used correctly this is, if non-stationarity in errors variance and bias is modeled, the Total Laws must be taken into account. The results of this research show that the

Assessing the effect of selection with deltamethrin on biological parameters and detoxifying enzymes in Aedes aegypti (L.).

Science.gov (United States)

Alvarez-Gonzalez, Leslie C; Briceño, Arelis; Ponce-Garcia, Gustavo; Villanueva-Segura, O Karina; Davila-Barboza, Jesus A; Lopez-Monroy, Beatriz; Gutierrez-Rodriguez, Selene M; Contreras-Perera, Yamili; Rodriguez-Sanchez, Iram P; Flores, Adriana E

2017-11-01

Resistance to insecticides through one or several mechanisms has a cost for an insect in various parameters of its biological cycle. The present study evaluated the effect of deltamethrin on detoxifying enzymes and biological parameters in a population of Aedes aegypti selected for 15 generations. The enzyme activities of alpha- and beta-esterases, mixed-function oxidases and glutathione-S-transferases were determined during selection, along with biological parameters. Overexpression of mixed-function oxidases as a mechanism of metabolic resistance to deltamethrin was found. There were decreases in percentages of eggs hatching, pupation and age-specific survival and in total survival at the end of the selection (F 16 ). Although age-specific fecundity was not affected by selection with deltamethrin, total fertility, together with lower survival, significantly affected gross reproduction rate, gradually decreasing due to deltamethrin selection. Similarly, net reproductive rate and intrinsic growth rate were affected by selection. Alterations in life parameters could be due to the accumulation of noxious effects or deleterious genes related to detoxifying enzymes, specifically those coding for mixed-function oxidases, along with the presence of recessive alleles of the V1016I and F1534C mutations, associating deltamethrin resistance with fitness cost in Ae. aegypti. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Evaluation of selected postural parameters in children who practice kyokushin karate

Directory of Open Access Journals (Sweden)

Drzał-Grabiec Justyna

2014-10-01

Full Text Available Study aim: martial arts can be traced back thousands of years. Karate is one of the most common martial arts, and both children and adults practice it. The aim of the study was to evaluate selected body posture parameters in children aged 7–10 years who regularly practice karate.

Grey fuzzy logic approach for the optimization of DLC thin film coating process parameters using PACVD technique

Science.gov (United States)

Ghadai, R. K.; Das, P. P.; Shivakoti, I.; Mondal, S. C.; Swain, B. P.

2017-07-01

Diamond-like carbon (DLC) coatings are widely used in medical, manufacturing and aerospace industries due to their excellent mechanical, biological, optical and tribological properties. The selection of optimal process parameters for efficient characteristics of DLC film is always a challenging issue for the materials science researchers. The optimal combination of the process parameters involved in the deposition of DLC films provide a better result, which subsequently help other researchers to choose the process parameters. In the present work Grey Relation Analysis (GRA) and Fuzzy-logic are being used for the optimization of process parameters in DLC film coating by using plasma assist chemical vapour deposition (PACVD) technique. The bias voltage, bias frequency, deposition pressure, gas composition are considered as input process parameters and hardness (GPa), Young's modulus (GPa), ratio between diamond to graphic fraction, (Id/Ig) ratio are considered as response parameters. The input parameters are optimized by grey fuzzy analysis. The contribution of individual input parameter is done by ANOVA. In this analysis found that bias voltage having the least influence and gas composition has highest influence in the PACVD deposited DLC films. The grey fuzzy analysis results indicated that optimum results for bias voltage, bias frequency, deposition pressure, gas composition for the DLC thin films are -50 V, 6 kHz, 4 μbar and 60:40 % respectively.

Location-based Mobile Relay Selection and Impact of Inaccurate Path Loss Model Parameters

DEFF Research Database (Denmark)

Nielsen, Jimmy Jessen; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter

2010-01-01

In this paper we propose a relay selection scheme which uses collected location information together with a path loss model for relay selection, and analyze the performance impact of mobility and different error causes on this scheme. Performance is evaluated in terms of bit error rate...... by simulations. The SNR measurement based relay selection scheme proposed previously is unsuitable for use with fast moving users in e.g. vehicular scenarios due to a large signaling overhead. The proposed location based scheme is shown to work well with fast moving users due to a lower signaling overhead...... in these situations. As the location-based scheme relies on a path loss model to estimate link qualities and select relays, the sensitivity with respect to inaccurate estimates of the unknown path loss model parameters is investigated. The parameter ranges that result in useful performance were found...

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

Science.gov (United States)

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

Four-parameter model for polarization-resolved rough-surface BRDF.

Science.gov (United States)

Renhorn, Ingmar G E; Hallberg, Tomas; Bergström, David; Boreman, Glenn D

2011-01-17

A modeling procedure is demonstrated, which allows representation of polarization-resolved BRDF data using only four parameters: the real and imaginary parts of an effective refractive index with an added parameter taking grazing incidence absorption into account and an angular-scattering parameter determined from the BRDF measurement of a chosen angle of incidence, preferably close to normal incidence. These parameters allow accurate predictions of s- and p-polarized BRDF for a painted rough surface, over three decades of variation in BRDF magnitude. To characterize any particular surface of interest, the measurements required to determine these four parameters are the directional hemispherical reflectance (DHR) for s- and p-polarized input radiation and the BRDF at a selected angle of incidence. The DHR data describes the angular and polarization dependence, as well as providing the overall normalization constraint. The resulting model conserves energy and fulfills the reciprocity criteria.

Coastal flooding as a parameter in multi-criteria analysis for industrial site selection

Science.gov (United States)

Christina, C.; Memos, C.; Diakoulaki, D.

2014-12-01

Natural hazards can trigger major industrial accidents, which apart from affecting industrial installations may cause a series of accidents with serious impacts on human health and the environment far beyond the site boundary. Such accidents, also called Na-Tech (natural - technical) accidents, deserve particular attention since they can cause release of hazardous substances possibly resulting in severe environmental pollution, explosions and/or fires. There are different kinds of natural events or, in general terms, of natural causes of industrial accidents, such as landslides, hurricanes, high winds, tsunamis, lightning, cold/hot temperature, floods, heavy rains etc that have caused accidents. The scope of this paper is to examine the coastal flooding as a parameter in causing an industrial accident, such as the nuclear disaster in Fukushima, Japan, and the critical role of this parameter in industrial site selection. Land use planning is a complex procedure that requires multi-criteria decision analysis involving economic, environmental and social parameters. In this context the parameter of a natural hazard occurrence, such as coastal flooding, for industrial site selection should be set by the decision makers. In this paper it is evaluated the influence that has in the outcome of a multi-criteria decision analysis for industrial spatial planning the parameter of an accident risk triggered by coastal flooding. The latter is analyzed in the context of both sea-and-inland induced flooding.

Generation of input parameters for OSPM calculations. Sensitivity analysis of a method based on a questionnaire

Energy Technology Data Exchange (ETDEWEB)

Vignati, E.; Hertel, O.; Berkowicz, R. [National Environmental Research Inst., Dept. of Atmospheric Enviroment (Denmark); Raaschou-Nielsen, O. [Danish Cancer Society, Division of Cancer Epidemiology (Denmark)

1997-05-01

The method for generation of the input data for the calculations with OSPM is presented in this report. The described method which is based on information provided from a questionnaire, will be used for model calculations of long term exposure for a large number of children in connection with an epidemiological study. A test of the calculation method has been performed on a few locations in which detailed measurements of air pollution, meteorological data and traffic were available. Comparisons between measured and calculated concentrations were made for hourly, monthly and yearly values. Beside the measured concentrations, the test results were compared to results obtained with the optimal street configuration data and measured traffic. The main conclusions drawn from this investigation are: (1) The calculation method works satisfactory well for long term averages, whereas the uncertainties are high when short term averages are considered. (2) The street width is one of the most crucial input parameters for the calculation of street pollution levels for both short and long term averages. Using H.C. Andersens Boulevard as an example, it was shown that estimation of street width based on traffic amount can lead to large overestimation of the concentration levels (in this case 50% for NO{sub x} and 30% for NO{sub 2}). (3) The street orientation and geometry is important for prediction of short term concentrations but this importance diminished for longer term averages. (4) The uncertainties in diurnal traffic profiles can influence the accuracy of short term averages, but are less important for long term averages. The correlation is good between modelled and measured concentrations when the actual background concentrations are replaced with the generated values. Even though extreme situations are difficult to reproduce with this method, the comparison between the yearly averaged modelled and measured concentrations is very good. (LN) 20 refs.

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

International Nuclear Information System (INIS)

M. Gross

2004-01-01

The purpose of this scientific analysis is to define the sampled values of stochastic (random) input parameters for (1) rockfall calculations in the lithophysal and nonlithophysal zones under vibratory ground motions, and (2) structural response calculations for the drip shield and waste package under vibratory ground motions. This analysis supplies: (1) Sampled values of ground motion time history and synthetic fracture pattern for analysis of rockfall in emplacement drifts in nonlithophysal rock (Section 6.3 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (2) Sampled values of ground motion time history and rock mechanical properties category for analysis of rockfall in emplacement drifts in lithophysal rock (Section 6.4 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (3) Sampled values of ground motion time history and metal to metal and metal to rock friction coefficient for analysis of waste package and drip shield damage to vibratory motion in ''Structural Calculations of Waste Package Exposed to Vibratory Ground Motion'' (BSC 2004 [DIRS 167083]) and in ''Structural Calculations of Drip Shield Exposed to Vibratory Ground Motion'' (BSC 2003 [DIRS 163425]). The sampled values are indices representing the number of ground motion time histories, number of fracture patterns and rock mass properties categories. These indices are translated into actual values within the respective analysis and model reports or calculations. This report identifies the uncertain parameters and documents the sampled values for these parameters. The sampled values are determined by GoldSim V6.04.007 [DIRS 151202] calculations using appropriate distribution types and parameter ranges. No software development or model development was required for these calculations. The calculation of the sampled values allows parameter uncertainty to be incorporated into the rockfall and structural response calculations that support development of the seismic scenario for the

Analysis of the selected mechanical parameters of coating of filters protecting against hazardous infrared radiation.

Science.gov (United States)

Gralewicz, Grzegorz; Owczarek, Grzegorz; Kubrak, Janusz

2017-03-01

This article presents a comparison of the test results of selected mechanical parameters (hardness, Young's modulus, critical force for delamination) for protective filters intended for eye protection against harmful infrared radiation. Filters with reflective metallic films were studied, as well as interference filters developed at the Central Institute for Labour Protection - National Research Institute (CIOP-PIB). The test results of the selected mechanical parameters were compared with the test results, conducted in accordance with a standardised method, of simulating filter surface destruction that occurs during use.

Effects of primary selective laser trabeculoplasty on anterior segment parameters

Directory of Open Access Journals (Sweden)

Suzan Guven Yilmaz

2015-10-01

Full Text Available AIM:To investigate the effects of selective laser trabeculoplasty (SLT on the main numerical parameters of anterior segment with Pentacam rotating Scheimpflug camera in patients with ocular hypertension (OHT and primary open angle glaucoma (POAG.METHODS: Pentacam measurements of 45 eyes of 25 (15 females and 10 males patients (12 with OHT, 13 with POAG before and after SLT were obtained. Measurements were taken before and 1 and 3mo after SLT. Pentacam parameters were compared between OHT and POAG patients, and age groups (60y and older, and younger than 60y.RESULTS: The mean age of the patients was 57.8±13.9 (range 20-77y. Twelve patients (48% were younger than 60y, while 13 patients (52% were 60y and older. Measurements of pre-SLT and post-SLT 1mo were significantly different for the parameters of central corneal thickness (CCT and anterior chamber volume (ACV (P<0.05. These parameters returned back to pre-SLT values at post-SLT 3mo. Decrease of ACV at post-SLT 1mo was significantly higher in younger than 60y group than 60y and older group. There was no statistically significant difference in Pentacam parameters between OHT and POAG patients at pre- and post-treatment measurements (P>0.05.CONCLUSION:SLT leads to significant increase in CCT and decrease in ACV at the 1st month of the procedure. Effects of SLT on these anterior segment parameters, especially for CCT that interferes IOP measurement, should be considered to ensure accurate clinical interpretation.

Parameter and State Estimator for State Space Models

Directory of Open Access Journals (Sweden)

Ruifeng Ding

2014-01-01

Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

Directory of Open Access Journals (Sweden)

N. Oosthuizen

2018-05-01

Full Text Available The demand for water resources is rapidly growing, placing more strain on access to water and its management. In order to appropriately manage water resources, there is a need to accurately quantify available water resources. Unfortunately, the data required for such assessment are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In this study, the uncertainty related to the estimation of water resources of two sub-basins of the Limpopo River Basin – the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe – is assessed. Input data (and model parameters are significant sources of uncertainty that should be quantified. In southern Africa water use data are among the most unreliable sources of model input data because available databases generally consist of only licensed information and actual use is generally unknown. The study assesses how these uncertainties impact the estimation of surface water resources of the sub-basins. Data on farm reservoirs and irrigated areas from various sources were collected and used to run the model. Many farm dams and large irrigation areas are located in the upper parts of the Mogalakwena sub-basin. Results indicate that water use uncertainty is small. Nevertheless, the medium to low flows are clearly impacted. The simulated mean monthly flows at the outlet of the Mogalakwena sub-basin were between 22.62 and 24.68 Mm3 per month when incorporating only the uncertainty related to the main physical runoff generating parameters. The range of total predictive uncertainty of the model increased to between 22.15 and 24.99 Mm3 when water use data such as small farm and large reservoirs and irrigation were included. For the Shashe sub-basin incorporating only uncertainty related to the main runoff parameters resulted in mean monthly flows between 11.66 and 14.54 Mm3. The range of predictive uncertainty changed to between 11.66 and 17

CBM First-level Event Selector Input Interface Demonstrator

Science.gov (United States)

Hutter, Dirk; de Cuveland, Jan; Lindenstruth, Volker

2017-10-01

CBM is a heavy-ion experiment at the future FAIR facility in Darmstadt, Germany. Featuring self-triggered front-end electronics and free-streaming read-out, event selection will exclusively be done by the First Level Event Selector (FLES). Designed as an HPC cluster with several hundred nodes its task is an online analysis and selection of the physics data at a total input data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES performs timeslice building, which combines the data from all given input links to self-contained, potentially overlapping processing intervals and distributes them to compute nodes. Partitioning the input data streams into specialized containers allows performing this task very efficiently. The FLES Input Interface defines the linkage between the FEE and the FLES data transport framework. A custom FPGA PCIe board, the FLES Interface Board (FLIB), is used to receive data via optical links and transfer them via DMA to the host’s memory. The current prototype of the FLIB features a Kintex-7 FPGA and provides up to eight 10 GBit/s optical links. A custom FPGA design has been developed for this board. DMA transfers and data structures are optimized for subsequent timeslice building. Index tables generated by the FPGA enable fast random access to the written data containers. In addition the DMA target buffers can directly serve as InfiniBand RDMA source buffers without copying the data. The usage of POSIX shared memory for these buffers allows data access from multiple processes. An accompanying HDL module has been developed to integrate the FLES link into the front-end FPGA designs. It implements the front-end logic interface as well as the link protocol. Prototypes of all Input Interface components have been implemented and integrated into the FLES test framework. This allows the implementation and evaluation of the foreseen CBM read-out chain.

Comparison of selected immunological parameters in silver and polar foxes

International Nuclear Information System (INIS)

Kostro, K.; Woloszyn, S.

1996-01-01

The aim of the work was to compare selected nonspecific immunological parameters in silver and polar foxes. It was found that out of three mitogens (Con A, La and PWM) only Con A stimulated the strongest lymphocyte proliferation in both species of foxes. Mean stimulation indices and the absolute values of incorporated thymidine were significantly higher in polar foxes. The average percentages of phagocytes and the fatal indices of neutrophils were comparable in both species of foxes, while the mean value of the phagocyte index was much higher in the silver fox. However, in polar foxes a higher concentration of lysozyme in the blood serum was found. The differences in the parameters may influence the susceptibility of these foxes to some infections already at the level of nonspecific primary response. (author)

Effects of primary selective laser trabeculoplasty on anterior segment parameters

Science.gov (United States)

Guven Yilmaz, Suzan; Palamar, Melis; Yusifov, Emil; Ates, Halil; Egrilmez, Sait; Yagci, Ayse

2015-01-01

AIM To investigate the effects of selective laser trabeculoplasty (SLT) on the main numerical parameters of anterior segment with Pentacam rotating Scheimpflug camera in patients with ocular hypertension (OHT) and primary open angle glaucoma (POAG). METHODS Pentacam measurements of 45 eyes of 25 (15 females and 10 males) patients (12 with OHT, 13 with POAG) before and after SLT were obtained. Measurements were taken before and 1 and 3mo after SLT. Pentacam parameters were compared between OHT and POAG patients, and age groups (60y and older, and younger than 60y). RESULTS The mean age of the patients was 57.8±13.9 (range 20-77y). Twelve patients (48%) were younger than 60y, while 13 patients (52%) were 60y and older. Measurements of pre-SLT and post-SLT 1mo were significantly different for the parameters of central corneal thickness (CCT) and anterior chamber volume (ACV) (P0.05). CONCLUSION SLT leads to significant increase in CCT and decrease in ACV at the 1st month of the procedure. Effects of SLT on these anterior segment parameters, especially for CCT that interferes IOP measurement, should be considered to ensure accurate clinical interpretation. PMID:26558208

Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model

Science.gov (United States)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.

2012-12-01

Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root

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

Science.gov (United States)

Aksoy, A.; Yuzugullu, O.

2017-12-01

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

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

Science.gov (United States)

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

2017-09-01

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

How to Select the most Relevant Roughness Parameters of a Surface: Methodology Research Strategy

Science.gov (United States)

Bobrovskij, I. N.

2018-01-01

In this paper, the foundations for new methodology creation which provides solving problem of surfaces structure new standards parameters huge amount conflicted with necessary actual floors quantity of surfaces structure parameters which is related to measurement complexity decreasing are considered. At the moment, there is no single assessment of the importance of a parameters. The approval of presented methodology for aerospace cluster components surfaces allows to create necessary foundation, to develop scientific estimation of surfaces texture parameters, to obtain material for investigators of chosen technological procedure. The methods necessary for further work, the creation of a fundamental reserve and development as a scientific direction for assessing the significance of microgeometry parameters are selected.

Input significance analysis: feature selection through synaptic ...

African Journals Online (AJOL)

Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected ... from the UCI Machine Learning Repository and executed in an online ... connectionist systems; evolving fuzzy neural network; connection weights; Garson's

Impact parameter and source selected correlation functions with a 4π multidetector

International Nuclear Information System (INIS)

Gourio, D.; Reposeur, T.; Assenard, M.; Germain, M.; Ardouin, D.; Eudes, P.; Lautridou, P.; Laville, J.L.; Lebrun, C.; Metivier, V.

1997-01-01

For the first time in the domain of (light charged) particle interferometry in nuclear physics, a complete study of proton an deuteron correlation functions is presented with both impact parameter and emission source selections. The correlations were determined for the system 129 Xe + nat Sn at 45 and 50 AMeV using the 4π multidetector INDRA at GANIL as an event selector as well as a particle correlator. Very short emission times are found for all the selections indicating possible contributions from a fast and preequilibrium process. (author)

Selective Laser Melting of Ti-45Nb Alloy

Directory of Open Access Journals (Sweden)

Holger Schwab

2015-04-01

Full Text Available Ti-45Nb is one of the potential alloys that can be applied for biomedical applications as implants due to its low Young’s modulus. Ti-45Nb (wt.% gas atomized powders were used to produce bulk samples by selective laser melting with three different parameter sets (energy inputs. A β-phase microstructure consisting of elliptical grains with an enriched edge of titanium was observed by scanning electron microscopy and X-ray diffraction studies. The mechanical properties of these samples were evaluated using hardness and compression tests, which suggested that the strength of the samples increases with increasing energy input within the range considered.

Load Estimation from Natural input Modal Analysis

DEFF Research Database (Denmark)

Aenlle, Manuel López; Brincker, Rune; Canteli, Alfonso Fernández

2005-01-01

One application of Natural Input Modal Analysis consists in estimating the unknown load acting on structures such as wind loads, wave loads, traffic loads, etc. In this paper, a procedure to determine loading from a truncated modal model, as well as the results of an experimental testing programme...... estimation. In the experimental program a small structure subjected to vibration was used to estimate the loading from the measurements and the experimental modal space. The modal parameters were estimated by Natural Input Modal Analysis and the scaling factors of the mode shapes obtained by the mass change...

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....

Input to the PRAST computer code used in the SRS probabilistic risk assessment

International Nuclear Information System (INIS)

Kearnaghan, D.P.

1992-01-01

The PRAST (Production Reactor Algorithm for Source Terms) computer code was developed by Westinghouse Savannah River Company and Science Application International Corporation for the quantification of source terms for the SRS Savannah River Site (SRS) Reactor Probabilistic Risk Assessment. PRAST requires as input a set of release fractions, decontamination factors, transfer fractions and source term characteristics that accurately reflect the conditions that are evaluated by PRAST. This document links the analyses which form the basis for the PRAST input parameters. In addition, it gives the distribution of the input parameters that are uncertain and considered to be important to the evaluation of the source terms to the environment

Calculating the sensitivity of wind turbine loads to wind inputs using response surfaces

DEFF Research Database (Denmark)

Rinker, Jennifer M.

2016-01-01

at a low computational cost. Sobol sensitivity indices (SIs) can then be calculated with relative ease using the calibrated response surface. The proposed methodology is demonstrated by calculating the total sensitivity of the maximum blade root bending moment of the WindPACT 5 MW reference model to four......This paper presents a methodology to calculate wind turbine load sensitivities to turbulence parameters through the use of response surfaces. A response surface is a high-dimensional polynomial surface that can be calibrated to any set of input/output data and then used to generate synthetic data...... turbulence input parameters: a reference mean wind speed, a reference turbulence intensity, the Kaimal length scale, and a novel parameter reflecting the nonstationarity present in the inflow turbulence. The input/output data used to calibrate the response surface were generated for a previous project...

Optimization of surface roughness parameters in dry turning

OpenAIRE

R.A. Mahdavinejad; H. Sharifi Bidgoli

2009-01-01

Purpose: The precision of machine tools on one hand and the input setup parameters on the other hand, are strongly influenced in main output machining parameters such as stock removal, toll wear ratio and surface roughnes.Design/methodology/approach: There are a lot of input parameters which are effective in the variations of these output parameters. In CNC machines, the optimization of machining process in order to predict surface roughness is very important.Findings: From this point of view...

Evolution of Boolean networks under selection for a robust response to external inputs yields an extensive neutral space

Science.gov (United States)

Szejka, Agnes; Drossel, Barbara

2010-02-01

We study the evolution of Boolean networks as model systems for gene regulation. Inspired by biological networks, we select simultaneously for robust attractors and for the ability to respond to external inputs by changing the attractor. Mutations change the connections between the nodes and the update functions. In order to investigate the influence of the type of update functions, we perform our simulations with canalizing as well as with threshold functions. We compare the properties of the fitness landscapes that result for different versions of the selection criterion and the update functions. We find that for all studied cases the fitness landscape has a plateau with maximum fitness resulting in the fact that structurally very different networks are able to fulfill the same task and are connected by neutral paths in network (“genotype”) space. We find furthermore a connection between the attractor length and the mutational robustness, and an extremely long memory of the initial evolutionary stage.

WIPP Compliance Certification Application calculations parameters. Part 1: Parameter development

International Nuclear Information System (INIS)

Howarth, S.M.

1997-01-01

The Waste Isolation Pilot Plant (WIPP) in southeast New Mexico has been studied as a transuranic waste repository for the past 23 years. During this time, an extensive site characterization, design, construction, and experimental program was completed, which provided in-depth understanding of the dominant processes that are most likely to influence the containment of radionuclides for 10,000 years. Nearly 1,500 parameters were developed using information gathered from this program; the parameters were input to numerical models for WIPP Compliance Certification Application (CCA) Performance Assessment (PA) calculations. The CCA probabilistic codes frequently require input values that define a statistical distribution for each parameter. Developing parameter distributions begins with the assignment of an appropriate distribution type, which is dependent on the type, magnitude, and volume of data or information available. The development of the parameter distribution values may require interpretation or statistical analysis of raw data, combining raw data with literature values, scaling of lab or field data to fit code grid mesh sizes, or other transformation. Parameter development and documentation of the development process were very complicated, especially for those parameters based on empirical data; they required the integration of information from Sandia National Laboratories (SNL) code sponsors, parameter task leaders (PTLs), performance assessment analysts (PAAs), and experimental principal investigators (PIs). This paper, Part 1 of two parts, contains a discussion of the parameter development process, roles and responsibilities, and lessons learned. Part 2 will discuss parameter documentation, traceability and retrievability, and lessons learned from related audits and reviews

A comparative analysis of selected parameters of roofing used in the Polish construction industry

Directory of Open Access Journals (Sweden)

Radziszewska-Zielina Elżbieta

2014-06-01

Full Text Available Roofing is an important element in the construction of the roof. It is also one of the essential elements of the whole building. The choice of roofing should depend on technical parameters that affect the quality of the materials used and the price. The present paper is a comparative analysis of the properties of five roofing materials selected as examples with respect to twelve parameters. As can be seen from the comparative analysis of the roofing parameters, roofing tile is by far the best material, receiving the highest score in the ranking

Micro-Level Management of Agricultural Inputs: Emerging Approaches

Directory of Open Access Journals (Sweden)

Jonathan Weekley

2012-12-01

Full Text Available Through the development of superior plant varieties that benefit from high agrochemical inputs and irrigation, the agricultural Green Revolution has doubled crop yields, yet introduced unintended impacts on environment. An expected 50% growth in world population during the 21st century demands novel integration of advanced technologies and low-input production systems based on soil and plant biology, targeting precision delivery of inputs synchronized with growth stages of crop plants. Further, successful systems will integrate subsurface water, air and nutrient delivery, real-time soil parameter data and computer-based decision-making to mitigate plant stress and actively manipulate microbial rhizosphere communities that stimulate productivity. Such an approach will ensure food security and mitigate impacts of climate change.

Automated corresponding point candidate selection for image registration using wavelet transformation neurla network with rotation invariant inputs and context information about neighboring candidates

Science.gov (United States)

Okumura, Hiroshi; Suezaki, Masashi; Sueyasu, Hideki; Arai, Kohei

2003-03-01

An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the 'RIN-net' means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training patterns, less training time, and higher selection accuracy than conventional method.

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

Derivation of parameters necessary for the evaluation of performance of sites for deep geological repositories with particular reference to bedded salt, Livermore, California. Volume II. Appendices

International Nuclear Information System (INIS)

Ashby, J.P.; Rawlings, G.E.; Soto, C.A.; Wood, D.F.; Chorley, D.W.

1979-12-01

The method of selection of parameters to be considered in the selection of a site for underground disposal of radioactive wastes is reported in volume 1. This volume contains the appendix to that report. The topics include: specific rock mechanics tests; drilling investigation techniques and equipment; geophysical surveying; theoretical study of a well text in a nonhomogeneous aquifer; and basic statistical and probability theory that may be used in the derivation of input parameters

Selection of operating parameters on the basis of hydrodynamics in centrifugal partition chromatography for the purification of nybomycin derivatives.

Science.gov (United States)

Adelmann, S; Baldhoff, T; Koepcke, B; Schembecker, G

2013-01-25

The selection of solvent systems in centrifugal partition chromatography (CPC) is the most critical point in setting up a separation. Therefore, lots of research was done on the topic in the last decades. But the selection of suitable operating parameters (mobile phase flow rate, rotational speed and mode of operation) with respect to hydrodynamics and pressure drop limit in CPC is still mainly driven by experience of the chromatographer. In this work we used hydrodynamic analysis for the prediction of most suitable operating parameters. After selection of different solvent systems with respect to partition coefficients for the target compound the hydrodynamics were visualized. Based on flow pattern and retention the operating parameters were selected for the purification runs of nybomycin derivatives that were carried out with a 200 ml FCPC(®) rotor. The results have proven that the selection of optimized operating parameters by analysis of hydrodynamics only is possible. As the hydrodynamics are predictable by the physical properties of the solvent system the optimized operating parameters can be estimated, too. Additionally, we found that dispersion and especially retention are improved if the less viscous phase is mobile. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

IFF, Full-Screen Input Menu Generator for FORTRAN Program

International Nuclear Information System (INIS)

Seidl, Albert

1991-01-01

1 - Description of program or function: The IFF-package contains input modules for use within FORTRAN programs. This package enables the programmer to easily include interactive menu-directed data input (module VTMEN1) and command-word processing (module INPCOM) into a FORTRAN program. 2 - Method of solution: No mathematical operations are performed. 3 - Restrictions on the complexity of the problem: Certain restrictions of use may arise from the dimensioning of arrays. Field lengths are defined via PARAMETER-statements

Third order TRANSPORT with MAD [Methodical Accelerator Design] input

International Nuclear Information System (INIS)

Carey, D.C.

1988-01-01

This paper describes computer-aided design codes for particle accelerators. Among the topics discussed are: input beam description; parameters and algebraic expressions; the physical elements; beam lines; operations; and third-order transfer matrix

KENO2MCNP, Version 5L, Conversion of Input Data between KENOV.a and MCNP File Formats

International Nuclear Information System (INIS)

2008-01-01

1 - Description of program or function: The KENO2MCNP program was written to convert KENO V.a input files to MCNP Format. This program currently only works with KENO Va geometries and will not work with geometries that contain more than a single array. A C++ graphical user interface was created that was linked to Fortran routines from KENO V.a that read the material library and Fortran routines from the MCNP Visual Editor that generate the MCNP input file. Either SCALE 5.0 or SCALE 5.1 cross section files will work with this release. 2 - Methods: The C++ binary executable reads the KENO V.a input file, the KENO V.a material library and SCALE data libraries. When an input file is read in, the input is stored in memory. The converter goes through and loads different sections of the input file into memory including parameters, composition, geometry information, array information and starting information. Many of the KENO V.a materials represent compositions that must be read from the KENO V.a material library. KENO2MCNP includes the KENO V.a FORTRAN routines used to read this material file for creating the MCNP materials. Once the file has been read in, the user must select 'Convert' to convert the file from KENO V.a to MCNP. This will generate the MCNP input file along with an output window that lists the KENO V.a composition information for the materials contained in the KENO V.a input file. The program can be run interactively by clicking on the executable or in batch mode from the command prompt. 3 - Restrictions on the complexity of the problem: Not all KENO V.a input files are supported. Only one array is allowed in the input file. Some of the more complex material descriptions also may not be converted

Self-Structured Organizing Single-Input CMAC Control for Robot Manipulator

Directory of Open Access Journals (Sweden)

ThanhQuyen Ngo

2011-09-01

Full Text Available This paper represents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of proposed control system so that the stability of the system can be guaranteed. The simulation results of robot manipulator are provided to verify the effectiveness of the proposed control methodology.

Input Scanners: A Growing Impact In A Diverse Marketplace

Science.gov (United States)

Marks, Kevin E.

1989-08-01

Just as newly invented photographic processes revolutionized the printing industry at the turn of the century, electronic imaging has affected almost every computer application today. To completely emulate traditionally mechanical means of information handling, computer based systems must be able to capture graphic images. Thus, there is a widespread need for the electronic camera, the digitizer, the input scanner. This paper will review how various types of input scanners are being used in many diverse applications. The following topics will be covered: - Historical overview of input scanners - New applications for scanners - Impact of scanning technology on select markets - Scanning systems issues

Parameter selection for Department of Energy spent nuclear fuel to be used in the Yucca Mountain Viability Assessment

International Nuclear Information System (INIS)

Fillmore, D.L.

1998-06-01

This report contains the chemical, physical, and radiological parameters that were chosen to represent the Department of Energy spent nuclear fuel in the Yucca Mountain Viability Assessment. It also contains the selected packaging requirements for the various fuel types and the criticality controls that were used. The data is reported for representative fuels in groups of fuels that were selected for the analysis. The justification for the selection of each parameter is given. The data reported was not generated under any Q.A. Program

Effect of heat input on dilution and heat affected zone in submerged ...

Indian Academy of Sciences (India)

Proper management of heat input in weld- ing is important .... total nugget area, heat transfer boundary length, and nugget parameter. 3. ... Predominant parameters that had greater influence on welding quality were identified as wire feed rate ...

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

Science.gov (United States)

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

2016-01-01

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

The parameter spreadsheets and their applications

International Nuclear Information System (INIS)

Schwitters, R.; Chao, A.; Chou, W.; Peterson, J.

1993-01-01

This paper is to announce that a set of parameter spreadsheets, using the Microsoft EXCEL software, has been developed for the SSC (and also for the LHC). In this program, the input (or control) parameters and the derived parameters are linked by equations that express the accelerator physics involved. A subgroup of parameters that are considered critical, or possible bottlenecks, has been highlighted under the category of open-quotes Flagsclose quotes. Given certain performance goals, one can use this program to open-quotes tuneclose quotes the input parameters in such a way that the flagged parameters do not exceed their acceptable range. During the past years, this program has been employed for the following purposes: (a) To guide the machine designs for various operation scenarios, (b) To generate a parameter list that is self-consistent and, (c) To study the impact of some proposed parameter changes (e.g., different choices of the rf frequency and bunch spacing)

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

Science.gov (United States)

Schut, H.; van Veen, A.

1995-01-01

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

Application of regional physically-based landslide early warning model: tuning of the input parameters and validation of the results

Science.gov (United States)

D'Ambrosio, Michele; Tofani, Veronica; Rossi, Guglielmo; Salvatici, Teresa; Tacconi Stefanelli, Carlo; Rosi, Ascanio; Benedetta Masi, Elena; Pazzi, Veronica; Vannocci, Pietro; Catani, Filippo; Casagli, Nicola

2017-04-01

The Aosta Valley region is located in North-West Alpine mountain chain. The geomorphology of the region is characterized by steep slopes, high climatic and altitude (ranging from 400 m a.s.l of Dora Baltea's river floodplain to 4810 m a.s.l. of Mont Blanc) variability. In the study area (zone B), located in Eastern part of Aosta Valley, heavy rainfall of about 800-900 mm per year is the main landslides trigger. These features lead to a high hydrogeological risk in all territory, as mass movements interest the 70% of the municipality areas (mainly shallow rapid landslides and rock falls). An in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted, with the aim to improve the reliability of deterministic model, named HIRESS (HIgh REsolution Stability Simulator). In particular, two campaigns of on site measurements and laboratory experiments were performed. The data obtained have been studied in order to assess the relationships existing among the different parameters and the bedrock lithology. The analyzed soils in 12 survey points are mainly composed of sand and gravel, with highly variable contents of silt. The range of effective internal friction angle (from 25.6° to 34.3°) and effective cohesion (from 0 kPa to 9.3 kPa) measured and the median ks (10E-6 m/s) value are consistent with the average grain sizes (gravelly sand). The data collected contributes to generate input map of parameters for HIRESS (static data). More static data are: volume weight, residual water content, porosity and grain size index. In order to improve the original formulation of the model, the contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. HIRESS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and in large areas using parallel computational techniques. The software

Residue-based Coordinated Selection and Parameter Design of Multiple Power System Stabilizers (PSSs)

DEFF Research Database (Denmark)

Su, Chi; Hu, Weihao; Fang, Jiakun

2013-01-01

data from time domain simulations. Then a coordinated approach for multiple PSS selection and parameter design based on residue method is proposed and realized in MATLAB m-files. Particle swarm optimization (PSO) is adopted in the coordination process. The IEEE 39-bus New England system model...

Parameter Selection for Department of Energy Spent Nuclear Fuel to be Used in the Yucca Mountain License Application

Energy Technology Data Exchange (ETDEWEB)

D. L. Fillmore

2003-10-01

This report contains the chemical, physical, and radiological parameters that were chosen to represent the U.S. Department of Energy spent nuclear fuel in the Yucca Mountain license application. It also contains the selected packaging requirements for the various fuel types and the criticality controls that were used. The data are reported for representative fuels and bounding fuels in groups of fuels that were selected for the analysis. The justification for the selection of each parameter is given. The data reported were not generated under any quality assurance program.

A Sensitivity Study for an Evaluation of Input Parameters Effect on a Preliminary Probabilistic Tsunami Hazard Analysis

Energy Technology Data Exchange (ETDEWEB)

Rhee, Hyun-Me; Kim, Min Kyu; Choi, In-Kil [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Sheen, Dong-Hoon [Chonnam National University, Gwangju (Korea, Republic of)

2014-10-15

The tsunami hazard analysis has been based on the seismic hazard analysis. The seismic hazard analysis has been performed by using the deterministic method and the probabilistic method. To consider the uncertainties in hazard analysis, the probabilistic method has been regarded as attractive approach. The various parameters and their weight are considered by using the logic tree approach in the probabilistic method. The uncertainties of parameters should be suggested by analyzing the sensitivity because the various parameters are used in the hazard analysis. To apply the probabilistic tsunami hazard analysis, the preliminary study for the Ulchin NPP site had been performed. The information on the fault sources which was published by the Atomic Energy Society of Japan (AESJ) had been used in the preliminary study. The tsunami propagation was simulated by using the TSUNAMI{sub 1}.0 which was developed by Japan Nuclear Energy Safety Organization (JNES). The wave parameters have been estimated from the result of tsunami simulation. In this study, the sensitivity analysis for the fault sources which were selected in the previous studies has been performed. To analyze the effect of the parameters, the sensitivity analysis for the E3 fault source which was published by AESJ was performed. The effect of the recurrence interval, the potential maximum magnitude, and the beta were suggested by the sensitivity analysis results. Level of annual exceedance probability has been affected by the recurrence interval.. Wave heights have been influenced by the potential maximum magnitude and the beta. In the future, the sensitivity analysis for the all fault sources in the western part of Japan which were published AESJ would be performed.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

Science.gov (United States)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Parameters Selection for Electropolishing Process of Products Made of Copper and Its Alloys

Directory of Open Access Journals (Sweden)

Maciąg T.

2017-09-01

Full Text Available Electropolishing is electrochemical method used in metal working that has a vital role in production of medical apparatus, in food or electric industry. The purpose of this paper is to determine optimal current parameters and time required for conducting electropolishing process from the perspective of changes of surface microgeometry. Furthermore, effect of different types of mechanical working used before electropolishing on final surface state was evaluated by observation in changes of topography. Research was conducted on electrolytic copper and brass. Analysis of surface geometry and its parameters (Ra, Sa was used as criterion describing efficiency of chemical electropolishing. Results of the experiment allow for current parameter optimization of electrochemical polishing process for selected non-ferrous alloys with preliminary mechanical preparation of the surface.

Summary report of the 3. research co-ordination meeting on development of reference input parameter library for nuclear model calculations of nuclear data (Phase 1: Starter File)

International Nuclear Information System (INIS)

Oblozinsky, P.

1997-09-01

The report contains the summary of the third and the last Research Co-ordination Meeting on ''Development of Reference Input Parameter Library for Nuclear Model Calculations of Nuclear Data (Phase I: Starter File)'', held at the ICTP, Trieste, Italy, from 26 to 29 May 1997. Details are given on the status of the Handbook and the Starter File - two major results of the project. (author)

Selected parameters of arabica coffee quality affected by its geographical origin

Directory of Open Access Journals (Sweden)

Alica Bobková

2017-01-01

Full Text Available The aim of this paper was to evaluate selected parameters of Arabica coffee quality. Arabica coffee beans originated from 21 different regions of the world. Parameters of their moisture content, water extract, water extract in dry matter, dry mater, caffeine and caffeine content in dry matter were assessed by the Slovak Technical Standard. Dry matter content ranged from 98.64 to 99.07%, the highest content was measured in sample from Cuba. Minimum dry matter content was detected in coffee beans from Mexico. Caffeine in studied samples ranged from 10 200 mg.kg-1 to 13 500 mg.kg-1. The lowest caffeine content was determined in Panama coffee, the highest was found in the sample from Indonesia. The results of moisture content and caffeine in dry mater were evaluated by the Food Code of the Slovak Republic and all observed parameters in the coffee beans meet the maximum levels given in legislation. By statistical procesing it can be seen that coffee samples originating from Ecuador, Indonesia and Nepal were similar in parameters of caffeine content and caffeine in dry matter. Other similar samples originating from Cuba, Peru, Ethiopia and Panama were statistically similar at dry matter content. Special statistical group was coffee from Salvador at the parameters of water extract and water extract in dry matter. Normal 0 21 false false false EN-GB X-NONE X-NONE

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.

Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis

Directory of Open Access Journals (Sweden)

Arun Kumar Srirangan

2016-06-01

Full Text Available Incoloy 800HT which was selected as one of the prominent material for fourth generation power plant can exhibit appreciable strength, good resistance to corrosion and oxidation in high temperature environment. This study focuses on the multi-objective optimization using grey relational analysis for Incoloy 800HT welded with tungsten inert arc welding process with N82 filler wire of diameter 1.2 mm. The welding input parameters play a vital role in determining desired weld quality. The experiments were conducted according to L9 orthogonal array. The input parameter chosen were the welding current, Voltage and welding speed. The output response for quality targets chosen were the ultimate tensile strength and yield strength (at room temperature, 750 °C and impact toughness. Grey relational analysis was applied to optimize the input parameters simultaneously considering multiple output variables. The optimal parameters combination was determined as A2B1C2 i.e. welding current at 110 A, voltage at 10 V and welding speed at 1.5 mm/s. ANOVA method was used to assess the significance of factors on the overall quality of the weldment. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics:

Sensitivity Analysis of Uncertainty Parameter based on MARS-LMR Code on SHRT-45R of EBR II

Energy Technology Data Exchange (ETDEWEB)

Kang, Seok-Ju; Kang, Doo-Hyuk; Seo, Jae-Seung [System Engineering and Technology Co., Daejeon (Korea, Republic of); Bae, Sung-Won [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Jeong, Hae-Yong [Sejong University, Seoul (Korea, Republic of)

2016-10-15

In order to assess the uncertainty quantification of the MARS-LMR code, the code has been improved by modifying the source code to accommodate calculation process required for uncertainty quantification. In the present study, a transient of Unprotected Loss of Flow(ULOF) is selected as typical cases of as Anticipated Transient without Scram(ATWS) which belongs to DEC category. The MARS-LMR input generation for EBR II SHRT-45R and execution works are performed by using the PAPIRUS program. The sensitivity analysis is carried out with Uncertainty Parameter of the MARS-LMR code for EBR-II SHRT-45R. Based on the results of sensitivity analysis, dominant parameters with large sensitivity to FoM are picked out. Dominant parameters selected are closely related to the development process of ULOF event.

Input reduction for long-term morphodynamic simulations in wave-dominated coastal settings

NARCIS (Netherlands)

Walstra, D.J.R.; Hoekstra, R.; Tonnon, P.K.; Ruessink, B.G.

2013-01-01

Input reduction is imperative to long-term (> years) morphodynamic simulations to avoid excessive computation times. Here, we introduce an input-reduction framework for wave-dominated coastal settings. Our framework comprises 4 steps, viz. (1) the selection of the duration of the original (full)

Seasonal variation in coat characteristics, tick loads, cortisol levels, some physiological parameters and temperature humidity index on Nguni cows raised in low- and high-input farms

Science.gov (United States)

Katiyatiya, C. L. F.; Muchenje, V.; Mushunje, A.

2015-06-01

Seasonal variations in hair length, tick loads, cortisol levels, haematological parameters (HP) and temperature humidity index (THI) in Nguni cows of different colours raised in two low-input farms, and a commercial stud was determined. The sites were chosen based on their production systems, climatic characteristics and geographical locations. Zazulwana and Komga are low-input, humid-coastal areas, while Honeydale is a high-input, dry-inland Nguni stud farm. A total of 103 cows, grouped according to parity, location and coat colour, were used in the study. The effects of location, coat colour, hair length and season were used to determine tick loads on different body parts, cortisol levels and HP in blood from Nguni cows. Highest tick loads were recorded under the tail and the lowest on the head of each of the animals ( P cows recorded the highest tick loads under the tails of all the cows used in the study from the three farms ( P cows with long hairs. Hair lengths were longest during the winter season in the coastal areas of Zazulwana and Honeydale ( P cows had significantly longer ( P heat stress in Nguni cows.

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

Science.gov (United States)

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

2016-12-01

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

Influence of tool geometry and processing parameters on welding defects and mechanical properties for friction stir welding of 6061 Aluminium alloy

Science.gov (United States)

Daneji, A.; Ali, M.; Pervaiz, S.

2018-04-01

Friction stir welding (FSW) is a form of solid state welding process for joining metals, alloys, and selective composites. Over the years, FSW development has provided an improved way of producing welding joints, and consequently got accepted in numerous industries such as aerospace, automotive, rail and marine etc. In FSW, the base metal properties control the material’s plastic flow under the influence of a rotating tool whereas, the process and tool parameters play a vital role in the quality of weld. In the current investigation, an array of square butt joints of 6061 Aluminum alloy was to be welded under varying FSW process and tool geometry related parameters, after which the resulting weld was evaluated for the corresponding mechanical properties and welding defects. The study incorporates FSW process and tool parameters such as welding speed, pin height and pin thread pitch as input parameters. However, the weld quality related defects and mechanical properties were treated as output parameters. The experimentation paves way to investigate the correlation between the inputs and the outputs. The correlation between inputs and outputs were used as tool to predict the optimized FSW process and tool parameters for a desired weld output of the base metals under investigation. The study also provides reflection on the effect of said parameters on a welding defect such as wormhole.

Sequential designs for sensitivity analysis of functional inputs in computer experiments

International Nuclear Information System (INIS)

Fruth, J.; Roustant, O.; Kuhnt, S.

2015-01-01

Computer experiments are nowadays commonly used to analyze industrial processes aiming at achieving a wanted outcome. Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on the response variable. In this work we focus on sensitivity analysis of a scalar-valued output of a time-consuming computer code depending on scalar and functional input parameters. We investigate a sequential methodology, based on piecewise constant functions and sequential bifurcation, which is both economical and fully interpretable. The new approach is applied to a sheet metal forming problem in three sequential steps, resulting in new insights into the behavior of the forming process over time. - Highlights: • Sensitivity analysis method for functional and scalar inputs is presented. • We focus on the discovery of most influential parts of the functional domain. • We investigate economical sequential methodology based on piecewise constant functions. • Normalized sensitivity indices are introduced and investigated theoretically. • Successful application to sheet metal forming on two functional inputs

DUSTMS-D: DISPOSAL UNIT SOURCE TERM - MULTIPLE SPECIES - DISTRIBUTED FAILURE DATA INPUT GUIDE.

Energy Technology Data Exchange (ETDEWEB)

SULLIVAN, T.M.

2006-01-01

the waste form), (b) diffusion release.(release from either a cylindrical, spherical, or rectangular wasteform), (c) dissolution release (uniform release over time due to dissolution of the wasteform surface), and (d) solubility limited release. The predicated wasteform releases are corrected for radioactive decay and ingrowth. A unique set of container failure and wasteform release parameters can be specified for each control volume with a container. Contaminant transport is modeled through a finite-difference solution of the advective transport equation with sources (wasteform release and ingrowth) and radioactive decay. Although DUST-MS simulates one-dimensional transport, it can be used to simulate migration down to an aquifer and then transport in the aquifer by running the code twice. A special subroutine allows the flux into the aquifer from the first simulation to be input as the flux at the upstream boundary in the aquifer. This document presents the models used to calculate release from a disposal facility, verification of the model, and instructions on the use of the DUST-MS code. In addition to DUST-MS, a preprocessor, DUSTINMS, which helps the code user create input decks for DUST-MS and a post-processor, GRAFMS, which takes selected output files and plots them on the computer terminal have been written. Use of these codes is also described. In using DUST-MS, as with all computer models, the validity of the predictions relies heavily on the validity of the input parameters. Often, the largest uncertainties arise from uncertainty in the input parameters. Therefore, it is crucial to document and support the use of these parameters. The DUST-MS code, because of its flexibility and ability to compute release rates quickly, is extremely useful for screening to determine the radionuclide released at the highest rate, parameter sensitivity analysis and, with proper choice of the input parameters, provide upper bounds to release rates.

Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input

OpenAIRE

Addo, Peter Martey

2014-01-01

This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.

Analytic uncertainty and sensitivity analysis of models with input correlations

Science.gov (United States)

Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu

2018-03-01

Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.

A Practical pedestrian approach to parsimonious regression with inaccurate inputs

Directory of Open Access Journals (Sweden)

Seppo Karrila

2014-04-01

Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.

Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis

Energy Technology Data Exchange (ETDEWEB)

Tencate, Alister J. [Department of Chemistry, Idaho State University, Pocatello, ID 83209 (United States); Kalivas, John H., E-mail: kalijohn@isu.edu [Department of Chemistry, Idaho State University, Pocatello, ID 83209 (United States); White, Alexander J. [Department of Physics and Optical Engineering, Rose-Hulman Institute of Technology, Terre Huate, IN 47803 (United States)

2016-05-19

New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of

Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis

International Nuclear Information System (INIS)

Tencate, Alister J.; Kalivas, John H.; White, Alexander J.

2016-01-01

New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of

THE EFFECT OF SELECTED PHYSICAL AND CHEMICAL PARAMETERS ON MICROBIOLOGICAL STATUS OF THE VISTULA RIVER NEAR WARSAW

Directory of Open Access Journals (Sweden)

Janusz Augustynowicz

2016-02-01

Full Text Available The types of organisms present in water reservoirs depend on water purity and biochemical processes that occur. Therefore, one of the methods of water quality assessment is to determine its condition by determining the biological indicators, including microbiological parameters. The aim of the experiment presented in this paper was to investigate the effects of selected physical and chemical parameters of water samples from the Vistula River on the microbiological status of water. The experiment was conducted in water samples collected in the central part of the Vistula River in Warsaw. The analyses of selected parameters were performed once a month throughout the year. Microbiological tests included: number of nitrogen fixing bacteria, MPN nitrifying bacteria, MPN sulfate-reducing bacteria. Physical and chemical parameters such as temperature, pH and total nitrogen content were determined in water samples. The results showed a correlation between temperature, pH and microbiological parameters. However, there was no significant correlation between the number of tested microorganisms and the concentration of total nitrogen in water samples.

Characterization of Input Current Interharmonics in Adjustable Speed Drives

DEFF Research Database (Denmark)

Soltani, Hamid; Davari, Pooya; Zare, Firuz

2017-01-01

This paper investigates the interharmonic generation process in the input current of double-stage Adjustable Speed Drives (ASDs) based on voltage source inverters and front-end diode rectifiers. The effects of the inverter output-side low order harmonics, caused by implementing the double......-edge symmetrical regularly sampled Space Vector Modulation (SVM) technique, on the input current interharmonic components are presented and discussed. Particular attention is also given to the influence of the asymmetrical regularly sampled modulation technique on the drive input current interharmonics....... The developed theoretical analysis predicts the drive interharmonic frequency locations with respect to the selected sampling strategies. Simulation and experimental results on a 2.5 kW ASD system verify the effectiveness of the theoretical analysis....

Input measurements in reprocessing plants

International Nuclear Information System (INIS)

Trincherini, P.R.; Facchetti, S.

1980-01-01

The aim of this work is to give a review of the methods and the problems encountered in measurements in 'input accountability tanks' of irradiated fuel treatment plants. This study was prompted by the conviction that more and more precise techniques and methods should be at the service of safeguards organizations and that ever greater efforts should be directed towards promoting knowledge of them among operators and all those general area of interest includes the nuclear fuel cycle. The overall intent is to show the necessity of selecting methods which produce measurements which are not only more precise but are absolutely reliable both for routine plant operation and for safety checks in the input area. A description and a critical evaluation of the most common physical and chemical methods are provided, together with an estimate of the precision and accuracy obtained in real operating conditions

Strategy of Cooling Parameters Selection in the Continuous Casting of Steel

Directory of Open Access Journals (Sweden)

Falkus J.

2016-03-01

Full Text Available This paper presents a strategy of the cooling parameters selection in the process of continuous steel casting. Industrial tests were performed at a slab casting machine at the Arcelor Mittal Poland Unit in Krakow. The tests covered 55 heats for 7 various steel grades. Based on the existing casting technology a numerical model of the continuous steel casting process was formulated. The numerical calculations were performed for three casting speeds - 0.6, 0.8 and 1 m min-1. An algorithm was presented that allows us to compute the values of the heat transfer coefficients for the secondary cooling zone. The correctness of the cooling parameter strategy was evaluated by inspecting the shell thickness, the length of the liquid core and the strand surface temperature. The ProCAST software package was used to construct the numerical model of continuous casting of steel.

Selected Parameters of Micro-Jet Cooling Gases in Hybrid Spraying Process

Directory of Open Access Journals (Sweden)

Szczucka-Lasota B.

2016-06-01

Full Text Available The innovative technology, like thermal spraying with a micro-jet cooling is one of the important modification of classical ultrasonic spraying methods. Using of micro-stream with gases like argon or nitrogen allows to cool the coating immediately after spraying, and thereby reduce the time of transition during the injection of each layer. As a result of the process, the fine dispersive structure of coatings is obtained during the shorter time in comparable to the classical high velocity oxygen fuel process (HVOF. The parameter of process and the type of stream equipment determine the quality of the obtained structure and thermal stress in the coating. The article presents the relationship between selected parameters of hybrid process and properties of the coatings. The presented technology should be adapted to the actual production of protective coating for machines and construction working in wear conditions.

A Method to Select Test Input Cases for Safety-critical Software

International Nuclear Information System (INIS)

Kim, Heeeun; Kang, Hyungook; Son, Hanseong

2013-01-01

This paper proposes a new testing methodology for effective and realistic quantification of RPS software failure probability. Software failure probability quantification is important factor in digital system safety assessment. In this study, the method for software test case generation is briefly described. The test cases generated by this method reflect the characteristics of safety-critical software and past inputs. Furthermore, the number of test cases can be reduced, but it is possible to perform exhaustive test. Aspect of software also can be reflected as failure data, so the final failure data can include the failure of software itself and external influences. Software reliability is generally accepted as the key factor in software quality since it quantifies software failures which can make a powerful system inoperative. In the KNITS (Korea Nuclear Instrumentation and Control Systems) project, the software for the fully digitalized reactor protection system (RPS) was developed under a strict procedure including unit testing and coverage measurement. Black box testing is one type of Verification and validation (V and V), in which given input values are entered and the resulting output values are compared against the expected output values. Programmable logic controllers (PLCs) were used in implementing critical systems and function block diagram (FBD) is a commonly used implementation language for PLC

RIP Input Tables From WAPDEG for LA Design Selection: Continuous Post-Closure Ventilation Design- Open Loop

International Nuclear Information System (INIS)

K.G. Mon; P.K. Mast; R. Howard; J.H. Lee

1999-01-01

The purpose of this calculation is to document (1) the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b). Software Routine Report for WAPDEG (Version 3.09) simulations used to analyze waste package degradation and failure under the repository exposure conditions characterized by the open loop option of the post-closure ventilation design and, (2) post-processing of these results into tables of waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems version 5.19.0 1 (RIP) computer program (Golder Associates 1998). Specifically, the WAPDEG simulations discussed in this calculation correspond to waste package emplacement conditions (repository environment and design) defined in the Total System Performance Assessment-Viability Assessment (TSPA-VA), with the exception that the open loop option of the post-closure ventilation License Application Design Selection (LADS) Design Alternative (Design Alternative 3b) was analyzed. The open loop post-closure ventilation design alternative, under which airways to the surface remain open after repository closure, could result in substantial cooling and drying of the potential repository. In open loop post-closure ventilation, expanded air heated by waste decay would move up an exhaust shaft, pulling denser, cooler air into the repository through intake shafts. The exchange of air with the atmosphere could remove more heat and moisture. As a result of the enhanced ventilation relative to the TSPA-VA base-case design, different temperature and relative humidity time histories at the waste package surface are calculated (input to the WAPDEG simulations), and consequently different waste package failure histories (as calculated by WAPDEG) result

HF Parameters of Induction Motor

Directory of Open Access Journals (Sweden)

M. N. Benallal

2017-09-01

Full Text Available This article describes the results of experimental studies of HF input and primary parameters. A simulation model in Matlab SimulinkTM of multiphase windings as ladder circuit of coils is developed. A method for determining the primary parameters of ladder equivalent circuits is presented.

On the selection of user-defined parameters in data-driven stochastic subspace identification

Science.gov (United States)

Priori, C.; De Angelis, M.; Betti, R.

2018-02-01

The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

Stabilization of (state, input)-disturbed CSTRs through the port-Hamiltonian systems approach

OpenAIRE

Lu, Yafei; Fang, Zhou; Gao, Chuanhou

2017-01-01

It is a universal phenomenon that the state and input of the continuous stirred tank reactor (CSTR) systems are both disturbed. This paper proposes a (state, input)-disturbed port-Hamiltonian framework that can be used to model and further designs a stochastic passivity based controller to asymptotically stabilize in probability the (state, input)-disturbed CSTR (sidCSTR) systems. The opposite entropy function and the availability function are selected as the Hamiltonian for the model and con...

Response sensitivity of barrel neuron subpopulations to simulated thalamic input.

Science.gov (United States)

Pesavento, Michael J; Rittenhouse, Cynthia D; Pinto, David J

2010-06-01

Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.

Effect of heat input on the microstructure and mechanical properties of gas tungsten arc welded AISI 304 stainless steel joints

International Nuclear Information System (INIS)

Kumar, Subodh; Shahi, A.S.

2011-01-01

Highlights: → Welding procedure is established for welding 6 mm thick AISI 304 using GTAW process. → Mechanical properties of the weld joints are influenced strongly by the heat input. → Highest tensile strength of 657.32 MPa is achieved by joints using low heat input. → Welding parameters affect heat input and hence microstructure of weld joints. → Extent of grain coarsening in the HAZ increases with increase in the heat input. -- Abstract: Influence of heat input on the microstructure and mechanical properties of gas tungsten arc welded 304 stainless steel (SS) joints was studied. Three heat input combinations designated as low heat (2.563 kJ/mm), medium heat (2.784 kJ/mm) and high heat (3.017 kJ/mm) were selected from the operating window of the gas tungsten arc welding process (GTAW) and weld joints made using these combinations were subjected to microstructural evaluations and tensile testing so as to analyze the effect of thermal arc energy on the microstructure and mechanical properties of these joints. The results of this investigation indicate that the joints made using low heat input exhibited higher ultimate tensile strength (UTS) than those welded with medium and high heat input. Significant grain coarsening was observed in the heat affected zone (HAZ) of all the joints and it was found that the extent of grain coarsening in the heat affected zone increased with increase in the heat input. For the joints investigated in this study it was also found that average dendrite length and inter-dendritic spacing in the weld zone increases with increase in the heat input which is the main reason for the observable changes in the tensile properties of the weld joints welded with different arc energy inputs.

Genetic parameters and selection gains for Euterpe oleracea in juvenile phase

Directory of Open Access Journals (Sweden)

João Tomé de Farias Neto

2012-09-01

Full Text Available Genetics parameters and selection gains, obtained 36 months after planting, are presented and discussed for progenies of open pollinated population of açai palm for plant height (AP, plant diameter (DPC, number of live leaves ( NFV and tiller number (NP, based on the linear mixed model methodology (REML / BLUP. The thirty progenies were evaluated in a randomized blocks design with three replications and plots of five plants, spaced at 6m x 4m. The values obtained for individual heritability (0.55, 0.44, 0.38 and 0.43 and for progeny means (0.64, 0.54, 0.58 and 0.64 for AP, DPC, NFV and NP, respectively, were expressives, which indicates the possibility of genetic progress with the selection. The accuracy among the genetics values predicted and the true were of 0.802 for height, 0.736 for diameter, 0.760 for number of live leaves and 0.797 for tiller number. With the exception of NFV character, the coefficients of individual genetic variation were high (>10%, confirming the potential of the population for selection. Predicted genetic gains of 89.3% were obtained for the character AP and 2.1% for DCP, with the selection of the twenty top individuals. Correlation was found between height and diameter of the plant. Among ages, for the same characters, positive correlations of mean magnitudes were found.

Multi-response optimization of process parameters in biogas production from food waste using Taguchi – Grey relational analysis

International Nuclear Information System (INIS)

Deepanraj, B.; Sivasubramanian, V.; Jayaraj, S.

2017-01-01

Highlights: • Influence of process parameters on biogas production was experimentally investigated. • The optimum conditions were determined using Taguchi based Grey relational analysis. • Percentage contribution of chosen parameters were determined using ANOVA. • Empirical relationship between the input and output variables were derived. - Abstract: In the present study, the influence of process parameters and pretreatment on biogas production, volatile solid degradation and COD degradation during anaerobic digestion of food waste were experimentally investigated. Using Taguchi based Grey relational analysis, the optimum condition for anaerobic digestion was found. Taguchi technique was coupled with grey relational analysis to obtain a grey relational grade for evaluating multiple outputs. A L_1_6 orthogonal array was selected and designed for five parameters varied through four levels by applying Taguchi’s design of experiments. The optimum level values of parameters obtained for anaerobic digestion of food waste is solid concentration of 7.5% TS, pH of 7, temperature of 50 °C, C/N ratio of 20.19 and ultrasonication pretreatment. Percentage contribution of input parameters on output was determined using ANOVA. The results showed that pretreatment is the prominent parameter that contributes towards output responses followed by pH, solid concentration, temperature and C/N ratio.

Model parameter updating using Bayesian networks

International Nuclear Information System (INIS)

Treml, C.A.; Ross, Timothy J.

2004-01-01

This paper outlines a model parameter updating technique for a new method of model validation using a modified model reference adaptive control (MRAC) framework with Bayesian Networks (BNs). The model parameter updating within this method is generic in the sense that the model/simulation to be validated is treated as a black box. It must have updateable parameters to which its outputs are sensitive, and those outputs must have metrics that can be compared to that of the model reference, i.e., experimental data. Furthermore, no assumptions are made about the statistics of the model parameter uncertainty, only upper and lower bounds need to be specified. This method is designed for situations where a model is not intended to predict a complete point-by-point time domain description of the item/system behavior; rather, there are specific points, features, or events of interest that need to be predicted. These specific points are compared to the model reference derived from actual experimental data. The logic for updating the model parameters to match the model reference is formed via a BN. The nodes of this BN consist of updateable model input parameters and the specific output values or features of interest. Each time the model is executed, the input/output pairs are used to adapt the conditional probabilities of the BN. Each iteration further refines the inferred model parameters to produce the desired model output. After parameter updating is complete and model inputs are inferred, reliabilities for the model output are supplied. Finally, this method is applied to a simulation of a resonance control cooling system for a prototype coupled cavity linac. The results are compared to experimental data.

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.

Sensitivity Analysis of Unsaturated Flow and Contaminant Transport with Correlated Parameters

Science.gov (United States)

Relative contributions from uncertainties in input parameters to the predictive uncertainties in unsaturated flow and contaminant transport are investigated in this study. The objectives are to: (1) examine the effects of input parameter correlations on the sensitivity of unsaturated flow and conta...

Fetal biometric parameters: Reference charts for a non-selected risk population from Uberaba, Brazil.

Science.gov (United States)

Peixoto, Alberto Borges; da Cunha Caldas, Taciana Mara Rodrigues; Dulgheroff, Fernando Felix; Martins, Wellington P; Araujo Júnior, Edward

2017-03-01

To establish reference charts for fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil. A retrospective cross-sectional study was performed among 5656 non-selected risk singleton pregnant women between 14 and 41 weeks of gestation. The ultrasound exams were performed during routine visits of second and third trimesters. Biparietal diameter (BPD) was measured at the level of the thalami and cavum septi pellucidi. Head circumference (HC) was calculated by the following formula: HC = 1.62*(BPD + occipital frontal diameter, OFD). Abdominal circumference (AC) was measured using the following formula: AC = (anteroposterior diameter + transverse abdominal diameter) × 1.57. Femur diaphysis length (FDL) was obtained in the longest axis of femur without including the distal femoral epiphysis. The estimated fetal weight (EFW) was obtained by the Hadlock formula. Polynomial regressions were performed to obtain the best-fit model for each fetal biometric parameter as the function of gestational age (GA). The mean, standard deviations ( SD ), minimum and maximum of BPD (cm), HC (cm), AC (cm), FDL (cm) and EFW (g) were 6.9 ± 1.9 (2.3 - 10.5), 24.51 ± 6.61 (9.1 - 36.4), 22.8 ± 7.3 (7.5 - 41.1), 4.9 ± 1.6 (1.2 - 8.1) and 1365 ± 1019 (103 - 4777), respectively. Second-degree polynomial regressions between the evaluated parameters and GA resulted in the following formulas: BPD = -4.044 + 0.540 × GA - 0.0049 × GA 2 ( R 2 = 0.97); HC= -15.420 + 2.024 GA - 0.0199 × GA 2 ( R 2 = 0.98); AC = -9.579 + 1.329 × GA - 0.0055 × GA 2 ( R 2 = 0.97); FDL = -3.778 + 0.416 × GA - 0.0035 × GA 2 ( R 2 = 0.98) and EFW = 916 - 123 × GA + 4.70 × GA 2 ( R 2 = 0.96); respectively. Reference charts for the fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil, were established.

Age-Related Interference between the Selection of Input-Output Modality Mappings and Postural Control—a Pilot Study

Science.gov (United States)

Stelzel, Christine; Schauenburg, Gesche; Rapp, Michael A.; Heinzel, Stephan; Granacher, Urs

2017-01-01

Age-related decline in executive functions and postural control due to degenerative processes in the central nervous system have been related to increased fall-risk in old age. Many studies have shown cognitive-postural dual-task interference in old adults, but research on the role of specific executive functions in this context has just begun. In this study, we addressed the question whether postural control is impaired depending on the coordination of concurrent response-selection processes related to the compatibility of input and output modality mappings as compared to impairments related to working-memory load in the comparison of cognitive dual and single tasks. Specifically, we measured total center of pressure (CoP) displacements in healthy female participants aged 19–30 and 66–84 years while they performed different versions of a spatial one-back working memory task during semi-tandem stance on an unstable surface (i.e., balance pad) while standing on a force plate. The specific working-memory tasks comprised: (i) modality compatible single tasks (i.e., visual-manual or auditory-vocal tasks), (ii) modality compatible dual tasks (i.e., visual-manual and auditory-vocal tasks), (iii) modality incompatible single tasks (i.e., visual-vocal or auditory-manual tasks), and (iv) modality incompatible dual tasks (i.e., visual-vocal and auditory-manual tasks). In addition, participants performed the same tasks while sitting. As expected from previous research, old adults showed generally impaired performance under high working-memory load (i.e., dual vs. single one-back task). In addition, modality compatibility affected one-back performance in dual-task but not in single-task conditions with strikingly pronounced impairments in old adults. Notably, the modality incompatible dual task also resulted in a selective increase in total CoP displacements compared to the modality compatible dual task in the old but not in the young participants. These results suggest

Can Simulation Credibility Be Improved Using Sensitivity Analysis to Understand Input Data Effects on Model Outcome?

Science.gov (United States)

Myers, Jerry G.; Young, M.; Goodenow, Debra A.; Keenan, A.; Walton, M.; Boley, L.

2015-01-01

Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations.

The Use of Asymptotic Functions for Determining Empirical Values of CN Parameter in Selected Catchments of Variable Land Cover

Directory of Open Access Journals (Sweden)

Wałęga Andrzej

2017-12-01

Full Text Available The aim of the study was to assess the applicability of asymptotic functions for determining the value of CN parameter as a function of precipitation depth in mountain and upland catchments. The analyses were carried out in two catchments: the Rudawa, left tributary of the Vistula, and the Kamienica, right tributary of the Dunajec. The input material included data on precipitation and flows for a multi-year period 1980–2012, obtained from IMGW PIB in Warsaw. Two models were used to determine empirical values of CNobs parameter as a function of precipitation depth: standard Hawkins model and 2-CN model allowing for a heterogeneous nature of a catchment area.

Selecting and optimizing eco-physiological parameters of Biome-BGC to reproduce observed woody and leaf biomass growth of Eucommia ulmoides plantation in China using Dakota optimizer

Science.gov (United States)

Miyauchi, T.; Machimura, T.

2013-12-01

In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the

Variance-based sensitivity indices for stochastic models with correlated inputs

Energy Technology Data Exchange (ETDEWEB)

Kala, Zdeněk [Brno University of Technology, Faculty of Civil Engineering, Department of Structural Mechanics Veveří St. 95, ZIP 602 00, Brno (Czech Republic)

2015-03-10

The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics.

Variance-based sensitivity indices for stochastic models with correlated inputs

International Nuclear Information System (INIS)

Kala, Zdeněk

2015-01-01

The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics

Calculating the sensitivity of wind turbine loads to wind inputs using response surfaces

International Nuclear Information System (INIS)

Rinker, Jennifer M.

2016-01-01

This paper presents a methodology to calculate wind turbine load sensitivities to turbulence parameters through the use of response surfaces. A response surface is a highdimensional polynomial surface that can be calibrated to any set of input/output data and then used to generate synthetic data at a low computational cost. Sobol sensitivity indices (SIs) can then be calculated with relative ease using the calibrated response surface. The proposed methodology is demonstrated by calculating the total sensitivity of the maximum blade root bending moment of the WindPACT 5 MW reference model to four turbulence input parameters: a reference mean wind speed, a reference turbulence intensity, the Kaimal length scale, and a novel parameter reflecting the nonstationarity present in the inflow turbulence. The input/output data used to calibrate the response surface were generated for a previous project. The fit of the calibrated response surface is evaluated in terms of error between the model and the training data and in terms of the convergence. The Sobol SIs are calculated using the calibrated response surface, and the convergence is examined. The Sobol SIs reveal that, of the four turbulence parameters examined in this paper, the variance caused by the Kaimal length scale and nonstationarity parameter are negligible. Thus, the findings in this paper represent the first systematic evidence that stochastic wind turbine load response statistics can be modeled purely by mean wind wind speed and turbulence intensity. (paper)

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

Science.gov (United States)

Golmohammadi, H; Dashtbozorgi, Z

2016-12-01

In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liquid chromatography based on molecular descriptors calculated from the optimized structures. Diverse kinds of molecular descriptors were calculated to encode the molecular structures of compounds, such as geometric, thermodynamic, electrostatic and quantum mechanical descriptors. The variable selection method of ERM was employed to select an optimum subset of descriptors. The five descriptors selected using ERM were used as inputs of SVM to predict the polarity parameter of organic compounds in methanol. The coefficient of determination, r 2 , between experimental and predicted polarity parameters for the prediction set by ERM and SVM were 0.952 and 0.982, respectively. Acceptable results specified that the ERM approach is a very effective method for variable selection and the predictive aptitude of the SVM model is superior to those obtained by ERM. The obtained results demonstrate that SVM can be used as a substitute influential modeling tool for QSPR studies.

Calibration of discrete element model parameters: soybeans

Science.gov (United States)

Ghodki, Bhupendra M.; Patel, Manish; Namdeo, Rohit; Carpenter, Gopal

2018-05-01

Discrete element method (DEM) simulations are broadly used to get an insight of flow characteristics of granular materials in complex particulate systems. DEM input parameters for a model are the critical prerequisite for an efficient simulation. Thus, the present investigation aims to determine DEM input parameters for Hertz-Mindlin model using soybeans as a granular material. To achieve this aim, widely acceptable calibration approach was used having standard box-type apparatus. Further, qualitative and quantitative findings such as particle profile, height of kernels retaining the acrylic wall, and angle of repose of experiments and numerical simulations were compared to get the parameters. The calibrated set of DEM input parameters includes the following (a) material properties: particle geometric mean diameter (6.24 mm); spherical shape; particle density (1220 kg m^{-3} ), and (b) interaction parameters such as particle-particle: coefficient of restitution (0.17); coefficient of static friction (0.26); coefficient of rolling friction (0.08), and particle-wall: coefficient of restitution (0.35); coefficient of static friction (0.30); coefficient of rolling friction (0.08). The results may adequately be used to simulate particle scale mechanics (grain commingling, flow/motion, forces, etc) of soybeans in post-harvest machinery and devices.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

Science.gov (United States)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

EARLY GUIDANCE FOR ASSIGNING DISTRIBUTION PARAMETERS TO GEOCHEMICAL INPUT TERMS TO STOCHASTIC TRANSPORT MODELS

International Nuclear Information System (INIS)

Kaplan, D; Margaret Millings, M

2006-01-01

Stochastic modeling is being used in the Performance Assessment program to provide a probabilistic estimate of the range of risk that buried waste may pose. The objective of this task was to provide early guidance for stochastic modelers for the selection of the range and distribution (e.g., normal, log-normal) of distribution coefficients (K d ) and solubility values (K sp ) to be used in modeling subsurface radionuclide transport in E- and Z-Area on the Savannah River Site (SRS). Due to the project's schedule, some modeling had to be started prior to collecting the necessary field and laboratory data needed to fully populate these models. For the interim, the project will rely on literature values and some statistical analyses of literature data as inputs. Based on statistical analyses of some literature sorption tests, the following early guidance was provided: (1) Set the range to an order of magnitude for radionuclides with K d values >1000 mL/g and to a factor of two for K d values of sp values -6 M and to a factor of two for K d values of >10 -6 M. This decision is based on the literature. (3) The distribution of K d values with a mean >1000 mL/g will be log-normally distributed. Those with a K d value <1000 mL/g will be assigned a normal distribution. This is based on statistical analysis of non-site-specific data. Results from on-going site-specific field/laboratory research involving E-Area sediments will supersede this guidance; these results are expected in 2007

Parameter extraction with neural networks

Science.gov (United States)

Cazzanti, Luca; Khan, Mumit; Cerrina, Franco

1998-06-01

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

A Regionalization Approach to select the final watershed parameter set among the Pareto solutions

Science.gov (United States)

Park, G. H.; Micheletty, P. D.; Carney, S.; Quebbeman, J.; Day, G. N.

2017-12-01

The calibration of hydrological models often results in model parameters that are inconsistent with those from neighboring basins. Considering that physical similarity exists within neighboring basins some of the physically related parameters should be consistent among them. Traditional manual calibration techniques require an iterative process to make the parameters consistent, which takes additional effort in model calibration. We developed a multi-objective optimization procedure to calibrate the National Weather Service (NWS) Research Distributed Hydrological Model (RDHM), using the Nondominant Sorting Genetic Algorithm (NSGA-II) with expert knowledge of the model parameter interrelationships one objective function. The multi-objective algorithm enables us to obtain diverse parameter sets that are equally acceptable with respect to the objective functions and to choose one from the pool of the parameter sets during a subsequent regionalization step. Although all Pareto solutions are non-inferior, we exclude some of the parameter sets that show extremely values for any of the objective functions to expedite the selection process. We use an apriori model parameter set derived from the physical properties of the watershed (Koren et al., 2000) to assess the similarity for a given parameter across basins. Each parameter is assigned a weight based on its assumed similarity, such that parameters that are similar across basins are given higher weights. The parameter weights are useful to compute a closeness measure between Pareto sets of nearby basins. The regionalization approach chooses the Pareto parameter sets that minimize the closeness measure of the basin being regionalized. The presentation will describe the results of applying the regionalization approach to a set of pilot basins in the Upper Colorado basin as part of a NASA-funded project.

Assessing the Impacts of Zimbabwe’s Agricultural Vouchers Input Program

OpenAIRE

Mazvimavi, Kizito; Murendo, Conrad; Minde, Isaac J.; Kunzekweguta, Machiweyi

2013-01-01

Using data from ICRISAT 2010/11 household and fertilizer retailer surveys, the study reveals that open vouchers enhance farmers input choice. The targeting of vulnerable farmers was efficient in selecting, households with less livestock ownership and those affected by HIV/AIDS. The use of open vouchers enabled retailers to sale agricultural inputs, boost revenue and link them to suppliers. The use of open voucher is preferable in areas where retailer’s infrastructure and mobile telephone netw...

Optimisation of milling parameters using neural network

Directory of Open Access Journals (Sweden)

Lipski Jerzy

2017-01-01

Full Text Available The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3. The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3 and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone, connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

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

Science.gov (United States)

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

1997-04-01

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

Sensitivity analysis of complex models: Coping with dynamic and static inputs

International Nuclear Information System (INIS)

Anstett-Collin, F.; Goffart, J.; Mara, T.; Denis-Vidal, L.

2015-01-01

In this paper, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the one of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen–Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient sampling-based strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a building energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house. - Highlights: • Sensitivity analysis of models with uncertain static and dynamic inputs is performed. • Karhunen–Loève (KL) decomposition of the spatio/temporal inputs is performed. • The influence of the dynamic inputs is studied through the modes of the KL expansion. • The proposed approach is applied to a building energy model. • Impact of weather data and material properties on performance of real house is given

Application of a Linear Input/Output Model to Tankless Water Heaters

Energy Technology Data Exchange (ETDEWEB)

Butcher T.; Schoenbauer, B.

2011-12-31

In this study, the applicability of a linear input/output model to gas-fired, tankless water heaters has been evaluated. This simple model assumes that the relationship between input and output, averaged over both active draw and idle periods, is linear. This approach is being applied to boilers in other studies and offers the potential to make a small number of simple measurements to obtain the model parameters. These parameters can then be used to predict performance under complex load patterns. Both condensing and non-condensing water heaters have been tested under a very wide range of load conditions. It is shown that this approach can be used to reproduce performance metrics, such as the energy factor, and can be used to evaluate the impacts of alternative draw patterns and conditions.

Liver kinetics of glucose analogs measured in pigs by PET: importance of dual-input blood sampling

DEFF Research Database (Denmark)

Munk, O L; Bass, L; Roelsgaard, K

2001-01-01

-input functions were very similar. CONCLUSION: Compartmental analysis of MG and FDG kinetics using dynamic PET data requires measurements of dual-input activity concentrations. Using the dual-input function, physiologically reasonable parameter estimates of K1, k2, and Vp were obtained, whereas the use......Metabolic processes studied by PET are quantified traditionally using compartmental models, which relate the time course of the tracer concentration in tissue to that in arterial blood. For liver studies, the use of arterial input may, however, cause systematic errors to the estimated kinetic...... parameters, because of ignorance of the dual blood supply from the hepatic artery and the portal vein to the liver. METHODS: Six pigs underwent PET after [15O]carbon monoxide inhalation, 3-O-[11C]methylglucose (MG) injection, and [18F]FDG injection. For the glucose scans, PET data were acquired for 90 min...

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

Science.gov (United States)

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

2017-09-01

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

Improved time series prediction with a new method for selection of model parameters

International Nuclear Information System (INIS)

Jade, A M; Jayaraman, V K; Kulkarni, B D

2006-01-01

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

Input parameters and scenarios, including economic inputs

DEFF Research Database (Denmark)

Boklund, Anette; Hisham Beshara Halasa, Tariq

2012-01-01

scenarios, we excluded hobby-type farms1 In the vaccination scenarios, herds within the vaccination zone were simulated to be vaccinated 14 days after detection of the first herd or when 10, 20, 30 or 50 herds were infected. All herds within the zones were simulated to be vaccinated. We used vaccination...... zones of either a 1, 2, 3 or 5 km. In some scenarios, hobby herds were not vaccinated. In one scenario, no sheep were vaccinated, and in another scenario no swine were vaccinated. from depopulation in zones. The resources for depopulation were estimated to 4,800 swine and 2,000 ruminants a day...

Unimolecular Logic Gate with Classical Input by Single Gold Atoms.

Science.gov (United States)

Skidin, Dmitry; Faizy, Omid; Krüger, Justus; Eisenhut, Frank; Jancarik, Andrej; Nguyen, Khanh-Hung; Cuniberti, Gianaurelio; Gourdon, Andre; Moresco, Francesca; Joachim, Christian

2018-02-27

By a combination of solution and on-surface chemistry, we synthesized an asymmetric starphene molecule with two long anthracenyl input branches and a short naphthyl output branch on the Au(111) surface. Starting from this molecule, we could demonstrate the working principle of a single molecule NAND logic gate by selectively contacting single gold atoms by atomic manipulation to the longer branches of the molecule. The logical input "1" ("0") is defined by the interaction (noninteraction) of a gold atom with one of the input branches. The output is measured by scanning tunneling spectroscopy following the shift in energy of the electronic tunneling resonances at the end of the short branch of the molecule.

Selecting Design Parameters for Flying Vehicles

Science.gov (United States)

Makeev, V. I.; Strel'nikova, E. A.; Trofimenko, P. E.; Bondar', A. V.

2013-09-01

Studying the influence of a number of design parameters of solid-propellant rockets on the longitudinal and lateral dispersion is an important applied problem. A mathematical model of a rigid body of variable mass moving in a disturbed medium exerting both wave drag and friction is considered. The model makes it possible to determine the coefficients of aerodynamic forces and moments, which affect the motion of vehicles, and to assess the effect of design parameters on their accuracy

The influence of paint dispersion parameters on the spectral selectivity of black-pigmented coatings

Energy Technology Data Exchange (ETDEWEB)

Gunde, M.K.; Orel, Z.C. [National Institute of Chemistry, Ljubljana (Slovenia); Hutchins, M.G. [Oxford Brookes University, Oxford (United Kingdom). School of Engineering

2003-10-31

The optical properties of variously prepared black-pigmented solar absorbing paints were calculated in terms of their effective absorption and scattering abilities. The phenomenological two-parameter Kubelka-Munk effective medium theory was applied. Paints with the same composition were prepared for different degrees of pigment dispersion and characterized by the average size of pigment agglomerates present in the pigment/vehicle system. Prepared paints were applied to aluminium foil in two ways, by coil coating and by spraying. The size of coarse pigment particles and the paint application technique influence the spectral selectivity and thus determine the final performance of spectrally selective surfaces. (author)

Evaluation of Injection Molding Process Parameters for Manufacturing Polyethylene Terephthalate

Directory of Open Access Journals (Sweden)

Marwah O.M.F.

2017-01-01

Full Text Available Quality control is an important aspect in manufacturing process. The quality of product in injection moulding is influenced by injection moulding process parameter. In this study, the effect of injection moulding parameter on defects quantity of PET preform was investigated. Optimizing the parameter of injection moulding process is critical to enhance productivity where parameters must operate at an optimum level for an acceptable performance. Design of Experiment (DOE by factorial design approach was used to find an optimum parameter setting and reduce the defects. In this case study, Minitab 17 software was used to analyses the data. The selected input parameters were mould hot runner temperature, water cooling chiller temperature 1 and water cooling chiller temperature 2. Meanwhile, the output for the process was defects quantity of the preform. The relationship between input and output of the process was analyzed using regression method and Analysis of Variance (ANOVA. In order to interpolate the experiment data, mathematical modeling was used which consists of different types of regression equation. Next, from the model, 95% confidence level (p-value was considered and the significant parameter was figured out. This study involved a collaboration with a preform injection moulding company which was Nilai Legasi Plastik Sdn Bhd. The collaboration enabled the researchers to collect the data and also help the company to improve the quality of its production. The results of the study showed that the optimum parameter setting that could reduce the defect quantity of preform was MHR= 88°C, CT1= 24°C and CT2= 27°C. The comparison defect quantity analysis between current companies setting with the optimum setting showed improvement by 21% reduction of defect quantity at the optimum setting. Finally, from the optimization plot, the validation error between the prediction value and experiment was 1.72%. The result proved that quality of products

On the issue of selecting technical and operational parameters for buses in urban passenger routes

Directory of Open Access Journals (Sweden)

Rudzinskyi V.V.

2017-10-01

Full Text Available Problems of a public transport bus service in urban areas were analyzed. The aim of the article is to determine actual operational parameters of buses during passenger transportation in Zhytomyr. Ways of determining technical and operational parameters of buses were developed using visual and tabular methods of city buses real-time speed and acceleration performance registration by GPS-monitoring system with the help of a communicational and informational intelligent transport system of the city. Experimental studies of city buses motion parameters were presented. A comprehensive survey of passenger traffic and conditions of public transport functioning in Zhytomyr was carried out. The values of technical and operational parameters of buses on city routes were obtained. Preliminarily conclusions and recommendations considering the criteria for selecting the optimal rolling stock for a bus network of the city were suggested.

Effect of Sacroiliac Joint Manipulation on Selected Gait Parameters in Healthy Subjects.

Science.gov (United States)

Wójtowicz, Sebastian; Sajko, Igor; Hadamus, Anna; Mosiołek, Anna; Białoszewski, Dariusz

2017-08-31

The sacroiliac joints have complicated biomechanics. While the movements in the joints are small, they exert a significant effect on gait. This study aimed to assess how sacroiliac joint manipulation influences selected gait parameters. The study enrolled 57 healthy subjects. The experimental group consisted of 26 participants diagnosed with dysfunction of one sacroiliac joint. The control group was composed of 31 persons. All subjects from the experimental group underwent sacroiliac joint manipulation. The experimental group showed significant lengthening of the step on both sides and the stride length in this group increased as well. Moreover, the duration of the stride increased (p=0.000826). The maximum midfoot pressure was higher and maximum heel pressure decreased. The differences were statistically significant. 1. Subclinical dysfunctions of the sacroiliac joints may cause functional gait disturbance. 2. Manipulation of the iliosacral joint exerts a significant effect on gait parameters, which may lead to improved gait economy and effec-tiveness. 3. Following manipulation of one iliosacral joint, altered gait parameters are noted on both the manipulated side and the contralateral side, which may translate into improved quality of locomotion.

Hydraulic actuator mechanism to control aircraft spoiler movements through dual input commands

Science.gov (United States)

Irick, S. C. (Inventor)

1981-01-01

An aircraft flight spoiler control mechanism is described. The invention enables the conventional, primary spoiler control system to retain its operational characteristics while accommodating a secondary input controlled by a conventional computer system to supplement the settings made by the primary input. This is achieved by interposing springs between the primary input and the spoiler control unit. The springs are selected to have a stiffness intermediate to the greater force applied by the primary control linkage and the lesser resistance offered by the spoiler control unit. Thus, operation of the primary input causes the control unit to yield before the springs, yet, operation of the secondary input, acting directly on the control unit, causes the springs to yield and absorb adjustments before they are transmitted into the primary control system.

Selection of entropy-measure parameters for knowledge discovery in heart rate variability data.

Science.gov (United States)

Mayer, Christopher C; Bachler, Martin; Hörtenhuber, Matthias; Stocker, Christof; Holzinger, Andreas; Wassertheurer, Siegfried

2014-01-01

Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery. This study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing rChon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds rF and rL for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test. The first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = rChon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning rF and rL showed that there is no optimal choice, but r = rF = rL is reasonable with r = rChon or r = 0.2σ. Some of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical

Analysis of the selected optical parameters of filters protecting against hazardous infrared radiation

OpenAIRE

Gralewicz, Grzegorz; Owczarek, Grzegorz

2016-01-01

The paper analyses the selected optical parameters of protective optic filters used for protection of the eyes against hazardous radiation within the visible (VIS) and near infrared (NIR) spectrum range. The indexes characterizing transmission and reflection of optic radiation incident on the filter are compared. As it follows from the completed analysis, the newly developed interference filters provide more effective blocking of infrared radiation in comparison with the currently used protec...

H∞ Loop Shaping Control of Input Saturated Systems with Norm-Bounded Parametric Uncertainty

Directory of Open Access Journals (Sweden)

Renan Lima Pereira

2015-01-01

Full Text Available This paper proposes a gain-scheduling control design strategy for a class of linear systems with the presence of both input saturation constraints and norm-bounded parametric uncertainty. LMI conditions are derived in order to obtain a gain-scheduled controller that ensures the robust stability and performance of the closed loop system. The main steps to obtain such a controller are given. Differently from other gain-scheduled approaches in the literature, this one focuses on the problem of H∞ loop shaping control design with input saturation nonlinearity and norm-bounded uncertainty to reduce the effect of the disturbance input on the controlled outputs. Here, the design problem has been formulated in the four-block H∞ synthesis framework, in which it is possible to describe the parametric uncertainty and the input saturation nonlinearity as perturbations to normalized coprime factors of the shaped plant. As a result, the shaped plant is represented as a linear parameter-varying (LPV system while the norm-bounded uncertainty and input saturation are incorporated. This procedure yields a linear parameter-varying structure for the controller that ensures the stability of the polytopic LPV shaped plant from the vertex property. Finally, the effectiveness of the method is illustrated through application to a physical system: a VTOL “vertical taking-off landing” helicopter.

Screening key parameters related to passive system performance based on Analytic Hierarchy Process

International Nuclear Information System (INIS)

Ma, Guohang; Yu, Yu; Huang, Xiong; Peng, Yuan; Ma, Nan; Shan, Zuhua; Niu, Fenglei; Wang, Shengfei

2015-01-01

Highlights: • An improved AHP method is presented for screening key parameters used in passive system reliability analysis. • We take the special bottom parameters as criterion for calculation and the abrupt change of the results are verified. • Combination weights are also affected by uncertainty of input parameters. - Abstract: Passive safety system is widely used in the new generation nuclear power plant (NPP) designs such as AP1000 to improve the reactor safety benefitting from its simple construction and less request for human intervene. However, the functional failure induced by uncertainty in the system thermal–hydraulic (T–H) performance becomes one of the main contributors to system operational failure since the system operates based on natural circulation, which should be considered in the system reliability evaluation. In order to improve the calculation efficiency the key parameters which significantly affect the system T–H characteristics can be screened and then be analyzed in detail. The Analytical Hierarchy Process (AHP) is one of the efficient methods to analyze the influence of the parameters on a passive system based on the experts’ experience. The passive containment cooling system (PCCS) in AP1000 is one of the typical passive safety systems, nevertheless too many parameters need to be analyzed and the T–H model itself is more complicated, so the traditional AHP method should be mended to use for screening key parameters efficiently. In this paper, we adapt the improved method in hierarchy construction and experts’ opinions integration, some parameters at the bottom justly in the traditional hierarchy are studied as criterion layer in improved AHP, the rationality of the method and the effect of abrupt change with the data are verified. The passive containment cooling system (PCCS) in AP1000 is evaluated as an example, and four key parameters are selected from 49 inputs

Selected environmental considerations and their measuring parameters for nuclear power plant siting

International Nuclear Information System (INIS)

Norris, J.A.

1975-01-01

The site selection process for nuclear power stations encompasses a broad range of considerations. A categorization of these considerations consistent with the needs of the U. S. Atomic Energy Commission, as the regulatory agency, and of the utility company involves these major areas of concern. They are issues related to safety, environmental impact, and engineering/economics. The more important environmental considerations and their measuring parameters presented in this paper include biota, ecological systems and water quality, land use, aesthetics, water availability, and meteorology. (U.S.)

Fetal biometric parameters: Reference charts for a non-selected risk population from Uberaba, Brazil

Directory of Open Access Journals (Sweden)

Alberto Borges Peixoto

2017-03-01

Full Text Available Objective: To establish reference charts for fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil. Methods: A retrospective cross-sectional study was performed among 5656 non-selected risk singleton pregnant women between 14 and 41 weeks of gestation. The ultrasound exams were performed during routine visits of second and third trimesters. Biparietal diameter (BPD was measured at the level of the thalami and cavum septi pellucidi. Head circumference (HC was calculated by the following formula: HC = 1.62*(BPD + occipital frontal diameter, OFD. Abdominal circumference (AC was measured using the following formula: AC = (anteroposterior diameter + transverse abdominal diameter × 1.57. Femur diaphysis length (FDL was obtained in the longest axis of femur without including the distal femoral epiphysis. The estimated fetal weight (EFW was obtained by the Hadlock formula. Polynomial regressions were performed to obtain the best-fit model for each fetal biometric parameter as the function of gestational age (GA. Results: The mean, standard deviations (SD, minimum and maximum of BPD (cm, HC (cm, AC (cm, FDL (cm and EFW (g were 6.9 ± 1.9 (2.3 – 10.5, 24.51 ± 6.61 (9.1 – 36.4, 22.8 ± 7.3 (7.5 – 41.1, 4.9 ± 1.6 (1.2 – 8.1 and 1365 ± 1019 (103 – 4777, respectively. Second-degree polynomial regressions between the evaluated parameters and GA resulted in the following formulas: BPD = –4.044 + 0.540 × GA – 0.0049 × GA² (R² = 0.97; HC= –15.420 + 2.024 GA – 0.0199 × GA² (R² = 0.98; AC = –9.579 + 1.329 × GA – 0.0055 × GA² (R² = 0.97; FDL = –3.778 + 0.416 × GA – 0.0035 × GA² (R² = 0.98 and EFW = 916 – 123 × GA + 4.70 × GA² (R² = 0.96; respectively. Conclusion: Reference charts for the fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil, were established.

Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning

1993-01-01

Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...

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.

Control Board Digital Interface Input Devices – Touchscreen, Trackpad, or Mouse?

Energy Technology Data Exchange (ETDEWEB)

Thomas A. Ulrich; Ronald L. Boring; Roger Lew

2015-08-01

The authors collaborated with a power utility to evaluate input devices for use in the human system interface (HSI) for a new digital Turbine Control System (TCS) at a nuclear power plant (NPP) undergoing a TCS upgrade. A standalone dynamic software simulation of the new digital TCS and a mobile kiosk were developed to conduct an input device study to evaluate operator preference and input device effectiveness. The TCS software presented the anticipated HSI for the TCS and mimicked (i.e., simulated) the turbine systems’ responses to operator commands. Twenty-four licensed operators from the two nuclear power units participated in the study. Three input devices were tested: a trackpad, mouse, and touchscreen. The subjective feedback from the survey indicates the operators preferred the touchscreen interface. The operators subjectively rated the touchscreen as the fastest and most comfortable input device given the range of tasks they performed during the study, but also noted a lack of accuracy for selecting small targets. The empirical data suggest the mouse input device provides the most consistent performance for screen navigation and manipulating on screen controls. The trackpad input device was both empirically and subjectively found to be the least effective and least desired input device.

Generic MIDI devices as new input for specialized software

Directory of Open Access Journals (Sweden)

Marcin Badurowicz

2016-12-01

Full Text Available There are plenty of sublime devices, including input devices, for all kinds of specialists working with computers available on the market. Furthermore, the more specific solutions are needed, the more expensive and complicated they are. At the time when many people prefer to try as many things as possible before selecting the specific learning paths, both high price and high entry threshold, can appear as blockers. In the paper, there are selected some hardware and software solutions for facilitating the work of the professionals , who expect more analog-like interfaces and more natural ways to control computers presented. Additionally, the authors describe original software and hardware solution that allows the use of wide range MIDI devices as custom input devices. The concept of the software made is being presented, as well as some results of initial interaction of different kind of professionals and the proposed solution software and hardware.

Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs

Energy Technology Data Exchange (ETDEWEB)

Liao, Qifeng, E-mail: liaoqf@shanghaitech.edu.cn [School of Information Science and Technology, ShanghaiTech University, Shanghai 200031 (China); Lin, Guang, E-mail: guanglin@purdue.edu [Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States)

2016-07-15

In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments.

A new method of hybrid frequency hopping signals selection and blind parameter estimation

Science.gov (United States)

Zeng, Xiaoyu; Jiao, Wencheng; Sun, Huixian

2018-04-01

Frequency hopping communication is widely used in military communications at home and abroad. In the case of single-channel reception, it is scarce to process multiple frequency hopping signals both effectively and simultaneously. A method of hybrid FH signals selection and blind parameter estimation is proposed. The method makes use of spectral transformation, spectral entropy calculation and PRI transformation basic theory to realize the sorting and parameter estimation of the components in the hybrid frequency hopping signal. The simulation results show that this method can correctly classify the frequency hopping component signal, and the estimated error of the frequency hopping period is about 5% and the estimated error of the frequency hopping frequency is less than 1% when the SNR is 10dB. However, the performance of this method deteriorates seriously at low SNR.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Science.gov (United States)

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Directory of Open Access Journals (Sweden)

Huanqing Cui

2017-03-01

Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Effect of Heat Input on Geometry of Austenitic Stainless Steel Weld Bead on Low Carbon Steel

Science.gov (United States)

Saha, Manas Kumar; Hazra, Ritesh; Mondal, Ajit; Das, Santanu

2018-05-01

Among different weld cladding processes, gas metal arc welding (GMAW) cladding becomes a cost effective, user friendly, versatile method for protecting the surface of relatively lower grade structural steels from corrosion and/or erosion wear by depositing high grade stainless steels onto them. The quality of cladding largely depends upon the bead geometry of the weldment deposited. Weld bead geometry parameters, like bead width, reinforcement height, depth of penetration, and ratios like reinforcement form factor (RFF) and penetration shape factor (PSF) determine the quality of the weld bead geometry. Various process parameters of gas metal arc welding like heat input, current, voltage, arc travel speed, mode of metal transfer, etc. influence formation of bead geometry. In the current experimental investigation, austenite stainless steel (316) weld beads are formed on low alloy structural steel (E350) by GMAW using 100% CO2 as the shielding gas. Different combinations of current, voltage and arc travel speed are chosen so that heat input increases from 0.35 to 0.75 kJ/mm. Nine number of weld beads are deposited and replicated twice. The observations show that weld bead width increases linearly with increase in heat input, whereas reinforcement height and depth of penetration do not increase with increase in heat input. Regression analysis is done to establish the relationship between heat input and different geometrical parameters of weld bead. The regression models developed agrees well with the experimental data. Within the domain of the present experiment, it is observed that at higher heat input, the weld bead gets wider having little change in penetration and reinforcement; therefore, higher heat input may be recommended for austenitic stainless steel cladding on low alloy steel.

Improved Detection of Extended Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli in Input and Output Samples of German Biogas Plants by a Selective Pre-Enrichment Procedure

Science.gov (United States)

Schauss, Thorsten; Glaeser, Stefanie P.; Gütschow, Alexandra; Dott, Wolfgang; Kämpfer, Peter

2015-01-01

The presence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli was investigated in input (manure from livestock husbandry) and output samples of six German biogas plants in 2012 (one sampling per biogas plant) and two German biogas plants investigated in an annual cycle four times in 2013/2014. ESBL-producing Escherichia coli were cultured by direct plating on CHROMagar ESBL from input samples in the range of 100 to 104 colony forming units (CFU) per g dry weight but not from output sample. This initially indicated a complete elimination of ESBL-producing E. coli by the biogas plant process. Detected non target bacteria were assigned to the genera Acinetobacter, Pseudomonas, Bordetella, Achromobacter, Castellaniella, and Ochrobactrum. A selective pre-enrichment procedure increased the detection efficiency of ESBL-producing E. coli in input samples and enabled the detection in five of eight analyzed output samples. In total 119 ESBL-producing E. coli were isolated from input and 46 from output samples. Most of the E. coli isolates carried CTX-M-type and/or TEM-type beta lactamases (94%), few SHV-type beta lactamase (6%). Sixty-four bla CTX-M genes were characterized more detailed and assigned mainly to CTX-M-groups 1 (85%) and 9 (13%), and one to group 2. Phylogenetic grouping of 80 E. coli isolates showed that most were assigned to group A (71%) and B1 (27%), only one to group D (2%). Genomic fingerprinting and multilocus sequence typing (MLST) showed a high clonal diversity with 41 BOX-types and 19 ST-types. The two most common ST-types were ST410 and ST1210. Antimicrobial susceptibility testing of 46 selected ESBL-producing E. coli revealed that several isolates were additionally resistant to other veterinary relevant antibiotics and some grew on CHROMagar STEC but shiga-like toxine (SLT) genes were not detected. Resistance to carbapenems was not detected. In summary the study showed for the first time the presence of ESBL-producing E. coli in

Improved detection of extended spectrum beta-lactamase (ESBL-producing Escherichia coli in input and output samples of German biogas plants by a selective pre-enrichment procedure.

Directory of Open Access Journals (Sweden)

Thorsten Schauss

Full Text Available The presence of extended-spectrum beta-lactamase (ESBL-producing Escherichia coli was investigated in input (manure from livestock husbandry and output samples of six German biogas plants in 2012 (one sampling per biogas plant and two German biogas plants investigated in an annual cycle four times in 2013/2014. ESBL-producing Escherichia coli were cultured by direct plating on CHROMagar ESBL from input samples in the range of 100 to 104 colony forming units (CFU per g dry weight but not from output sample. This initially indicated a complete elimination of ESBL-producing E. coli by the biogas plant process. Detected non target bacteria were assigned to the genera Acinetobacter, Pseudomonas, Bordetella, Achromobacter, Castellaniella, and Ochrobactrum. A selective pre-enrichment procedure increased the detection efficiency of ESBL-producing E. coli in input samples and enabled the detection in five of eight analyzed output samples. In total 119 ESBL-producing E. coli were isolated from input and 46 from output samples. Most of the E. coli isolates carried CTX-M-type and/or TEM-type beta lactamases (94%, few SHV-type beta lactamase (6%. Sixty-four blaCTX-M genes were characterized more detailed and assigned mainly to CTX-M-groups 1 (85% and 9 (13%, and one to group 2. Phylogenetic grouping of 80 E. coli isolates showed that most were assigned to group A (71% and B1 (27%, only one to group D (2%. Genomic fingerprinting and multilocus sequence typing (MLST showed a high clonal diversity with 41 BOX-types and 19 ST-types. The two most common ST-types were ST410 and ST1210. Antimicrobial susceptibility testing of 46 selected ESBL-producing E. coli revealed that several isolates were additionally resistant to other veterinary relevant antibiotics and some grew on CHROMagar STEC but shiga-like toxine (SLT genes were not detected. Resistance to carbapenems was not detected. In summary the study showed for the first time the presence of ESBL-producing E

Differentiation of malignant and benign pulmonary nodules with first-pass dual-input perfusion CT

International Nuclear Information System (INIS)

Yuan, Xiaodong; Quan, Changbin; Cao, Jianxia; Ao, Guokun; Tian, Yuan; Li, Hong; Zhang, Jing

2013-01-01

To assess diagnostic performance of dual-input CT perfusion for distinguishing malignant from benign solitary pulmonary nodules (SPNs). Fifty-six consecutive subjects with SPNs underwent contrast-enhanced 320-row multidetector dynamic volume CT. The dual-input maximum slope CT perfusion analysis was employed to calculate the pulmonary flow (PF), bronchial flow (BF), and perfusion index (PI,=PF/(PF+BF)). Differences in perfusion parameters between malignant and benign tumours were assessed with histopathological diagnosis as the gold standard. Diagnostic value of the perfusion parameters was calculated using the receiver-operating characteristic (ROC) curve analysis. Amongst 56 SPNs, statistically significant differences in all three perfusion parameters were revealed between malignant and benign tumours. The PI demonstrated the biggest difference between malignancy and benignancy: 0.30 ± 0.07 vs. 0.51 ± 0.13, P < 0.001. The area under the PI ROC curve was 0.92, the largest of the three perfusion parameters, producing a sensitivity of 0.95, specificity of 0.83, positive likelihood ratio (+LR) of 5.59, and negative likelihood ratio (-LR) of 0.06 in identifying malignancy. The PI derived from the dual-input maximum slope CT perfusion analysis is a valuable biomarker for identifying malignancy in SPNs. PI may be potentially useful for lung cancer treatment planning and forecasting the therapeutic effect of radiotherapy treatment. (orig.)

Blind CP-OFDM and ZP-OFDM Parameter Estimation in Frequency Selective Channels

Directory of Open Access Journals (Sweden)

Vincent Le Nir

2009-01-01

Full Text Available A cognitive radio system needs accurate knowledge of the radio spectrum it operates in. Blind modulation recognition techniques have been proposed to discriminate between single-carrier and multicarrier modulations and to estimate their parameters. Some powerful techniques use autocorrelation- and cyclic autocorrelation-based features of the transmitted signal applying to OFDM signals using a Cyclic Prefix time guard interval (CP-OFDM. In this paper, we propose a blind parameter estimation technique based on a power autocorrelation feature applying to OFDM signals using a Zero Padding time guard interval (ZP-OFDM which in particular excludes the use of the autocorrelation- and cyclic autocorrelation-based techniques. The proposed technique leads to an efficient estimation of the symbol duration and zero padding duration in frequency selective channels, and is insensitive to receiver phase and frequency offsets. Simulation results are given for WiMAX and WiMedia signals using realistic Stanford University Interim (SUI and Ultra-Wideband (UWB IEEE 802.15.4a channel models, respectively.

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection

Directory of Open Access Journals (Sweden)

Fabrizia Caiazzo

2018-04-01

Full Text Available Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti–6Al–4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection.

Science.gov (United States)

Caiazzo, Fabrizia; Caggiano, Alessandra

2018-04-20

Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti⁻6Al⁻4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

Science.gov (United States)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Effect of Planned Early Recommended Ambulation Technique on Selected Post caesarean Biophysiological Health Parameters

Directory of Open Access Journals (Sweden)

Jyoti V. Dube

2014-01-01

Full Text Available Background: Caesarean section has been a part of human culture since ancient times. It has been used effectively throughout the 20th century and among the major abdominal surgeries, it is the most common, oldest worldwide surgery performed in obstetrics. Despite the life saving advantages, there are several adverse consequences of caesarean delivery for a woman and to her household. The rate and risk of these complications increases due to the increasing incidence mainly in countries like India. The role of nurse midwife is to act in the best interest of patient and newborn and make the patient independent in carrying out the activities of daily living as soon as possible. This can lead to a faster recovery and shorter hospital stay. Also it can indirectly help in reducing the complications associated with prolonged bed rest and can improve the maternal newborn bonding. Aim and Objectives: The present study was done to evaluate the effect of planned early ambulation on selected biophysiological health parameters of post caesarean patients. Material and Methods: The study included total 500 study subjects, 250 in experimental and 250 in control group. Quasi experimental approach with multiple time series design was adopted for the study. The experimental group was given an early planned recommended ambulation technique starting from the day of surgery. This consisted of deep breathing exercise, cough exercise, leg exercise and early mobilization. Over and above, the routine general health care was given by the doctors and nurses. The control group received only by routine general care by doctors and nurses and mobilization on third post operative day as per strategy adopted by the hospital. The deep breathing exercises, coughing exercises and leg exercises were not given routinely and hence were not given to the control group. Post caesarean biophysiological parameters chart was used to assess the selected parameters for first five post operative

How Do Parameters of Motor Response Influence Selective Inhibition? Evidence from the Stop-Signal Paradigm

Directory of Open Access Journals (Sweden)

Chien Hui Tang

2011-05-01

Full Text Available The ability to selectively inhibit the execution of an action while performing other ones is crucial in humans' multitasking daily life. The current study aims to compare selective inhibition for choice reaction involving two effectors or response directions. We adopted a variation of the stop-signal paradigm to examine how selective inhibition is modulated by the way potential motor responses are combined and inhibited. Experiment 1 investigated selective inhibition under different combinations of effectors, namely “index and middle fingers” versus “hand and foot”. The results showed SSRT of the index finger was longer when the other response option was the foot than the middle finger. Experiment 2 examined how selective inhibition differs between selective stopping of effectors and movement directions, and that for most of the situations SSRT is longer for stopping a response based on its direction than effector. After equating complexity of response mapping between direction and effector conditions in Experiment 2, Experiment 3 still showed that SSRT differs between selecting direction or effectors. To summarize, SSRT varies depending on the way response effectors are paired and selectively stopped. Selective inhibition is thus likely not amodal and may involve different inhibitory mechanisms depending on parameters specifying the motor response.

Cell size spatial convergence analysis on GOTHIC distributed parameter models for studying hydrogen mixing behaviour in CANDU containments

International Nuclear Information System (INIS)

Yim, K.; Wong, R.C.

1995-01-01

Gas mixing phenomena can be modelled using distributed parameter codes such as GOTHIC, but the selection of the optimum cell size is an important user input. The tradeoff between accuracy and practical computation times affect the choice of cell sizes, where small cells provide better accuracy at the expense of longer computing time. A study on cell size effect on hydrogen distribution is presented for the problem of hydrogen mixing behaviour in a typical CANDU reactor containment following a severe reactor accident. Optimal cell sizes were found for different room volumes, hydrogen release profiles and elevations using spatial convergence criteria. The findings of this study provide the technical basis for the cell size selection in the GOTHIC distributed parameter models used for analysing hydrogen mixing behaviour. (author). 1 ref., 1 tab., 13 figs

Numerical research of influence of laser radiation parameters on the formation of intermetallic phases from metal powders in selective laser melting technology

Science.gov (United States)

Agapovichev, A. V.; Knyazeva, A. G.; Smelov, V. G.

2017-10-01

A large number of factors influence the quality of the material obtained with selective laser melting. Through correct understanding and managing these factors, it is possible to achieve the necessary quality of the materials, which is highly competitive to the traditional production methods. The technique of selective laser melting is a complex process in which a large number of parameters affect the quality of the final product. The complexity of the process of selective laser melting consists of many thermal, physical and chemical interactions, which are influenced by a large number of parameters. The main parameters of SLM are scanning rate, laser radiation power and layer thickness. In the framework of this paper, there was made an attempt to take into account real physical and chemical processes taking place during the selective laser melting of an Ni-Al alloy.

Selection of Forklift Unit for Warehouse Operation by Applying Multi-Criteria Analysis

Directory of Open Access Journals (Sweden)

Predrag Atanasković

2013-07-01

Full Text Available This paper presents research related to the choice of the criteria that can be used to perform an optimal selection of the forklift unit for warehouse operation. The analysis has been done with the aim of exploring the requirements and defining relevant criteria that are important when investment decision is made for forklift procurement, and based on the conducted research by applying multi-criteria analysis, to determine the appropriate parameters and their relative weights that form the input data and database for selection of the optimal handling unit. This paper presents an example of choosing the optimal forklift based on the selected criteria for the purpose of making the relevant investment decision.

The effect of harmeful elements in production of iron in relation to input and output material balance

Directory of Open Access Journals (Sweden)

P. Besta

2012-07-01

Full Text Available The main objectives of blast-furnace operators include maximum production of pig iron of required chemical composition at minimal cost. This can be ensured only in case of quality raw material basis and trouble-free operation of blast-furnace. Both parameters are influenced by the concentration of undesirable elements. The negative elements contained in the blast-furnace raw materials cause many technological problems in the sintering as well as in the blast-furnace process. These are mainly heavy metals and alkaline carbonates. The article deals with the analysis of material balance of zinc and selected alkaline carbonates contents in the input raw materials and output products of the blast-furnace.

[Prosody, speech input and language acquisition].

Science.gov (United States)

Jungheim, M; Miller, S; Kühn, D; Ptok, M

2014-04-01

In order to acquire language, children require speech input. The prosody of the speech input plays an important role. In most cultures adults modify their code when communicating with children. Compared to normal speech this code differs especially with regard to prosody. For this review a selective literature search in PubMed and Scopus was performed. Prosodic characteristics are a key feature of spoken language. By analysing prosodic features, children gain knowledge about underlying grammatical structures. Child-directed speech (CDS) is modified in a way that meaningful sequences are highlighted acoustically so that important information can be extracted from the continuous speech flow more easily. CDS is said to enhance the representation of linguistic signs. Taking into consideration what has previously been described in the literature regarding the perception of suprasegmentals, CDS seems to be able to support language acquisition due to the correspondence of prosodic and syntactic units. However, no findings have been reported, stating that the linguistically reduced CDS could hinder first language acquisition.

Genetic parameters and simultaneous selection for root yield, adaptability and stability of cassava genotypes

Directory of Open Access Journals (Sweden)

João Tomé de Farias Neto

2013-12-01

Full Text Available The objective of this work was to estimate genetic parameters and to evaluate simultaneous selection for root yield and for adaptability and stability of cassava genotypes. The effects of genotypes were assumed as fixed and random, and the mixed model methodology (REML/Blup was used to estimate genetic parameters and the harmonic mean of the relative performance of genotypic values (HMRPGV, for simultaneous selection purposes. Ten genotypes were analyzed in a complete randomized block design, with four replicates. The experiment was carried out in the municipalities of Altamira, Santarém, and Santa Luzia do Pará in the state of Pará, Brazil, in the growing seasons of 2009/2010, 2010/2011, and 2011/2012. Roots were harvested 12 months after planting, in all tested locations. Root yield had low coefficients of genotypic variation (4.25% and broad-sense heritability of individual plots (0.0424, which resulted in low genetic gain. Due to the low genotypic correlation (0.15, genotype classification as to root yield varied according to the environment. Genotypes CPATU 060, CPATU 229, and CPATU 404 stood out as to their yield, adaptability, and stability.

Input-Admittance Passivity Compliance for Grid-Connected Converters with LCL Filter

DEFF Research Database (Denmark)

Diaz, Enrique Rodriguez; Freijedo, Francisco D.; Guerrero, Josep M.

2018-01-01

This work presents a design methodology and its experimental validation for the input-admittance passivity compliance of LCL grid-connected converters. The designs of the LCL filter parameters and discrete controller are addressed systematically, and suitable design guidelines are provided......-admittance passivity compliance....

The selection criteria elements of X-ray optics system

Science.gov (United States)

Plotnikova, I. V.; Chicherina, N. V.; Bays, S. S.; Bildanov, R. G.; Stary, O.

2018-01-01

At the design of new modifications of x-ray tomography there are difficulties in the right choice of elements of X-ray optical system. Now this problem is solved by practical consideration, selection of values of the corresponding parameters - tension on an x-ray tube taking into account the thickness and type of the studied material. For reduction of time and labor input of design it is necessary to create the criteria of the choice, to determine key parameters and characteristics of elements. In the article two main elements of X-ray optical system - an x-ray tube and the detector of x-ray radiation - are considered. Criteria of the choice of elements, their key characteristics, the main dependences of parameters, quality indicators and also recommendations according to the choice of elements of x-ray systems are received.

The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.

Science.gov (United States)

Park, Heesu; Dong, Suh-Yeon; Lee, Miran; Youn, Inchan

2017-07-24

Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

Influence of Process Parameters on the Quality of Aluminium Alloy EN AW 7075 Using Selective Laser Melting (SLM)

Science.gov (United States)

Kaufmann, N.; Imran, M.; Wischeropp, T. M.; Emmelmann, C.; Siddique, S.; Walther, F.

Selective laser melting (SLM) is an additive manufacturing process, forming the desired geometry by selective layer fusion of powder material. Unlike conventional manufacturing processes, highly complex parts can be manufactured with high accuracy and little post processing. Currently, different steel, aluminium, titanium and nickel-based alloys have been successfully processed; however, high strength aluminium alloy EN AW 7075 has not been processed with satisfying quality. The main focus of the investigation is to develop the SLM process for the wide used aluminium alloy EN AW 7075. Before process development, the gas-atomized powder material was characterized in terms of statistical distribution: size and shape. A wide range of process parameters were selected to optimize the process in terms of optimum volume density. The investigations resulted in a relative density of over 99%. However, all laser-melted parts exhibit hot cracks which typically appear in aluminium alloy EN AW 7075 during the welding process. Furthermore the influence of processing parameters on the chemical composition of the selected alloy was determined.

Thermal Hydraulic design parameters study for severe accidents using neural networks

Energy Technology Data Exchange (ETDEWEB)

Roh, Chang Hyun; Chang, Soon Heung [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Chang, Keun Sun [Sunmoon University, Asan (Korea, Republic of)

1998-12-31

To provide the information on severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore, was performed to investigate the effect of thermal hydraulic design parameters on severe accident progression of pressurized water reactors (PWRs). Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among nine parameters. For training, different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3 and 4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout (SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to the other six parameters. 9 refs., 5 tabs. (Author)

Thermal Hydraulic design parameters study for severe accidents using neural networks

Energy Technology Data Exchange (ETDEWEB)

Roh, Chang Hyun; Chang, Soon Heung [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Chang, Keun Sun [Sunmoon University, Asan (Korea, Republic of)

1997-12-31

To provide the information on severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore, was performed to investigate the effect of thermal hydraulic design parameters on severe accident progression of pressurized water reactors (PWRs). Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among nine parameters. For training, different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3 and 4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout (SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to the other six parameters. 9 refs., 5 tabs. (Author)

Influence of selecting secondary settling tank sub-models on the calibration of WWTP models – A global sensitivity analysis using BSM2

DEFF Research Database (Denmark)

Ramin, Elham; Flores Alsina, Xavier; Sin, Gürkan

2014-01-01

This study investigates the sensitivity of wastewater treatment plant (WWTP) model performance to the selection of one-dimensional secondary settling tanks (1-D SST) models with first-order and second-order mathematical structures. We performed a global sensitivity analysis (GSA) on the benchmark...... simulation model No.2 with the input uncertainty associated to the biokinetic parameters in the activated sludge model No. 1 (ASM1), a fractionation parameter in the primary clarifier, and the settling parameters in the SST model. Based on the parameter sensitivity rankings obtained in this study......, the settling parameters were found to be as influential as the biokinetic parameters on the uncertainty of WWTP model predictions, particularly for biogas production and treated water quality. However, the sensitivity measures were found to be dependent on the 1-D SST models selected. Accordingly, we suggest...

Methods of control of inaccuracy in calculation of nuclear power plant decommissioning parameters - 16383

International Nuclear Information System (INIS)

Ondra, Frantisek; Daniska, Vladimir; Rehak, Ivan; Necas, Vladimir

2009-01-01

The aim of the article is a development of analytical methodology for evaluation of input data inaccuracies impact on calculation of cost and other output decommissioning parameters. This methodology is based on analytical model calculations using the OMEGA code and taking into account the probability of input data inaccuracies occurrence also. To achieve about mentioned aim, the article identifies possible sources of input data inaccuracies and analyzes their level of impact on output parameters. Then the methodology for calculation of input parameters inaccuracies impact is developed, based on analytical model calculation. The model calculation takes into consideration output parameters impact on cost and other decommissioning output parameters in analytical way. The methodology used in model calculations is original, more over it implements the international standardized structure (IAEA, OECD/NEA, EC) [6] of decommissioning cost for the first time. A probabilistic occurrence of input data inaccuracies is taken into consideration and implemented in the methodology developed. A correction factors matrix for evaluation of input data inaccuracies impact on decommissioning output parameters is set up. The matrix contains parameters based on model calculations using the proposed methodology. Finally the methodology for application of correction factor matrix is proposed and tested; the methodology is used for calculation of contingency in the standardized structure which reflected the level of input data inaccuracies. The cost for individual decommissioning projects for common nuclear power plants are in the range 300 - 500 mil. EUR. Contingencies are from 10% to 30%, depending on the level of detailed during preparation of decommissioning projects. A implementation about mentioned methodology in the OMEGA code improves the accuracy of contingency. Consequently it makes calculated contingency more trustworthy and makes calculated decommissioning cost closer to reality

Antenna Correlation From Input Parameters for Arbitrary Topologies and Terminations

DEFF Research Database (Denmark)

Alrabadi, Osama; Andersen, Jørgen Bach; Pedersen, Gert Frølund

2012-01-01

The spatial correlation between pairs of antennas in a system comprised of N RF ports is found by extending the N × N scattering matrix to (N + 1)×(N + 1) spatial scattering matrix, where the extra space dimension accounts for the reference port patterns. The lossless property of the spatial...... scattering matrix in a 3D uniform field is employed for expressing the spatial correlation between the port patterns at arbitrary complex terminations merely from the reference scattering parameters and the complex terminations without any far-field calculation....

Fukushima Daiichi unit 1 uncertainty analysis--Preliminary selection of uncertain parameters and analysis methodology

Energy Technology Data Exchange (ETDEWEB)

Cardoni, Jeffrey N.; Kalinich, Donald A.

2014-02-01

Sandia National Laboratories (SNL) plans to conduct uncertainty analyses (UA) on the Fukushima Daiichi unit (1F1) plant with the MELCOR code. The model to be used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). However, that study only examined a handful of various model inputs and boundary conditions, and the predictions yielded only fair agreement with plant data and current release estimates. The goal of this uncertainty study is to perform a focused evaluation of uncertainty in core melt progression behavior and its effect on key figures-of-merit (e.g., hydrogen production, vessel lower head failure, etc.). In preparation for the SNL Fukushima UA work, a scoping study has been completed to identify important core melt progression parameters for the uncertainty analysis. The study also lays out a preliminary UA methodology.

Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?

Directory of Open Access Journals (Sweden)

Anna Carter

2016-01-01

Full Text Available The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island generated at a very high spatial resolution (<1 m2 using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale microclimate layers (within about 2–3°C of observed values required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles, while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.

Parameter uncertainty effects on variance-based sensitivity analysis

International Nuclear Information System (INIS)

Yu, W.; Harris, T.J.

2009-01-01

In the past several years there has been considerable commercial and academic interest in methods for variance-based sensitivity analysis. The industrial focus is motivated by the importance of attributing variance contributions to input factors. A more complete understanding of these relationships enables companies to achieve goals related to quality, safety and asset utilization. In a number of applications, it is possible to distinguish between two types of input variables-regressive variables and model parameters. Regressive variables are those that can be influenced by process design or by a control strategy. With model parameters, there are typically no opportunities to directly influence their variability. In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters. A sequential analysis is proposed, where first an sensitivity analysis is performed with respect to the regressive variables. In the second step, the uncertainty effects arising from the model parameters are included. This strategy can be quite useful in understanding process variability and in developing strategies to reduce overall variability. When this method is used for nonlinear models which are linear in the parameters, analytical solutions can be utilized. In the more general case of models that are nonlinear in both the regressive variables and the parameters, either first order approximations can be used, or numerically intensive methods must be used

Selection of design parameters of diffusion barrier in a passive 222Rn sampler based on activated charcoal adsorption

International Nuclear Information System (INIS)

Wei Suxia

1992-01-01

A method concerning selection of design parameters of diffusion barrier in a passive 222 Rn sampler based on activated charcoal adsorption. The proper parameter value of diffusion barrier is obtained by means of linearization of 222 Rn adsorption versus the exposure time. Thus, the influence of temperature on measured results may be greatly decreased, and higher sensitivity of the detector may be maintained

THE EFFECT OF SELECTED PHYSICAL AND CHEMICAL PARAMETERS ON MICROBIOLOGICAL STATUS OF THE VISTULA RIVER NEAR WARSAW

OpenAIRE

Janusz Augustynowicz; Mariusz Nierebiński; Małgorzata Zawada; Russel Russel

2016-01-01

The types of organisms present in water reservoirs depend on water purity and biochemical processes that occur. Therefore, one of the methods of water quality assessment is to determine its condition by determining the biological indicators, including microbiological parameters. The aim of the experiment presented in this paper was to investigate the effects of selected physical and chemical parameters of water samples from the Vistula River on the microbiological status of water. The experim...

Multimedia Environmental Pollutant Assessment System (MEPAS) application guidance. Guidelines for evaluating MEPAS input parameters for Version 3.1

International Nuclear Information System (INIS)

Buck, J.W.; Whelan, G.; Droppo, J.G. Jr.; Strenge, D.L.; Castleton, K.J.; McDonald, J.P.; Sato, C.; Streile, G.P.

1995-02-01

The Multimedia Environmental Pollutant Assessment System (MEPAS) was developed by Pacific Northwest Laboratory (PNL) for the U.S. Department of Energy (DOE) Office of Environment, Safety and Health and Office of Environmental Management and Environmental Restoration. MEPAS is a set of computer codes developed to provide decision makers with risk information integrated for hazardous, radioactive, and mixed-waste sites based on their potential hazard to public health. It is applicable to a wide range of environmental management and regulatory conditions, including inactive sites covered under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) and active air and water releases covered under the Clean Air Act, the Clean Water Act, and the Resource Conservation and Recovery Act. MEPAS integrates contaminant release, transport, and exposure models into a single system. An interactive user interface assists the investigator in defining problems, assembling data and entering input, and developing reports. PNL has compiled two documents that explain the methodology behind the MEPAS model and instruct the user in how to input, retrieve, and evaluate data. This report contains detailed guidelines for defining the input data required to conduct an analysis with MEPAS. Entries for each variable have a short definition, units, and text explaining what a variable is and how it can be quantified. As appropriate, ranges and typical values are given. This report also contains listings of the input screens (worksheets) that are used in the MEPAS user interface for these variables

Selected parameters of the corneal deformation in the Corvis tonometer.

Science.gov (United States)

Koprowski, Robert; Lyssek-Boron, Anita; Nowinska, Anna; Wylegala, Edward; Kasprzak, Henryk; Wrobel, Zygmunt

2014-05-03

Contemporary ophthalmology knows many methods of measuring intraocular pressure, namely the methods of non-contact and impression applanation tonometry. In non-contact applanation tonometers, e.g. the Corvis, the corneal flattening is caused by an air puff. Image registration of the corneal deflection performed by a tonometer enables to determine other interesting biomechanical parameters of the eye, which are not available in the tonometer. The measurement of new selected parameters is presented in this paper. Images with an M × N × I resolution of 200 × 576 × 140 pixels were acquired from the Corvis device in the source recording format *.cst. A total of 13'400 2D images of patients examined routinely in the Clinical Department of Ophthalmology, in District Railway Hospital in Katowice, Poland, were analysed in accordance with the Declaration of Helsinki. A new method has been proposed for the analysis of corneal deflection images in the Corvis tonometer with the use of the Canny edge detection method, mathematical morphology methods and context-free operations. The resulting image analysis tool allows determination of the response of the cornea and the entire eyeball to an air puff. The paper presents the method that enables the measurement of the amplitude of curvature changes in the frequency range from 150 to 500 Hz and automatic designation of the eyeball movement direction. The analysis of these data resulted in 3 new features of dynamics of the eye reaction to an air puff. Classification of these features enabled to propose 4 classes of deformation. The proposed algorithm allows to obtain reproducible results fully automatically at a time of 5 s per patient using the Core i5 CPU M460 @ 2.5GHz 4GB of RAM. The paper presents the possibility of using a profiled algorithm of image analysis, proposed by the authors, to measure additional cornea deformation parameters. The new tool enables automatic measurement of the additional new

Optimization model of peach production relevant to input energies – Yield function in Chaharmahal va Bakhtiari province, Iran

International Nuclear Information System (INIS)

Ghatrehsamani, Shirin; Ebrahimi, Rahim; Kazi, Salim Newaz; Badarudin Badry, Ahmad; Sadeghinezhad, Emad

2016-01-01

The aim of this study was to determine the amount of input–output energy used in peach production and to develop an optimal model of production in Chaharmahal va Bakhtiari province, Iran. Data were collected from 100 producers by administering a questionnaire in face-to-face interviews. Farms were selected based on random sampling method. Results revealed that the total energy of production is 47,951.52 MJ/ha and the highest share of energy consumption belongs to chemical fertilizers (35.37%). Consumption of direct energy was 47.4% while indirect energy was 52.6%. Also, Total energy consumption was divided into two groups; renewable and non-renewable (19.2% and 80.8% respectively). Energy use efficiency, Energy productivity, Specific energy and Net energy were calculated as 0.433, 0.228 (kg/MJ), 4.38 (MJ/kg) and −27,161.722 (MJ/ha), respectively. According to the negative sign for Net energy, if special strategy is used, energy dismiss will decrease and negative effect of some parameters could be omitted. In the present case the amount is indicating decimate of production energy. In addition, energy efficiency was not high enough. Some of the input energies were applied to machinery, chemical fertilizer, water irrigation and electricity which had significant effect on increasing production and MPP (marginal physical productivity) was determined for variables. This parameter was positive for energy groups namely; machinery, diesel fuel, chemical fertilizer, water irrigation and electricity while it was negative for other kind of energy such as chemical pesticides and human labor. Finally, there is a need to pursue a new policy to force producers to undertake energy-efficient practices to establish sustainable production systems without disrupting the natural resources. In addition, extension activities are needed to improve the efficiency of energy consumption and to sustain the natural resources. - Highlights: • Replacing non-renewable energy with renewable

Influence of selected test parameters on measured values during the MSCR test

Science.gov (United States)

Benešová, Lucie; Valentin, Jan

2017-09-01

One of today’s most commonly used test on a Dynamic Shear Rheometer (DSR) is the Multiple Stress Creep Recovery (MSCR) test. The test is described in the standard EN 16659, which is valid in the Czech Republic since October 2016. The principle of the test is based on repeated loading and recovering of a bitumen sample, according to which it is possible to determine the percentage of elastic recovery (R) and non-recoverable creep compliance (Jnr) of the bituminous binder. This method has been recently promoted as the most suitable test for assessing the resistance of bituminous binders to permanent deformation. The test is performed at higher temperatures and is particularly suitable for modified bituminous binders. The paper deals with the comparison of the different input parameters set on the DSR device - different levels of stress, temperature of test, the geometry of the measuring device and also a comparison of the results for a different number of loading cycles. The research study was focused mainly on modified bituminous binders, but to compare the MSCR test it is performed even with conventional paving grade binders.

Estimating relations between temperature, relative humidity as independed variables and selected water quality parameters in Lake Manzala, Egypt

Directory of Open Access Journals (Sweden)

Gehan A.H. Sallam

2018-03-01

Full Text Available In Egypt, Lake Manzala is the largest and the most productive lake of northern coastal lakes. In this study, the continuous measurements data of the Real Time Water Quality Monitoring stations in Lake Manzala were statistically analyzed to measure the regional and seasonal variations of the selected water quality parameters in relation to the change of air temperature and relative humidity. Simple formulas are elaborated using the DataFit software to predict the selected water quality parameters of the Lake including pH, Dissolved Oxygen (DO, Electrical Conductivity (EC, Total Dissolved Solids (TDS, Turbidity, and Chlorophyll as a function of air temperature, relative humidity and quantities and qualities of the drainage water that discharge into the lake. An empirical positive relation was found between air temperature and the relative humidity and pH, EC and TDS and negative relation with DO. There is no significant effect on the other two parameters of turbidity and chlorophyll.

The role of the input scale in parton distribution analyses

International Nuclear Information System (INIS)

Jimenez-Delgado, Pedro

2012-01-01

A first systematic study of the effects of the choice of the input scale in global determinations of parton distributions and QCD parameters is presented. It is shown that, although in principle the results should not depend on these choices, in practice a relevant dependence develops as a consequence of what is called procedural bias. This uncertainty should be considered in addition to other theoretical and experimental errors, and a practical procedure for its estimation is proposed. Possible sources of mistakes in the determination of QCD parameter from parton distribution analysis are pointed out.

Automating Risk Assessments of Hazardous Material Shipments for Transportation Routes and Mode Selection

International Nuclear Information System (INIS)

Dolphin, Barbara H.; Richins, William D.; Novascone, Stephen R.

2010-01-01

The METEOR project at Idaho National Laboratory (INL) successfully addresses the difficult problem in risk assessment analyses of combining the results from bounding deterministic simulation results with probabilistic (Monte Carlo) risk assessment techniques. This paper describes a software suite designed to perform sensitivity and cost/benefit analyses on selected transportation routes and vehicles to minimize risk associated with the shipment of hazardous materials. METEOR uses Monte Carlo techniques to estimate the probability of an accidental release of a hazardous substance along a proposed transportation route. A METEOR user selects the mode of transportation, origin and destination points, and charts the route using interactive graphics. Inputs to METEOR (many selections built in) include crash rates for the specific aircraft, soil/rock type and population densities over the proposed route, and bounding limits for potential accident types (velocity, temperature, etc.). New vehicle, materials, and location data are added when available. If the risk estimates are unacceptable, the risks associated with alternate transportation modes or routes can be quickly evaluated and compared. Systematic optimizing methods will provide the user with the route and vehicle selection identified with the lowest risk of hazardous material release. The effects of a selected range of potential accidents such as vehicle impact, fire, fuel explosions, excessive containment pressure, flooding, etc. are evaluated primarily using hydrocodes capable of accurately simulating the material response of critical containment components. Bounding conditions that represent credible accidents (i.e; for an impact event, velocity, orientations, and soil conditions) are used as input parameters to the hydrocode models yielding correlation functions relating accident parameters to component damage. The Monte Carlo algorithms use random number generators to make selections at the various decision

Combining control input with flight path data to evaluate pilot performance in transport aircraft.

Science.gov (United States)

Ebbatson, Matt; Harris, Don; Huddlestone, John; Sears, Rodney

2008-11-01

When deriving an objective assessment of piloting performance from flight data records, it is common to employ metrics which purely evaluate errors in flight path parameters. The adequacy of pilot performance is evaluated from the flight path of the aircraft. However, in large jet transport aircraft these measures may be insensitive and require supplementing with frequency-based measures of control input parameters. Flight path and control input data were collected from pilots undertaking a jet transport aircraft conversion course during a series of symmetric and asymmetric approaches in a flight simulator. The flight path data were analyzed for deviations around the optimum flight path while flying an instrument landing approach. Manipulation of the flight controls was subject to analysis using a series of power spectral density measures. The flight path metrics showed no significant differences in performance between the symmetric and asymmetric approaches. However, control input frequency domain measures revealed that the pilots employed highly different control strategies in the pitch and yaw axes. The results demonstrate that to evaluate pilot performance fully in large aircraft, it is necessary to employ performance metrics targeted at both the outer control loop (flight path) and the inner control loop (flight control) parameters in parallel, evaluating both the product and process of a pilot's performance.

Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space.

Science.gov (United States)

Marvuglia, Antonino; Kanevski, Mikhail; Benetto, Enrico

2015-10-01

Toxicity characterization of chemical emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose-response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amount of data on the properties of the chemical compounds being assessed, which are hard to collect or hardly publicly available (especially for thousands of less common or newly developed chemicals), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodology to reduce the number of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory analysis (using dimensionality reduction techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estimating the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network--GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive analysis, the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge

Input-output supervisor

International Nuclear Information System (INIS)

Dupuy, R.

1970-01-01

The input-output supervisor is the program which monitors the flow of informations between core storage and peripheral equipments of a computer. This work is composed of three parts: 1 - Study of a generalized input-output supervisor. With sample modifications it looks like most of input-output supervisors which are running now on computers. 2 - Application of this theory on a magnetic drum. 3 - Hardware requirement for time-sharing. (author) [fr

Strategy for a consistent selection of radionuclide migration parameters for the Belgian safety and feasibility case-1

International Nuclear Information System (INIS)

Bruggeman, C.; Maes, N.; Salah, S.; Brassinnes, S.; Van Geet, M.

2010-01-01

Document available in extended abstract form only. The purpose of this presentation is to describe the strategy for the selection of retention and migration parameters for safety-relevant nuclides that was developed in the framework of the Belgian Safety and Feasibility Case SFC-1. A geochemical database containing state-of-the-art retention and migration parameters of all safety-relevant radionuclides, is ideally based on a thermodynamic understanding and an ability to accurately describe the geochemical and transport behaviour of all these radionuclides under the geochemical conditions that are considered for a reference host formation. In Belgium, this reference formation is Boom Clay. The parameters will be used in Performance Assessment (PA) calculations, and therefore must also be adapted to PA models. Since these models currently use only a four parameters for every radionuclide, the whole geochemical and transport behaviour must be comprised to a very limited parameter set that describe on the one hand chemical retention within the Boom Clay formation, and on the other hand transport through the Boom Clay formation. Chemical retention considers two concepts: 1) a concentration limit (S), which represents the mobile concentration of a nuclide present in the aqueous phase under undisturbed far field Boom Clay conditions; 2) a retardation (R/Kd) factor, which represents the uptake of a mobile nuclide by the inorganic and organic phases present in the Boom Clay formation. For mobility/migration two additional concepts are introduced: 3) the diffusion accessible porosity (η), which is the total physical space available for transport of a nuclide. The maximum value of η is limited by the water content of the formation; 4) the pore diffusion coefficient (Dp), which represents the transport velocity of a nuclide in a diffusion-dominated system. Within the framework of SFC-1, primary focus is laid on the compilation of parameter ranges, instead of individual &apos

Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

Directory of Open Access Journals (Sweden)

Huiguo Chen

2017-01-01

Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.

Centrifuge modeling of one-step outflow tests for unsaturated parameter estimations

Directory of Open Access Journals (Sweden)

H. Nakajima

2006-01-01

Full Text Available Centrifuge modeling of one-step outflow tests were carried out using a 2-m radius geotechnical centrifuge, and the cumulative outflow and transient pore water pressure were measured during the tests at multiple gravity levels. Based on the scaling laws of centrifuge modeling, the measurements generally showed reasonable agreement with prototype data calculated from forward simulations with input parameters determined from standard laboratory tests. The parameter optimizations were examined for three different combinations of input data sets using the test measurements. Within the gravity level examined in this study up to 40g, the optimized unsaturated parameters compared well when accurate pore water pressure measurements were included along with cumulative outflow as input data. With its capability to implement variety of instrumentations under well controlled initial and boundary conditions and to shorten testing time, the centrifuge modeling technique is attractive as an alternative experimental method that provides more freedom to set inverse problem conditions for the parameter estimation.

Centrifuge modeling of one-step outflow tests for unsaturated parameter estimations

Science.gov (United States)

Nakajima, H.; Stadler, A. T.

2006-10-01

Centrifuge modeling of one-step outflow tests were carried out using a 2-m radius geotechnical centrifuge, and the cumulative outflow and transient pore water pressure were measured during the tests at multiple gravity levels. Based on the scaling laws of centrifuge modeling, the measurements generally showed reasonable agreement with prototype data calculated from forward simulations with input parameters determined from standard laboratory tests. The parameter optimizations were examined for three different combinations of input data sets using the test measurements. Within the gravity level examined in this study up to 40g, the optimized unsaturated parameters compared well when accurate pore water pressure measurements were included along with cumulative outflow as input data. With its capability to implement variety of instrumentations under well controlled initial and boundary conditions and to shorten testing time, the centrifuge modeling technique is attractive as an alternative experimental method that provides more freedom to set inverse problem conditions for the parameter estimation.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

Science.gov (United States)

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Effects of input uncertainty on cross-scale crop modeling

Science.gov (United States)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input

Selective Laser Sintering of PA2200: Effects of print parameters on density, accuracy, and surface roughness

Energy Technology Data Exchange (ETDEWEB)

Bajric, Sendin [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-06-12

Additive manufacturing needs a broader selection of materials for part production. In order for the Los Alamos National Laboratory (LANL) to investigate new materials for selective laser sintering (SLS), this paper reviews research on the effect of print parameters on part density, accuracy, and surface roughness of polyamide 12 (PA12, PA2200). The literature review serves to enhance the understanding of how changing the laser powder, scan speed, etc. will affect the mechanical properties of a commercial powder. By doing so, this understanding will help the investigation of new materials for SLS.

Input-output model for MACCS nuclear accident impacts estimation¹

Energy Technology Data Exchange (ETDEWEB)

Outkin, Alexander V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bixler, Nathan E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vargas, Vanessa N [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-01-27

Since the original economic model for MACCS was developed, better quality economic data (as well as the tools to gather and process it) and better computational capabilities have become available. The update of the economic impacts component of the MACCS legacy model will provide improved estimates of business disruptions through the use of Input-Output based economic impact estimation. This paper presents an updated MACCS model, bases on Input-Output methodology, in which economic impacts are calculated using the Regional Economic Accounting analysis tool (REAcct) created at Sandia National Laboratories. This new GDP-based model allows quick and consistent estimation of gross domestic product (GDP) losses due to nuclear power plant accidents. This paper outlines the steps taken to combine the REAcct Input-Output-based model with the MACCS code, describes the GDP loss calculation, and discusses the parameters and modeling assumptions necessary for the estimation of long-term effects of nuclear power plant accidents.

Tailoring Selective Laser Melting Process Parameters for NiTi Implants

Science.gov (United States)

Bormann, Therese; Schumacher, Ralf; Müller, Bert; Mertmann, Matthias; de Wild, Michael

2012-12-01

Complex-shaped NiTi constructions become more and more essential for biomedical applications especially for dental or cranio-maxillofacial implants. The additive manufacturing method of selective laser melting allows realizing complex-shaped elements with predefined porosity and three-dimensional micro-architecture directly out of the design data. We demonstrate that the intentional modification of the applied energy during the SLM-process allows tailoring the transformation temperatures of NiTi entities within the entire construction. Differential scanning calorimetry, x-ray diffraction, and metallographic analysis were employed for the thermal and structural characterizations. In particular, the phase transformation temperatures, the related crystallographic phases, and the formed microstructures of SLM constructions were determined for a series of SLM-processing parameters. The SLM-NiTi exhibits pseudoelastic behavior. In this manner, the properties of NiTi implants can be tailored to build smart implants with pre-defined micro-architecture and advanced performance.

Working fluid selection for organic Rankine cycles - Impact of uncertainty of fluid properties

DEFF Research Database (Denmark)

Frutiger, Jerome; Andreasen, Jesper Graa; Liu, Wei

2016-01-01

This study presents a generic methodology to select working fluids for ORC (Organic Rankine Cycles)taking into account property uncertainties of the working fluids. A Monte Carlo procedure is described as a tool to propagate the influence of the input uncertainty of the fluid parameters on the ORC...... modeloutput, and provides the 95%-confidence interval of the net power output with respect to the fluid property uncertainties. The methodology has been applied to a molecular design problem for an ORCusing a low-temperature heat source and consisted of the following four parts: 1) formulation...... of processmodels and constraints 2) selection of property models, i.e. Penge Robinson equation of state 3)screening of 1965 possible working fluid candidates including identification of optimal process parametersbased on Monte Carlo sampling 4) propagating uncertainty of fluid parameters to the ORC netpower output...

Effect Assessment the Impact of Filler Types on the Input Design Parameter of Flexible Pavements

Directory of Open Access Journals (Sweden)

Sahar S. Neham

2017-08-01

Full Text Available To meet the requirements of flexible pavements (safety, economy, limited the stresses on the natural subgrade and a smooth ride, good quality material of surface course must be used so to prevent pavement distresses caused by the different types of loadings (structural and environmental loadings, while the resilient modulus is important input data when flexible pavement was designed, it is selected to show its effect by different types of mineral filler as a partial replacement. In this paving mix, to improve the quality of the mix material and to represent the effect of these replacements materials on the elastic characterization by measuring the resilient modulus of hot mix asphalt (HMA: Fly Ash (FA, Ordinary Portland Cement (OPC, Hydrated Lime (HL and Silica Fume (SF are used as a partial percent of filler (Limestone Dust (LSD replacement, where these materials are locally available including (40-50 penetration grade asphalt binder. To achieve the goal of study; asphalt concrete mixes are prepared at their optimum asphalt content using Marshall Method of mix design. Four replacement percent’s were used; 0, 1.5, 3.0 and 4.5 percent by total weight of aggregate for each filler types. According to ASTM D4123 criteria (Resilient Modulus was tested by UTM¬25. Mixes modified with (FA, (OPC, (HL and (SF were found to have average improvement in the value of Resilient Modulus by (13.37, 9.63, 11.14, 24.00 % at 1.5 percent of filler replacement and by (24.54, 16.63, 18.73, 38.31 % at 3.0 percent of filler replacement also the percent of improvement is: (39.55, 26.36, 29.82, 58.30 at 4.5percent of filler replacement sequentially.

Parameter selection for peak alignment in chromatographic sample profiling: Objective quality indicators and use of control samples

NARCIS (Netherlands)

Peters, S.; van Velzen, E.; Janssen, H.-G.

2009-01-01

In chromatographic profiling applications, peak alignment is often essential as most chromatographic systems exhibit small peak shifts over time. When using currently available alignment algorithms, there are several parameters that determine the outcome of the alignment process. Selecting the

Input modeling with phase-type distributions and Markov models theory and applications

CERN Document Server

Buchholz, Peter; Felko, Iryna

2014-01-01

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system...

Parameter Subset Selection Techniques for Problems in Mathematical Biology

DEFF Research Database (Denmark)

Olsen, Christian; Smith, Ralph; Tran, Hien

2015-01-01

Patient-specific models for diagnostics and treatment planning require reliable parameter estimation and model predictions. Mathematical models of physiological systems are often formulated as systems of nonlinear ODEs with many parameters and few options for measuring all state variables....... Consequently, it can be difficult to determine which parameters can reliably be estimated from the available data. This investigation highlights some pitfalls associated with parameters that are unidentifiable in the sense that they are not uniquely determined by responses, and presents methods for recognizing...... and addressing identifiability problems. These methods quantify the magnitude of parameter influence through sensitivity analysis, and parameter interactions that might complicate unambiguous parameter estimation. The methods will be demonstrated using five examples of increasing complexity, as well...