Characteristic length scale of input data in distributed models: implications for modeling grain size

Science.gov (United States)

Artan, Guleid A.; Neale, C. M. U.; Tarboton, D. G.

2000-01-01

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m2 modeling grid size. A modeling grid size of about 10×10 m2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.

How model and input uncertainty impact maize yield simulations in West Africa

Science.gov (United States)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

Statistical screening of input variables in a complex computer code

International Nuclear Information System (INIS)

Krieger, T.J.

1982-01-01

A method is presented for ''statistical screening'' of input variables in a complex computer code. The object is to determine the ''effective'' or important input variables by estimating the relative magnitudes of their associated sensitivity coefficients. This is accomplished by performing a numerical experiment consisting of a relatively small number of computer runs with the code followed by a statistical analysis of the results. A formula for estimating the sensitivity coefficients is derived. Reference is made to an earlier work in which the method was applied to a complex reactor code with good results

Input Variability Facilitates Unguided Subcategory Learning in Adults

Science.gov (United States)

Eidsvåg, Sunniva Sørhus; Austad, Margit; Plante, Elena; Asbjørnsen, Arve E.

2015-01-01

Purpose: This experiment investigated whether input variability would affect initial learning of noun gender subcategories in an unfamiliar, natural language (Russian), as it is known to assist learning of other grammatical forms. Method: Forty adults (20 men, 20 women) were familiarized with examples of masculine and feminine Russian words. Half…

Latitudinal and seasonal variability of the micrometeor input function: A study using model predictions and observations from Arecibo and PFISR

Science.gov (United States)

Fentzke, J. T.; Janches, D.; Sparks, J. J.

2009-05-01

In this work, we use a semi-empirical model of the micrometeor input function (MIF) together with meteor head-echo observations obtained with two high power and large aperture (HPLA) radars, the 430 MHz Arecibo Observatory (AO) radar in Puerto Rico (18°N, 67°W) and the 450 MHz Poker flat incoherent scatter radar (PFISR) in Alaska (65°N, 147°W), to study the seasonal and geographical dependence of the meteoric flux in the upper atmosphere. The model, recently developed by Janches et al. [2006a. Modeling the global micrometeor input function in the upper atmosphere observed by high power and large aperture radars. Journal of Geophysical Research 111] and Fentzke and Janches [2008. A semi-empirical model of the contribution from sporadic meteoroid sources on the meteor input function observed at arecibo. Journal of Geophysical Research (Space Physics) 113 (A03304)], includes an initial mass flux that is provided by the six known meteor sources (i.e. orbital families of dust) as well as detailed modeling of meteoroid atmospheric entry and ablation physics. In addition, we use a simple ionization model to treat radar sensitivity issues by defining minimum electron volume density production thresholds required in the meteor head-echo plasma for detection. This simplified approach works well because we use observations from two radars with similar frequencies, but different sensitivities and locations. This methodology allows us to explore the initial input of particles and how it manifests in different parts of the MLT as observed by these instruments without the need to invoke more sophisticated plasma models, which are under current development. The comparisons between model predictions and radar observations show excellent agreement between diurnal, seasonal, and latitudinal variability of the detected meteor rate and radial velocity distributions, allowing us to understand how individual meteoroid populations contribute to the overall flux at a particular

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

Framework for Modelling Multiple Input Complex Aggregations for Interactive Installations

DEFF Research Database (Denmark)

Padfield, Nicolas; Andreasen, Troels

2012-01-01

on fuzzy logic and provides a method for variably balancing interaction and user input with the intention of the artist or director. An experimental design is presented, demonstrating an intuitive interface for parametric modelling of a complex aggregation function. The aggregation function unifies...

A new interpretation and validation of variance based importance measures for models with correlated inputs

Science.gov (United States)

Hao, Wenrui; Lu, Zhenzhou; Li, Luyi

2013-05-01

In order to explore the contributions by correlated input variables to the variance of the output, a novel interpretation framework of importance measure indices is proposed for a model with correlated inputs, which includes the indices of the total correlated contribution and the total uncorrelated contribution. The proposed indices accurately describe the connotations of the contributions by the correlated input to the variance of output, and they can be viewed as the complement and correction of the interpretation about the contributions by the correlated inputs presented in "Estimation of global sensitivity indices for models with dependent variables, Computer Physics Communications, 183 (2012) 937-946". Both of them contain the independent contribution by an individual input. Taking the general form of quadratic polynomial as an illustration, the total correlated contribution and the independent contribution by an individual input are derived analytically, from which the components and their origins of both contributions of correlated input can be clarified without any ambiguity. In the special case that no square term is included in the quadratic polynomial model, the total correlated contribution by the input can be further decomposed into the variance contribution related to the correlation of the input with other inputs and the independent contribution by the input itself, and the total uncorrelated contribution can be further decomposed into the independent part by interaction between the input and others and the independent part by the input itself. Numerical examples are employed and their results demonstrate that the derived analytical expressions of the variance-based importance measure are correct, and the clarification of the correlated input contribution to model output by the analytical derivation is very important for expanding the theory and solutions of uncorrelated input to those of the correlated one.

Modeling inputs to computer models used in risk assessment

International Nuclear Information System (INIS)

Iman, R.L.

1987-01-01

Computer models for various risk assessment applications are closely scrutinized both from the standpoint of questioning the correctness of the underlying mathematical model with respect to the process it is attempting to model and from the standpoint of verifying that the computer model correctly implements the underlying mathematical model. A process that receives less scrutiny, but is nonetheless of equal importance, concerns the individual and joint modeling of the inputs. This modeling effort clearly has a great impact on the credibility of results. Model characteristics are reviewed in this paper that have a direct bearing on the model input process and reasons are given for using probabilities-based modeling with the inputs. The authors also present ways to model distributions for individual inputs and multivariate input structures when dependence and other constraints may be present

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.

Importance analysis for models with correlated variables and its sparse grid solution

International Nuclear Information System (INIS)

Li, Luyi; Lu, Zhenzhou

2013-01-01

For structural models involving correlated input variables, a novel interpretation for variance-based importance measures is proposed based on the contribution of the correlated input variables to the variance of the model output. After the novel interpretation of the variance-based importance measures is compared with the existing ones, two solutions of the variance-based importance measures of the correlated input variables are built on the sparse grid numerical integration (SGI): double-loop nested sparse grid integration (DSGI) method and single loop sparse grid integration (SSGI) method. The DSGI method solves the importance measure by decreasing the dimensionality of the input variables procedurally, while SSGI method performs importance analysis through extending the dimensionality of the inputs. Both of them can make full use of the advantages of the SGI, and are well tailored for different situations. By analyzing the results of several numerical and engineering examples, it is found that the novel proposed interpretation about the importance measures of the correlated input variables is reasonable, and the proposed methods for solving importance measures are efficient and accurate. -- Highlights: •The contribution of correlated variables to the variance of the output is analyzed. •A novel interpretation for variance-based indices of correlated variables is proposed. •Two solutions for variance-based importance measures of correlated variables are built

ETFOD: a point model physics code with arbitrary input

International Nuclear Information System (INIS)

Rothe, K.E.; Attenberger, S.E.

1980-06-01

ETFOD is a zero-dimensional code which solves a set of physics equations by minimization. The technique used is different than normally used, in that the input is arbitrary. The user is supplied with a set of variables from which he specifies which variables are input (unchanging). The remaining variables become the output. Presently the code is being used for ETF reactor design studies. The code was written in a manner to allow easy modificaton of equations, variables, and physics calculations. The solution technique is presented along with hints for using the code

A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.

Science.gov (United States)

He, Yan-Lin; Geng, Zhi-Qiang; Xu, Yuan; Zhu, Qun-Xiong

2015-09-01

In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

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. 

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

Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types

NARCIS (Netherlands)

Akçay, A.E.; Biller, B.

2014-01-01

We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In

Uncertainty importance measure for models with correlated normal variables

International Nuclear Information System (INIS)

Hao, Wenrui; Lu, Zhenzhou; Wei, Pengfei

2013-01-01

In order to explore the contributions by correlated input variables to the variance of the model output, the contribution decomposition of the correlated input variables based on Mara's definition is investigated in detail. By taking the quadratic polynomial output without cross term as an illustration, the solution of the contribution decomposition is derived analytically using the statistical inference theory. After the correction of the analytical solution is validated by the numerical examples, they are employed to two engineering examples to show their wide application. The derived analytical solutions can directly be used to recognize the contributions by the correlated input variables in case of the quadratic or linear polynomial output without cross term, and the analytical inference method can be extended to the case of higher order polynomial output. Additionally, the origins of the interaction contribution of the correlated inputs are analyzed, and the comparisons of the existing contribution indices are completed, on which the engineer can select the suitable indices to know the necessary information. At last, the degeneration of the correlated inputs to the uncorrelated ones and some computational issues are discussed in concept

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.

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

Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

Energy Technology Data Exchange (ETDEWEB)

Costa, Valter Magalhaes; Pereira, Iraci Martinez, E-mail: valter.costa@usp.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because a previous diagnosis allows the correction of the fault and, like this, to prevent the production stopped, improving operator's security and it's not provoking economics losses. The objective of this work is to build a set, using bivariate correlation and canonical correlation, which will be the set of input variables of an artificial neural network to monitor the greater number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. Initially, for the input set of neural network we selected the variables: nuclear power, primary circuit flow rate, control/safety rod position and difference in pressure in the core of the reactor, because almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The nuclear power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures; the primary circuit flow rate has the function of energy transport by removing the nucleus heat. An artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables. (author)

Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

International Nuclear Information System (INIS)

Costa, Valter Magalhaes; Pereira, Iraci Martinez

2011-01-01

The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because a previous diagnosis allows the correction of the fault and, like this, to prevent the production stopped, improving operator's security and it's not provoking economics losses. The objective of this work is to build a set, using bivariate correlation and canonical correlation, which will be the set of input variables of an artificial neural network to monitor the greater number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. Initially, for the input set of neural network we selected the variables: nuclear power, primary circuit flow rate, control/safety rod position and difference in pressure in the core of the reactor, because almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The nuclear power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures; the primary circuit flow rate has the function of energy transport by removing the nucleus heat. An artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables. (author)

Sensitivity of a complex urban air quality model to input data

International Nuclear Information System (INIS)

Seigneur, C.; Tesche, T.W.; Roth, P.M.; Reid, L.E.

1981-01-01

In recent years, urban-scale photochemical simulation models have been developed that are of practical value for predicting air quality and analyzing the impacts of alternative emission control strategies. Although the performance of some urban-scale models appears to be acceptable, the demanding data requirements of such models have prompted concern about the costs of data acquistion, which might be high enough to preclude use of photochemical models for many urban areas. To explore this issue, sensitivity studies with the Systems Applications, Inc. (SAI) Airshed Model, a grid-based time-dependent photochemical dispersion model, have been carried out for the Los Angeles basin. Reductions in the amount and quality of meteorological, air quality and emission data, as well as modifications of the model gridded structure, have been analyzed. This paper presents and interprets the results of 22 sensitivity studies. A sensitivity-uncertainty index is defined to rank input data needs for an urban photochemical model. The index takes into account the sensitivity of model predictions to the amount of input data, the costs of data acquistion, and the uncertainties in the air quality model input variables. The results of these sensitivity studies are considered in light of the limitations of specific attributes of the Los Angeles basin and of the modeling conditions (e.g., choice of wind model, length of simulation time). The extent to which the results may be applied to other urban areas also is discussed

PCC/SRC, PCC and SRC Calculation from Multivariate Input for Sensitivity Analysis

International Nuclear Information System (INIS)

Iman, R.L.; Shortencarier, M.J.; Johnson, J.D.

1995-01-01

1 - Description of program or function: PCC/SRC is designed for use in conjunction with sensitivity analyses of complex computer models. PCC/SRC calculates the partial correlation coefficients (PCC) and the standardized regression coefficients (SRC) from the multivariate input to, and output from, a computer model. 2 - Method of solution: PCC/SRC calculates the coefficients on either the original observations or on the ranks of the original observations. These coefficients provide alternative measures of the relative contribution (importance) of each of the various input variables to the observed variations in output. Relationships between the coefficients and differences in their interpretations are identified. If the computer model output has an associated time or spatial history, PCC/SRC will generate a graph of the coefficients over time or space for each input-variable, output- variable combination of interest, indicating the importance of each input value over time or space. 3 - Restrictions on the complexity of the problem - Maxima of: 100 observations, 100 different time steps or intervals between successive dependent variable readings, 50 independent variables (model input), 20 dependent variables (model output). 10 ordered triples specifying intervals between dependent variable readings

Construction of an input sensitivity variable CAMAC module for measuring DC voltage

International Nuclear Information System (INIS)

Noda, Nobuaki.

1979-03-01

In on-line experimental data processing systems, the collection of DC voltage data is frequently required. In plasma confinement experiments, for example, the range of input voltage is very wide from over 1 kV applied to photomultiplier tubes to 10 mV full scale of the controller output for ionization vacuum gauges. A DC voltmeter CAMAC module with variable input range, convenient for plasma experiments and inexpensive, has been constructed for trial. The number of input channels is 16, and the input range is changeable in six steps from +-10 mV to +-200 V; these are all set by commands from a computer. The module is actually used for the on-line data processing system for JIPP T-2 experiment. The ideas behind its development, and the functions, features and usage of the module are described in this report. (J.P.N.)

Wide Input Range Power Converters Using a Variable Turns Ratio Transformer

DEFF Research Database (Denmark)

Ouyang, Ziwei; Andersen, Michael A. E.

2016-01-01

A new integrated transformer with variable turns ratio is proposed to enable dc-dc converters operating over a wide input voltage range. The integrated transformer employs a new geometry of magnetic core with “four legs”, two primary windings with orthogonal arrangement, and “8” shape connection...... of diagonal secondary windings, in order to make the transformer turns ratio adjustable by controlling the phase between the two current excitations subjected to the two primary windings. Full-bridge boost dc-dc converter is employed with the proposed transformer to demonstrate the feasibility of the variable...

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.

Modelling Implicit Communication in Multi-Agent Systems with Hybrid Input/Output Automata

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Marta Capiluppi

2012-10-01

Full Text Available We propose an extension of Hybrid I/O Automata (HIOAs to model agent systems and their implicit communication through perturbation of the environment, like localization of objects or radio signals diffusion and detection. To this end we decided to specialize some variables of the HIOAs whose values are functions both of time and space. We call them world variables. Basically they are treated similarly to the other variables of HIOAs, but they have the function of representing the interaction of each automaton with the surrounding environment, hence they can be output, input or internal variables. Since these special variables have the role of simulating implicit communication, their dynamics are specified both in time and space, because they model the perturbations induced by the agent to the environment, and the perturbations of the environment as perceived by the agent. Parallel composition of world variables is slightly different from parallel composition of the other variables, since their signals are summed. The theory is illustrated through a simple example of agents systems.

A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

Science.gov (United States)

Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

2017-10-01

Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal

Multi input single output model predictive control of non-linear bio-polymerization process

Energy Technology Data Exchange (ETDEWEB)

Arumugasamy, Senthil Kumar; Ahmad, Z. [School of Chemical Engineering, Univerisiti Sains Malaysia, Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang (Malaysia)

2015-05-15

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.

Uncertainty and variability in computational and mathematical models of cardiac physiology.

Science.gov (United States)

Mirams, Gary R; Pathmanathan, Pras; Gray, Richard A; Challenor, Peter; Clayton, Richard H

2016-12-01

Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for

Dose uncertainties for large solar particle events: Input spectra variability and human geometry approximations

International Nuclear Information System (INIS)

Townsend, Lawrence W.; Zapp, E. Neal

1999-01-01

The true uncertainties in estimates of body organ absorbed dose and dose equivalent, from exposures of interplanetary astronauts to large solar particle events (SPEs), are essentially unknown. Variations in models used to parameterize SPE proton spectra for input into space radiation transport and shielding computer codes can result in uncertainty about the reliability of dose predictions for these events. Also, different radiation transport codes and their input databases can yield significant differences in dose predictions, even for the same input spectra. Different results may also be obtained for the same input spectra and transport codes if different spacecraft and body self-shielding distributions are assumed. Heretofore there have been no systematic investigations of the variations in dose and dose equivalent resulting from these assumptions and models. In this work we present a study of the variability in predictions of organ dose and dose equivalent arising from the use of different parameters to represent the same incident SPE proton data and from the use of equivalent sphere approximations to represent human body geometry. The study uses the BRYNTRN space radiation transport code to calculate dose and dose equivalent for the skin, ocular lens and bone marrow using the October 1989 SPE as a model event. Comparisons of organ dose and dose equivalent, obtained with a realistic human geometry model and with the oft-used equivalent sphere approximation, are also made. It is demonstrated that variations of 30-40% in organ dose and dose equivalent are obtained for slight variations in spectral fitting parameters obtained when various data points are included or excluded from the fitting procedure. It is further demonstrated that extrapolating spectra from low energy (≤30 MeV) proton fluence measurements, rather than using fluence data extending out to 100 MeV results in dose and dose equivalent predictions that are underestimated by factors as large as 2

Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest.

Science.gov (United States)

Delpierre, Nicolas; Berveiller, Daniel; Granda, Elena; Dufrêne, Eric

2016-04-01

Although the analysis of flux data has increased our understanding of the interannual variability of carbon inputs into forest ecosystems, we still know little about the determinants of wood growth. Here, we aimed to identify which drivers control the interannual variability of wood growth in a mesic temperate deciduous forest. We analysed a 9-yr time series of carbon fluxes and aboveground wood growth (AWG), reconstructed at a weekly time-scale through the combination of dendrometer and wood density data. Carbon inputs and AWG anomalies appeared to be uncorrelated from the seasonal to interannual scales. More than 90% of the interannual variability of AWG was explained by a combination of the growth intensity during a first 'critical period' of the wood growing season, occurring close to the seasonal maximum, and the timing of the first summer growth halt. Both atmospheric and soil water stress exerted a strong control on the interannual variability of AWG at the study site, despite its mesic conditions, whilst not affecting carbon inputs. Carbon sink activity, not carbon inputs, determined the interannual variations in wood growth at the study site. Our results provide a functional understanding of the dependence of radial growth on precipitation observed in dendrological studies. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Variance-based sensitivity indices for models with dependent inputs

International Nuclear Information System (INIS)

Mara, Thierry A.; Tarantola, Stefano

2012-01-01

Computational models are intensively used in engineering for risk analysis or prediction of future outcomes. Uncertainty and sensitivity analyses are of great help in these purposes. Although several methods exist to perform variance-based sensitivity analysis of model output with independent inputs only a few are proposed in the literature in the case of dependent inputs. This is explained by the fact that the theoretical framework for the independent case is set and a univocal set of variance-based sensitivity indices is defined. In the present work, we propose a set of variance-based sensitivity indices to perform sensitivity analysis of models with dependent inputs. These measures allow us to distinguish between the mutual dependent contribution and the independent contribution of an input to the model response variance. Their definition relies on a specific orthogonalisation of the inputs and ANOVA-representations of the model output. In the applications, we show the interest of the new sensitivity indices for model simplification setting. - Highlights: ► Uncertainty and sensitivity analyses are of great help in engineering. ► Several methods exist to perform variance-based sensitivity analysis of model output with independent inputs. ► We define a set of variance-based sensitivity indices for models with dependent inputs. ► Inputs mutual contributions are distinguished from their independent contributions. ► Analytical and computational tests are performed and discussed.

Graphical user interface for input output characterization of single variable and multivariable highly nonlinear systems

Directory of Open Access Journals (Sweden)

Shahrukh Adnan Khan M. D.

2017-01-01

Full Text Available This paper presents a Graphical User Interface (GUI software utility for the input/output characterization of single variable and multivariable nonlinear systems by obtaining the sinusoidal input describing function (SIDF of the plant. The software utility is developed on MATLAB R2011a environment. The developed GUI holds no restriction on the nonlinearity type, arrangement and system order; provided that output(s of the system is obtainable either though simulation or experiments. An insight to the GUI and its features are presented in this paper and example problems from both single variable and multivariable cases are demonstrated. The formulation of input/output behavior of the system is discussed and the nucleus of the MATLAB command underlying the user interface has been outlined. Some of the industries that would benefit from this software utility includes but not limited to aerospace, defense technology, robotics and automotive.

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

Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

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Koichi Kobayashi

2013-01-01

Full Text Available We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

Not All Children Agree: Acquisition of Agreement when the Input Is Variable

Science.gov (United States)

Miller, Karen

2012-01-01

In this paper we investigate the effect of variable input on the acquisition of grammar. More specifically, we examine the acquisition of the third person singular marker -s on the auxiliary "do" in comprehension and production in two groups of children who are exposed to similar varieties of English but that differ with respect to adult…

Continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics

International Nuclear Information System (INIS)

Chen, Haixia; Zhang, Jing

2007-01-01

We propose a scheme for continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics. The quantum cloning machine yields M identical optimal clones from N replicas of a coherent state and N replicas of its phase conjugate. This scheme can be straightforwardly implemented with the setups accessible at present since its optical implementation only employs simple linear optical elements and homodyne detection. Compared with the original scheme for continuous-variable quantum cloning with phase-conjugate input modes proposed by Cerf and Iblisdir [Phys. Rev. Lett. 87, 247903 (2001)], which utilized a nondegenerate optical parametric amplifier, our scheme loses the output of phase-conjugate clones and is regarded as irreversible quantum cloning

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...

On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model

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Daniela Molinari

2017-09-01

Full Text Available IN-depth SYnthetic Model for Flood Damage Estimation (INSYDE is a model for the estimation of flood damage to residential buildings at the micro-scale. This study investigates the sensitivity of INSYDE to the accuracy of input data. Starting from the knowledge of input parameters at the scale of individual buildings for a case study, the level of detail of input data is progressively downgraded until the condition in which a representative value is defined for all inputs at the census block scale. The analysis reveals that two conditions are required to limit the errors in damage estimation: the representativeness of representatives values with respect to micro-scale values and the local knowledge of the footprint area of the buildings, being the latter the main extensive variable adopted by INSYDE. Such a result allows for extending the usability of the model at the meso-scale, also in different countries, depending on the availability of aggregated building data.

Input Selection for Return Temperature Estimation in Mixing Loops using Partial Mutual Information with Flow Variable Delay

DEFF Research Database (Denmark)

Overgaard, Anders; Kallesøe, Carsten Skovmose; Bendtsen, Jan Dimon

2017-01-01

adgang til data, er ønsker at skabe en datadreven model til kontrol. Grundet den store mængde tilgængelig data anvendes der en metode til valg af inputs kaldet "Partial Mutual Information" (PMI). Denne artikel introducerer en metode til at inkluderer flow variable forsinkelser i PMI. Data fra en...... kontorbygning i Bjerringbro anvendes til analyse. Det vises at "Mutual Information" og et "Generalized Regression Neural Network" begge forbedres ved at anvende flow variabelt forsinkelse i forhold til at anvende konstante delay....

Optimization modeling of U.S. renewable electricity deployment using local input variables

Science.gov (United States)

Bernstein, Adam

For the past five years, state Renewable Portfolio Standard (RPS) laws have been a primary driver of renewable electricity (RE) deployments in the United States. However, four key trends currently developing: (i) lower natural gas prices, (ii) slower growth in electricity demand, (iii) challenges of system balancing intermittent RE within the U.S. transmission regions, and (iv) fewer economical sites for RE development, may limit the efficacy of RPS laws over the remainder of the current RPS statutes' lifetime. An outsized proportion of U.S. RE build occurs in a small number of favorable locations, increasing the effects of these variables on marginal RE capacity additions. A state-by-state analysis is necessary to study the U.S. electric sector and to generate technology specific generation forecasts. We used LP optimization modeling similar to the National Renewable Energy Laboratory (NREL) Renewable Energy Development System (ReEDS) to forecast RE deployment across the 8 U.S. states with the largest electricity load, and found state-level RE projections to Year 2031 significantly lower than thoseimplied in the Energy Information Administration (EIA) 2013 Annual Energy Outlook forecast. Additionally, the majority of states do not achieve their RPS targets in our forecast. Combined with the tendency of prior research and RE forecasts to focus on larger national and global scale models, we posit that further bottom-up state and local analysis is needed for more accurate policy assessment, forecasting, and ongoing revision of variables as parameter values evolve through time. Current optimization software eliminates much of the need for algorithm coding and programming, allowing for rapid model construction and updating across many customized state and local RE parameters. Further, our results can be tested against the empirical outcomes that will be observed over the coming years, and the forecast deviation from the actuals can be attributed to discrete parameter

A non-linear dimension reduction methodology for generating data-driven stochastic input models

Science.gov (United States)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-06-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low

A non-linear dimension reduction methodology for generating data-driven stochastic input models

International Nuclear Information System (INIS)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-01-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology

Unitary input DEA model to identify beef cattle production systems typologies

Directory of Open Access Journals (Sweden)

Eliane Gonçalves Gomes

2012-08-01

Full Text Available The cow-calf beef production sector in Brazil has a wide variety of operating systems. This suggests the identification and the characterization of homogeneous regions of production, with consequent implementation of actions to achieve its sustainability. In this paper we attempted to measure the performance of 21 livestock modal production systems, in their cow-calf phase. We measured the performance of these systems, considering husbandry and production variables. The proposed approach is based on data envelopment analysis (DEA. We used unitary input DEA model, with apparent input orientation, together with the efficiency measurements generated by the inverted DEA frontier. We identified five modal production systems typologies, using the isoefficiency layers approach. The results showed that the knowledge and the processes management are the most important factors for improving the efficiency of beef cattle production systems.

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.

A new chance-constrained DEA model with birandom input and output data

OpenAIRE

Tavana, M.; Shiraz, R. K.; Hatami-Marbini, A.

2013-01-01

The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-...

Analysis of reactor capital costs and correlated sampling of economic input variables - 15342

International Nuclear Information System (INIS)

Ganda, F.; Kim, T.K.; Taiwo, T.A.; Wigeland, R.

2015-01-01

In this paper we present work aimed at enhancing the capability to perform nuclear fuel cycle cost estimates and evaluation of financial risk. Reactor capital costs are of particular relevance, since they typically comprise about 60% to 70% of the calculated Levelized Cost of Electricity at Equilibrium (LCAE). The work starts with the collection of historical construction cost and construction duration of nuclear plants in the U.S. and France, as well as forecasted costs of nuclear plants currently under construction in the U.S. This data has the primary goal of supporting the introduction of an appropriate framework, supported in this paper by two case studies with historical data, which allows the development of solid and defensible assumptions on nuclear reactor capital costs. Work is also presented on the enhancement of the capability to model interdependence of cost estimates between facilities and uncertainties. The correlated sampling capabilities in the nuclear economic code NECOST have been expanded to include partial correlations between input variables, according to a given correlation matrix. Accounting for partial correlations correctly allows a narrowing, where appropriate, of the probability density function of the difference in the LCAE between alternative, but correlated, fuel cycles. It also allows the correct calculation of the standard deviation of the LCAE of multistage systems, which appears smaller than the correct value if correlated input costs are treated as uncorrelated. (authors)

A Model to Determinate the Influence of Probability Density Functions (PDFs of Input Quantities in Measurements

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Jesús Caja

2016-06-01

Full Text Available A method for analysing the effect of different hypotheses about the type of the input quantities distributions of a measurement model is presented here so that the developed algorithms can be simplified. As an example, a model of indirect measurements with optical coordinate measurement machine was employed to evaluate these different hypotheses. As a result of the different experiments, the assumption that the different variables of the model can be modelled as normal distributions is proved.

Urban vs. Rural CLIL: An Analysis of Input-Related Variables, Motivation and Language Attainment

Science.gov (United States)

Alejo, Rafael; Piquer-Píriz, Ana

2016-01-01

The present article carries out an in-depth analysis of the differences in motivation, input-related variables and linguistic attainment of the students at two content and language integrated learning (CLIL) schools operating within the same institutional and educational context, the Spanish region of Extremadura, and differing only in terms of…

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

Science.gov (United States)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the

Verification of models for ballistic movement time and endpoint variability.

Science.gov (United States)

Lin, Ray F; Drury, Colin G

2013-01-01

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.

The input and output management of solid waste using DEA models: A case study at Jengka, Pahang

Science.gov (United States)

Mohamed, Siti Rosiah; Ghazali, Nur Fadzrina Mohd; Mohd, Ainun Hafizah

2017-08-01

Data Envelopment Analysis (DEA) as a tool for obtaining performance indices has been used extensively in several of organizations sector. The ways to improve the efficiency of Decision Making Units (DMUs) is impractical because some of inputs and outputs are uncontrollable and in certain situation its produce weak efficiency which often reflect the impact for operating environment. Based on the data from Alam Flora Sdn. Bhd Jengka, the researcher wants to determine the efficiency of solid waste management (SWM) in town Jengka Pahang using CCRI and CCRO model of DEA and duality formulation with vector average input and output. Three input variables (length collection in meter, frequency time per week in hour and number of garbage truck) and 2 outputs variables (frequency collection and the total solid waste collection in kilogram) are analyzed. As a conclusion, it shows only three roads from 23 roads are efficient that achieve efficiency score 1. Meanwhile, 20 other roads are in an inefficient management.

Inhalation Exposure Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

K. Rautenstrauch

2004-09-10

This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.

Inhalation Exposure Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

K. Rautenstrauch

2004-01-01

This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception

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.

Comparison of different snow model formulations and their responses to input uncertainties in the Upper Indus Basin

Science.gov (United States)

Pritchard, David; Fowler, Hayley; Forsythe, Nathan; O'Donnell, Greg; Rutter, Nick; Bardossy, Andras

2017-04-01

Snow and glacier melt in the mountainous Upper Indus Basin (UIB) sustain water supplies, irrigation networks, hydropower production and ecosystems in extensive downstream lowlands. Understanding hydrological and cryospheric sensitivities to climatic variability and change in the basin is therefore critical for local, national and regional water resources management. Assessing these sensitivities using numerical modelling is challenging, due to limitations in the quality and quantity of input and evaluation data, as well as uncertainties in model structures and parameters. This study explores how these uncertainties in inputs and process parameterisations affect distributed simulations of ablation in the complex climatic setting of the UIB. The role of model forcing uncertainties is explored using combinations of local observations, remote sensing and reanalysis - including the high resolution High Asia Refined Analysis - to generate multiple realisations of spatiotemporal model input fields. Forcing a range of model structures with these input fields then provides an indication of how different ablation parameterisations respond to uncertainties and perturbations in climatic drivers. Model structures considered include simple, empirical representations of melt processes through to physically based, full energy balance models with multi-physics options for simulating snowpack evolution (including an adapted version of FSM). Analysing model input and structural uncertainties in this way provides insights for methodological choices in climate sensitivity assessments of data-sparse, high mountain catchments. Such assessments are key for supporting water resource management in these catchments, particularly given the potential complications of enhanced warming through elevation effects or, in the case of the UIB, limited understanding of how and why local climate change signals differ from broader patterns.

Environmental Transport Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

M. Wasiolek

2004-01-01

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573])

Environmental Transport Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. Wasiolek

2004-09-10

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis

'Quantization' of stochastic variables: description and effects on the input noise sources in a BWR

International Nuclear Information System (INIS)

Matthey, M.

1979-01-01

A set of macrostochastic and discrete variables, with Markovian properties, is used to characterize the state of a BWR, whose input noise sources are of interest. The ratio between the auto-power spectral density (APSD) of the neutron noise fluctuations and the square modulus of the transfer function (SMTF) defines 'the total input noise source' (TINS), the components of which are the different noise source corresponding to the relevant variables. A white contribution to TINS arises from the birth and death processes of neutrons in the reactor and corresponds to a 'shot noise' (SN). Non-white contributions arise from fluctuations of the neutron cross-sections caused by fuel temperature and steam content variations. These terms called 'Flicker noises' (FN) are characterized by cut-off frequencies related to time constants of reactivity feedback effects. The respective magnitudes of the shot and flicker noises depend not only on the frequency, the feedback reactivity coefficients or the power of the reactor, but also on the 'quantization' of the continuous variables introduced such as fuel temperature and steam content. The effects of this last 'quantization' on the shapes of the noise sources and their sum are presented in this paper. (author)

Modeling Short-Range Soil Variability and its Potential Use in Variable-Rate Treatment of Experimental Plots

Directory of Open Access Journals (Sweden)

A Moameni

2011-02-01

Full Text Available Abstract In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics

Pandemic recovery analysis using the dynamic inoperability input-output model.

Science.gov (United States)

Santos, Joost R; Orsi, Mark J; Bond, Erik J

2009-12-01

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model((1,2)) and the dynamic inoperability input-output model (DIIM).((3)) These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

Gait variability: methods, modeling and meaning

Directory of Open Access Journals (Sweden)

Hausdorff Jeffrey M

2005-07-01

Full Text Available Abstract The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting.

Agricultural and Environmental Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-01-01

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). 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 the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN

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

Modeling Recognition Memory Using the Similarity Structure of Natural Input

Science.gov (United States)

Lacroix, Joyca P. W.; Murre, Jaap M. J.; Postma, Eric O.; van den Herik, H. Jaap

2006-01-01

The natural input memory (NAM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed…

Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

International Nuclear Information System (INIS)

Costa, Valter Magalhaes

2011-01-01

The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operator's security and it's not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( Δ P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and Δ P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network

Agricultural and Environmental Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-06-20

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). 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 the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN.

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

Modeling recognition memory using the similarity structure of natural input

NARCIS (Netherlands)

Lacroix, J.P.W.; Murre, J.M.J.; Postma, E.O.; van den Herik, H.J.

2006-01-01

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During

'Fingerprints' of four crop models as affected by soil input data aggregation

DEFF Research Database (Denmark)

Angulo, Carlos; Gaiser, Thomas; Rötter, Reimund P

2014-01-01

for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil...... properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation. No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation....... In this study we used four crop models (SIMPLACE, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo...

Statistical learning from nonrecurrent experience with discrete input variables and recursive-error-minimization equations

Science.gov (United States)

Carter, Jeffrey R.; Simon, Wayne E.

1990-08-01

Neural networks are trained using Recursive Error Minimization (REM) equations to perform statistical classification. Using REM equations with continuous input variables reduces the required number of training experiences by factors of one to two orders of magnitude over standard back propagation. Replacing the continuous input variables with discrete binary representations reduces the number of connections by a factor proportional to the number of variables reducing the required number of experiences by another order of magnitude. Undesirable effects of using recurrent experience to train neural networks for statistical classification problems are demonstrated and nonrecurrent experience used to avoid these undesirable effects. 1. THE 1-41 PROBLEM The statistical classification problem which we address is is that of assigning points in ddimensional space to one of two classes. The first class has a covariance matrix of I (the identity matrix) the covariance matrix of the second class is 41. For this reason the problem is known as the 1-41 problem. Both classes have equal probability of occurrence and samples from both classes may appear anywhere throughout the ddimensional space. Most samples near the origin of the coordinate system will be from the first class while most samples away from the origin will be from the second class. Since the two classes completely overlap it is impossible to have a classifier with zero error. The minimum possible error is known as the Bayes error and

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

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

Science.gov (United States)

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis

DEFF Research Database (Denmark)

Jiang, Jiuchuan; Jaeger, Manfred

2015-01-01

distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods......Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability...... use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit...

Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City

OpenAIRE

Priska Arindya Purnama

2017-01-01

The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Yt) sequence expected to be effected by an input series (Xt) and other inputs in a group called a noise series (Nt). Multi input transfer function model obtained is (b1,s1,r1) (b2,s2,r2) (b3,s3,r3) (b4,s4,r4)(pn,qn) = (0,0,0)...

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Science.gov (United States)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer

Directory of Open Access Journals (Sweden)

T. Youssef

2013-01-01

Full Text Available This paper presents an unknown input Proportional Multiple-Integral Observer (PIO for synchronization of chaotic systems based on Takagi-Sugeno (TS fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.

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

Simplifying BRDF input data for optical signature modeling

Science.gov (United States)

Hallberg, Tomas; Pohl, Anna; Fagerström, Jan

2017-05-01

Scene simulations of optical signature properties using signature codes normally requires input of various parameterized measurement data of surfaces and coatings in order to achieve realistic scene object features. Some of the most important parameters are used in the model of the Bidirectional Reflectance Distribution Function (BRDF) and are normally determined by surface reflectance and scattering measurements. Reflectance measurements of the spectral Directional Hemispherical Reflectance (DHR) at various incident angles can normally be performed in most spectroscopy labs, while measuring the BRDF is more complicated or may not be available at all in many optical labs. We will present a method in order to achieve the necessary BRDF data directly from DHR measurements for modeling software using the Sandford-Robertson BRDF model. The accuracy of the method is tested by modeling a test surface by comparing results from using estimated and measured BRDF data as input to the model. These results show that using this method gives no significant loss in modeling accuracy.

Improved Stabilization Conditions for Nonlinear Systems with Input and State Delays via T-S Fuzzy Model

Directory of Open Access Journals (Sweden)

Chang Che

2018-01-01

Full Text Available This paper focuses on the problem of nonlinear systems with input and state delays. The considered nonlinear systems are represented by Takagi-Sugeno (T-S fuzzy model. A new state feedback control approach is introduced for T-S fuzzy systems with input delay and state delays. A new Lyapunov-Krasovskii functional is employed to derive less conservative stability conditions by incorporating a recently developed Wirtinger-based integral inequality. Based on the Lyapunov stability criterion, a series of linear matrix inequalities (LMIs are obtained by using the slack variables and integral inequality, which guarantees the asymptotic stability of the closed-loop system. Several numerical examples are given to show the advantages of the proposed results.

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.

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.

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.

Seasonal variability of salinity and circulation in a silled estuarine fjord: A numerical model study

Science.gov (United States)

Kawase, Mitsuhiro; Bang, Bohyun

2013-12-01

A three-dimensional hydrodynamic model is used to study seasonal variability of circulation and hydrography in Hood Canal, Washington, United States, an estuarine fjord that develops seasonally hypoxic conditions. The model is validated with data from year 2006, and is shown to be capable of quantitatively realistic simulation of hydrographic variability. Sensitivity experiments show the largest cause of seasonal variability to be that of salinity at the mouth of the fjord, which drives an annual deep water renewal in late summer-early autumn. Variability of fresh water input from the watershed also causes significant but secondary changes, especially in winter. Local wind stress has little effect over the seasonal timescale. Further experiments, in which one forcing parameter is abruptly altered while others are kept constant, show that outside salinity change induces an immediate response in the exchange circulation that, however, decays as a transient as the system equilibrates. In contrast, a change in the river input initiates gradual adjustment towards a new equilibrium value for the exchange transport. It is hypothesized that the spectral character of the system response to river variability will be redder than to salinity variability. This is demonstrated with a stochastically forced, semi-analytical model of fjord exchange circulation. While the exchange circulation in Hood Canal appears less sensitive to the river variability than to the outside hydrography at seasonal timescales, at decadal and longer timescales both could become significant factors in affecting the exchange circulation.

Impact of Formal Financial Market Participation on Farm Size and Expenditure on Variable Farm Inputs: The Case of Maize Farmers in Ghana

OpenAIRE

Awunyo-Vitor, Dadson; Al-Hassan, Ramatu M.; Sarpong, Daniel B.

2014-01-01

The study examined maize farmers’ participation in the formal financial market and its impact on farm size and expenditure on variable farm inputs. A multistage sampling method was used in selecting 595 maize farmers from the seven districts in Ashanti and Brong Ahafo Regions of Ghana. A structured questionnaire and interview schedule were used to elicit information from the respondents. The impact of formal financial market participation on farm size and expenditure on variable inputs was es...

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

Science.gov (United States)

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

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.

Investigation of RADTRAN Stop Model input parameters for truck stops

International Nuclear Information System (INIS)

Griego, N.R.; Smith, J.D.; Neuhauser, K.S.

1996-01-01

RADTRAN is a computer code for estimating the risks and consequences as transport of radioactive materials (RAM). RADTRAN was developed and is maintained by Sandia National Laboratories for the US Department of Energy (DOE). For incident-free transportation, the dose to persons exposed while the shipment is stopped is frequently a major percentage of the overall dose. This dose is referred to as Stop Dose and is calculated by the Stop Model. Because stop dose is a significant portion of the overall dose associated with RAM transport, the values used as input for the Stop Model are important. Therefore, an investigation of typical values for RADTRAN Stop Parameters for truck stops was performed. The resulting data from these investigations were analyzed to provide mean values, standard deviations, and histograms. Hence, the mean values can be used when an analyst does not have a basis for selecting other input values for the Stop Model. In addition, the histograms and their characteristics can be used to guide statistical sampling techniques to measure sensitivity of the RADTRAN calculated Stop Dose to the uncertainties in the stop model input parameters. This paper discusses the details and presents the results of the investigation of stop model input parameters at truck stops

A novel methodology improves reservoir characterization models using geologic fuzzy variables

Energy Technology Data Exchange (ETDEWEB)

Soto B, Rodolfo [DIGITOIL, Maracaibo (Venezuela); Soto O, David A. [Texas A and M University, College Station, TX (United States)

2004-07-01

One of the research projects carried out in Cusiana field to explain its rapid decline during the last years was to get better permeability models. The reservoir of this field has a complex layered system that it is not easy to model using conventional methods. The new technique included the development of porosity and permeability maps from cored wells following the same trend of the sand depositions for each facie or layer according to the sedimentary facie and the depositional system models. Then, we used fuzzy logic to reproduce those maps in three dimensions as geologic fuzzy variables. After multivariate statistical and factor analyses, we found independence and a good correlation coefficient between the geologic fuzzy variables and core permeability and porosity. This means, the geologic fuzzy variable could explain the fabric, the grain size and the pore geometry of the reservoir rock trough the field. Finally, we developed a neural network permeability model using porosity, gamma ray and the geologic fuzzy variable as input variables. This model has a cross-correlation coefficient of 0.873 and average absolute error of 33% compared with the actual model with a correlation coefficient of 0.511 and absolute error greater than 250%. We tested different methodologies, but this new one showed dramatically be a promiser way to get better permeability models. The use of the models have had a high impact in the explanation of well performance and workovers, and reservoir simulation models. (author)

Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City

Directory of Open Access Journals (Sweden)

Priska Arindya Purnama

2017-11-01

Full Text Available The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Yt sequence expected to be effected by an input series (Xt and other inputs in a group called a noise series (Nt. Multi input transfer function model obtained is (b1,s1,r1 (b2,s2,r2 (b3,s3,r3 (b4,s4,r4(pn,qn = (0,0,0 (23,0,0 (1,2,0 (0,0,0 ([5,8],2 and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.

Determination of the arterial input function in mouse-models using clinical MRI

International Nuclear Information System (INIS)

Theis, D.; Fachhochschule Giessen-Friedberg; Keil, B.; Heverhagen, J.T.; Klose, K.J.; Behe, M.; Fiebich, M.

2008-01-01

Dynamic contrast enhanced magnetic resonance imaging is a promising method for quantitative analysis of tumor perfusion and is increasingly used in study of cancer in small animal models. In those studies the determination of the arterial input function (AIF) of the target tissue can be the first step. Series of short-axis images of the heart were acquired during administration of a bolus of Gd-DTPA using saturation-recovery gradient echo pulse sequences. The AIF was determined from the changes of the signal intensity in the left ventricle. The native T1 relaxation times and AIF were determined for 11 mice. An average value of (1.16 ± 0.09) s for the native T1 relaxation time was measured. However, the AIF showed significant inter animal variability, as previously observed by other authors. The inter-animal variability shows, that a direct measurement of the AIF is reasonable to avoid significant errors. The proposed method for determination of the AIF proved to be reliable. (orig.)

Evaluating nuclear physics inputs in core-collapse supernova models

Science.gov (United States)

Lentz, E.; Hix, W. R.; Baird, M. L.; Messer, O. E. B.; Mezzacappa, A.

Core-collapse supernova models depend on the details of the nuclear and weak interaction physics inputs just as they depend on the details of the macroscopic physics (transport, hydrodynamics, etc.), numerical methods, and progenitors. We present preliminary results from our ongoing comparison studies of nuclear and weak interaction physics inputs to core collapse supernova models using the spherically-symmetric, general relativistic, neutrino radiation hydrodynamics code Agile-Boltztran. We focus on comparisons of the effects of the nuclear EoS and the effects of improving the opacities, particularly neutrino--nucleon interactions.

Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi-Sugeno-Kang fuzzy model

International Nuclear Information System (INIS)

Galdi, V.; Piccolo, A.; Siano, P.

2009-01-01

Nowadays, incentives and financing options for developing renewable energy facilities and the new development in variable speed wind technology make wind energy a competitive source if compared with conventional generation ones. In order to improve the effectiveness of variable speed wind systems, adaptive control systems able to cope with time variances of the system under control are necessary. On these basis, a data driven designing methodology for TSK fuzzy models design is presented in this paper. The methodology, on the basis of given input-output numerical data, generates the 'best' TSK fuzzy model able to estimate with high accuracy the maximum extractable power from a variable speed wind turbine. The design methodology is based on fuzzy clustering methods for partitioning the input-output space combined with genetic algorithms (GA), and recursive least-squares (LS) optimization methods for model parameter adaptation

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

Effect of the spatiotemporal variability of rainfall inputs in water quality integrated catchment modelling for dissolved oxygen concentrations

Science.gov (United States)

Moreno Ródenas, Antonio Manuel; Cecinati, Francesca; ten Veldhuis, Marie-Claire; Langeveld, Jeroen; Clemens, Francois

2016-04-01

Maintaining water quality standards in highly urbanised hydrological catchments is a worldwide challenge. Water management authorities struggle to cope with changing climate and an increase in pollution pressures. Water quality modelling has been used as a decision support tool for investment and regulatory developments. This approach led to the development of integrated catchment models (ICM), which account for the link between the urban/rural hydrology and the in-river pollutant dynamics. In the modelled system, rainfall triggers the drainage systems of urban areas scattered along a river. When flow exceeds the sewer infrastructure capacity, untreated wastewater enters the natural system by combined sewer overflows. This results in a degradation of the river water quality, depending on the magnitude of the emission and river conditions. Thus, being capable of representing these dynamics in the modelling process is key for a correct assessment of the water quality. In many urbanised hydrological systems the distances between draining sewer infrastructures go beyond the de-correlation length of rainfall processes, especially, for convective summer storms. Hence, spatial and temporal scales of selected rainfall inputs are expected to affect water quality dynamics. The objective of this work is to evaluate how the use of rainfall data from different sources and with different space-time characteristics affects modelled output concentrations of dissolved oxygen in a simplified ICM. The study area is located at the Dommel, a relatively small and sensitive river flowing through the city of Eindhoven (The Netherlands). This river stretch receives the discharge of the 750,000 p.e. WWTP of Eindhoven and from over 200 combined sewer overflows scattered along its length. A pseudo-distributed water quality model has been developed in WEST (mikedhi.com); this is a lumped-physically based model that accounts for urban drainage processes, WWTP and river dynamics for several

Estimating net present value variability for deterministic models

NARCIS (Netherlands)

van Groenendaal, W.J.H.

1995-01-01

For decision makers the variability in the net present value (NPV) of an investment project is an indication of the project's risk. So-called risk analysis is one way to estimate this variability. However, risk analysis requires knowledge about the stochastic character of the inputs. For large,

Motivation Monitoring and Assessment Extension for Input-Process-Outcome Game Model

Science.gov (United States)

Ghergulescu, Ioana; Muntean, Cristina Hava

2014-01-01

This article proposes a Motivation Assessment-oriented Input-Process-Outcome Game Model (MotIPO), which extends the Input-Process-Outcome game model with game-centred and player-centred motivation assessments performed right from the beginning of the game-play. A feasibility case-study involving 67 participants playing an educational game and…

High Temperature Test Facility Preliminary RELAP5-3D Input Model Description

Energy Technology Data Exchange (ETDEWEB)

Bayless, Paul David [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-12-01

A RELAP5-3D input model is being developed for the High Temperature Test Facility at Oregon State University. The current model is described in detail. Further refinements will be made to the model as final as-built drawings are released and when system characterization data are available for benchmarking the input model.

A generic method for automatic translation between input models for different versions of simulation codes

International Nuclear Information System (INIS)

Serfontein, Dawid E.; Mulder, Eben J.; Reitsma, Frederik

2014-01-01

A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is presented as a generic method for producing codes for the automatic translation of input models from the format of one code version to another, or even to that of a completely different code. Normally, such translations are done manually. However, input model files, such as for the VSOP codes, often are very large and may consist of many thousands of numeric entries that make no particular sense to the human eye. Therefore the task, of for instance nuclear regulators, to verify the accuracy of such translated files can be very difficult and cumbersome. This may cause translation errors not to be picked up, which may have disastrous consequences later on when a reactor with such a faulty design is built. Therefore a generic algorithm for producing such automatic translation codes may ease the translation and verification process to a great extent. It will also remove human error from the process, which may significantly enhance the accuracy and reliability of the process. The developed algorithm also automatically creates a verification log file which permanently record the names and values of each variable used, as well as the list of meanings of all the possible values. This should greatly facilitate reactor licensing applications

A generic method for automatic translation between input models for different versions of simulation codes

Energy Technology Data Exchange (ETDEWEB)

Serfontein, Dawid E., E-mail: Dawid.Serfontein@nwu.ac.za [School of Mechanical and Nuclear Engineering, North West University (PUK-Campus), PRIVATE BAG X6001 (Internal Post Box 360), Potchefstroom 2520 (South Africa); Mulder, Eben J. [School of Mechanical and Nuclear Engineering, North West University (South Africa); Reitsma, Frederik [Calvera Consultants (South Africa)

2014-05-01

A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is presented as a generic method for producing codes for the automatic translation of input models from the format of one code version to another, or even to that of a completely different code. Normally, such translations are done manually. However, input model files, such as for the VSOP codes, often are very large and may consist of many thousands of numeric entries that make no particular sense to the human eye. Therefore the task, of for instance nuclear regulators, to verify the accuracy of such translated files can be very difficult and cumbersome. This may cause translation errors not to be picked up, which may have disastrous consequences later on when a reactor with such a faulty design is built. Therefore a generic algorithm for producing such automatic translation codes may ease the translation and verification process to a great extent. It will also remove human error from the process, which may significantly enhance the accuracy and reliability of the process. The developed algorithm also automatically creates a verification log file which permanently record the names and values of each variable used, as well as the list of meanings of all the possible values. This should greatly facilitate reactor licensing applications.

Rose bush leaf and internode expansion dynamics: analysis and development of a model capturing interplant variability

Directory of Open Access Journals (Sweden)

Sabine eDemotes-Mainard

2013-10-01

Full Text Available Bush rose architecture, among other factors, such as plant health, determines plant visual quality. The commercial product is the individual plant and interplant variability may be high within a crop. Thus, both mean plant architecture and interplant variability should be studied. Expansion is an important feature of architecture, but it has been little studied at the level of individual organs in bush roses. We investigated the expansion kinetics of primary shoot organs, to develop a model reproducing the organ expansion of real crops from non destructive input variables. We took interplant variability in expansion kinetics and the model’s ability to simulate this variability into account. Changes in leaflet and internode dimensions over thermal time were recorded for primary shoot expansion, on 83 plants from three crops grown in different climatic conditions and densities. An empirical model was developed, to reproduce organ expansion kinetics for individual plants of a real crop of bush rose primary shoots. Leaflet or internode length was simulated as a logistic function of thermal time. The model was evaluated by cross-validation. We found that differences in leaflet or internode expansion kinetics between phytomer positions and between plants at a given phytomer position were due mostly to large differences in time of organ expansion and expansion rate, rather than differences in expansion duration. Thus, in the model, the parameters linked to expansion duration were predicted by values common to all plants, whereas variability in final size and organ expansion time was captured by input data. The model accurately simulated leaflet and internode expansion for individual plants (RMSEP = 7.3% and 10.2% of final length, respectively. Thus, this study defines the measurements required to simulate expansion and provides the first model simulating organ expansion in rosebush to capture interplant variability.

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.

Variability of concrete properties: experimental characterisation and probabilistic modelling for calcium leaching

International Nuclear Information System (INIS)

De Larrard, Th.

2010-09-01

Evaluating structures durability requires taking into account the variability of material properties. The thesis has two main aspects: on the one hand, an experimental campaign aimed at quantifying the variability of many indicators of concrete behaviour; on the other hand, a simple numerical model for calcium leaching is developed in order to implement probabilistic methods so as to estimate the lifetime of structures such as those related to radioactive waste disposal. The experimental campaign consisted in following up two real building sites, and quantifying the variability of these indicators, studying their correlation, and characterising the random fields variability for the considered variables (especially the correlation length). To draw any conclusion from the accelerated leaching tests with ammonium nitrate by overcoming the effects of temperature, an inverse analysis tool based on the theory of artificial neural networks was developed. Simple numerical tools are presented to investigate the propagation of variability in durability issues, quantify the influence of this variability on the lifespan of structures and explain the variability of the input parameters of the numerical model and the physical measurable quantities of the material. (author)

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

Science.gov (United States)

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Model reduction of nonlinear systems subject to input disturbances

KAUST Repository

Ndoye, Ibrahima

2017-07-10

The method of convex optimization is used as a tool for model reduction of a class of nonlinear systems in the presence of disturbances. It is shown that under some conditions the nonlinear disturbed system can be approximated by a reduced order nonlinear system with similar disturbance-output properties to the original plant. The proposed model reduction strategy preserves the nonlinearity and the input disturbance nature of the model. It guarantees a sufficiently small error between the outputs of the original and the reduced-order systems, and also maintains the properties of input-to-state stability. The matrices of the reduced order system are given in terms of a set of linear matrix inequalities (LMIs). The paper concludes with a demonstration of the proposed approach on model reduction of a nonlinear electronic circuit with additive disturbances.

Data input guide for SWIFT II. The Sandia waste-isolation flow and transport model for fractured media, Release 4.84

International Nuclear Information System (INIS)

Reeves, M.; Ward, D.S.; Johns, N.D.; Cranwell, R.M.

1986-04-01

This report is one of three which describes the SWIFT II computer code. The code simulates flow and transport processes in geologic media which may be fractured. SWIFT II was developed for use in the analysis of deep geologic facilities for nuclear-waste disposal. This user's manual should permit the analyst to use the code effectively by facilitating the preparation of input data. A second companion document discusses the theory and implementation of the models employed by the SWIFT II code. A third document provides illustrative problems for instructional purposes. This report contains detailed descriptions of the input data along with an appendix of the input diagnostics. The use of auxiliary files, unit conversions, and program variable descriptors also are included in this document

Extended Fitts' model of pointing time in eye-gaze input system - Incorporating effects of target shape and movement direction into modeling.

Science.gov (United States)

Murata, Atsuo; Fukunaga, Daichi

2018-04-01

This study attempted to investigate the effects of the target shape and the movement direction on the pointing time using an eye-gaze input system and extend Fitts' model so that these factors are incorporated into the model and the predictive power of Fitts' model is enhanced. The target shape, the target size, the movement distance, and the direction of target presentation were set as within-subject experimental variables. The target shape included: a circle, and rectangles with an aspect ratio of 1:1, 1:2, 1:3, and 1:4. The movement direction included eight directions: upper, lower, left, right, upper left, upper right, lower left, and lower right. On the basis of the data for identifying the effects of the target shape and the movement direction on the pointing time, an attempt was made to develop a generalized and extended Fitts' model that took into account the movement direction and the target shape. As a result, the generalized and extended model was found to fit better to the experimental data, and be more effective for predicting the pointing time for a variety of human-computer interaction (HCI) task using an eye-gaze input system. Copyright © 2017. Published by Elsevier Ltd.

Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

Directory of Open Access Journals (Sweden)

Szeląg Bartosz

2017-09-01

Full Text Available The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM, random forests (RF, k-nearest neighbour (k-NN and of Kernel regression (K. Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Science.gov (United States)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

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.

Description of the CONTAIN input model for the Dodewaard nuclear power plant

International Nuclear Information System (INIS)

Velema, E.J.

1992-02-01

This report describes the ECN standard CONTAIN input model for the Dodewaard Nuclear Power Plant (NPP) that has been developed by ECN. This standard input model will serve as a basis for analyses of the phenomena which may occur inside the Dodewaard containment in the event of a postulated severe accident. Boundary conditions for specific containment analyses can easily be implemented in the input model. as a result ECN will be able to respond quickly on requests for analyses from the utilities of the authorities. The report also includes brief descriptions of the Dodewaard NPP and the CONTAIN computer program. (author). 7 refs.; 5 figs.; 3 tabs

Screening important inputs in models with strong interaction properties

International Nuclear Information System (INIS)

Saltelli, Andrea; Campolongo, Francesca; Cariboni, Jessica

2009-01-01

We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs. The method proposed here represents an improvement over the best available practice for this setting when dealing with models having strong interaction effects. When the sample size is sufficiently high the same design can also be used to obtain accurate quantitative estimates of the variance-based sensitivity measures: the same simulations can be used to obtain estimates of the variance-based measures according to the Sobol' and the Jansen formulas. Results demonstrate that Sobol' is more efficient for the computation of the first-order indices, while Jansen performs better for the computation of the total indices.

Screening important inputs in models with strong interaction properties

Energy Technology Data Exchange (ETDEWEB)

Saltelli, Andrea [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy); Campolongo, Francesca [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy)], E-mail: francesca.campolongo@jrc.it; Cariboni, Jessica [European Commission, Joint Research Centre, 21020 Ispra, Varese (Italy)

2009-07-15

We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs. The method proposed here represents an improvement over the best available practice for this setting when dealing with models having strong interaction effects. When the sample size is sufficiently high the same design can also be used to obtain accurate quantitative estimates of the variance-based sensitivity measures: the same simulations can be used to obtain estimates of the variance-based measures according to the Sobol' and the Jansen formulas. Results demonstrate that Sobol' is more efficient for the computation of the first-order indices, while Jansen performs better for the computation of the total indices.

Plasticity of the cis-regulatory input function of a gene.

Directory of Open Access Journals (Sweden)

Avraham E Mayo

2006-04-01

Full Text Available The transcription rate of a gene is often controlled by several regulators that bind specific sites in the gene's cis-regulatory region. The combined effect of these regulators is described by a cis-regulatory input function. What determines the form of an input function, and how variable is it with respect to mutations? To address this, we employ the well-characterized lac operon of Escherichia coli, which has an elaborate input function, intermediate between Boolean AND-gate and OR-gate logic. We mapped in detail the input function of 12 variants of the lac promoter, each with different point mutations in the regulator binding sites, by means of accurate expression measurements from living cells. We find that even a few mutations can significantly change the input function, resulting in functions that resemble Pure AND gates, OR gates, or single-input switches. Other types of gates were not found. The variant input functions can be described in a unified manner by a mathematical model. The model also lets us predict which functions cannot be reached by point mutations. The input function that we studied thus appears to be plastic, in the sense that many of the mutations do not ruin the regulation completely but rather result in new ways to integrate the inputs.

Specification and Aggregation Errors in Environmentally Extended Input-Output Models

NARCIS (Netherlands)

Bouwmeester, Maaike C.; Oosterhaven, Jan

This article considers the specification and aggregation errors that arise from estimating embodied emissions and embodied water use with environmentally extended national input-output (IO) models, instead of with an environmentally extended international IO model. Model specification errors result

A PRODUCTIVITY EVALUATION MODEL BASED ON INPUT AND OUTPUT ORIENTATIONS

Directory of Open Access Journals (Sweden)

C.O. Anyaeche

2012-01-01

Full Text Available

ENGLISH ABSTRACT: Many productivity models evaluate either the input or the output performances using standalone techniques. This sometimes gives divergent views of the same system’s results. The work reported in this article, which simultaneously evaluated productivity from both orientations, was applied on real life data. The results showed losses in productivity (–2% and price recovery (–8% for the outputs; the inputs showed productivity gain (145% but price recovery loss (–63%. These imply losses in product performances but a productivity gain in inputs. The loss in the price recovery of inputs indicates a problem in the pricing policy. This model is applicable in product diversification.

AFRIKAANSE OPSOMMING: Die meeste produktiwiteitsmodelle evalueer of die inset- of die uitsetverrigting deur gebruik te maak van geïsoleerde tegnieke. Dit lei soms tot uiteenlopende perspektiewe van dieselfde sisteem se verrigting. Hierdie artikel evalueer verrigting uit beide perspektiewe en gebruik ware data. Die resultate toon ‘n afname in produktiwiteit (-2% en prysherwinning (-8% vir die uitsette. Die insette toon ‘n toename in produktiwiteit (145%, maar ‘n afname in prysherwinning (-63%. Dit impliseer ‘n afname in produkverrigting, maar ‘n produktiwiteitstoename in insette. Die afname in die prysherwinning van insette dui op ‘n problem in die prysvasstellingbeleid. Hierdie model is geskik vir produkdiversifikasie.

Remote Sensing Analysis of Malawi's Agricultural Inputs Subsidy and Climate Variability Impacts on Productivity

Science.gov (United States)

Galford, G. L.; Fiske, G. J.; Sedano, F.; Michelson, H.

2016-12-01

Agriculture in sub-Saharan Africa is characterized by smallholder production and low yields ( 1 ton ha-1 year-1 since records began in 1961) for staple food crops such as maize (Zea mays). Many years of low-input farming have depleted much of the region's agricultural land of critical soil carbon and nitrogen, further reducing yield potentials. Malawi is a 98,000 km2 subtropical nation with a short rainy season from November to May, with most rainfall occurring between December and mid-April. This short growing season supports the cultivation of one primary crop, maize. In Malawi, many smallholder farmers face annual nutrient deficits as nutrients removed as grain harvest and residues are beyond replenishment levels. As a result, Malawi has had stagnant maize yields averaging 1.2 ton ha-1 year-1 for decades. After multiple years of drought and widespread hunger in the early 2000s, Malawi introduced an agricultural input support program (fertilizer and seed subsidy) in time for the 2006 harvest that was designed to restore soil nutrients, improve maize production, and decrease dependence on food aid. Malawi's subsidy program targets 50-67% of smallholder farmers who cultivate half a hectare or less, yet collectively supply 80% of the country's maize. The country has achieved significant increases in crop yields (now 2 tons/ha/year) and, as our analysis shows, benefited from a new resilience against drought. We utilized Landsat time series to determine cropland extent from 2000-present and identify areas of marginal and/or intermittent production. We found a strong latitudinal gradient of precipitation variability from north to south in CHIRPS data. We used the precipitation variability to normalize trends in a productivity proxy derived from MODIS EVI. After normalization of productivity to precipitation variability, we found significant productivity trends correlated to subsidy distribution. This work was conducted with Google's Earth Engine, a cloud-based platform

Wideband Small-Signal Input dq Admittance Modeling of Six-Pulse Diode Rectifiers

DEFF Research Database (Denmark)

Yue, Xiaolong; Wang, Xiongfei; Blaabjerg, Frede

2018-01-01

This paper studies the wideband small-signal input dq admittance of six-pulse diode rectifiers. Considering the frequency coupling introduced by ripple frequency harmonics of d-and q-channel switching function, the proposed model successfully predicts the small-signal input dq admittance of six......-pulse diode rectifiers in high frequency regions that existing models fail to explain. Simulation and experimental results verify the accuracy of the proposed model....

Climatological variability in modeling of long-term regional transport and deposition of air pollutants

International Nuclear Information System (INIS)

Shannon, J.D.

1984-01-01

In a growing number of emission policy analyses, regulatory proceedings, and cost/benefit assessments, numerical models of long-range transport and deposition of air pollutants have been exercised to estimate source-receptor (S-R) relationships--for the particular meteorological conditions input to the model. The representativeness of the meteorological conditions, or the variability of the model estimates with climatological input from different years or corresponding seasons from different years, is seldom evaluated. Here, two full years (1980 and 1981) of meteorological data, as well as data from January and July of 1978, are used in the Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model (Shannon, 1981), one of the eight Memorandum of Intent (MOI) models, to estimate deposition for the S-R matrix combination of eleven source regions and nine receptors used in the MOI reports. (S-R matrices of dimensions 40 by 9 were also examined in the MOI reports.) Improvements in the ASTRAP model and in the emission inventory since the earlier work require recalculation of the two-month 1978 simulation in order for the comparison to isolate the effect of meteorological variability. The source regions are listed, and the receptor regions are provide. For completeness, an additional source region, the western states and provinces, has been added, as well as a total for the 48 contiguous states and 10 provinces. 4 references, 9 tables

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

Assessing geotechnical centrifuge modelling in addressing variably saturated flow in soil and fractured rock.

Science.gov (United States)

Jones, Brendon R; Brouwers, Luke B; Van Tonder, Warren D; Dippenaar, Matthys A

2017-05-01

The vadose zone typically comprises soil underlain by fractured rock. Often, surface water and groundwater parameters are readily available, but variably saturated flow through soil and rock are oversimplified or estimated as input for hydrological models. In this paper, a series of geotechnical centrifuge experiments are conducted to contribute to the knowledge gaps in: (i) variably saturated flow and dispersion in soil and (ii) variably saturated flow in discrete vertical and horizontal fractures. Findings from the research show that the hydraulic gradient, and not the hydraulic conductivity, is scaled for seepage flow in the geotechnical centrifuge. Furthermore, geotechnical centrifuge modelling has been proven as a viable experimental tool for the modelling of hydrodynamic dispersion as well as the replication of similar flow mechanisms for unsaturated fracture flow, as previously observed in literature. Despite the imminent challenges of modelling variable saturation in the vadose zone, the geotechnical centrifuge offers a powerful experimental tool to physically model and observe variably saturated flow. This can be used to give valuable insight into mechanisms associated with solid-fluid interaction problems under these conditions. Findings from future research can be used to validate current numerical modelling techniques and address the subsequent influence on aquifer recharge and vulnerability, contaminant transport, waste disposal, dam construction, slope stability and seepage into subsurface excavations.

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.

An introduction to latent variable growth curve modeling concepts, issues, and application

CERN Document Server

Duncan, Terry E; Strycker, Lisa A

2013-01-01

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters-growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group is...

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.

Frequency Preference Response to Oscillatory Inputs in Two-dimensional Neural Models: A Geometric Approach to Subthreshold Amplitude and Phase Resonance.

Science.gov (United States)

Rotstein, Horacio G

2014-01-01

We investigate the dynamic mechanisms of generation of subthreshold and phase resonance in two-dimensional linear and linearized biophysical (conductance-based) models, and we extend our analysis to account for the effect of simple, but not necessarily weak, types of nonlinearities. Subthreshold resonance refers to the ability of neurons to exhibit a peak in their voltage amplitude response to oscillatory input currents at a preferred non-zero (resonant) frequency. Phase-resonance refers to the ability of neurons to exhibit a zero-phase (or zero-phase-shift) response to oscillatory input currents at a non-zero (phase-resonant) frequency. We adapt the classical phase-plane analysis approach to account for the dynamic effects of oscillatory inputs and develop a tool, the envelope-plane diagrams, that captures the role that conductances and time scales play in amplifying the voltage response at the resonant frequency band as compared to smaller and larger frequencies. We use envelope-plane diagrams in our analysis. We explain why the resonance phenomena do not necessarily arise from the presence of imaginary eigenvalues at rest, but rather they emerge from the interplay of the intrinsic and input time scales. We further explain why an increase in the time-scale separation causes an amplification of the voltage response in addition to shifting the resonant and phase-resonant frequencies. This is of fundamental importance for neural models since neurons typically exhibit a strong separation of time scales. We extend this approach to explain the effects of nonlinearities on both resonance and phase-resonance. We demonstrate that nonlinearities in the voltage equation cause amplifications of the voltage response and shifts in the resonant and phase-resonant frequencies that are not predicted by the corresponding linearized model. The differences between the nonlinear response and the linear prediction increase with increasing levels of the time scale separation between

Calibration of controlling input models for pavement management system.

Science.gov (United States)

2013-07-01

The Oklahoma Department of Transportation (ODOT) is currently using the Deighton Total Infrastructure Management System (dTIMS) software for pavement management. This system is based on several input models which are computational backbones to dev...

Effect of variable heat input on the heat transfer characteristics in an Organic Rankine Cycle system

Directory of Open Access Journals (Sweden)

Aboaltabooq Mahdi Hatf Kadhum

2016-01-01

Full Text Available This paper analyzes the heat transfer characteristics of an ORC evaporator applied on a diesel engine using measured data from experimental work such as flue gas mass flow rate and flue gas temperature. A mathematical model was developed with regard to the preheater, boiler and the superheater zones of a counter flow evaporator. Each of these zones has been subdivided into a number of cells. The hot source of the ORC cycle was modeled. The study involves the variable heat input's dependence on the ORC system's heat transfer characteristics, with especial emphasis on the evaporator. The results show that the refrigerant's heat transfer coefficient has a higher value for a 100% load from the diesel engine, and decreases with the load decrease. Also, on the exhaust gas side, the heat transfer coefficient decreases with the decrease of the load. The refrigerant's heat transfer coefficient increased normally with the evaporator's tube length in the preheater zone, and then increases rapidly in the boiler zone, followed by a decrease in the superheater zone. The exhaust gases’ heat transfer coefficient increased with the evaporator’ tube length in all zones. The results were compared with result by other authors and were found to be in agreement.

Quality assurance of weather data for agricultural system model input

Science.gov (United States)

It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

Input-output interactions and optimal monetary policy

DEFF Research Database (Denmark)

Petrella, Ivan; Santoro, Emiliano

2011-01-01

This paper deals with the implications of factor demand linkages for monetary policy design in a two-sector dynamic general equilibrium model. Part of the output of each sector serves as a production input in both sectors, in accordance with a realistic input–output structure. Strategic...... complementarities induced by factor demand linkages significantly alter the transmission of shocks and amplify the loss of social welfare under optimal monetary policy, compared to what is observed in standard two-sector models. The distinction between value added and gross output that naturally arises...... in this context is of key importance to explore the welfare properties of the model economy. A flexible inflation targeting regime is close to optimal only if the central bank balances inflation and value added variability. Otherwise, targeting gross output variability entails a substantial increase in the loss...

Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control.

Science.gov (United States)

Kotz, Kaylee; Cinar, Ali; Mei, Yong; Roggendorf, Amy; Littlejohn, Elizabeth; Quinn, Laurie; Rollins, Derrick K

2014-11-26

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.

Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

Directory of Open Access Journals (Sweden)

G Khalili-Zadeh-Mahani

2016-07-01

Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

Development of the MARS input model for Kori nuclear units 1 transient analyzer

International Nuclear Information System (INIS)

Hwang, M.; Kim, K. D.; Lee, S. W.; Lee, Y. J.; Lee, W. J.; Chung, B. D.; Jeong, J. J.

2004-11-01

KAERI has been developing the 'NSSS transient analyzer' based on best-estimate codes for Kori Nuclear Units 1 plants. The MARS and RETRAN codes have been used as the best-estimate codes for the NSSS transient analyzer. Among these codes, the MARS code is adopted for realistic analysis of small- and large-break loss-of-coolant accidents, of which break size is greater than 2 inch diameter. So it is necessary to develop the MARS input model for Kori Nuclear Units 1 plants. This report includes the input model (hydrodynamic component and heat structure models) requirements and the calculation note for the MARS input data generation for Kori Nuclear Units 1 plant analyzer (see the Appendix). In order to confirm the validity of the input data, we performed the calculations for a steady state at 100 % power operation condition and a double-ended cold leg break LOCA. The results of the steady-state calculation agree well with the design data. The results of the LOCA calculation seem to be reasonable and consistent with those of other best-estimate calculations. Therefore, the MARS input data can be used as a base input deck for the MARS transient analyzer for Kori Nuclear Units 1

Development of the RETRAN input model for Ulchin 3/4 visual system analyzer

International Nuclear Information System (INIS)

Lee, S. W.; Kim, K. D.; Lee, Y. J.; Lee, W. J.; Chung, B. D.; Jeong, J. J.; Hwang, M. K.

2004-01-01

As a part of the Long-Term Nuclear R and D program, KAERI has developed the so-called Visual System Analyzer (ViSA) based on best-estimate codes. The MARS and RETRAN codes are used as the best-estimate codes for ViSA. Between these two codes, the RETRAN code is used for realistic analysis of Non-LOCA transients and small-break loss-of-coolant accidents, of which break size is less than 3 inch diameter. So it is necessary to develop the RETRAN input model for Ulchin 3/4 plants (KSNP). In recognition of this, the RETRAN input model for Ulchin 3/4 plants has been developed. This report includes the input model requirements and the calculation note for the input data generation (see the Appendix). In order to confirm the validity of the input data, the calculations are performed for a steady state at 100 % power operation condition, inadvertent reactor trip and RCP trip. The results of the steady-state calculation agree well with the design data. The results of the other transient calculations seem to be reasonable and consistent with those of other best-estimate calculations. Therefore, the RETRAN input data can be used as a base input deck for the RETRAN transient analyzer for Ulchin 3/4. Moreover, it is found that Core Protection Calculator (CPC) module, which is modified by Korea Electric Power Research Institute (KEPRI), is well adapted to ViSA

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

A Design Method of Robust Servo Internal Model Control with Control Input Saturation

OpenAIRE

山田, 功; 舩見, 洋祐

2001-01-01

In the present paper, we examine a design method of robust servo Internal Model Control with control input saturation. First of all, we clarify the condition that Internal Model Control has robust servo characteristics for the system with control input saturation. From this consideration, we propose new design method of Internal Model Control with robust servo characteristics. A numerical example to illustrate the effectiveness of the proposed method is shown.

Evaluation of globally available precipitation data products as input for water balance models

Science.gov (United States)

Lebrenz, H.; Bárdossy, A.

2009-04-01

Subject of this study is the evaluation of globally available precipitation data products, which are intended to be used as input variables for water balance models in ungauged basins. The selected data sources are a) the Global Precipitation Climatology Centre (GPCC), b) the Global Precipitation Climatology Project (GPCP) and c) the Climate Research Unit (CRU), resulting into twelve globally available data products. The data products imply different data bases, different derivation routines and varying resolutions in time and space. For validation purposes, the ground data from South Africa were screened on homogeneity and consistency by various tests and an outlier detection using multi-linear regression was performed. External Drift Kriging was subsequently applied on the ground data and the resulting precipitation arrays were compared to the different products with respect to quantity and variance.

Evaluating the uncertainty of input quantities in measurement models

Science.gov (United States)

Possolo, Antonio; Elster, Clemens

2014-06-01

The Guide to the Expression of Uncertainty in Measurement (GUM) gives guidance about how values and uncertainties should be assigned to the input quantities that appear in measurement models. This contribution offers a concrete proposal for how that guidance may be updated in light of the advances in the evaluation and expression of measurement uncertainty that were made in the course of the twenty years that have elapsed since the publication of the GUM, and also considering situations that the GUM does not yet contemplate. Our motivation is the ongoing conversation about a new edition of the GUM. While generally we favour a Bayesian approach to uncertainty evaluation, we also recognize the value that other approaches may bring to the problems considered here, and focus on methods for uncertainty evaluation and propagation that are widely applicable, including to cases that the GUM has not yet addressed. In addition to Bayesian methods, we discuss maximum-likelihood estimation, robust statistical methods, and measurement models where values of nominal properties play the same role that input quantities play in traditional models. We illustrate these general-purpose techniques in concrete examples, employing data sets that are realistic but that also are of conveniently small sizes. The supplementary material available online lists the R computer code that we have used to produce these examples (stacks.iop.org/Met/51/3/339/mmedia). Although we strive to stay close to clause 4 of the GUM, which addresses the evaluation of uncertainty for input quantities, we depart from it as we review the classes of measurement models that we believe are generally useful in contemporary measurement science. We also considerably expand and update the treatment that the GUM gives to Type B evaluations of uncertainty: reviewing the state-of-the-art, disciplined approach to the elicitation of expert knowledge, and its encapsulation in probability distributions that are usable in

Analysis of Design Variables of Annular Linear Induction Electromagnetic Pump using an MHD Model

Energy Technology Data Exchange (ETDEWEB)

Kwak, Jae Sik; Kim, Hee Reyoung [Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

2015-05-15

The generated force is affected by lots of factors including electrical input, hydrodynamic flow, geometrical shape, and so on. These factors, which are the design variables of an ALIP, should be suitably analyzed to optimally design an ALIP. Analysis on the developed pressure and efficiency of the ALIP according to the change of design variables is required for the ALIP satisfying requirements. In this study, the design variables of the ALIP are analyzed by using ideal MHD analysis model. Electromagnetic force and efficiency are derived by analyzing the main design variables such as pump core length, inner core diameter, flow gap and turns of coils. The developed pressure and efficiency of the ALIP were derived and analyzed on the change of the main variables such as pump core length, inner core diameter, flow gap, and turns of coils of the ALIP.

CONSTRUCTION OF A DYNAMIC INPUT-OUTPUT MODEL WITH A HUMAN CAPITAL BLOCK

Directory of Open Access Journals (Sweden)

Baranov A. O.

2017-03-01

Full Text Available The accumulation of human capital is an important factor of economic growth. It seems to be useful to include «human capital» as a factor of a macroeconomic model, as it helps to take into account the quality differentiation of the workforce. Most of the models usually distinguish labor force by the levels of education, while some of the factors remain unaccounted. Among them are health status and culture development level, which influence productivity level as well as gross product reproduction. Inclusion of the human capital block to the interindustry model can help to make it more reliable for economic development forecasting. The article presents a mathematical description of the extended dynamic input-output model (DIOM with a human capital block. The extended DIOM is based on the Input-Output Model from The KAMIN system (the System of Integrated Analyses of Interindustrial Information developed at the Institute of Economics and Industrial Engineering of the Siberian Branch of the Academy of Sciences of the Russian Federation and at the Novosibirsk State University. The extended input-output model can be used to analyze and forecast development of Russian economy.

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.

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.

GASFLOW computer code (physical models and input data)

International Nuclear Information System (INIS)

Muehlbauer, Petr

2007-11-01

The GASFLOW computer code was developed jointly by the Los Alamos National Laboratory, USA, and Forschungszentrum Karlsruhe, Germany. The code is primarily intended for calculations of the transport, mixing, and combustion of hydrogen and other gases in nuclear reactor containments and in other facilities. The physical models and the input data are described, and a commented simple calculation is presented

Recurrent network models for perfect temporal integration of fluctuating correlated inputs.

Directory of Open Access Journals (Sweden)

Hiroshi Okamoto

2009-06-01

Full Text Available Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

Short-term to seasonal variability in factors driving primary productivity in a shallow estuary: Implications for modeling production

Science.gov (United States)

Canion, Andy; MacIntyre, Hugh L.; Phipps, Scott

2013-10-01

The inputs of primary productivity models may be highly variable on short timescales (hourly to daily) in turbid estuaries, but modeling of productivity in these environments is often implemented with data collected over longer timescales. Daily, seasonal, and spatial variability in primary productivity model parameters: chlorophyll a concentration (Chla), the downwelling light attenuation coefficient (kd), and photosynthesis-irradiance response parameters (Pmchl, αChl) were characterized in Weeks Bay, a nitrogen-impacted shallow estuary in the northern Gulf of Mexico. Variability in primary productivity model parameters in response to environmental forcing, nutrients, and microalgal taxonomic marker pigments were analysed in monthly and short-term datasets. Microalgal biomass (as Chla) was strongly related to total phosphorus concentration on seasonal scales. Hourly data support wind-driven resuspension as a major source of short-term variability in Chla and light attenuation (kd). The empirical relationship between areal primary productivity and a combined variable of biomass and light attenuation showed that variability in the photosynthesis-irradiance response contributed little to the overall variability in primary productivity, and Chla alone could account for 53-86% of the variability in primary productivity. Efforts to model productivity in similar shallow systems with highly variable microalgal biomass may benefit the most by investing resources in improving spatial and temporal resolution of chlorophyll a measurements before increasing the complexity of models used in productivity modeling.

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.

Modeling and optimization of process variables of wire-cut electric discharge machining of super alloy Udimet-L605

Directory of Open Access Journals (Sweden)

Somvir Singh Nain

2017-02-01

Full Text Available This paper presents the behavior of Udimet-L605 after wire electric discharge machining and evaluating the WEDM process using sophisticated machine learning approaches. The experimental work is depicted on the basis of Taguchi orthogonal L27 array, considering six input variables and three interactions. Three models such as support vector machine algorithms based on PUK kernel, non-linear regression and multi-linear regression have been proposed to examine the variance between experimental and predicted outcome and preferred the preeminent model based on its evaluation parameters performance and graph analysis. The grey relational analysis is the relevant approach to obtain the best grouping of input variables for maximum material removal rate and minimum surface roughness. Based on statistical analysis, it has been concluded that pulse-on time, interaction between pulse-on time x pulse-off time, spark-gap voltage and wire tension are the momentous variable for surface roughness while the pulse-on time, spark-gap voltage and pulse-off time are the momentous variables for material removal rate. The micro structural and compositional changes on the surface of work material were examined by means of SEM and EDX analysis. The thickness of the white layer and the recast layer formation increases with increases in the pulse-on time duration.

Development of an Input Suite for an Orthotropic Composite Material Model

Science.gov (United States)

Hoffarth, Canio; Shyamsunder, Loukham; Khaled, Bilal; Rajan, Subramaniam; Goldberg, Robert K.; Carney, Kelly S.; Dubois, Paul; Blankenhorn, Gunther

2017-01-01

An orthotropic three-dimensional material model suitable for use in modeling impact tests has been developed that has three major components elastic and inelastic deformations, damage and failure. The material model has been implemented as MAT213 into a special version of LS-DYNA and uses tabulated data obtained from experiments. The prominent features of the constitutive model are illustrated using a widely-used aerospace composite the T800S3900-2B[P2352W-19] BMS8-276 Rev-H-Unitape fiber resin unidirectional composite. The input for the deformation model consists of experimental data from 12 distinct experiments at a known temperature and strain rate: tension and compression along all three principal directions, shear in all three principal planes, and off axis tension or compression tests in all three principal planes, along with other material constants. There are additional input associated with the damage and failure models. The steps in using this model are illustrated composite characterization tests, verification tests and a validation test. The results show that the developed and implemented model is stable and yields acceptably accurate results.

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

Input vs. Output Taxation—A DSGE Approach to Modelling Resource Decoupling

Directory of Open Access Journals (Sweden)

Marek Antosiewicz

2016-04-01

Full Text Available Environmental taxes constitute a crucial instrument aimed at reducing resource use through lower production losses, resource-leaner products, and more resource-efficient production processes. In this paper we focus on material use and apply a multi-sector dynamic stochastic general equilibrium (DSGE model to study two types of taxation: tax on material inputs used by industry, energy, construction, and transport sectors, and tax on output of these sectors. We allow for endogenous adoption of resource-saving technologies. We calibrate the model for the EU27 area using an IO matrix. We consider taxation introduced from 2021 and simulate its impact until 2050. We compare the taxes along their ability to induce reduction in material use and raise revenue. We also consider the effect of spending this revenue on reduction of labour taxation. We find that input and output taxation create contrasting incentives and have opposite effects on resource efficiency. The material input tax induces investment in efficiency-improving technology which, in the long term, results in GDP and employment by 15%–20% higher than in the case of a comparable output tax. We also find that using revenues to reduce taxes on labour has stronger beneficial effects for the input tax.

Modeling the short-run effect of fiscal stimuli on GDP : A new semi-closed input-output model

NARCIS (Netherlands)

Chen, Quanrun; Dietzenbacher, Erik; Los, Bart; Yang, Cuihong

In this study, we propose a new semi-closed input-output model, which reconciles input-output analysis with modern consumption theories. It can simulate changes in household consumption behavior when exogenous stimulus policies lead to higher disposable income levels. It is useful for quantifying

Modeling the short-run effect of fiscal stimuli on GDP : A new semi-closed input-output model

NARCIS (Netherlands)

Chen, Quanrun; Dietzenbacher, Erik; Los, Bart; Yang, Cuihong

2016-01-01

In this study, we propose a new semi-closed input-output model, which reconciles input-output analysis with modern consumption theories. It can simulate changes in household consumption behavior when exogenous stimulus policies lead to higher disposable income levels. It is useful for quantifying

Environmental Transport Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. A. Wasiolek

2003-06-27

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699

Environmental Transport Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Wasiolek, M. A.

2003-01-01

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699], Section 6.2). Parameter values

Input modelling of ASSERT-PV V2R8M1 for RUFIC fuel bundle

Energy Technology Data Exchange (ETDEWEB)

Park, Joo Hwan; Suk, Ho Chun

2001-02-01

This report describes the input modelling for subchannel analysis of CANFLEX-RU (RUFIC) fuel bundle which has been developed for an advanced fuel bundle of CANDU-6 reactor, using ASSERT-PV V2R8M1 code. Execution file of ASSERT-PV V2R8M1 code was recently transferred from AECL under JRDC agreement between KAERI and AECL. SSERT-PV V2R8M1 which is quite different from COBRA-IV-i code has been developed for thermalhydraulic analysis of CANDU-6 fuel channel by subchannel analysis method and updated so that 43-element CANDU fuel geometry can be applied. Hence, ASSERT code can be applied to the subchannel analysis of RUFIC fuel bundle. The present report was prepared for ASSERT input modelling of RUFIC fuel bundle. Since the ASSERT results highly depend on user's input modelling, the calculation results may be quite different among the user's input models. The objective of the present report is the preparation of detail description of the background information for input data and gives credibility of the calculation results.

Input modelling of ASSERT-PV V2R8M1 for RUFIC fuel bundle

International Nuclear Information System (INIS)

Park, Joo Hwan; Suk, Ho Chun

2001-02-01

This report describes the input modelling for subchannel analysis of CANFLEX-RU (RUFIC) fuel bundle which has been developed for an advanced fuel bundle of CANDU-6 reactor, using ASSERT-PV V2R8M1 code. Execution file of ASSERT-PV V2R8M1 code was recently transferred from AECL under JRDC agreement between KAERI and AECL. SSERT-PV V2R8M1 which is quite different from COBRA-IV-i code has been developed for thermalhydraulic analysis of CANDU-6 fuel channel by subchannel analysis method and updated so that 43-element CANDU fuel geometry can be applied. Hence, ASSERT code can be applied to the subchannel analysis of RUFIC fuel bundle. The present report was prepared for ASSERT input modelling of RUFIC fuel bundle. Since the ASSERT results highly depend on user's input modelling, the calculation results may be quite different among the user's input models. The objective of the present report is the preparation of detail description of the background information for input data and gives credibility of the calculation results

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.

Modeling and Control of a Dual-Input Isolated Full-Bridge Boost Converter

DEFF Research Database (Denmark)

Zhang, Zhe; Thomsen, Ole Cornelius; Andersen, Michael A. E.

2012-01-01

In this paper, a steady-state model, a large-signal (LS) model and an ac small-signal (SS) model for a recently proposed dual-input transformer-isolated boost converter are derived respectively by the switching flow-graph (SFG) nonlinear modeling technique. Based upon the converter’s model...

The use of synthetic input sequences in time series modeling

International Nuclear Information System (INIS)

Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.

2008-01-01

In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure

Logistics flows and enterprise input-output models: aggregate and disaggregate analysis

NARCIS (Netherlands)

Albino, V.; Yazan, Devrim; Messeni Petruzzelli, A.; Okogbaa, O.G.

2011-01-01

In the present paper, we propose the use of enterprise input-output (EIO) models to describe and analyse the logistics flows considering spatial issues and related environmental effects associated with production and transportation processes. In particular, transportation is modelled as a specific

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.

The MARINA model (Model to Assess River Inputs of Nutrients to seAs)

NARCIS (Netherlands)

Strokal, Maryna; Kroeze, Carolien; Wang, Mengru; Bai, Zhaohai; Ma, Lin

2016-01-01

Chinese agriculture has been developing fast towards industrial food production systems that discharge nutrient-rich wastewater into rivers. As a result, nutrient export by rivers has been increasing, resulting in coastal water pollution. We developed a Model to Assess River Inputs of Nutrients

Calibration of uncertain inputs to computer models using experimentally measured quantities and the BMARS emulator

International Nuclear Information System (INIS)

Stripling, H.F.; McClarren, R.G.; Kuranz, C.C.; Grosskopf, M.J.; Rutter, E.; Torralva, B.R.

2011-01-01

We present a method for calibrating the uncertain inputs to a computer model using available experimental data. The goal of the procedure is to produce posterior distributions of the uncertain inputs such that when samples from the posteriors are used as inputs to future model runs, the model is more likely to replicate (or predict) the experimental response. The calibration is performed by sampling the space of the uncertain inputs, using the computer model (or, more likely, an emulator for the computer model) to assign weights to the samples, and applying the weights to produce the posterior distributions and generate predictions of new experiments within confidence bounds. The method is similar to the Markov chain Monte Carlo (MCMC) calibration methods with independent sampling with the exception that we generate samples beforehand and replace the candidate acceptance routine with a weighting scheme. We apply our method to the calibration of a Hyades 2D model of laser energy deposition in beryllium. We employ a Bayesian Multivariate Adaptive Regression Splines (BMARS) emulator as a surrogate for Hyades 2D. We treat a range of uncertainties in our system, including uncertainties in the experimental inputs, experimental measurement error, and systematic experimental timing errors. The results of the calibration are posterior distributions that both agree with intuition and improve the accuracy and decrease the uncertainty in experimental predictions. (author)

Modelling a variable valve timing spark ignition engine using different neural networks

Energy Technology Data Exchange (ETDEWEB)

Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group

2004-10-01

In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)

Input modelling of ASSERT-PV V2R8M1 for RUFIC fuel bundle

Energy Technology Data Exchange (ETDEWEB)

Park, Joo Hwan; Suk, Ho Chun

2001-02-01

This report describes the input modelling for subchannel analysis of CANFLEX-RU (RUFIC) fuel bundle which has been developed for an advanced fuel bundle of CANDU-6 reactor, using ASSERT-PV V2R8M1 code. Execution file of ASSERT-PV V2R8M1 code was recently transferred from AECL under JRDC agreement between KAERI and AECL. SSERT-PV V2R8M1 which is quite different from COBRA-IV-i code has been developed for thermalhydraulic analysis of CANDU-6 fuel channel by subchannel analysis method and updated so that 43-element CANDU fuel geometry can be applied. Hence, ASSERT code can be applied to the subchannel analysis of RUFIC fuel bundle. The present report was prepared for ASSERT input modelling of RUFIC fuel bundle. Since the ASSERT results highly depend on user's input modelling, the calculation results may be quite different among the user's input models. The objective of the present report is the preparation of detail description of the background information for input data and gives credibility of the calculation results.

VSC Input-Admittance Modeling and Analysis Above the Nyquist Frequency for Passivity-Based Stability Assessment

DEFF Research Database (Denmark)

Harnefors, Lennart; Finger, Raphael; Wang, Xiongfei

2017-01-01

The interconnection stability of a gridconnected voltage-source converter (VSC) can be assessed via the dissipative properties of its input admittance. In this paper, the modeling of the current control loop is revisited with the aim to improve the accuracy of the input-admittance model above...

COGEDIF - automatic TORT and DORT input generation from MORSE combinatorial geometry models

International Nuclear Information System (INIS)

Castelli, R.A.; Barnett, D.A.

1992-01-01

COGEDIF is an interactive utility which was developed to automate the preparation of two and three dimensional geometrical inputs for the ORNL-TORT and DORT discrete ordinates programs from complex three dimensional models described using the MORSE combinatorial geometry input description. The program creates either continuous or disjoint mesh input based upon the intersections of user defined meshing planes and the MORSE body definitions. The composition overlay of the combinatorial geometry is used to create the composition mapping of the discretized geometry based upon the composition found at the centroid of each of the mesh cells. This program simplifies the process of using discrete orthogonal mesh cells to represent non-orthogonal geometries in large models which require mesh sizes of the order of a million cells or more. The program was specifically written to take advantage of the new TORT disjoint mesh option which was developed at ORNL

Comprehensive Information Retrieval and Model Input Sequence (CIRMIS)

International Nuclear Information System (INIS)

Friedrichs, D.R.

1977-04-01

The Comprehensive Information Retrieval and Model Input Sequence (CIRMIS) was developed to provide the research scientist with man--machine interactive capabilities in a real-time environment, and thereby produce results more quickly and efficiently. The CIRMIS system was originally developed to increase data storage and retrieval capabilities and ground-water model control for the Hanford site. The overall configuration, however, can be used in other areas. The CIRMIS system provides the user with three major functions: retrieval of well-based data, special application for manipulating surface data or background maps, and the manipulation and control of ground-water models. These programs comprise only a portion of the entire CIRMIS system. A complete description of the CIRMIS system is given in this report. 25 figures, 7 tables

Global modeling of land water and energy balances. Part III: Interannual variability

Science.gov (United States)

Shmakin, A.B.; Milly, P.C.D.; Dunne, K.A.

2002-01-01

The Land Dynamics (LaD) model is tested by comparison with observations of interannual variations in discharge from 44 large river basins for which relatively accurate time series of monthly precipitation (a primary model input) have recently been computed. When results are pooled across all basins, the model explains 67% of the interannual variance of annual runoff ratio anomalies (i.e., anomalies of annual discharge volume, normalized by long-term mean precipitation volume). The new estimates of basin precipitation appear to offer an improvement over those from a state-of-the-art analysis of global precipitation (the Climate Prediction Center Merged Analysis of Precipitation, CMAP), judging from comparisons of parallel model runs and of analyses of precipitation-discharge correlations. When the new precipitation estimates are used, the performance of the LaD model is comparable to, but not significantly better than, that of a simple, semiempirical water-balance relation that uses only annual totals of surface net radiation and precipitation. This implies that the LaD simulations of interannual runoff variability do not benefit substantially from information on geographical variability of land parameters or seasonal structure of interannual variability of precipitation. The aforementioned analyses necessitated the development of a method for downscaling of long-term monthly precipitation data to the relatively short timescales necessary for running the model. The method merges the long-term data with a reference dataset of 1-yr duration, having high temporal resolution. The success of the method, for the model and data considered here, was demonstrated in a series of model-model comparisons and in the comparisons of modeled and observed interannual variations of basin discharge.

Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm.

Science.gov (United States)

Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S

2012-12-01

Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost. Copyright © 2012 Elsevier Ltd. All rights reserved.

System Identification for Nonlinear FOPDT Model with Input-Dependent Dead-Time

DEFF Research Database (Denmark)

Sun, Zhen; Yang, Zhenyu

2011-01-01

An on-line iterative method of system identification for a kind of nonlinear FOPDT system is proposed in the paper. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its dead time depends on the input signal and the other parameters are time dependent....

Effect of input variability on the quality of laser shock processing

Energy Technology Data Exchange (ETDEWEB)

Arif, Abul Fazal M. [King Fahd University of Petroleum and Minerals, Dhahran (Saudi Arabia)

2009-10-15

Laser shock processing (LSP) involves high-energy laser radiation combined with suitable overlays to generate highpressure pulses on the surface of the metal. The stress wave generated due to high pressure pulses propagates into the material causing the surface layer to yield and plastically deform, and thereby, develop a significant residual compressive stress in the surface region of the substrate material. The developed compressive stress field is beneficial to improve surface properties such as fatigue, wear, and corrosion. To improve the understanding of the shock hardening process, investigation into the physical processes involved is necessary. In the first part of this paper, the temporal variation in the pressure intensity and spot size is calculated by using a two-dimensional recoil pressure prediction model. Using an explicit non-linear FEA code, ANSYS LS-DYNA, the deformation behavior and residual stresses in the substrate material are predicted. In the second part, a probabilistic approach to the modeling and analysis of LSP is presented in this paper. Various factors that affect the probabilistic performance of the LSP are grouped into categories and a select number of factors known to be significant, for which the variability could be assessed, are modeled as random variables (such as recoil pressure, laser beam spot size, substrate material properties and others). The potential of the probabilistic approach in predicting the structural integrity of the laser-shocked components is addressed

Effect of input variability on the quality of laser shock processing

International Nuclear Information System (INIS)

Arif, Abul Fazal M.

2009-01-01

Laser shock processing (LSP) involves high-energy laser radiation combined with suitable overlays to generate highpressure pulses on the surface of the metal. The stress wave generated due to high pressure pulses propagates into the material causing the surface layer to yield and plastically deform, and thereby, develop a significant residual compressive stress in the surface region of the substrate material. The developed compressive stress field is beneficial to improve surface properties such as fatigue, wear, and corrosion. To improve the understanding of the shock hardening process, investigation into the physical processes involved is necessary. In the first part of this paper, the temporal variation in the pressure intensity and spot size is calculated by using a two-dimensional recoil pressure prediction model. Using an explicit non-linear FEA code, ANSYS LS-DYNA, the deformation behavior and residual stresses in the substrate material are predicted. In the second part, a probabilistic approach to the modeling and analysis of LSP is presented in this paper. Various factors that affect the probabilistic performance of the LSP are grouped into categories and a select number of factors known to be significant, for which the variability could be assessed, are modeled as random variables (such as recoil pressure, laser beam spot size, substrate material properties and others). The potential of the probabilistic approach in predicting the structural integrity of the laser-shocked components is addressed

Input and language development in bilingually developing children.

Science.gov (United States)

Hoff, Erika; Core, Cynthia

2013-11-01

Language skills in young bilingual children are highly varied as a result of the variability in their language experiences, making it difficult for speech-language pathologists to differentiate language disorder from language difference in bilingual children. Understanding the sources of variability in bilingual contexts and the resulting variability in children's skills will help improve language assessment practices by speech-language pathologists. In this article, we review literature on bilingual first language development for children under 5 years of age. We describe the rate of development in single and total language growth, we describe effects of quantity of input and quality of input on growth, and we describe effects of family composition on language input and language growth in bilingual children. We provide recommendations for language assessment of young bilingual children and consider implications for optimizing children's dual language development. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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.

Input/Output linearizing control of a nuclear reactor

International Nuclear Information System (INIS)

Perez C, V.

1994-01-01

The feedback linearization technique is an approach to nonlinear control design. The basic idea is to transform, by means of algebraic methods, the dynamics of a nonlinear control system into a full or partial linear system. As a result of this linearization process, the well known basic linear control techniques can be used to obtain some desired dynamic characteristics. When full linearization is achieved, the method is referred to as input-state linearization, whereas when partial linearization is achieved, the method is referred to as input-output linearization. We will deal with the latter. By means of input-output linearization, the dynamics of a nonlinear system can be decomposed into an external part (input-output), and an internal part (unobservable). Since the external part consists of a linear relationship among the output of the plant and the auxiliary control input mentioned above, it is easy to design such an auxiliary control input so that we get the output to behave in a predetermined way. Since the internal dynamics of the system is known, we can check its dynamics behavior on order of to ensure that the internal states are bounded. The linearization method described here can be applied to systems with one-input/one-output, as well as to systems with multiple-inputs/multiple-outputs. Typical control problems such as stabilization and reference path tracking can be solved using this technique. In this work, the input/output linearization theory is presented, as well as the problem of getting the output variable to track some desired trayectories. Further, the design of an input/output control system applied to the nonlinear model of a research nuclear reactor is included, along with the results obtained by computer simulation. (Author)

Development of the MARS input model for Ulchin 1/2 transient analyzer

International Nuclear Information System (INIS)

Jeong, J. J.; Kim, K. D.; Lee, S. W.; Lee, Y. J.; Chung, B. D.; Hwang, M.

2003-03-01

KAERI has been developing the NSSS transient analyzer based on best-estimate codes for Ulchin 1/2 plants. The MARS and RETRAN code are used as the best-estimate codes for the NSSS transient analyzer. Among the two codes, the MARS code is to be used for realistic analysis of small- and large-break loss-of-coolant accidents, of which break size is greater than 2 inch diameter. This report includes the input model requirements and the calculation note for the Ulchin 1/2 MARS input data generation (see the Appendix). In order to confirm the validity of the input data, we performed the calculations for a steady state at 100 % power operation condition and a double-ended cold leg break LOCA. The results of the steady-state calculation agree well with the design data. The results of the LOCA calculation seem to be reasonable and consistent with those of other best-estimate calculations. Therefore, the MARS input data can be used as a base input deck for the MARS transient analyzer for Ulchin 1/2

Development of the MARS input model for Ulchin 3/4 transient analyzer

International Nuclear Information System (INIS)

Jeong, J. J.; Kim, K. D.; Lee, S. W.; Lee, Y. J.; Lee, W. J.; Chung, B. D.; Hwang, M. G.

2003-12-01

KAERI has been developing the NSSS transient analyzer based on best-estimate codes.The MARS and RETRAN code are adopted as the best-estimate codes for the NSSS transient analyzer. Among these two codes, the MARS code is to be used for realistic analysis of small- and large-break loss-of-coolant accidents, of which break size is greater than 2 inch diameter. This report includes the MARS input model requirements and the calculation note for the MARS input data generation (see the Appendix) for Ulchin 3/4 plant analyzer. In order to confirm the validity of the input data, we performed the calculations for a steady state at 100 % power operation condition and a double-ended cold leg break LOCA. The results of the steady-state calculation agree well with the design data. The results of the LOCA calculation seem to be reasonable and consistent with those of other best-estimate calculations. Therefore, the MARS input data can be used as a base input deck for the MARS transient analyzer for Ulchin 3/4

Quantifying variability in earthquake rupture models using multidimensional scaling: application to the 2011 Tohoku earthquake

KAUST Repository

Razafindrakoto, Hoby

2015-04-22

Finite-fault earthquake source inversion is an ill-posed inverse problem leading to non-unique solutions. In addition, various fault parametrizations and input data may have been used by different researchers for the same earthquake. Such variability leads to large intra-event variability in the inferred rupture models. One way to understand this problem is to develop robust metrics to quantify model variability. We propose a Multi Dimensional Scaling (MDS) approach to compare rupture models quantitatively. We consider normalized squared and grey-scale metrics that reflect the variability in the location, intensity and geometry of the source parameters. We test the approach on two-dimensional random fields generated using a von Kármán autocorrelation function and varying its spectral parameters. The spread of points in the MDS solution indicates different levels of model variability. We observe that the normalized squared metric is insensitive to variability of spectral parameters, whereas the grey-scale metric is sensitive to small-scale changes in geometry. From this benchmark, we formulate a similarity scale to rank the rupture models. As case studies, we examine inverted models from the Source Inversion Validation (SIV) exercise and published models of the 2011 Mw 9.0 Tohoku earthquake, allowing us to test our approach for a case with a known reference model and one with an unknown true solution. The normalized squared and grey-scale metrics are respectively sensitive to the overall intensity and the extension of the three classes of slip (very large, large, and low). Additionally, we observe that a three-dimensional MDS configuration is preferable for models with large variability. We also find that the models for the Tohoku earthquake derived from tsunami data and their corresponding predictions cluster with a systematic deviation from other models. We demonstrate the stability of the MDS point-cloud using a number of realizations and jackknife tests, for

Quantifying variability in earthquake rupture models using multidimensional scaling: application to the 2011 Tohoku earthquake

KAUST Repository

Razafindrakoto, Hoby; Mai, Paul Martin; Genton, Marc G.; Zhang, Ling; Thingbaijam, Kiran Kumar

2015-01-01

Finite-fault earthquake source inversion is an ill-posed inverse problem leading to non-unique solutions. In addition, various fault parametrizations and input data may have been used by different researchers for the same earthquake. Such variability leads to large intra-event variability in the inferred rupture models. One way to understand this problem is to develop robust metrics to quantify model variability. We propose a Multi Dimensional Scaling (MDS) approach to compare rupture models quantitatively. We consider normalized squared and grey-scale metrics that reflect the variability in the location, intensity and geometry of the source parameters. We test the approach on two-dimensional random fields generated using a von Kármán autocorrelation function and varying its spectral parameters. The spread of points in the MDS solution indicates different levels of model variability. We observe that the normalized squared metric is insensitive to variability of spectral parameters, whereas the grey-scale metric is sensitive to small-scale changes in geometry. From this benchmark, we formulate a similarity scale to rank the rupture models. As case studies, we examine inverted models from the Source Inversion Validation (SIV) exercise and published models of the 2011 Mw 9.0 Tohoku earthquake, allowing us to test our approach for a case with a known reference model and one with an unknown true solution. The normalized squared and grey-scale metrics are respectively sensitive to the overall intensity and the extension of the three classes of slip (very large, large, and low). Additionally, we observe that a three-dimensional MDS configuration is preferable for models with large variability. We also find that the models for the Tohoku earthquake derived from tsunami data and their corresponding predictions cluster with a systematic deviation from other models. We demonstrate the stability of the MDS point-cloud using a number of realizations and jackknife tests, for

Sensitivity Analysis of Input Parameters for a Dynamic Food Chain Model DYNACON

International Nuclear Information System (INIS)

Hwang, Won Tae; Lee, Geun Chang; Han, Moon Hee; Cho, Gyu Seong

2000-01-01

The sensitivity analysis of input parameters for a dynamic food chain model DYNACON was conducted as a function of deposition data for the long-lived radionuclides ( 137 Cs, 90 Sr). Also, the influence of input parameters for the short and long-terms contamination of selected foodstuffs (cereals, leafy vegetables, milk) was investigated. The input parameters were sampled using the LHS technique, and their sensitivity indices represented as PRCC. The sensitivity index was strongly dependent on contamination period as well as deposition data. In case of deposition during the growing stages of plants, the input parameters associated with contamination by foliar absorption were relatively important in long-term contamination as well as short-term contamination. They were also important in short-term contamination in case of deposition during the non-growing stages. In long-term contamination, the influence of input parameters associated with foliar absorption decreased, while the influence of input parameters associated with root uptake increased. These phenomena were more remarkable in case of the deposition of non-growing stages than growing stages, and in case of 90 Sr deposition than 137 Cs deposition. In case of deposition during growing stages of pasture, the input parameters associated with the characteristics of cattle such as feed-milk transfer factor and daily intake rate of cattle were relatively important in contamination of milk

Modeling of heat transfer into a heat pipe for a localized heat input zone

International Nuclear Information System (INIS)

Rosenfeld, J.H.

1987-01-01

A general model is presented for heat transfer into a heat pipe using a localized heat input. Conduction in the wall of the heat pipe and boiling in the interior structure are treated simultaneously. The model is derived from circumferential heat transfer in a cylindrical heat pipe evaporator and for radial heat transfer in a circular disk with boiling from the interior surface. A comparison is made with data for a localized heat input zone. Agreement between the theory and the model is good. This model can be used for design purposes if a boiling correlation is available. The model can be extended to provide improved predictions of heat pipe performance

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

Air temperature variability in a high-elevation Himalayan catchment

NARCIS (Netherlands)

Heynen, Martin; Miles, Evan; Ragettli, Silvan; Buri, Pascal; Immerzeel, Walter W.; Pellicciotti, Francesca

2016-01-01

Air temperature is a key control of processes affecting snow and glaciers in high-elevation catchments, including melt, snowfall and sublimation. It is therefore a key input variable to models of land-surface-atmosphere interaction. Despite this importance, its spatial variability is poorly

Input-Output model for waste management plan for Nigeria | Njoku ...

African Journals Online (AJOL)

An Input-Output Model for Waste Management Plan has been developed for Nigeria based on Leontief concept and life cycle analysis. Waste was considered as source of pollution, loss of resources, and emission of green house gasses from bio-chemical treatment and decomposition, with negative impact on the ...

DIMITRI 1.0: Beschrijving en toepassing van een dynamisch input-output model

NARCIS (Netherlands)

Wilting HC; Blom WF; Thomas R; Idenburg AM; LAE

2001-01-01

DIMITRI, the Dynamic Input-Output Model to study the Impacts of Technology Related Innovations, was developed in the framework of the RIVM Environment and Economy project to answer questions about interrelationships between economy, technology and the environment. DIMITRI, a meso-economic model,

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

Science.gov (United States)

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Science.gov (United States)

Sullivan, Sylvia C.; Lee, Dongmin; Oreopoulos, Lazaros; Nenes, Athanasios

2016-05-01

Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.

Variable identification in group method of data handling methodology

Energy Technology Data Exchange (ETDEWEB)

Pereira, Iraci Martinez, E-mail: martinez@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil)

2011-07-01

The Group Method of Data Handling - GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a preselected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. A Monitoring and Diagnosis System was developed based on GMDH and Artificial Neural Network - ANN methodologies, and applied to the IPEN research Reactor IEA-R1. The GMDH was used to study the best set of variables to be used to train an ANN, resulting in a best monitoring variable estimative. The system performs the monitoring by comparing these estimative calculated values with measured ones. The IPEN Reactor Data Acquisition System is composed of 58 variables (process and nuclear variables). As the GMDH is a self-organizing methodology, the input variables choice is made automatically, and the real input variables used in the Monitoring and Diagnosis System were not showed in the final result. This work presents a study of variable identification of GMDH methodology by means of an algorithm that works in parallel with the GMDH algorithm and traces the initial variables paths, resulting in an identification of the variables that composes the best Monitoring and Diagnosis Model. (author)

Variable identification in group method of data handling methodology

International Nuclear Information System (INIS)

Pereira, Iraci Martinez; Bueno, Elaine Inacio

2011-01-01

The Group Method of Data Handling - GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a preselected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. A Monitoring and Diagnosis System was developed based on GMDH and Artificial Neural Network - ANN methodologies, and applied to the IPEN research Reactor IEA-R1. The GMDH was used to study the best set of variables to be used to train an ANN, resulting in a best monitoring variable estimative. The system performs the monitoring by comparing these estimative calculated values with measured ones. The IPEN Reactor Data Acquisition System is composed of 58 variables (process and nuclear variables). As the GMDH is a self-organizing methodology, the input variables choice is made automatically, and the real input variables used in the Monitoring and Diagnosis System were not showed in the final result. This work presents a study of variable identification of GMDH methodology by means of an algorithm that works in parallel with the GMDH algorithm and traces the initial variables paths, resulting in an identification of the variables that composes the best Monitoring and Diagnosis Model. (author)

The Mixed Effects of Phonetic Input Variability on Relative Ease of L2 Learning: Evidence from English Learners’ Production of French and Spanish Stop-Rhotic Clusters

Directory of Open Access Journals (Sweden)

Laura Colantoni

2018-04-01

Full Text Available We examined the consequences of within-category phonetic variability in the input on non-native learners’ production accuracy. Following previous empirical research on the L2 acquisition of phonetics and the lexicon, we tested the hypothesis that phonetic variability facilitates learning by analyzing English-speaking learners’ production of French and Spanish word-medial stop-rhotic clusters, which differ from their English counterparts in terms of stop and rhotic voicing and manner. Crucially, for both the stops and rhotics, there are differences in within-language variability. Twenty native speakers per language and 39 L1 English-learners of French (N = 20 and Spanish (N = 19 of intermediate and advanced proficiency performed a carrier-sentence reading task. A given parameter was deemed to have been acquired when the learners’ production fell within the range of attested native speaker values. An acoustic analysis of the data partially supports the facilitative effect of phonetic variability. To account for the unsupported hypotheses, we discuss a number of issues, including the difficulty of measuring variability, the need to determine the extent to which learners’ perception shapes intake, and the challenge of teasing apart the effects of input variability from those of transferred L1 articulatory patterns.

Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff, Sediment, and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties.

Science.gov (United States)

Boluwade, Alaba; Madramootoo, Chandra

2013-01-01

Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km 2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed "Reference". Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.

Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

DEFF Research Database (Denmark)

Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

2018-01-01

current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

Development of an Input Model to MELCOR 1.8.5 for the Ringhals 3 PWR

International Nuclear Information System (INIS)

Nilsson, Lars

2004-12-01

An input file to the severe accident code MELCOR 1.8.5 has been developed for the Swedish pressurized water reactor Ringhals 3. The aim was to produce a file that can be used for calculations of various postulated severe accident scenarios, although the first application is specifically on cases involving large hydrogen production. The input file is rather detailed with individual modelling of all three cooling loops. The report describes the basis for the Ringhals 3 model and the input preparation step by step and is illustrated by nodalization schemes of the different plant systems. Present version of the report is restricted to the fundamental MELCOR input preparation, and therefore most of the figures of Ringhals 3 measurements and operating parameters are excluded here. These are given in another, complete version of the report, for limited distribution, which includes tables for pertinent data of all components. That version contains appendices with a complete listing of the input files as well as tables of data compiled from a RELAP5 file, that was a major basis for the MELCOR input for the cooling loops. The input was tested in steady-state calculations in order to simulate the initial conditions at current nominal operating conditions in Ringhals 3 for 2775 MW thermal power. The results of the steady-state calculations are presented in the report. Calculations with the MELCOR model will then be carried out of certain accident sequences for comparison with results from earlier MAAP4 calculations. That work will be reported separately

Optimization of a prognostic biosphere model for terrestrial biomass and atmospheric CO2 variability

International Nuclear Information System (INIS)

Saito, M.; Ito, A.; Maksyutov, S.

2014-01-01

This study investigates the capacity of a prognostic biosphere model to simulate global variability in atmospheric CO 2 concentrations and vegetation carbon dynamics under current environmental conditions. Global data sets of atmospheric CO 2 concentrations, above-ground biomass (AGB), and net primary productivity (NPP) in terrestrial vegetation were assimilated into the biosphere model using an inverse modeling method combined with an atmospheric transport model. In this process, the optimal physiological parameters of the biosphere model were estimated by minimizing the misfit between observed and modeled values, and parameters were generated to characterize various biome types. Results obtained using the model with the optimized parameters correspond to the observed seasonal variations in CO 2 concentration and their annual amplitudes in both the Northern and Southern Hemispheres. In simulating the mean annual AGB and NPP, the model shows improvements in estimating the mean magnitudes and probability distributions for each biome, as compared with results obtained using prior simulation parameters. However, the model is less efficient in its simulation of AGB for forest type biomes. This misfit suggests that more accurate values of input parameters, specifically, grid mean AGB values and seasonal variabilities in physiological parameters, are required to improve the performance of the simulation model. (authors)

An analytical model for an input/output-subsystem

International Nuclear Information System (INIS)

Roemgens, J.

1983-05-01

An input/output-subsystem of one or several computers if formed by the external memory units and the peripheral units of a computer system. For these subsystems mathematical models are established, taking into account the special properties of the I/O-subsystems, in order to avoid planning errors and to allow for predictions of the capacity of such systems. Here an analytical model is presented for the magnetic discs of a I/O-subsystem, using analytical methods for the individual waiting queues or waiting queue networks. Only I/O-subsystems of IBM-computer configurations are considered, which can be controlled by the MVS operating system. After a description of the hardware and software components of these I/O-systems, possible solutions from the literature are presented and discussed with respect to their applicability in IBM-I/O-subsystems. Based on these models a special scheme is developed which combines the advantages of the literature models and avoids the disadvantages in part. (orig./RW) [de

Confounding of three binary-variables counterfactual model

OpenAIRE

Liu, Jingwei; Hu, Shuang

2011-01-01

Confounding of three binary-variables counterfactual model is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses, the sufficient conditions...

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.

Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling

Directory of Open Access Journals (Sweden)

Jens G. Balchen

1995-04-01

Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.

Lysimeter data as input to performance assessment models

International Nuclear Information System (INIS)

McConnell, J.W. Jr.

1998-01-01

The Field Lysimeter Investigations: Low-Level Waste Data Base Development Program is obtaining information on the performance of radioactive waste forms in a disposal environment. Waste forms fabricated using ion-exchange resins from EPICOR-117 prefilters employed in the cleanup of the Three Mile Island (TMI) Nuclear Power Station are being tested to develop a low-level waste data base and to obtain information on survivability of waste forms in a disposal environment. The program includes reviewing radionuclide releases from those waste forms in the first 7 years of sampling and examining the relationship between code input parameters and lysimeter data. Also, lysimeter data are applied to performance assessment source term models, and initial results from use of data in two models are presented

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

Science.gov (United States)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Scientific and technical advisory committee review of the nutrient inputs to the watershed model

Science.gov (United States)

The following is a report by a STAC Review Team concerning the methods and documentation used by the Chesapeake Bay Partnership for evaluation of nutrient inputs to Phase 6 of the Chesapeake Bay Watershed Model. The “STAC Review of the Nutrient Inputs to the Watershed Model” (previously referred to...

Tritium Records to Trace Stratospheric Moisture Inputs in Antarctica

Science.gov (United States)

Fourré, E.; Landais, A.; Cauquoin, A.; Jean-Baptiste, P.; Lipenkov, V.; Petit, J.-R.

2018-03-01

Better assessing the dynamic of stratosphere-troposphere exchange is a key point to improve our understanding of the climate dynamic in the East Antarctica Plateau, a region where stratospheric inputs are expected to be important. Although tritium (3H or T), a nuclide naturally produced mainly in the stratosphere and rapidly entering the water cycle as HTO, seems a first-rate tracer to study these processes, tritium data are very sparse in this region. We present the first high-resolution measurements of tritium concentration over the last 50 years in three snow pits drilled at the Vostok station. Natural variability of the tritium records reveals two prominent frequencies, one at about 10 years (to be related to the solar Schwabe cycles) and the other one at a shorter periodicity: despite dating uncertainty at this short scale, a good correlation is observed between 3H and Na+ and an anticorrelation between 3H and δ18O measured on an individual pit. The outputs from the LMDZ Atmospheric General Circulation Model including stable water isotopes and tritium show the same 3H-δ18O anticorrelation and allow further investigation on the associated mechanism. At the interannual scale, the modeled 3H variability matches well with the Southern Annular Mode index. At the seasonal scale, we show that modeled stratospheric tritium inputs in the troposphere are favored in winter cold and dry conditions.

Regional disaster impact analysis: comparing Input-Output and Computable General Equilibrium models

NARCIS (Netherlands)

Koks, E.E.; Carrera, L.; Jonkeren, O.; Aerts, J.C.J.H.; Husby, T.G.; Thissen, M.; Standardi, G.; Mysiak, J.

2016-01-01

A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input-output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of

A new control-oriented transient model of variable geometry turbocharger

International Nuclear Information System (INIS)

Bahiuddin, Irfan; Mazlan, Saiful Amri; Imaduddin, Fitrian; Ubaidillah

2017-01-01

The flow input of a variable geometry turbocharger turbine is highly unsteady due to rapid and periodic pressure dynamics in engine combustion chambers. Several VGT control methods have been developed to recover more energy from the highly pulsating exhaust gas flow. To develop a control system for the highly pulsating flow condition, an accurate and valid unsteady model is required. This study focuses on the derivation of governing the unsteady control-oriented model (COM) for a turbine of an actively controlled turbocharger (ACT). The COM has the capability to predict the turbocharger behaviour regarding the instantaneous turbine actual and isentropic powers in different effective throat areas. The COM is a modified version of a conventional mean value model (MVM) with an additional feature to calculate the turbine angular velocity and torque for determining the actual power. The simulation results were further compared with experimental data in two general scenarios. The first scenario was simulations on fixed geometry positions. The second simulation scenario considered the nozzle movement after receiving a signal from the controller in different cases. The comparison between simulation and experimental results showed similarities in the recovered power behaviours the turbine inlet area increases or vice versa. The model also has proved its reliability to replicate general behaviour as in the example of ACT cases presented in this paper. However, the model is incapable to replicate the detailed and complicated phenomena, such as choking effect and hysteresis effect. - Highlights: • A control-oriented model of a variable geometry turbocharger turbine is proposed. • Isentropic and actual power behaviour estimations on turbocharger turbine. • A simulation tool for developing active control systems of turbocharger turbines.

Input data requirements for performance modelling and monitoring of photovoltaic plants

DEFF Research Database (Denmark)

Gavriluta, Anamaria Florina; Spataru, Sergiu; Sera, Dezso

2018-01-01

This work investigates the input data requirements in the context of performance modeling of thin-film photovoltaic (PV) systems. The analysis focuses on the PVWatts performance model, well suited for on-line performance monitoring of PV strings, due to its low number of parameters and high......, modelling the performance of the PV modules at high irradiances requires a dataset of only a few hundred samples in order to obtain a power estimation accuracy of ~1-2\\%....

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)

The effect of long-term changes in plant inputs on soil carbon stocks

Science.gov (United States)

Georgiou, K.; Li, Z.; Torn, M. S.

2017-12-01

Soil organic carbon (SOC) is the largest actively-cycling terrestrial reservoir of C and an integral component of thriving natural and managed ecosystems. C input interventions (e.g., litter removal or organic amendments) are common in managed landscapes and present an important decision for maintaining healthy soils in sustainable agriculture and forestry. Furthermore, climate and land-cover change can also affect the amount of plant C inputs that enter the soil through changes in plant productivity, allocation, and rooting depth. Yet, the processes that dictate the response of SOC to such changes in C inputs are poorly understood and inadequately represented in predictive models. Long-term litter manipulations are an invaluable resource for exploring key controls of SOC storage and validating model representations. Here we explore the response of SOC to long-term changes in plant C inputs across a range of biomes and soil types. We synthesize and analyze data from long-term litter manipulation field experiments, and focus our meta-analysis on changes to total SOC stocks, microbial biomass carbon, and mineral-associated (`protected') carbon pools and explore the relative contribution of above- versus below-ground C inputs. Our cross-site data comparison reveals that divergent SOC responses are observed between forest sites, particularly for treatments that increase C inputs to the soil. We explore trends among key variables (e.g., microbial biomass to SOC ratios) that inform soil C model representations. The assembled dataset is an important benchmark for evaluating process-based hypotheses and validating divergent model formulations.

Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images.

Science.gov (United States)

Sanz-Requena, Roberto; Prats-Montalbán, José Manuel; Martí-Bonmatí, Luis; Alberich-Bayarri, Ángel; García-Martí, Gracián; Pérez, Rosario; Ferrer, Alberto

2015-08-01

To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate. © 2014 Wiley Periodicals, Inc.

Evaluating the effects of model structure and meteorological input data on runoff modelling in an alpine headwater basin

Science.gov (United States)

Schattan, Paul; Bellinger, Johannes; Förster, Kristian; Schöber, Johannes; Huttenlau, Matthias; Kirnbauer, Robert; Achleitner, Stefan

2017-04-01

Modelling water resources in snow-dominated mountainous catchments is challenging due to both, short concentration times and a highly variable contribution of snow melt in space and time from complex terrain. A number of model setups exist ranging from physically based models to conceptional models which do not attempt to represent the natural processes in a physically meaningful way. Within the flood forecasting system for the Tyrolean Inn River two serially linked hydrological models with differing process representation are used. Non- glacierized catchments are modelled by a semi-distributed, water balance model (HQsim) based on the HRU-approach. A fully-distributed energy and mass balance model (SES), purpose-built for snow- and icemelt, is used for highly glacierized headwater catchments. Previous work revealed uncertainties and limitations within the models' structures regarding (i) the representation of snow processes in HQsim, (ii) the runoff routing of SES, and (iii) the spatial resolution of the meteorological input data in both models. To overcome these limitations, a "strengths driven" model coupling is applied. Instead of linking the models serially, a vertical one-way coupling of models has been implemented. The fully-distributed snow modelling of SES is combined with the semi-distributed HQsim structure, allowing to benefit from soil and runoff routing schemes in HQsim. A monte-carlo based modelling experiment was set up to evaluate the resulting differences in the runoff prediction due to the improved model coupling and a refined spatial resolution of the meteorological forcing. The experiment design follows a gradient of spatial discretisation of hydrological processes and meteorological forcing data with a total of six different model setups for the alpine headwater basin of the Fagge River in the Tyrolean Alps. In general, all setups show a good performance for this particular basin. It is therefore planned to include other basins with differing

Input-constrained model predictive control via the alternating direction method of multipliers

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Frison, Gianluca; Andersen, Martin S.

2014-01-01

This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP......) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly structured quadratic program. These quadratic programs are solved using a Riccati iteration procedure, and a structure-exploiting interior-point method, respectively. The computational cost per iteration...... is quadratic in the dimensions of the controlled system, and linear in the length of the prediction horizon. Simulations show that the approach proposed in this paper is more than an order of magnitude faster than several state-of-the-art quadratic programming algorithms, and that the difference in computation...

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

Science.gov (United States)

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

Directory of Open Access Journals (Sweden)

Holger Hoffmann

Full Text Available We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Development of an Input Model to MELCOR 1.8.5 for the Oskarshamn 3 BWR

Energy Technology Data Exchange (ETDEWEB)

Nilsson, Lars [Lentek, Nykoeping (Sweden)

2006-05-15

An input model has been prepared to the code MELCOR 1.8.5 for the Swedish Oskarshamn 3 Boiling Water Reactor (O3). This report describes the modelling work and the various files which comprise the input deck. Input data are mainly based on original drawings and system descriptions made available by courtesy of OKG AB. Comparison and check of some primary system data were made against an O3 input file to the SCDAP/RELAP5 code that was used in the SARA project. Useful information was also obtained from the FSAR (Final Safety Analysis Report) for O3 and the SKI report '2003 Stoerningshandboken BWR'. The input models the O3 reactor at its current state with the operating power of 3300 MW{sub th}. One aim with this work is that the MELCOR input could also be used for power upgrading studies. All fuel assemblies are thus assumed to consist of the new Westinghouse-Atom's SVEA-96 Optima2 fuel. MELCOR is a severe accident code developed by Sandia National Laboratory under contract from the U.S. Nuclear Regulatory Commission (NRC). MELCOR is a successor to STCP (Source Term Code Package) and has thus a long evolutionary history. The input described here is adapted to the latest version 1.8.5 available when the work began. It was released the year 2000, but a new version 1.8.6 was distributed recently. Conversion to the new version is recommended. (During the writing of this report still another code version, MELCOR 2.0, has been announced to be released within short.) In version 1.8.5 there is an option to describe the accident progression in the lower plenum and the melt-through of the reactor vessel bottom in more detail by use of the Bottom Head (BH) package developed by Oak Ridge National Laboratory especially for BWRs. This is in addition to the ordinary MELCOR COR package. Since problems arose running with the BH input two versions of the O3 input deck were produced, a NONBH and a BH deck. The BH package is no longer a separate package in the new 1

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.

Eutrophication Modeling Using Variable Chlorophyll Approach

International Nuclear Information System (INIS)

Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.

2016-01-01

In this study, eutrophication was investigated in Lake Ontario to identify the interactions among effective drivers. The complexity of such phenomenon was modeled using a system dynamics approach based on a consideration of constant and variable stoichiometric ratios. The system dynamics approach is a powerful tool for developing object-oriented models to simulate complex phenomena that involve feedback effects. Utilizing stoichiometric ratios is a method for converting the concentrations of state variables. During the physical segmentation of the model, Lake Ontario was divided into two layers, i.e., the epilimnion and hypolimnion, and differential equations were developed for each layer. The model structure included 16 state variables related to phytoplankton, herbivorous zooplankton, carnivorous zooplankton, ammonium, nitrate, dissolved phosphorus, and particulate and dissolved carbon in the epilimnion and hypolimnion during a time horizon of one year. The results of several tests to verify the model, close to 1 Nash-Sutcliff coefficient (0.98), the data correlation coefficient (0.98), and lower standard errors (0.96), have indicated well-suited model’s efficiency. The results revealed that there were significant differences in the concentrations of the state variables in constant and variable stoichiometry simulations. Consequently, the consideration of variable stoichiometric ratios in algae and nutrient concentration simulations may be applied in future modeling studies to enhance the accuracy of the results and reduce the likelihood of inefficient control policies.

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

Science.gov (United States)

Yaghouby, Farid; Sunderam, Sridhar

2015-04-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. Copyright © 2015 Elsevier Ltd. All rights reserved.

A quantitative approach to modeling the information processing of NPP operators under input information overload

International Nuclear Information System (INIS)

Kim, Jong Hyun; Seong, Poong Hyun

2002-01-01

This paper proposes a quantitative approach to modeling the information processing of NPP operators. The aim of this work is to derive the amount of the information processed during a certain control task under input information overload. We primarily develop the information processing model having multiple stages, which contains information flow. Then the uncertainty of the information is quantified using the Conant's model, a kind of information theory. We also investigate the applicability of this approach to quantifying the information reduction of operators under the input information overload

Post-BEMUSE Reflood Model Input Uncertainty Methods (PREMIUM) Benchmark Phase II: Identification of Influential Parameters

International Nuclear Information System (INIS)

Kovtonyuk, A.; Petruzzi, A.; D'Auria, F.

2015-01-01

The objective of the Post-BEMUSE Reflood Model Input Uncertainty Methods (PREMIUM) benchmark is to progress on the issue of the quantification of the uncertainty of the physical models in system thermal-hydraulic codes by considering a concrete case: the physical models involved in the prediction of core reflooding. The PREMIUM benchmark consists of five phases. This report presents the results of Phase II dedicated to the identification of the uncertain code parameters associated with physical models used in the simulation of reflooding conditions. This identification is made on the basis of the Test 216 of the FEBA/SEFLEX programme according to the following steps: - identification of influential phenomena; - identification of the associated physical models and parameters, depending on the used code; - quantification of the variation range of identified input parameters through a series of sensitivity calculations. A procedure for the identification of potentially influential code input parameters has been set up in the Specifications of Phase II of PREMIUM benchmark. A set of quantitative criteria has been as well proposed for the identification of influential IP and their respective variation range. Thirteen participating organisations, using 8 different codes (7 system thermal-hydraulic codes and 1 sub-channel module of a system thermal-hydraulic code) submitted Phase II results. The base case calculations show spread in predicted cladding temperatures and quench front propagation that has been characterized. All the participants, except one, predict a too fast quench front progression. Besides, the cladding temperature time trends obtained by almost all the participants show oscillatory behaviour which may have numeric origins. Adopted criteria for identification of influential input parameters differ between the participants: some organisations used the set of criteria proposed in Specifications 'as is', some modified the quantitative thresholds

Nonlinear model-based robust control of a nuclear reactor using adaptive PIF gains and variable structure controller

International Nuclear Information System (INIS)

Park, Moon Ghu; Cho, Nam Zin

1993-01-01

A Nonlinear model-based Hybrid Controller (NHC) is developed which consists of the adaptive proportional-integral-feedforward (PIF) gains and variable structure controller. The controller has the robustness against modeling uncertainty and is applied to the trajectory tracking control of single-input, single-output nonlinear systems. The essence of the scheme is to divide the control into four different terms. Namely, the adaptive P-I-F gains and variable structure controller are used to accomplish the specific control actions by each terms. The robustness of the controller is guaranteed by the feedback of estimated uncertainty and the performance specification given by the adaptation of PIF gains using the second method of Lyapunov. The variable structure controller is incorporated to regulate the initial peak of the tracking error during the parameter adaptation is not settled yet. The newly developed NHC method is applied to the power tracking control of a nuclear reactor and the simulation results show great improvement in tracking performance compared with the conventional model-based control methods. (Author)

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

Directory of Open Access Journals (Sweden)

H. Kreibich

2016-05-01

Full Text Available Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB.In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Method and system to estimate variables in an integrated gasification combined cycle (IGCC) plant

Science.gov (United States)

Kumar, Aditya; Shi, Ruijie; Dokucu, Mustafa

2013-09-17

System and method to estimate variables in an integrated gasification combined cycle (IGCC) plant are provided. The system includes a sensor suite to measure respective plant input and output variables. An extended Kalman filter (EKF) receives sensed plant input variables and includes a dynamic model to generate a plurality of plant state estimates and a covariance matrix for the state estimates. A preemptive-constraining processor is configured to preemptively constrain the state estimates and covariance matrix to be free of constraint violations. A measurement-correction processor may be configured to correct constrained state estimates and a constrained covariance matrix based on processing of sensed plant output variables. The measurement-correction processor is coupled to update the dynamic model with corrected state estimates and a corrected covariance matrix. The updated dynamic model may be configured to estimate values for at least one plant variable not originally sensed by the sensor suite.

Lithium inputs to subduction zones

NARCIS (Netherlands)

Bouman, C.; Elliott, T.R.; Vroon, P.Z.

2004-01-01

We have studied the sedimentary and basaltic inputs of lithium to subduction zones. Various sediments from DSDP and ODP drill cores in front of the Mariana, South Sandwich, Banda, East Sunda and Lesser Antilles island arcs have been analysed and show highly variable Li contents and δ

Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Quantified carbon input for maintaining existing soil organic carbon stocks in global wheat systems

Science.gov (United States)

Wang, G.

2017-12-01

Soil organic carbon (SOC) dynamics in croplands is a crucial component of global carbon (C) cycle. Depending on local environmental conditions and management practices, typical C input is generally required to reduce or reverse C loss in agricultural soils. No studies have quantified the critical C input for maintaining SOC at global scale with high resolution. Such information will provide a baseline map for assessing soil C dynamics under potential changes in management practices and climate, and thus enable development of management strategies to reduce C footprint from farm to regional scales. We used the soil C model RothC to simulate the critical C input rates needed to maintain existing soil C level at 0.1°× 0.1° resolution in global wheat systems. On average, the critical C input was estimated to be 2.0 Mg C ha-1 yr-1, with large spatial variability depending on local soil and climatic conditions. Higher C inputs are required in wheat system of central United States and western Europe, mainly due to the higher current soil C stocks present in these regions. The critical C input could be effectively estimated using a summary model driven by current SOC level, mean annual temperature, precipitation, and soil clay content.

Critical carbon input to maintain current soil organic carbon stocks in global wheat systems.

Science.gov (United States)

Wang, Guocheng; Luo, Zhongkui; Han, Pengfei; Chen, Huansheng; Xu, Jingjing

2016-01-13

Soil organic carbon (SOC) dynamics in croplands is a crucial component of global carbon (C) cycle. Depending on local environmental conditions and management practices, typical C input is generally required to reduce or reverse C loss in agricultural soils. No studies have quantified the critical C input for maintaining SOC at global scale with high resolution. Such information will provide a baseline map for assessing soil C dynamics under potential changes in management practices and climate, and thus enable development of management strategies to reduce C footprint from farm to regional scales. We used the soil C model RothC to simulate the critical C input rates needed to maintain existing soil C level at 0.1° × 0.1° resolution in global wheat systems. On average, the critical C input was estimated to be 2.0 Mg C ha(-1) yr(-1), with large spatial variability depending on local soil and climatic conditions. Higher C inputs are required in wheat system of central United States and western Europe, mainly due to the higher current soil C stocks present in these regions. The critical C input could be effectively estimated using a summary model driven by current SOC level, mean annual temperature, precipitation, and soil clay content.

Influential input parameters for reflood model of MARS code

Energy Technology Data Exchange (ETDEWEB)

Oh, Deog Yeon; Bang, Young Seok [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

2012-10-15

Best Estimate (BE) calculation has been more broadly used in nuclear industries and regulations to reduce the significant conservatism for evaluating Loss of Coolant Accident (LOCA). Reflood model has been identified as one of the problems in BE calculation. The objective of the Post BEMUSE Reflood Model Input Uncertainty Methods (PREMIUM) program of OECD/NEA is to make progress the issue of the quantification of the uncertainty of the physical models in system thermal hydraulic codes, by considering an experimental result especially for reflood. It is important to establish a methodology to identify and select the parameters influential to the response of reflood phenomena following Large Break LOCA. For this aspect, a reference calculation and sensitivity analysis to select the dominant influential parameters for FEBA experiment are performed.

Does linguistic input play the same role in language learning for children with and without early brain injury?

Science.gov (United States)

Rowe, Meredith L; Levine, Susan C; Fisher, Joan A; Goldin-Meadow, Susan

2009-01-01

Children with unilateral pre- or perinatal brain injury (BI) show remarkable plasticity for language learning. Previous work highlights the important role that lesion characteristics play in explaining individual variation in plasticity in the language development of children with BI. The current study examines whether the linguistic input that children with BI receive from their caregivers also contributes to this early plasticity, and whether linguistic input plays a similar role in children with BI as it does in typically developing (TD) children. Growth in vocabulary and syntactic production is modeled for 80 children (53 TD, 27 BI) between 14 and 46 months. Findings indicate that caregiver input is an equally potent predictor of vocabulary growth in children with BI and in TD children. In contrast, input is a more potent predictor of syntactic growth for children with BI than for TD children. Controlling for input, lesion characteristics (lesion size, type, seizure history) also affect the language trajectories of children with BI. Thus, findings illustrate how both variability in the environment (linguistic input) and variability in the organism (lesion characteristics) work together to contribute to plasticity in language learning.

Effect of input data variability on estimations of the equivalent constant temperature time for microbial inactivation by HTST and retort thermal processing.

Science.gov (United States)

Salgado, Diana; Torres, J Antonio; Welti-Chanes, Jorge; Velazquez, Gonzalo

2011-08-01

Consumer demand for food safety and quality improvements, combined with new regulations, requires determining the processor's confidence level that processes lowering safety risks while retaining quality will meet consumer expectations and regulatory requirements. Monte Carlo calculation procedures incorporate input data variability to obtain the statistical distribution of the output of prediction models. This advantage was used to analyze the survival risk of Mycobacterium avium subspecies paratuberculosis (M. paratuberculosis) and Clostridium botulinum spores in high-temperature short-time (HTST) milk and canned mushrooms, respectively. The results showed an estimated 68.4% probability that the 15 sec HTST process would not achieve at least 5 decimal reductions in M. paratuberculosis counts. Although estimates of the raw milk load of this pathogen are not available to estimate the probability of finding it in pasteurized milk, the wide range of the estimated decimal reductions, reflecting the variability of the experimental data available, should be a concern to dairy processors. Knowledge of the C. botulinum initial load and decimal thermal time variability was used to estimate an 8.5 min thermal process time at 110 °C for canned mushrooms reducing the risk to 10⁻⁹ spores/container with a 95% confidence. This value was substantially higher than the one estimated using average values (6.0 min) with an unacceptable 68.6% probability of missing the desired processing objective. Finally, the benefit of reducing the variability in initial load and decimal thermal time was confirmed, achieving a 26.3% reduction in processing time when standard deviation values were lowered by 90%. In spite of novel technologies, commercialized or under development, thermal processing continues to be the most reliable and cost-effective alternative to deliver safe foods. However, the severity of the process should be assessed to avoid under- and over

Use of regional climate model simulations as an input for hydrological models for the Hindukush-Karakorum-Himalaya region

NARCIS (Netherlands)

Akhtar, M.; Ahmad, N.; Booij, Martijn J.

2009-01-01

The most important climatological inputs required for the calibration and validation of hydrological models are temperature and precipitation that can be derived from observational records or alternatively from regional climate models (RCMs). In this paper, meteorological station observations and

Remote sensing inputs to water demand modeling

Science.gov (United States)

Estes, J. E.; Jensen, J. R.; Tinney, L. R.; Rector, M.

1975-01-01

In an attempt to determine the ability of remote sensing techniques to economically generate data required by water demand models, the Geography Remote Sensing Unit, in conjunction with the Kern County Water Agency of California, developed an analysis model. As a result it was determined that agricultural cropland inventories utilizing both high altitude photography and LANDSAT imagery can be conducted cost effectively. In addition, by using average irrigation application rates in conjunction with cropland data, estimates of agricultural water demand can be generated. However, more accurate estimates are possible if crop type, acreage, and crop specific application rates are employed. An analysis of the effect of saline-alkali soils on water demand in the study area is also examined. Finally, reference is made to the detection and delineation of water tables that are perched near the surface by semi-permeable clay layers. Soil salinity prediction, automated crop identification on a by-field basis, and a potential input to the determination of zones of equal benefit taxation are briefly touched upon.

PERMODELAN INDEKS HARGA KONSUMEN INDONESIA DENGAN MENGGUNAKAN MODEL INTERVENSI MULTI INPUT

KAUST Repository

Novianti, Putri Wikie

2017-01-24

There are some events which are expected effecting CPI’s fluctuation, i.e. financial crisis 1997/1998, fuel price risings, base year changing’s, independence of Timor-Timur (October 1999), and Tsunami disaster in Aceh (December 2004). During re-search period, there were eight fuel price risings and four base year changing’s. The objective of this research is to obtain multi input intervention model which can des-cribe magnitude and duration of each event effected to CPI. Most of intervention re-searches that have been done are only contain of an intervention with single input, ei-ther step or pulse function. Multi input intervention was used in Indonesia CPI case because there are some events which are expected effecting CPI. Based on the result, those events were affecting CPI. Additionally, other events, such as Ied on January 1999, events on April 2002, July 2003, December 2005, and September 2008, were affecting CPI too. In general, those events gave positive effect to CPI, except events on April 2002 and July 2003 which gave negative effects.

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

From LCC to LCA Using a Hybrid Input Output Model – A Maritime Case Study

DEFF Research Database (Denmark)

Kjær, Louise Laumann; Pagoropoulos, Aris; Hauschild, Michael Zwicky

2015-01-01

As companies try to embrace life cycle thinking, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) have proven to be powerful tools. In this paper, an Environmental Input-Output model is used for analysis as it enables an LCA using the same economic input data as LCC. This approach helps...

Variable thickness transient ground-water flow model. Volume 3. Program listings

International Nuclear Information System (INIS)

Reisenauer, A.E.

1979-12-01

The Assessment of Effectiveness of Geologic Isolation Systems (AEGIS) Program is developing and applying the methodology for assessing the far-field, long-term post-closure safety of deep geologic nuclear waste repositories. AEGIS is being performed by Pacific Northwest Laboratory (PNL) under contract with the Office of Nuclear Waste Isolation (OWNI) for the Department of Energy (DOE). One task within AEGIS is the development of methodology for analysis of the consequences (water pathway) from loss of repository containment as defined by various release scenarios. Analysis of the long-term, far-field consequences of release scenarios requires the application of numerical codes which simulate the hydrologic systems, model the transport of released radionuclides through the hydrologic systems to the biosphere, and, where applicable, assess the radiological dose to humans. Hydrologic and transport models are available at several levels of complexity or sophistication. Model selection and use are determined by the quantity and quality of input data. Model development under AEGIS and related programs provides three levels of hydrologic models, two levels of transport models, and one level of dose models (with several separate models). This is the third of 3 volumes of the description of the VTT (Variable Thickness Transient) Groundwater Hydrologic Model - second level (intermediate complexity) two-dimensional saturated groundwater flow

Soil process-oriented modelling of within-field variability based on high-resolution 3D soil type distribution maps.

Science.gov (United States)

Bönecke, Eric; Lück, Erika; Gründling, Ralf; Rühlmann, Jörg; Franko, Uwe

2016-04-01

Today, the knowledge of within-field variability is essential for numerous purposes, including practical issues, such as precision and sustainable soil management. Therefore, process-oriented soil models have been applied for a considerable time to answer question of spatial soil nutrient and water dynamics, although, they can only be as consistent as their variation and resolution of soil input data. Traditional approaches, describe distribution of soil types, soil texture or other soil properties for greater soil units through generalised point information, e.g. from classical soil survey maps. Those simplifications are known to be afflicted with large uncertainties. Varying soil, crop or yield conditions are detected even within such homogenised soil units. However, recent advances of non-invasive soil survey and on-the-go monitoring techniques, made it possible to obtain vertical and horizontal dense information (3D) about various soil properties, particularly soil texture distribution which serves as an essential soil key variable affecting various other soil properties. Thus, in this study we based our simulations on detailed 3D soil type distribution (STD) maps (4x4 m) to adjacently built-up sufficient informative soil profiles including various soil physical and chemical properties. Our estimates of spatial STD are based on high-resolution lateral and vertical changes of electrical resistivity (ER), detected by a relatively new multi-sensor on-the-go ER monitoring device. We performed an algorithm including fuzzy-c-mean (FCM) logic and traditional soil classification to estimate STD from those inverted and layer-wise available ER data. STD is then used as key input parameter for our carbon, nitrogen and water transport model. We identified Pedological horizon depths and inferred hydrological soil variables (field capacity, permanent wilting point) from pedotransferfunctions (PTF) for each horizon. Furthermore, the spatial distribution of soil organic carbon

Handwriting generates variable visual input to facilitate symbol learning

Science.gov (United States)

Li, Julia X.; James, Karin H.

2015-01-01

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing two hypotheses: That handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5 year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: three involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and three involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the six conditions (N=72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. PMID:26726913

Transport coefficient computation based on input/output reduced order models

Science.gov (United States)

Hurst, Joshua L.

The guiding purpose of this thesis is to address the optimal material design problem when the material description is a molecular dynamics model. The end goal is to obtain a simplified and fast model that captures the property of interest such that it can be used in controller design and optimization. The approach is to examine model reduction analysis and methods to capture a specific property of interest, in this case viscosity, or more generally complex modulus or complex viscosity. This property and other transport coefficients are defined by a input/output relationship and this motivates model reduction techniques that are tailored to preserve input/output behavior. In particular Singular Value Decomposition (SVD) based methods are investigated. First simulation methods are identified that are amenable to systems theory analysis. For viscosity, these models are of the Gosling and Lees-Edwards type. They are high order nonlinear Ordinary Differential Equations (ODEs) that employ Periodic Boundary Conditions. Properties can be calculated from the state trajectories of these ODEs. In this research local linear approximations are rigorously derived and special attention is given to potentials that are evaluated with Periodic Boundary Conditions (PBC). For the Gosling description LTI models are developed from state trajectories but are found to have limited success in capturing the system property, even though it is shown that full order LTI models can be well approximated by reduced order LTI models. For the Lees-Edwards SLLOD type model nonlinear ODEs will be approximated by a Linear Time Varying (LTV) model about some nominal trajectory and both balanced truncation and Proper Orthogonal Decomposition (POD) will be used to assess the plausibility of reduced order models to this system description. An immediate application of the derived LTV models is Quasilinearization or Waveform Relaxation. Quasilinearization is a Newton's method applied to the ODE operator

A Core Language for Separate Variability Modeling

DEFF Research Database (Denmark)

Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina

2014-01-01

Separate variability modeling adds variability to a modeling language without requiring modifications of the language or the supporting tools. We define a core language for separate variability modeling using a single kind of variation point to define transformations of software artifacts in object...... hierarchical dependencies between variation points via copying and flattening. Thus, we reduce a model with intricate dependencies to a flat executable model transformation consisting of simple unconditional local variation points. The core semantics is extremely concise: it boils down to two operational rules...

Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions.

Science.gov (United States)

Luo, Zhongkui; Feng, Wenting; Luo, Yiqi; Baldock, Jeff; Wang, Enli

2017-10-01

Soil organic carbon (SOC) dynamics are regulated by the complex interplay of climatic, edaphic and biotic conditions. However, the interrelation of SOC and these drivers and their potential connection networks are rarely assessed quantitatively. Using observations of SOC dynamics with detailed soil properties from 90 field trials at 28 sites under different agroecosystems across the Australian cropping regions, we investigated the direct and indirect effects of climate, soil properties, carbon (C) inputs and soil C pools (a total of 17 variables) on SOC change rate (r C , Mg C ha -1  yr -1 ). Among these variables, we found that the most influential variables on r C were the average C input amount and annual precipitation, and the total SOC stock at the beginning of the trials. Overall, C inputs (including C input amount and pasture frequency in the crop rotation system) accounted for 27% of the relative influence on r C , followed by climate 25% (including precipitation and temperature), soil C pools 24% (including pool size and composition) and soil properties (such as cation exchange capacity, clay content, bulk density) 24%. Path analysis identified a network of intercorrelations of climate, soil properties, C inputs and soil C pools in determining r C . The direct correlation of r C with climate was significantly weakened if removing the effects of soil properties and C pools, and vice versa. These results reveal the relative importance of climate, soil properties, C inputs and C pools and their complex interconnections in regulating SOC dynamics. Ignorance of the impact of changes in soil properties, C pool composition and C input (quantity and quality) on SOC dynamics is likely one of the main sources of uncertainty in SOC predictions from the process-based SOC models. © 2017 John Wiley & Sons Ltd.

ANALYSIS OF THE BANDUNG CHANGES EXCELLENT POTENTIAL THROUGH INPUT-OUTPUT MODEL USING INDEX LE MASNE

Directory of Open Access Journals (Sweden)

Teti Sofia Yanti

2017-03-01

Full Text Available Input-Output Table is arranged to present an overview of the interrelationships and interdependence between units of activity (sector production in the whole economy. Therefore the input-output models are complete and comprehensive analytical tool. The usefulness of input-output tables is an analysis of the economic structure of the national/regional level which covers the structure of production and value-added (GDP of each sector. For the purposes of planning and evaluation of the outcomes of development that is comprehensive both national and smaller scale (district/city, a model for regional development planning approach can use the model input-output analysis. Analysis of Bandung Economic Structure did use Le Masne index, by comparing the coefficients of the technology in 2003 and 2008, of which nearly 50% change. The trade sector has grown very conspicuous than other areas, followed by the services of road transport and air transport services, the development priorities and investment Bandung should be directed to these areas, this is due to these areas can be thrust and be power attraction for the growth of other areas. The areas that experienced the highest decrease was Industrial Chemicals and Goods from Chemistry, followed by Oil and Refinery Industry Textile Industry Except For Garment.

The Canadian Defence Input-Output Model DIO Version 4.41

Science.gov (United States)

2011-09-01

Request to develop DND tailored Input/Output Model. Electronic communication from AllenWeldon to Team Leader, Defence Economics Team onMarch 12, 2011...and similar contain- ers 166 1440 Handbags, wallets and similar personal articles such as eyeglass and cigar cases and coin purses 167 1450 Cotton yarn...408 3600 Radar and radio navigation equipment 409 3619 Semi-conductors 410 3621 Printed circuits 411 3622 Integrated circuits 412 3623 Other electronic

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 linearizing tracking control of induction machine with the included magnetic saturation

DEFF Research Database (Denmark)

Dolinar, Drago; Ljusev, Petar; Stumberger, Gorazd

2003-01-01

The tracking control design of an induction motor, based on input-output linearisation with magnetic saturation included is addressed. The magnetic saturation is represented by a nonlinear magnetising curve for the iron core and is used in the control, the observer of the state variables......, and in the load torque estimator. An input-output linearising control is used to achieve better tracking performances. It is based on the mixed 'stator current - rotor flux linkage' induction motor model with magnetic saturation considered in the stationary reference frame. Experimental results show...... that the proposed input-output linearising tracking control with saturation included behaves considerably better than the one without saturation, and that it introduces smaller position and speed errors, and better motor stiffness on account of the increased computational complexity....

Low-level waste shallow land disposal source term model: Data input guides

International Nuclear Information System (INIS)

Sullivan, T.M.; Suen, C.J.

1989-07-01

This report provides an input guide for the computational models developed to predict the rate of radionuclide release from shallow land disposal of low-level waste. Release of contaminants depends on four processes: water flow, container degradation, waste from leaching, and contaminant transport. The computer code FEMWATER has been selected to predict the movement of water in an unsaturated porous media. The computer code BLT (Breach, Leach, and Transport), a modification of FEMWASTE, has been selected to predict the processes of container degradation (Breach), contaminant release from the waste form (Leach), and contaminant migration (Transport). In conjunction, these two codes have the capability to account for the effects of disposal geometry, unsaturated/water flow, container degradation, waste form leaching, and migration of contaminants releases within a single disposal trench. In addition to the input requirements, this report presents the fundamental equations and relationships used to model the four different processes previously discussed. Further, the appendices provide a representative sample of data required by the different models. 14 figs., 27 tabs

PLEXOS Input Data Generator

Energy Technology Data Exchange (ETDEWEB)

2017-02-01

The PLEXOS Input Data Generator (PIDG) is a tool that enables PLEXOS users to better version their data, automate data processing, collaborate in developing inputs, and transfer data between different production cost modeling and other power systems analysis software. PIDG can process data that is in a generalized format from multiple input sources, including CSV files, PostgreSQL databases, and PSS/E .raw files and write it to an Excel file that can be imported into PLEXOS with only limited manual intervention.

Temporal rainfall estimation using input data reduction and model inversion

Science.gov (United States)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a

Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope

Directory of Open Access Journals (Sweden)

Cheng-Yang Chang

2017-10-01

Full Text Available Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the “open loop sensitivity” of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs.

Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope.

Science.gov (United States)

Chang, Cheng-Yang; Chen, Tsung-Lin

2017-10-31

Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT) material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the "open loop sensitivity" of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs.

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

Development of algorithm for depreciation costs allocation in dynamic input-output industrial enterprise model

Directory of Open Access Journals (Sweden)

Keller Alevtina

2017-01-01

Full Text Available The article considers the issue of allocation of depreciation costs in the dynamic inputoutput model of an industrial enterprise. Accounting the depreciation costs in such a model improves the policy of fixed assets management. It is particularly relevant to develop the algorithm for the allocation of depreciation costs in the construction of dynamic input-output model of an industrial enterprise, since such enterprises have a significant amount of fixed assets. Implementation of terms of the adequacy of such an algorithm itself allows: evaluating the appropriateness of investments in fixed assets, studying the final financial results of an industrial enterprise, depending on management decisions in the depreciation policy. It is necessary to note that the model in question for the enterprise is always degenerate. It is caused by the presence of zero rows in the matrix of capital expenditures by lines of structural elements unable to generate fixed assets (part of the service units, households, corporate consumers. The paper presents the algorithm for the allocation of depreciation costs for the model. This algorithm was developed by the authors and served as the basis for further development of the flowchart for subsequent implementation with use of software. The construction of such algorithm and its use for dynamic input-output models of industrial enterprises is actualized by international acceptance of the effectiveness of the use of input-output models for national and regional economic systems. This is what allows us to consider that the solutions discussed in the article are of interest to economists of various industrial enterprises.

A Water-Withdrawal Input-Output Model of the Indian Economy.

Science.gov (United States)

Bogra, Shelly; Bakshi, Bhavik R; Mathur, Ritu

2016-02-02

Managing freshwater allocation for a highly populated and growing economy like India can benefit from knowledge about the effect of economic activities. This study transforms the 2003-2004 economic input-output (IO) table of India into a water withdrawal input-output model to quantify direct and indirect flows. This unique model is based on a comprehensive database compiled from diverse public sources, and estimates direct and indirect water withdrawal of all economic sectors. It distinguishes between green (rainfall), blue (surface and ground), and scarce groundwater. Results indicate that the total direct water withdrawal is nearly 3052 billion cubic meter (BCM) and 96% of this is used in agriculture sectors with the contribution of direct green water being about 1145 BCM, excluding forestry. Apart from 727 BCM direct blue water withdrawal for agricultural, other significant users include "Electricity" with 64 BCM, "Water supply" with 44 BCM and other industrial sectors with nearly 14 BCM. "Construction", "miscellaneous food products"; "Hotels and restaurants"; "Paper, paper products, and newsprint" are other significant indirect withdrawers. The net virtual water import is found to be insignificant compared to direct water used in agriculture nationally, while scarce ground water associated with crops is largely contributed by northern states.

Metocean input data for drift models applications: Loustic study

International Nuclear Information System (INIS)

Michon, P.; Bossart, C.; Cabioc'h, M.

1995-01-01

Real-time monitoring and crisis management of oil slicks or floating structures displacement require a good knowledge of local winds, waves and currents used as input data for operational drift models. Fortunately, thanks to world-wide and all-weather coverage, satellite measurements have recently enabled the introduction of new methods for the remote sensing of the marine environment. Within a French joint industry project, a procedure has been developed using basically satellite measurements combined to metocean models in order to provide marine operators' drift models with reliable wind, wave and current analyses and short term forecasts. Particularly, a model now allows the calculation of the drift current, under the joint action of wind and sea-state, thus radically improving the classical laws. This global procedure either directly uses satellite wind and waves measurements (if available on the study area) or indirectly, as calibration of metocean models results which are brought to the oil slick or floating structure location. The operational use of this procedure is reported here with an example of floating structure drift offshore from the Brittany coasts

Analysis on relation between safety input and accidents

Institute of Scientific and Technical Information of China (English)

YAO Qing-guo; ZHANG Xue-mu; LI Chun-hui

2007-01-01

The number of safety input directly determines the level of safety, and there exists dialectical and unified relations between safety input and accidents. Based on the field investigation and reliable data, this paper deeply studied the dialectical relationship between safety input and accidents, and acquired the conclusions. The security situation of the coal enterprises was related to the security input rate, being effected little by the security input scale, and build the relationship model between safety input and accidents on this basis, that is the accident model.

Scaling precipitation input to spatially distributed hydrological models by measured snow distribution

Directory of Open Access Journals (Sweden)

Christian Vögeli

2016-12-01

Full Text Available Accurate knowledge on snow distribution in alpine terrain is crucial for various applicationssuch as flood risk assessment, avalanche warning or managing water supply and hydro-power.To simulate the seasonal snow cover development in alpine terrain, the spatially distributed,physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolationsof observations from automatic weather stations (AWS, leading to errors in the spatial distributionof atmospheric forcing. With recent advances in remote sensing techniques, maps of snowdepth can be acquired with high spatial resolution and accuracy. In this work, maps of the snowdepth distribution, calculated from summer and winter digital surface models based on AirborneDigital Sensors (ADS, are used to scale precipitation input data, with the aim to improve theaccuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method toscale and redistribute precipitation is presented and the performance is analysed. The scalingmethod is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation,with a simple air temperature threshold used for the determination of the precipitation phase.It was found that the accuracy of spatial snow distribution could be improved significantly forthe simulated domain. The standard deviation of absolute snow depth error is reduced up toa factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reducedwhen using representative input sources for the simulation domain. For inter-annual scaling, themodel performance could also be improved, even when using a remote sensing dataset from adifferent winter. In conclusion, using remote sensing data to process precipitation input, complexprocesses such as preferential snow deposition and snow relocation due to wind or avalanches,can be substituted and modelling performance of spatial snow distribution is improved.

SISTEM KONTROL OTOMATIK DENGAN MODEL SINGLE-INPUT-DUAL-OUTPUT DALAM KENDALI EFISIENSI UMUR-PEMAKAIAN INSTRUMEN

Directory of Open Access Journals (Sweden)

S.N.M.P. Simamora

2014-10-01

Full Text Available Efficiency condition occurs when the value of the used outputs compared to the resource total that has been used almost close to the value 1 (absolute environment. An instrument to achieve efficiency if the power output level has decreased significantly in the life of the instrument used, if it compared to the previous condition, when the instrument is not equipped with additional systems (or proposed model improvement. Even more effective if the inputs model that are used in unison to achieve a homogeneous output. On this research has been designed and implemented the automatic control system for models of single input-dual-output, wherein the sampling instruments used are lamp and fan. Source voltage used is AC (alternate-current and tested using quantitative research methods and instrumentation (with measuring instruments are observed. The results obtained demonstrate the efficiency of the instrument experienced a significant current model of single-input-dual-output applied separately instrument trials such as lamp and fan when it compared to the condition or state before. And the result show that the design has been built, can also run well.

VAM2D: Variably saturated analysis model in two dimensions

International Nuclear Information System (INIS)

Huyakorn, P.S.; Kool, J.B.; Wu, Y.S.

1991-10-01

This report documents a two-dimensional finite element model, VAM2D, developed to simulate water flow and solute transport in variably saturated porous media. Both flow and transport simulation can be handled concurrently or sequentially. The formulation of the governing equations and the numerical procedures used in the code are presented. The flow equation is approximated using the Galerkin finite element method. Nonlinear soil moisture characteristics and atmospheric boundary conditions (e.g., infiltration, evaporation and seepage face), are treated using Picard and Newton-Raphson iterations. Hysteresis effects and anisotropy in the unsaturated hydraulic conductivity can be taken into account if needed. The contaminant transport simulation can account for advection, hydrodynamic dispersion, linear equilibrium sorption, and first-order degradation. Transport of a single component or a multi-component decay chain can be handled. The transport equation is approximated using an upstream weighted residual method. Several test problems are presented to verify the code and demonstrate its utility. These problems range from simple one-dimensional to complex two-dimensional and axisymmetric problems. This document has been produced as a user's manual. It contains detailed information on the code structure along with instructions for input data preparation and sample input and printed output for selected test problems. Also included are instructions for job set up and restarting procedures. 44 refs., 54 figs., 24 tabs

Bayesian modeling of measurement error in predictor variables

NARCIS (Netherlands)

Fox, Gerardus J.A.; Glas, Cornelis A.W.

2003-01-01

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between

A Probabilistic Model for Propagating Ungauged Basin Runoff Prediction Variability and Uncertainty Into Estuarine Water Quality Dynamics and Water Quality-Based Management Decisions

Science.gov (United States)

Anderson, R.; Gronewold, A.; Alameddine, I.; Reckhow, K.

2008-12-01

The latest official assessment of United States (US) surface water quality indicates that pathogens are a leading cause of coastal shoreline water quality standard violations. Rainfall-runoff and hydrodynamic water quality models are commonly used to predict fecal indicator bacteria (FIB) concentrations in these waters and to subsequently identify climate change, land use, and pollutant mitigation scenarios which might improve water quality and lead to reinstatement of a designated use. While decay, settling, and other loss kinetics dominate FIB fate and transport in freshwater systems, previous authors identify tidal advection as a dominant fate and transport process in coastal estuaries. As a result, acknowledging hydrodynamic model input (e.g. watershed runoff) variability and parameter (e.g tidal dynamics parameter) uncertainty is critical to building a robust coastal water quality model. Despite the widespread application of watershed models (and associated model calibration procedures), we find model inputs and parameters are commonly encoded as deterministic point estimates (as opposed to random variables), an approach which effectively ignores potential sources of variability and uncertainty. Here, we present an innovative approach to building, calibrating, and propagating uncertainty and variability through a coupled data-based mechanistic (DBM) rainfall-runoff and tidal prism water quality model. While we apply the model to an ungauged tributary of the Newport River Estuary (one of many currently impaired shellfish harvesting waters in Eastern North Carolina), our model can be used to evaluate water quality restoration scenarios for coastal waters with a wide range of designated uses. We begin by calibrating the DBM rainfall-runoff model, as implemented in the IHACRES software package, using a regionalized calibration approach. We then encode parameter estimates as random variables (in the rainfall-runoff component of our comprehensive model) via the

Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs

Energy Technology Data Exchange (ETDEWEB)

Woods, J.; Winkler, J.; Christensen, D.; Hancock, E.

2014-08-01

Building energy simulations can be used to predict a building's interior conditions, along with the energy use associated with keeping these conditions comfortable. These models simulate the loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the space conditioning equipment, and then calculate the building's temperature and humidity throughout the year. The indoor temperature and humidity are affected not only by the loads and the space conditioning equipment, but also by the capacitance of the building materials, which buffer changes in temperature and humidity. This research developed an empirical method to extract whole-house model inputs for use with a more accurate moisture capacitance model (the effective moisture penetration depth model). The experimental approach was to subject the materials in the house to a square-wave relative humidity profile, measure all of the moisture transfer terms (e.g., infiltration, air conditioner condensate) and calculate the only unmeasured term: the moisture absorption into the materials. After validating the method with laboratory measurements, we performed the tests in a field house. A least-squares fit of an analytical solution to the measured moisture absorption curves was used to determine the three independent model parameters representing the moisture buffering potential of this house and its furnishings. Follow on tests with realistic latent and sensible loads showed good agreement with the derived parameters, especially compared to the commonly-used effective capacitance approach. These results show that the EMPD model, once the inputs are known, is an accurate moisture buffering model.

Handbook of latent variable and related models

CERN Document Server

Lee, Sik-Yum

2011-01-01

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Measurement of Laser Weld Temperatures for 3D Model Input

Energy Technology Data Exchange (ETDEWEB)

Dagel, Daryl [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grossetete, Grant [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Maccallum, Danny O. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-10-01

Laser welding is a key joining process used extensively in the manufacture and assembly of critical components for several weapons systems. Sandia National Laboratories advances the understanding of the laser welding process through coupled experimentation and modeling. This report summarizes the experimental portion of the research program, which focused on measuring temperatures and thermal history of laser welds on steel plates. To increase confidence in measurement accuracy, researchers utilized multiple complementary techniques to acquire temperatures during laser welding. This data serves as input to and validation of 3D laser welding models aimed at predicting microstructure and the formation of defects and their impact on weld-joint reliability, a crucial step in rapid prototyping of weapons components.

User manual of the multicompenent variably - saturated flow and transport model HP1

International Nuclear Information System (INIS)

Jacques, D.; Simunek, J.

2005-06-01

This report describes a new comprehensive simulation tool HP1 (HYDRUS1D-PHREEQC) that was obtained by coupling the HYDRUS-1D one-dimensional variably-saturated water flow and solute transport model with the PHREEQC geochemical code. The HP1 code incorporates modules simulating (1) transient water flow in variably-saturated media, (2) transport of multiple components, and (3) mixed equilibrium/kinetic geochemical reactions. The program numerically solves the Richards equation for variably-saturated water flow and advection-dispersion type equations for heat and solute transport. The flow equation incorporates a sink term to account for water uptake by plant roots. The heat transport equation considers transport due to conduction and convection with flowing water. The solute transport equations consider advective-dispersive transport in the liquid phase. The program can simulate a broad range of low-temperature biogeochemical reactions in water, soil and ground water systems including interactions with minerals, gases, exchangers, and sorption surfaces, based on thermodynamic equilibrium, kinetics, or mixed equilibrium-kinetic reactions. The program may be used to analyze water and solute movement in unsaturated, partially saturated, or fully saturated porous media. The flow region may be composed of nonuniform soils or sediments. Flow and transport can occur in the vertical, horizontal, or a generally inclined direction. The water flow part of the model can deal with prescribed head and flux boundaries, boundaries controlled by atmospheric conditions, as well as free drainage boundary conditions. The governing flow and transport equations were solved numerically using Galerkin-type linear finite element schemes. To test the accuracy of the coupling procedures implemented in HP1, simulation results were compared with (i) HYDRUS-1D for transport problems of multiple components subject to sequential first-order decay, (ii) PHREEQC for steady-state flow conditions, and

Linear and quadratic models of point process systems: contributions of patterned input to output.

Science.gov (United States)

Lindsay, K A; Rosenberg, J R

2012-08-01

In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike. Copyright © 2012 Elsevier Ltd. All rights reserved.

The economic impact of multifunctional agriculture in Dutch regions: An input-output model

NARCIS (Netherlands)

Heringa, P.W.; Heide, van der C.M.; Heijman, W.J.M.

2013-01-01

Multifunctional agriculture is a broad concept lacking a precise definition. Moreover, little is known about the societal importance of multifunctional agriculture. This paper is an empirical attempt to fill this gap. To this end, an input-output model was constructed for multifunctional agriculture

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

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

Science.gov (United States)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

International Nuclear Information System (INIS)

Wang, Qinpeng; Augenbroe, Godfried; Kim, Ji-Hyun; Gu, Li

2016-01-01

Highlights: • A meta-analysis framework for a stochastic characterization of occupancy variables. • Sensitivity ranking of occupancy variability against all other sources of uncertainty. • Sensitivity of occupant presence for building energy consumption is low. • Accurate mean knowledge is sufficient for predicting building energy consumption. • Prediction of peak demand behavior requires stochastic occupancy modeling. - Abstract: Occupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the meta-analysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

Efficient uncertainty quantification of a fully nonlinear and dispersive water wave model with random inputs

DEFF Research Database (Denmark)

Bigoni, Daniele; Engsig-Karup, Allan Peter; Eskilsson, Claes

2016-01-01

A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description...... at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental...... benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained...

A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

Directory of Open Access Journals (Sweden)

H.J. Foster

2001-01-01

Full Text Available Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests, has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry

Variability Properties of Four Million Sources in the TESS Input Catalog Observed with the Kilodegree Extremely Little Telescope Survey

Science.gov (United States)

Oelkers, Ryan J.; Rodriguez, Joseph E.; Stassun, Keivan G.; Pepper, Joshua; Somers, Garrett; Kafka, Stella; Stevens, Daniel J.; Beatty, Thomas G.; Siverd, Robert J.; Lund, Michael B.; Kuhn, Rudolf B.; James, David; Gaudi, B. Scott

2018-01-01

The Kilodegree Extremely Little Telescope (KELT) has been surveying more than 70% of the celestial sphere for nearly a decade. While the primary science goal of the survey is the discovery of transiting, large-radii planets around bright host stars, the survey has collected more than 106 images, with a typical cadence between 10–30 minutes, for more than four million sources with apparent visual magnitudes in the approximate range 7TESS Input catalog and the AAVSO Variable Star Index to precipitate the follow-up and classification of each source. The catalog is maintained as a living database on the Filtergraph visualization portal at the URL https://filtergraph.com/kelt_vars.

Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration

Science.gov (United States)

Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim

2015-04-01

In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.

Impact of magnetic saturation on the input-output linearising tracking control of an induction motor

DEFF Research Database (Denmark)

Dolinar, Drago; Ljusev, Petar; Stumberger, Gorazd

2004-01-01

This paper deals with the tracking control design of an induction motor, based on input-output linearization with magnetic saturation included. Magnetic saturation is represented by the nonlinear magnetizing curve of the iron core and is used in the control design, the observer of state variables......, and in the load torque estimator. An input-output linearising control is used to achieve better tracking performances of the drive. It is based on the mixed ”stator current - rotor flux linkage” induction motor model with magnetic saturation considered in the stationary reference frame. Experimental results show...... that the proposed input-output linearising tracking control with the included saturation behaves considerably better than the one without saturation, and that it introduces smaller position and speed errors, and better motor stiffness on account of the increased computational complexity....

Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

Directory of Open Access Journals (Sweden)

Wahiba Yaïci

2017-08-01

Full Text Available In recent years, there has been a strong growth in solar power generation industries. The need for highly efficient and optimised solar thermal energy systems, stand-alone or grid connected photovoltaic systems, has substantially increased. This requires the development of efficient and reliable performance prediction capabilities of solar heat and power production over the day. This contribution investigates the effect of the number of input variables on both the accuracy and the reliability of the artificial neural network (ANN method for predicting the performance parameters of a solar energy system. This paper describes the ANN models and the optimisation process in detail for predicting performance. Comparison with experimental data from a solar energy system tested in Ottawa, Canada during two years under different weather conditions demonstrates the good prediction accuracy attainable with each of the models using reduced input variables. However, it is likely true that the degree of model accuracy would gradually decrease with reduced inputs. Overall, the results of this study demonstrate that the ANN technique is an effective approach for predicting the performance of highly non-linear energy systems. The suitability of the modelling approach using ANNs as a practical engineering tool in renewable energy system performance analysis and prediction is clearly demonstrated.

Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: a shared input DEA-model.

Science.gov (United States)

Rogge, Nicky; De Jaeger, Simon

2012-10-01

This paper proposed an adjusted "shared-input" version of the popular efficiency measurement technique Data Envelopment Analysis (DEA) that enables evaluating municipality waste collection and processing performances in settings in which one input (waste costs) is shared among treatment efforts of multiple municipal solid waste fractions. The main advantage of this version of DEA is that it not only provides an estimate of the municipalities overall cost efficiency but also estimates of the municipalities' cost efficiency in the treatment of the different fractions of municipal solid waste (MSW). To illustrate the practical usefulness of the shared input DEA-model, we apply the model to data on 293 municipalities in Flanders, Belgium, for the year 2008. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multiregional input-output model for the evaluation of Spanish water flows.

Science.gov (United States)

Cazcarro, Ignacio; Duarte, Rosa; Sánchez Chóliz, Julio

2013-01-01

We construct a multiregional input-output model for Spain, in order to evaluate the pressures on the water resources, virtual water flows, and water footprints of the regions, and the water impact of trade relationships within Spain and abroad. The study is framed with those interregional input-output models constructed to study water flows and impacts of regions in China, Australia, Mexico, or the UK. To build our database, we reconcile regional IO tables, national and regional accountancy of Spain, trade and water data. Results show an important imbalance between origin of water resources and final destination, with significant water pressures in the South, Mediterranean, and some central regions. The most populated and dynamic regions of Madrid and Barcelona are important drivers of water consumption in Spain. Main virtual water exporters are the South and Central agrarian regions: Andalusia, Castile-La Mancha, Castile-Leon, Aragon, and Extremadura, while the main virtual water importers are the industrialized regions of Madrid, Basque country, and the Mediterranean coast. The paper shows the different location of direct and indirect consumers of water in Spain and how the economic trade and consumption pattern of certain areas has significant impacts on the availability of water resources in other different and often drier regions.

Amplification factor variable amplifier

NARCIS (Netherlands)

Akitsugu, Oshita; Nauta, Bram

2007-01-01

PROBLEM TO BE SOLVED: To provide an amplification factor variable amplifier capable of achieving temperature compensation of an amplification factor over a wide variable amplification factor range. ; SOLUTION: A Gilbert type amplification factor variable amplifier 11 amplifies an input signal and

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

On the redistribution of existing inputs using the spherical frontier dea model

Directory of Open Access Journals (Sweden)

José Virgilio Guedes de Avellar

2010-04-01

Full Text Available The Spherical Frontier DEA Model (SFM (Avellar et al., 2007 was developed to be used when one wants to fairly distribute a new and fixed input to a group of Decision Making Units (DMU's. SFM's basic idea is to distribute this new and fixed input in such a way that every DMU will be placed on an efficiency frontier with a spherical shape. We use SFM to analyze the problems that appear when one wants to redistribute an already existing input to a group of DMU's such that the total sum of this input will remain constant. We also analyze the case in which this total sum may vary.O Modelo de Fronteira Esférica (MFE (Avellar et al., 2007 foi desenvolvido para ser usado quando se deseja distribuir de maneira justa um novo insumo a um conjunto de unidades tomadoras de decisão (DMU's, da sigla em inglês, Decision Making Units. A ideia básica do MFE é a de distribuir esse novo insumo de maneira que todas as DMU's sejam colocadas numa fronteira de eficiência com um formato esférico. Neste artigo, usamos MFE para analisar o problema que surge quando se deseja redistribuir um insumo já existente para um grupo de DMU's de tal forma que a soma desse insumo para todas as DMU's se mantenha constante. Também analisamos o caso em que essa soma possa variar.

How to get rid of W: a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, H.; Oud, J.

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

How to get rid of W : a latent variables approach to modelling spatially lagged variables

NARCIS (Netherlands)

Folmer, Henk; Oud, Johan

2008-01-01

In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

Internal variability of a 3-D ocean model

Directory of Open Access Journals (Sweden)

Bjarne Büchmann

2016-11-01

Full Text Available The Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve ‘model skill’ and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry.An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found – in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events.A particular event is examined in detail to shed light on how the ensemble ‘behaves’ in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

Science.gov (United States)

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

Directory of Open Access Journals (Sweden)

Connor Hyunju Kim

2016-01-01

Full Text Available The magnetosphere is a major source of energy for the Earth’s ionosphere and thermosphere (IT system. Current IT models drive the upper atmosphere using empirically calculated magnetospheric energy input. Thus, they do not sufficiently capture the storm-time dynamics, particularly at high latitudes. To improve the prediction capability of IT models, a physics-based magnetospheric input is necessary. Here, we use the Open Global General Circulation Model (OpenGGCM coupled with the Coupled Thermosphere Ionosphere Model (CTIM. OpenGGCM calculates a three-dimensional global magnetosphere and a two-dimensional high-latitude ionosphere by solving resistive magnetohydrodynamic (MHD equations with solar wind input. CTIM calculates a global thermosphere and a high-latitude ionosphere in three dimensions using realistic magnetospheric inputs from the OpenGGCM. We investigate whether the coupled model improves the storm-time IT responses by simulating a geomagnetic storm that is preceded by a strong solar wind pressure front on August 24, 2005. We compare the OpenGGCM-CTIM results with low-earth-orbit satellite observations and with the model results of Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe. CTIPe is an up-to-date version of CTIM that incorporates more IT dynamics such as a low-latitude ionosphere and a plasmasphere, but uses empirical magnetospheric input. OpenGGCM-CTIM reproduces localized neutral density peaks at ~ 400 km altitude in the high-latitude dayside regions in agreement with in situ observations during the pressure shock and the early phase of the storm. Although CTIPe is in some sense a much superior model than CTIM, it misses these localized enhancements. Unlike the CTIPe empirical input models, OpenGGCM-CTIM more faithfully produces localized increases of both auroral precipitation and ionospheric electric fields near the high-latitude dayside region after the pressure shock and after the storm onset

Generalized latent variable modeling multilevel, longitudinal, and structural equation models

CERN Document Server

Skrondal, Anders; Rabe-Hesketh, Sophia

2004-01-01

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.

Flow variability and hillslope hydrology

Energy Technology Data Exchange (ETDEWEB)

Huff, D D; O' Neill, R V; Emanuel, W R; Elwood, J W; Newbold, J D

1982-01-01

Examination of spatial variability of streamflow in headwater areas can provide important insight about factors that influence hillslope hydrology. Detailed observations of variations in stream channel input, based on a tracer experiment, indicate that topography alone cannot explain flow variability. However, determination of changes in channel input on a small spatial scale can provide valuable clues to factors, such as structural geology that control subsurface flows.

An Approach for Generating Precipitation Input for Worst-Case Flood Modelling

Science.gov (United States)

Felder, Guido; Weingartner, Rolf

2015-04-01

There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this study proposes a method of deriving representative spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte-Carlo rainfall-runoff model allows for the testing of a wide range of different spatio-temporal distributions of an extreme precipitation event and therefore for the generation of a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the worst-case catchment reactions on the system input can be derived. The spatio-temporal distributions leading to the highest peak discharges are identified and can eventually be used for further investigations.

Amplification factor variable amplifier

NARCIS (Netherlands)

Akitsugu, Oshita; Nauta, Bram

2010-01-01

PROBLEM TO BE SOLVED: To provide an amplification factor variable amplifier capable of achieving temperature compensation of an amplification factor over a wide variable amplification factor range. ;SOLUTION: A Gilbert type amplification factor variable amplifier 11 amplifies an input signal and can

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

Science.gov (United States)

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

Phylogenetic mixtures and linear invariants for equal input models.

Science.gov (United States)

Casanellas, Marta; Steel, Mike

2017-04-01

The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).

In-process tool rotational speed variation with constant heat input in friction stir welding of AZ31 sheets with variable thickness

Science.gov (United States)

Buffa, Gianluca; Campanella, Davide; Forcellese, Archimede; Fratini, Livan; Simoncini, Michela

2017-10-01

In the present work, friction stir welding experiments on AZ31 magnesium alloy sheets, characterized by a variable thickness along the welding line, were carried out. The approach adapted during welding consisted in maintaining constant the heat input to the joint. To this purpose, the rotational speed of the pin tool was increased with decreasing thickness and decreased with increasing thickness in order to obtain the same temperatures during welding. The amount by which the rotational speed was changed as a function of the sheet thickness was defined on the basis of the results given by FEM simulations of the FSW process. Finally, the effect of the in-process variation of the tool rotational speed on the mechanical and microstructural properties of FSWed joints was analysed by comparing both the nominal stress vs. nominal strain curves and microstructure of FSWed joints obtained in different process conditions. It was observed that FSW performed by keeping constant the heat input to the joint leads to almost coincident results both in terms of the curve shape, ultimate tensile strength and ultimate elongation values, and microstructure.

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

Usefulness of non-linear input-output models for economic impact analyses in tourism and recreation

NARCIS (Netherlands)

Klijs, J.; Peerlings, J.H.M.; Heijman, W.J.M.

2015-01-01

In tourism and recreation management it is still common practice to apply traditional input–output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input–output (NLIO) model, in which price-induced input

Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

Science.gov (United States)

Miller, L. D.; Tom, C.; Nualchawee, K.

1977-01-01

A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

Non-perturbative inputs for gluon distributions in the hadrons

International Nuclear Information System (INIS)

Ermolaev, B.I.; Troyan, S.I.

2017-01-01

Description of hadronic reactions at high energies is conventionally done in the framework of QCD factorization. All factorization convolutions comprise non-perturbative inputs mimicking non-perturbative contributions and perturbative evolution of those inputs. We construct inputs for the gluon-hadron scattering amplitudes in the forward kinematics and, using the optical theorem, convert them into inputs for gluon distributions in the hadrons, embracing the cases of polarized and unpolarized hadrons. In the first place, we formulate mathematical criteria which any model for the inputs should obey and then suggest a model satisfying those criteria. This model is based on a simple reasoning: after emitting an active parton off the hadron, the remaining set of spectators becomes unstable and therefore it can be described through factors of the resonance type, so we call it the resonance model. We use it to obtain non-perturbative inputs for gluon distributions in unpolarized and polarized hadrons for all available types of QCD factorization: basic, K_T-and collinear factorizations. (orig.)

Non-perturbative inputs for gluon distributions in the hadrons

Energy Technology Data Exchange (ETDEWEB)

Ermolaev, B.I. [Ioffe Physico-Technical Institute, Saint Petersburg (Russian Federation); Troyan, S.I. [St. Petersburg Institute of Nuclear Physics, Gatchina (Russian Federation)

2017-03-15

Description of hadronic reactions at high energies is conventionally done in the framework of QCD factorization. All factorization convolutions comprise non-perturbative inputs mimicking non-perturbative contributions and perturbative evolution of those inputs. We construct inputs for the gluon-hadron scattering amplitudes in the forward kinematics and, using the optical theorem, convert them into inputs for gluon distributions in the hadrons, embracing the cases of polarized and unpolarized hadrons. In the first place, we formulate mathematical criteria which any model for the inputs should obey and then suggest a model satisfying those criteria. This model is based on a simple reasoning: after emitting an active parton off the hadron, the remaining set of spectators becomes unstable and therefore it can be described through factors of the resonance type, so we call it the resonance model. We use it to obtain non-perturbative inputs for gluon distributions in unpolarized and polarized hadrons for all available types of QCD factorization: basic, K{sub T}-and collinear factorizations. (orig.)

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

Science.gov (United States)

Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

2009-01-01

This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

On the explaining-away phenomenon in multivariate latent variable models.

Science.gov (United States)

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation

Directory of Open Access Journals (Sweden)

A. Arneth

2011-08-01

Full Text Available Due to its effects on the atmospheric lifetime of methane, the burdens of tropospheric ozone and growth of secondary organic aerosol, isoprene is central among the biogenic compounds that need to be taken into account for assessment of anthropogenic air pollution-climate change interactions. Lack of process-understanding regarding leaf isoprene production as well as of suitable observations to constrain and evaluate regional or global simulation results add large uncertainties to past, present and future emissions estimates. Focusing on contemporary climate conditions, we compare three global isoprene models that differ in their representation of vegetation and isoprene emission algorithm. We specifically aim to investigate the between- and within model variation that is introduced by varying some of the models' main features, and to determine which spatial and/or temporal features are robust between models and different experimental set-ups. In their individual standard configurations, the models broadly agree with respect to the chief isoprene sources and emission seasonality, with maximum monthly emission rates around 20–25 Tg C, when averaged by 30-degree latitudinal bands. They also indicate relatively small (approximately 5 to 10 % around the mean interannual variability of total global emissions. The models are sensitive to changes in one or more of their main model components and drivers (e.g., underlying vegetation fields, climate input which can yield increases or decreases in total annual emissions of cumulatively by more than 30 %. Varying drivers also strongly alters the seasonal emission pattern. The variable response needs to be interpreted in view of the vegetation emission capacities, as well as diverging absolute and regional distribution of light, radiation and temperature, but the direction of the simulated emission changes was not as uniform as anticipated. Our results highlight the need for modellers to evaluate their

Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy).

Science.gov (United States)

Tuo, Ye; Duan, Zheng; Disse, Markus; Chiogna, Gabriele

2016-12-15

Precipitation is often the most important input data in hydrological models when simulating streamflow. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauge station that is nearest to the centroid of each subbasin, which is eventually corrected using the elevation band method. This leads in general to inaccurate representation of subbasin precipitation input data, particularly in catchments with complex topography. To investigate the impact of different precipitation inputs on the SWAT model simulations in Alpine catchments, 13years (1998-2010) of daily precipitation data from four datasets including OP (Observed precipitation), IDW (Inverse Distance Weighting data), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission) has been considered. Both model performances (comparing simulated and measured streamflow data at the catchment outlet) as well as parameter and prediction uncertainties have been quantified. For all three subbasins, the use of elevation bands is fundamental to match the water budget. Streamflow predictions obtained using IDW inputs are better than those obtained using the other datasets in terms of both model performance and prediction uncertainty. Models using the CHIRPS product as input provide satisfactory streamflow estimation, suggesting that this satellite product can be applied to this data-scarce Alpine region. Comparing the performance of SWAT models using different precipitation datasets is therefore important in data-scarce regions. This study has shown that, precipitation is the main source of uncertainty, and different precipitation datasets in SWAT models lead to different best estimate ranges for the calibrated parameters. This has important implications for the interpretation of the simulated hydrological processes. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Stabilization of Networked Control Systems with Variable Delays and Saturating Inputs

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M. Mahmodi Kaleybar

2014-06-01

Full Text Available In this paper, improved conditions for the synthesis of static state-feedback controller are derived to stabilize networked control systems (NCSs subject to actuator saturation. Both of the data packet latency and dropout which deteriorate the performance of the closed-loop system are considered in the NCS model via variable delays. Two different techniques are employed to incorporate actuator saturation in the system description. Utilizing Lyapunov-Krasovskii Theorem, delay-dependent conditions are obtained in terms of linear matrix inequalities (LMIs to determine the static feedback gain. Moreover, an optimization problem is formulated in order to find the less conservative estimate for the region of attraction corresponding to different maximum allowable delays. Numerical examples are introduced to demonstrate the effectiveness and advantages of the proposed schemes.

Targeting the right input data to improve crop modeling at global level

Science.gov (United States)

Adam, M.; Robertson, R.; Gbegbelegbe, S.; Jones, J. W.; Boote, K. J.; Asseng, S.

2012-12-01

Designed for location-specific simulations, the use of crop models at a global level raises important questions. Crop models are originally premised on small unit areas where environmental conditions and management practices are considered homogeneous. Specific information describing soils, climate, management, and crop characteristics are used in the calibration process. However, when scaling up for global application, we rely on information derived from geographical information systems and weather generators. To run crop models at broad, we use a modeling platform that assumes a uniformly generated grid cell as a unit area. Specific weather, specific soil and specific management practices for each crop are represented for each of the cell grids. Studies on the impacts of the uncertainties of weather information and climate change on crop yield at a global level have been carried out (Osborne et al, 2007, Nelson et al., 2010, van Bussel et al, 2011). Detailed information on soils and management practices at global level are very scarce but recognized to be of critical importance (Reidsma et al., 2009). Few attempts to assess the impact of their uncertainties on cropping systems performances can be found. The objectives of this study are (i) to determine sensitivities of a crop model to soil and management practices, inputs most relevant to low input rainfed cropping systems, and (ii) to define hotspots of sensitivity according to the input data. We ran DSSAT v4.5 globally (CERES-CROPSIM) to simulate wheat yields at 45arc-minute resolution. Cultivar parameters were calibrated and validated for different mega-environments (results not shown). The model was run for nitrogen-limited production systems. This setting was chosen as the most representative to simulate actual yield (especially for low-input rainfed agricultural systems) and assumes crop growth to be free of any pest and diseases damages. We conducted a sensitivity analysis on contrasting management

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

Science.gov (United States)

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

2013-09-01

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

International trade inoperability input-output model (IT-IIM): theory and application.

Science.gov (United States)

Jung, Jeesang; Santos, Joost R; Haimes, Yacov Y

2009-01-01

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).

Detection of no-model input-output pairs in closed-loop systems.

Science.gov (United States)

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Linear latent variable models: the lava-package

DEFF Research Database (Denmark)

Holst, Klaus Kähler; Budtz-Jørgensen, Esben

2013-01-01

are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...

Hydrologic scales, cloud variability, remote sensing, and models: Implications for forecasting snowmelt and streamflow

Science.gov (United States)

Simpson, James J.; Dettinger, M.D.; Gehrke, F.; McIntire, T.J.; Hufford, Gary L.

2004-01-01

Accurate prediction of available water supply from snowmelt is needed if the myriad of human, environmental, agricultural, and industrial demands for water are to be satisfied, especially given legislatively imposed conditions on its allocation. Robust retrievals of hydrologic basin model variables (e.g., insolation or areal extent of snow cover) provide several advantages over the current operational use of either point measurements or parameterizations to help to meet this requirement. Insolation can be provided at hourly time scales (or better if needed during rapid melt events associated with flooding) and at 1-km spatial resolution. These satellite-based retrievals incorporate the effects of highly variable (both in space and time) and unpredictable cloud cover on estimates of insolation. The insolation estimates are further adjusted for the effects of basin topography using a high-resolution digital elevation model prior to model input. Simulations of two Sierra Nevada rivers in the snowmelt seasons of 1998 and 1999 indicate that even the simplest improvements in modeled insolation can improve snowmelt simulations, with 10%-20% reductions in root-mean-square errors. Direct retrieval of the areal extent of snow cover may mitigate the need to rely entirely on internal calculations of this variable, a reliance that can yield large errors that are difficult to correct until long after the season is complete and that often leads to persistent underestimates or overestimates of the volumes of the water to operational reservoirs. Agencies responsible for accurately predicting available water resources from the melt of snowpack [e.g., both federal (the National Weather Service River Forecast Centers) and state (the California Department of Water Resources)] can benefit by incorporating concepts developed herein into their operational forecasting procedures. ?? 2004 American Meteorological Society.

Inputs and spatial distribution patterns of Cr in Jiaozhou Bay

Science.gov (United States)

Yang, Dongfang; Miao, Zhenqing; Huang, Xinmin; Wei, Linzhen; Feng, Ming

2018-03-01

Cr pollution in marine bays has been one of the critical environmental issues, and understanding the input and spatial distribution patterns is essential to pollution control. In according to the source strengths of the major pollution sources, the input patterns of pollutants to marine bay include slight, moderate and heavy, and the spatial distribution are corresponding to three block models respectively. This paper analyzed input patterns and distributions of Cr in Jiaozhou Bay, eastern China based on investigation on Cr in surface waters during 1979-1983. Results showed that the input strengths of Cr in Jiaozhou Bay could be classified as moderate input and slight input, and the input strengths were 32.32-112.30 μg L-1 and 4.17-19.76 μg L-1, respectively. The input patterns of Cr included two patterns of moderate input and slight input, and the horizontal distributions could be defined by means of Block Model 2 and Block Model 3, respectively. In case of moderate input pattern via overland runoff, Cr contents were decreasing from the estuaries to the bay mouth, and the distribution pattern was parallel. In case of moderate input pattern via marine current, Cr contents were decreasing from the bay mouth to the bay, and the distribution pattern was parallel to circular. The Block Models were able to reveal the transferring process of various pollutants, and were helpful to understand the distributions of pollutants in marine bay.

Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models

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Robert B. Gramacy

2010-02-01

Full Text Available This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling. These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART, to treed Gaussian processes with jumps to the limiting linear model. It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007.

Uncertainty of input data for room acoustic simulations

DEFF Research Database (Denmark)

Jeong, Cheol-Ho; Marbjerg, Gerd; Brunskog, Jonas

2016-01-01

Although many room acoustic simulation models have been well established, simulation results will never be accurate with inaccurate and uncertain input data. This study addresses inappropriateness and uncertainty of input data for room acoustic simulations. Firstly, the random incidence absorption...... and scattering coefficients are insufficient when simulating highly non-diffuse rooms. More detailed information, such as the phase and angle dependence, can greatly improve the simulation results of pressure-based geometrical and wave-based models at frequencies well below the Schroeder frequency. Phase...... summarizes potential advanced absorption measurement techniques that can improve the quality of input data for room acoustic simulations. Lastly, plenty of uncertain input data are copied from unreliable sources. Software developers and users should be careful when spreading such uncertain input data. More...

Multi-wheat-model ensemble responses to interannual climatic variability

DEFF Research Database (Denmark)

Ruane, A C; Hudson, N I; Asseng, S

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and ......-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.......We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...

Statistical Dependence of Pipe Breaks on Explanatory Variables

Directory of Open Access Journals (Sweden)

Patricia Gómez-Martínez

2017-02-01

Full Text Available Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models.

Simplex-based optimization of numerical and categorical inputs in early bioprocess development: Case studies in HT chromatography.

Science.gov (United States)

Konstantinidis, Spyridon; Titchener-Hooker, Nigel; Velayudhan, Ajoy

2017-08-01

Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter-plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex-based method was further shown to be of equivalent efficiency to a DoE-based approach, represented here by D-Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex-variant is suited to development activities involving numerical and categorical inputs in early bioprocess development. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

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Adam ePonzi

2012-03-01

Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

Science.gov (United States)

Ponzi, Adam; Wickens, Jeff

2012-01-01

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

Science.gov (United States)

Ngan, Fong; Byun, Daewon; Kim, Hyuncheol; Lee, Daegyun; Rappenglück, Bernhard; Pour-Biazar, Arastoo

2012-07-01

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

A new environmental Kuznets curve? Relationship between direct material input and income per capita. Evidence from industrialised countries

Energy Technology Data Exchange (ETDEWEB)

Canas, Angela; Ferrao, Paulo; Conceicao, Pedro [IN+-Centre for Innovation, Technology and Policy Research, IST-Instituto Superior Tecnico, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

2003-09-01

Many studies have focused on the quantification of the input of materials into the economy. However, the insights provided by those studies have generally been limited. This paper attempts to bring analytical value to the discussion on 'dematerialization' considering direct material input (DMI) per capita as the dependent variable in a test of the environmental Kuznets curve (EKC). The explanatory variable is, as usual, gross domestic product per capita. The quadratic and cubic versions of the EKC are tested econometrically, using panel data ranging from 1960 to 1998 for 16 industrialised countries. The results indicate a strong and robust support for both the quadratic and cubic EKC relationships between material input and income, in industrialised economies. While the statistical support to both types of evolution may seem contradictory, it suggests that as industrialised economies grow, the intensity of material consumption first increases, but eventually starts exhibiting a decreasing trend after a certain income threshold is reached. The inverted-U, or quadratic, relationship is confirmed, even though for the ranges of income considered in this study, the trend is mostly on the increasing part of the inverted-U curve. However, the statistical support to the cubic specification suggests that these results need to be regarded with caution. Overall, the statistically stronger models are the quadratic and cubic models with country random effects, and the cubic model with country and year fixed effects.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

Science.gov (United States)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Are revised models better models? A skill score assessment of regional interannual variability

Science.gov (United States)

Sperber, Kenneth R.; Participating AMIP Modelling Groups

1999-05-01

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

Comparison of plasma input and reference tissue models for analysing [(11)C]flumazenil studies

NARCIS (Netherlands)

Klumpers, Ursula M. H.; Veltman, Dick J.; Boellaard, Ronald; Comans, Emile F.; Zuketto, Cassandra; Yaqub, Maqsood; Mourik, Jurgen E. M.; Lubberink, Mark; Hoogendijk, Witte J. G.; Lammertsma, Adriaan A.

2008-01-01

A single-tissue compartment model with plasma input is the established method for analysing [(11)C]flumazenil ([(11)C]FMZ) studies. However, arterial cannulation and measurement of metabolites are time-consuming. Therefore, a reference tissue approach is appealing, but this approach has not been

The economic impact of multifunctional agriculture in The Netherlands: A regional input-output model

NARCIS (Netherlands)

Heringa, P.W.; Heide, van der C.M.; Heijman, W.J.M.

2012-01-01

Multifunctional agriculture is a broad concept lacking a precise and uniform definition. Moreover, little is known about the societal importance of multifunctional agriculture. This paper is an empirical attempt to fill this gap. To this end, an input-output model is constructed for multifunctional

Noise and crosstalk in two quorum-sensing inputs of Vibrio fischeri

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Weiss Joel T

2011-09-01

Full Text Available Abstract Background One of the puzzles in bacterial quorum sensing is understanding how an organism integrates the information gained from multiple input signals. The marine bacterium Vibrio fischeri regulates its bioluminescence through a quorum sensing mechanism that receives input from three pheromone signals, including two acyl homoserine lactone (HSL signals. While the role of the 3-oxo-C6 homoserine lactone (3OC6HSL signal in activating the lux genes has been extensively studied and modeled, the role of the C8 homoserine lactone (C8HSL is less obvious, as it can either activate luminescence or block its activation. It remains unclear how crosstalk between C8HSL and 3OC6HSL affects the information that the bacterium obtains through quorum sensing. Results We have used microfluidic methods to measure the response of individual V.fischeri cells to combinations of C8HSL and 3OC6HSL. By measuring the fluorescence of individual V.fischeri cells containing a chromosomal gfp-reporter for the lux genes, we study how combinations of exogenous HSLs affect both the population average and the cell-to-cell variability of lux activation levels. At the level of a population average, the crosstalk between the C8HSL and 3OC6HSL inputs is well-described by a competitive inhibition model. At the level of individual cells, the heterogeneity in the lux response depends only on the average degree of activation, so that the noise in the output is not reduced by the presence of the second HSL signal. Overall we find that the mutual information between the signal inputs and the lux output is less than one bit. A nonlinear correlation between fluorescence and bioluminescence outputs from lux leads to different noise properties for these reporters. Conclusions The lux genes in V.fischeri do not appear to distinguish between the two HSL inputs, and even with two signal inputs the regulation of lux is extremely noisy. Hence the role of crosstalk from the C8HSL input

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 coupled physical-biological model of the Northern Gulf of Mexico shelf: model description, validation and analysis of phytoplankton variability

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K. Fennel

2011-07-01

Full Text Available The Texas-Louisiana shelf in the Northern Gulf of Mexico receives large inputs of nutrients and freshwater from the Mississippi/Atchafalaya River system. The nutrients stimulate high rates of primary production in the river plume, which contributes to the development of a large and recurring hypoxic area in summer, but the mechanistic links between hypoxia and river discharge of freshwater and nutrients are complex as the accumulation and vertical export of organic matter, the establishment and maintenance of vertical stratification, and the microbial degradation of organic matter are controlled by a non-linear interplay of factors. Unraveling these interactions will have to rely on a combination of observations and models. Here we present results from a realistic, 3-dimensional, physical-biological model with focus on a quantification of nutrient-stimulated phytoplankton growth, its variability and the fate of this organic matter. We demonstrate that the model realistically reproduces many features of observed nitrate and phytoplankton dynamics including observed property distributions and rates. We then contrast the environmental factors and phytoplankton source and sink terms characteristic of three model subregions that represent an ecological gradient from eutrophic to oligotrophic conditions. We analyze specifically the reasons behind the counterintuitive observation that primary production in the light-limited plume region near the Mississippi River delta is positively correlated with river nutrient input, and find that, while primary production and phytoplankton biomass are positively correlated with nutrient load, phytoplankton growth rate is not. This suggests that accumulation of biomass in this region is not primarily controlled bottom up by nutrient-stimulation, but top down by systematic differences in the loss processes.

Jointness through vessel capacity input in a multispecies fishery

DEFF Research Database (Denmark)

Hansen, Lars Gårn; Jensen, Carsten Lynge

2014-01-01

capacity. We develop a fixed but allocatable input model of purse seine fisheries capturing this particular type of jointness. We estimate the model for the Norwegian purse seine fishery and find that it is characterized by nonjointness, while estimations for this fishery using the standard models imply...... are typically modeled as either independent single species fisheries or using standard multispecies functional forms characterized by jointness in inputs. We argue that production of each species is essentially independent but that jointness may be caused by competition for fixed but allocable input of vessel...

Coevolution of variability models and related software artifacts

DEFF Research Database (Denmark)

Passos, Leonardo; Teixeira, Leopoldo; Dinztner, Nicolas

2015-01-01

models coevolve with other artifact types, we study a large and complex real-world variant-rich software system: the Linux kernel. Specifically, we extract variability-coevolution patterns capturing changes in the variability model of the Linux kernel with subsequent changes in Makefiles and C source...

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

Science.gov (United States)

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-08-23

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful

Parameter and model uncertainty in a life-table model for fine particles (PM2.5: a statistical modeling study

Directory of Open Access Journals (Sweden)

Jantunen Matti J

2007-08-01

Full Text Available Abstract Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5 are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i plausibility of mortality outcomes and (ii lag, and parameter uncertainties (iii exposure-response coefficients for different mortality outcomes, and (iv exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure

Variable selection in Logistic regression model with genetic algorithm.

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Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi

2018-02-01

Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

Associated and Mediating Variables Related to Job Satisfaction among Professionals from Mental Health Teams.

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Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie; Chiocchio, François

2018-06-01

Using a structural analysis, this study examines the relationship between job satisfaction among 315 mental health professionals from the province of Quebec (Canada) and a wide range of variables related to provider characteristics, team characteristics, processes, and emergent states, and organizational culture. We used the Job Satisfaction Survey to assess job satisfaction. Our conceptual framework integrated numerous independent variables adapted from the input-mediator-output-input (IMOI) model and the Integrated Team Effectiveness Model (ITEM). The structural equation model predicted 47% of the variance of job satisfaction. Job satisfaction was associated with eight variables: strong team support, participation in the decision-making process, closer collaboration, fewer conflicts among team members, modest knowledge production (team processes), firm affective commitment, multifocal identification (emergent states) and belonging to the nursing profession (provider characteristics). Team climate had an impact on six job satisfaction variables (team support, knowledge production, conflicts, affective commitment, collaboration, and multifocal identification). Results show that team processes and emergent states were mediators between job satisfaction and team climate. To increase job satisfaction among professionals, health managers need to pursue strategies that foster a positive climate within mental health teams.

Simplified quantitative treatment of uncertainty and interindividual variability in health risk assessment

International Nuclear Information System (INIS)

Bogen, K.T.

1993-01-01

A distinction between uncertainty (or the extent of lack of knowledge) and interindividual variability (or the extent of person-to-person heterogeneity) regarding the values of input variates must be maintained if a quantitative characterization of uncertainty in population risk or in individual risk is sought. Here, some practical methods are presented that should facilitate implementation of the analytic framework for uncertainty and variability proposed by Bogen and Spear. (1,2) Two types of methodology are discussed: one that facilitates the distinction between uncertainty and variability per se, and another that may be used to simplify quantitative analysis of distributed inputs representing either uncertainty or variability. A simple and a complex form for modeled increased risk are presented and then used to illustrate methods facilitating the distinction between uncertainty and variability in reference to characterization of both population and individual risk. Finally, a simple form of discrete probability calculus is proposed as an easily implemented, practical altemative to Monte-Carlo based procedures to quantitative integration of uncertainty and variability in risk assessment

High Resolution Modeling of the Thermospheric Response to Energy Inputs During the RENU-2 Rocket Flight

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Walterscheid, R. L.; Brinkman, D. G.; Clemmons, J. H.; Hecht, J. H.; Lessard, M.; Fritz, B.; Hysell, D. L.; Clausen, L. B. N.; Moen, J.; Oksavik, K.; Yeoman, T. K.

2017-12-01

The Earth's magnetospheric cusp provides direct access of energetic particles to the thermosphere. These particles produce ionization and kinetic (particle) heating of the atmosphere. The increased ionization coupled with enhanced electric fields in the cusp produces increased Joule heating and ion drag forcing. These energy inputs cause large wind and temperature changes in the cusp region. The Rocket Experiment for Neutral Upwelling -2 (RENU-2) launched from Andoya, Norway at 0745UT on 13 December 2015 into the ionosphere-thermosphere beneath the magnetic cusp. It made measurements of the energy inputs (e.g., precipitating particles, electric fields) and the thermospheric response to these energy inputs (e.g., neutral density and temperature, neutral winds). Complementary ground based measurements were made. In this study, we use a high resolution two-dimensional time-dependent non hydrostatic nonlinear dynamical model driven by rocket and ground based measurements of the energy inputs to simulate the thermospheric response during the RENU-2 flight. Model simulations will be compared to the corresponding measurements of the thermosphere to see what they reveal about thermospheric structure and the nature of magnetosphere-ionosphere-thermosphere coupling in the cusp. Acknowledgements: This material is based upon work supported by the National Aeronautics and Space Administration under Grants: NNX16AH46G and NNX13AJ93G. This research was also supported by The Aerospace Corporation's Technical Investment program

A statistical survey of heat input parameters into the cusp thermosphere

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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.

Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

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Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2017-08-22

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method

Persistence and ergodicity of plant disease model with markov conversion and impulsive toxicant input

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Zhao, Wencai; Li, Juan; Zhang, Tongqian; Meng, Xinzhu; Zhang, Tonghua

2017-07-01

Taking into account of both white and colored noises, a stochastic mathematical model with impulsive toxicant input is formulated. Based on this model, we investigate dynamics, such as the persistence and ergodicity, of plant infectious disease model with Markov conversion in a polluted environment. The thresholds of extinction and persistence in mean are obtained. By using Lyapunov functions, we prove that the system is ergodic and has a stationary distribution under certain sufficient conditions. Finally, numerical simulations are employed to illustrate our theoretical analysis.

The sensitivity of ecosystem service models to choices of input data and spatial resolution

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Bagstad, Kenneth J.; Cohen, Erika; Ancona, Zachary H.; McNulty, Steven; Sun, Ge

2018-01-01

Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.