WorldWideScience

Sample records for model output statistics

  1. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  2. On the statistical comparison of climate model output and climate data

    International Nuclear Information System (INIS)

    Solow, A.R.

    1991-01-01

    Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question. 9 refs

  3. On the statistical comparison of climate model output and climate data

    International Nuclear Information System (INIS)

    Solow, A.R.

    1990-01-01

    Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question

  4. texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables

    Directory of Open Access Journals (Sweden)

    Philip Leifeld

    2013-11-01

    Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.

  5. Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

    DEFF Research Database (Denmark)

    Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

    2013-01-01

    A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

  6. An Evaluation of Mesoscale Model Based Model Output Statistics (MOS) During the 2002 Olympic and Paralympic Winter Games

    National Research Council Canada - National Science Library

    Hart, Kenneth

    2003-01-01

    The skill of a mesoscale model based Model Output Statistics (MOS) system that provided hourly forecasts for 18 sites over northern Utah during the 2002 Winter Olympic and Paralympic Games is evaluated...

  7. Statistical Compression for Climate Model Output

    Science.gov (United States)

    Hammerling, D.; Guinness, J.; Soh, Y. J.

    2017-12-01

    Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.

  8. Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

    Science.gov (United States)

    Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

    2013-01-01

    Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

  9. A statistical-dynamical modeling approach for the simulation of local paleo proxy records using GCM output

    Energy Technology Data Exchange (ETDEWEB)

    Reichert, B.K.; Bengtsson, L. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Aakesson, O. [Sveriges Meteorologiska och Hydrologiska Inst., Norrkoeping (Sweden)

    1998-08-01

    Recent proxy data obtained from ice core measurements, dendrochronology and valley glaciers provide important information on the evolution of the regional or local climate. General circulation models integrated over a long period of time could help to understand the (external and internal) forcing mechanisms of natural climate variability. For a systematic interpretation of in situ paleo proxy records, a combined method of dynamical and statistical modeling is proposed. Local 'paleo records' can be simulated from GCM output by first undertaking a model-consistent statistical downscaling and then using a process-based forward modeling approach to obtain the behavior of valley glaciers and the growth of trees under specific conditions. The simulated records can be compared to actual proxy records in order to investigate whether e.g. the response of glaciers to climatic change can be reproduced by models and to what extent climate variability obtained from proxy records (with the main focus on the last millennium) can be represented. For statistical downscaling to local weather conditions, a multiple linear forward regression model is used. Daily sets of observed weather station data and various large-scale predictors at 7 pressure levels obtained from ECMWF reanalyses are used for development of the model. Daily data give the closest and most robust relationships due to the strong dependence on individual synoptic-scale patterns. For some local variables, the performance of the model can be further increased by developing seasonal specific statistical relationships. The model is validated using both independent and restricted predictor data sets. The model is applied to a long integration of a mixed layer GCM experiment simulating pre-industrial climate variability. The dynamical-statistical local GCM output within a region around Nigardsbreen glacier, Norway is compared to nearby observed station data for the period 1868-1993. Patterns of observed

  10. Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

    Science.gov (United States)

    Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

    2017-12-01

    A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

  11. A model and variance reduction method for computing statistical outputs of stochastic elliptic partial differential equations

    International Nuclear Information System (INIS)

    Vidal-Codina, F.; Nguyen, N.C.; Giles, M.B.; Peraire, J.

    2015-01-01

    We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations. Our method consists of three main ingredients: (1) the hybridizable discontinuous Galerkin (HDG) discretization of elliptic partial differential equations (PDEs), which allows us to obtain high-order accurate solutions of the governing PDE; (2) the reduced basis method for a new HDG discretization of the underlying PDE to enable real-time solution of the parameterized PDE in the presence of stochastic parameters; and (3) a multilevel variance reduction method that exploits the statistical correlation among the different reduced basis approximations and the high-fidelity HDG discretization to accelerate the convergence of the Monte Carlo simulations. The multilevel variance reduction method provides efficient computation of the statistical outputs by shifting most of the computational burden from the high-fidelity HDG approximation to the reduced basis approximations. Furthermore, we develop a posteriori error estimates for our approximations of the statistical outputs. Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance. We provide numerical examples to demonstrate the performance of the proposed method

  12. Finding the Root Causes of Statistical Inconsistency in Community Earth System Model Output

    Science.gov (United States)

    Milroy, D.; Hammerling, D.; Baker, A. H.

    2017-12-01

    Baker et al (2015) developed the Community Earth System Model Ensemble Consistency Test (CESM-ECT) to provide a metric for software quality assurance by determining statistical consistency between an ensemble of CESM outputs and new test runs. The test has proved useful for detecting statistical difference caused by compiler bugs and errors in physical modules. However, detection is only the necessary first step in finding the causes of statistical difference. The CESM is a vastly complex model comprised of millions of lines of code which is developed and maintained by a large community of software engineers and scientists. Any root cause analysis is correspondingly challenging. We propose a new capability for CESM-ECT: identifying the sections of code that cause statistical distinguishability. The first step is to discover CESM variables that cause CESM-ECT to classify new runs as statistically distinct, which we achieve via Randomized Logistic Regression. Next we use a tool developed to identify CESM components that define or compute the variables found in the first step. Finally, we employ the application Kernel GENerator (KGEN) created in Kim et al (2016) to detect fine-grained floating point differences. We demonstrate an example of the procedure and advance a plan to automate this process in our future work.

  13. Robust optimization of the output voltage of nanogenerators by statistical design of experiments

    KAUST Repository

    Song, Jinhui

    2010-09-01

    Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

  14. Robust optimization of the output voltage of nanogenerators by statistical design of experiments

    KAUST Repository

    Song, Jinhui; Xie, Huizhi; Wu, Wenzhuo; Roshan Joseph, V.; Jeff Wu, C. F.; Wang, Zhong Lin

    2010-01-01

    Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

  15. A formal statistical approach to representing uncertainty in rainfall-runoff modelling with focus on residual analysis and probabilistic output evaluation - Distinguishing simulation and prediction

    DEFF Research Database (Denmark)

    Breinholt, Anders; Møller, Jan Kloppenborg; Madsen, Henrik

    2012-01-01

    While there seems to be consensus that hydrological model outputs should be accompanied with an uncertainty estimate the appropriate method for uncertainty estimation is not agreed upon and a debate is ongoing between advocators of formal statistical methods who consider errors as stochastic...... and GLUE advocators who consider errors as epistemic, arguing that the basis of formal statistical approaches that requires the residuals to be stationary and conform to a statistical distribution is unrealistic. In this paper we take a formal frequentist approach to parameter estimation and uncertainty...... necessary but the statistical assumptions were nevertheless not 100% justified. The residual analysis showed that significant autocorrelation was present for all simulation models. We believe users of formal approaches to uncertainty evaluation within hydrology and within environmental modelling in general...

  16. Probing NWP model deficiencies by statistical postprocessing

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.

    2016-01-01

    The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational....... Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites....

  17. Parsing statistical machine translation output

    NARCIS (Netherlands)

    Carter, S.; Monz, C.; Vetulani, Z.

    2009-01-01

    Despite increasing research into the use of syntax during statistical machine translation, the incorporation of syntax into language models has seen limited success. We present a study of the discriminative abilities of generative syntax-based language models, over and above standard n-gram models,

  18. Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

    Science.gov (United States)

    Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

    2016-09-01

    We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

  19. Sanov and central limit theorems for output statistics of quantum Markov chains

    Energy Technology Data Exchange (ETDEWEB)

    Horssen, Merlijn van, E-mail: merlijn.vanhorssen@nottingham.ac.uk [School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD (United Kingdom); Guţă, Mădălin, E-mail: madalin.guta@nottingham.ac.uk [School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD (United Kingdom)

    2015-02-15

    In this paper, we consider the statistics of repeated measurements on the output of a quantum Markov chain. We establish a large deviations result analogous to Sanov’s theorem for the multi-site empirical measure associated to finite sequences of consecutive outcomes of a classical stochastic process. Our result relies on the construction of an extended quantum transition operator (which keeps track of previous outcomes) in terms of which we compute moment generating functions, and whose spectral radius is related to the large deviations rate function. As a corollary to this, we obtain a central limit theorem for the empirical measure. Such higher level statistics may be used to uncover critical behaviour such as dynamical phase transitions, which are not captured by lower level statistics such as the sample mean. As a step in this direction, we give an example of a finite system whose level-1 (empirical mean) rate function is independent of a model parameter while the level-2 (empirical measure) rate is not.

  20. Modelling innovation performance of European regions using multi-output neural networks.

    Science.gov (United States)

    Hajek, Petr; Henriques, Roberto

    2017-01-01

    Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  1. Modelling innovation performance of European regions using multi-output neural networks.

    Directory of Open Access Journals (Sweden)

    Petr Hajek

    Full Text Available Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  2. Statistics & Input-Output Measures for School Libraries in Colorado, 2002.

    Science.gov (United States)

    Colorado State Library, Denver.

    This document presents statistics and input-output measures for K-12 school libraries in Colorado for 2002. Data are presented by type and size of school, i.e., high schools (six categories ranging from 2,000 and over to under 300), junior high/middle schools (five categories ranging from 1,000-1,999 to under 300), elementary schools (four…

  3. Structured, Physically Inspired (Gray Box) Models Versus Black Box Modeling for Forecasting the Output Power of Photovoltaic Plants

    Czech Academy of Sciences Publication Activity Database

    Paulescu, M.; Brabec, Marek; Boata, R.; Badescu, V.

    2017-01-01

    Roč. 121, 15 February (2017), s. 792-802 ISSN 0360-5442 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : photovoltaic plant * output power * forecasting * fuzzy model * generalized additive model Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 4.520, year: 2016

  4. Output from Statistical Predictive Models as Input to eLearning Dashboards

    Directory of Open Access Journals (Sweden)

    Marlene A. Smith

    2015-06-01

    Full Text Available We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article’s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.

  5. The Metamorphosis of the Statistical Segmentation Output: Lexicalization during Artificial Language Learning

    Science.gov (United States)

    Fernandes, Tania; Kolinsky, Regine; Ventura, Paulo

    2009-01-01

    This study combined artificial language learning (ALL) with conventional experimental techniques to test whether statistical speech segmentation outputs are integrated into adult listeners' mental lexicon. Lexicalization was assessed through inhibitory effects of novel neighbors (created by the parsing process) on auditory lexical decisions to…

  6. WRF Model Output

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset contains WRF model output. There are three months of data: July 2012, July 2013, and January 2013. For each month, several simulations were made: A...

  7. Enhanced surrogate models for statistical design exploiting space mapping technology

    DEFF Research Database (Denmark)

    Koziel, Slawek; Bandler, John W.; Mohamed, Achmed S.

    2005-01-01

    We present advances in microwave and RF device modeling exploiting Space Mapping (SM) technology. We propose new SM modeling formulations utilizing input mappings, output mappings, frequency scaling and quadratic approximations. Our aim is to enhance circuit models for statistical analysis...

  8. CMAQ Model Output

    Data.gov (United States)

    U.S. Environmental Protection Agency — CMAQ and CMAQ-VBS model output. This dataset is not publicly accessible because: Files too large. It can be accessed through the following means: via EPA's NCC tape...

  9. Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity

    Science.gov (United States)

    Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.

    As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.

  10. Model validation and calibration based on component functions of model output

    International Nuclear Information System (INIS)

    Wu, Danqing; Lu, Zhenzhou; Wang, Yanping; Cheng, Lei

    2015-01-01

    The target in this work is to validate the component functions of model output between physical observation and computational model with the area metric. Based on the theory of high dimensional model representations (HDMR) of independent input variables, conditional expectations are component functions of model output, and the conditional expectations reflect partial information of model output. Therefore, the model validation of conditional expectations tells the discrepancy between the partial information of the computational model output and that of the observations. Then a calibration of the conditional expectations is carried out to reduce the value of model validation metric. After that, a recalculation of the model validation metric of model output is taken with the calibrated model parameters, and the result shows that a reduction of the discrepancy in the conditional expectations can help decrease the difference in model output. At last, several examples are employed to demonstrate the rationality and necessity of the methodology in case of both single validation site and multiple validation sites. - Highlights: • A validation metric of conditional expectations of model output is proposed. • HDRM explains the relationship of conditional expectations and model output. • An improved approach of parameter calibration updates the computational models. • Validation and calibration process are applied at single site and multiple sites. • Validation and calibration process show a superiority than existing methods

  11. A classical statistical model of heavy ion collisions

    International Nuclear Information System (INIS)

    Schmidt, R.; Teichert, J.

    1980-01-01

    The use of the computer code TRAJEC which represents the numerical realization of a classical statistical model for heavy ion collisions is described. The code calculates the results of a classical friction model as well as various multi-differential cross sections for heavy ion collisions. INPUT and OUTPUT information of the code are described. Two examples of data sets are given [ru

  12. Preliminary Retrospective Analysis of Daily Tomotherapy Output Constancy Checks Using Statistical Process Control.

    Science.gov (United States)

    Mezzenga, Emilio; D'Errico, Vincenzo; Sarnelli, Anna; Strigari, Lidia; Menghi, Enrico; Marcocci, Francesco; Bianchini, David; Benassi, Marcello

    2016-01-01

    The purpose of this study was to retrospectively evaluate the results from a Helical TomoTherapy Hi-Art treatment system relating to quality controls based on daily static and dynamic output checks using statistical process control methods. Individual value X-charts, exponentially weighted moving average charts, and process capability and acceptability indices were used to monitor the treatment system performance. Daily output values measured from January 2014 to January 2015 were considered. The results obtained showed that, although the process was in control, there was an out-of-control situation in the principal maintenance intervention for the treatment system. In particular, process capability indices showed a decreasing percentage of points in control which was, however, acceptable according to AAPM TG148 guidelines. Our findings underline the importance of restricting the acceptable range of daily output checks and suggest a future line of investigation for a detailed process control of daily output checks for the Helical TomoTherapy Hi-Art treatment system.

  13. Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

    Science.gov (United States)

    Hay, Lauren E.; LaFontaine, Jacob H.; Markstrom, Steven

    2014-01-01

    The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive

  14. The multi-factor energy input–output model

    International Nuclear Information System (INIS)

    Guevara, Zeus; Domingos, Tiago

    2017-01-01

    Energy input–output analysis (EIO analysis) is a noteworthy tool for the analysis of the role of energy in the economy. However, it has relied on models that provide a limited description of energy flows in the economic system and do not allow an adequate analysis of energy efficiency. This paper introduces a novel energy input–output model, the multi-factor energy input–output model (MF-EIO model), which is obtained from a partitioning of a hybrid-unit input–output system of the economy. This model improves on current models by describing the energy flows according to the processes of energy conversion and the levels of energy use in the economy. It characterizes the vector of total energy output as a function of seven factors: two energy efficiency indicators; two characteristics of end-use energy consumption; and three economic features of the rest of the economy. Moreover, it is consistent with the standard model for EIO analysis, i.e., the hybrid-unit model. This paper also introduces an approximate version of the MF-EIO model, which is equivalent to the former under equal energy prices for industries and final consumers, but requires less data processing. The latter is composed by two linked models: a model of the energy sector in physical units, and a model of the rest of the economy in monetary units. In conclusion, the proposed modelling framework improves EIO analysis and extends EIO applications to the accounting for energy efficiency of the economy. - Highlights: • A novel energy input–output model is introduced. • It allows a more adequate analysis of energy flows than current models. • It describes energy flows according to processes of energy conversion and use. • It can be used for other environmental applications (material use and emissions). • An approximate version of the model is introduced, simpler and less data intensive.

  15. Problems in Modelling Charge Output Accelerometers

    Directory of Open Access Journals (Sweden)

    Tomczyk Krzysztof

    2016-12-01

    Full Text Available The paper presents major issues associated with the problem of modelling change output accelerometers. The presented solutions are based on the weighted least squares (WLS method using transformation of the complex frequency response of the sensors. The main assumptions of the WLS method and a mathematical model of charge output accelerometers are presented in first two sections of this paper. In the next sections applying the WLS method to estimation of the accelerometer model parameters is discussed and the associated uncertainties are determined. Finally, the results of modelling a PCB357B73 charge output accelerometer are analysed in the last section of this paper. All calculations were executed using the MathCad software program. The main stages of these calculations are presented in Appendices A−E.

  16. About the use of rank transformation in sensitivity analysis of model output

    International Nuclear Information System (INIS)

    Saltelli, Andrea; Sobol', Ilya M

    1995-01-01

    Rank transformations are frequently employed in numerical experiments involving a computational model, especially in the context of sensitivity and uncertainty analyses. Response surface replacement and parameter screening are tasks which may benefit from a rank transformation. Ranks can cope with nonlinear (albeit monotonic) input-output distributions, allowing the use of linear regression techniques. Rank transformed statistics are more robust, and provide a useful solution in the presence of long tailed input and output distributions. As is known to practitioners, care must be employed when interpreting the results of such analyses, as any conclusion drawn using ranks does not translate easily to the original model. In the present note an heuristic approach is taken, to explore, by way of practical examples, the effect of a rank transformation on the outcome of a sensitivity analysis. An attempt is made to identify trends, and to correlate these effects to a model taxonomy. Employing sensitivity indices, whereby the total variance of the model output is decomposed into a sum of terms of increasing dimensionality, we show that the main effect of the rank transformation is to increase the relative weight of the first order terms (the 'main effects'), at the expense of the 'interactions' and 'higher order interactions'. As a result the influence of those parameters which influence the output mostly by way of interactions may be overlooked in an analysis based on the ranks. This difficulty increases with the dimensionality of the problem, and may lead to the failure of a rank based sensitivity analysis. We suggest that the models can be ranked, with respect to the complexity of their input-output relationship, by mean of an 'Association' index I y . I y may complement the usual model coefficient of determination R y 2 as a measure of model complexity for the purpose of uncertainty and sensitivity analysis

  17. Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

    KAUST Repository

    Castruccio, Stefano

    2014-03-01

    The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.

  18. Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment

    Energy Technology Data Exchange (ETDEWEB)

    Rashid, Md. Mamunur, E-mail: mdmamunur.rashid@mymail.unisa.edu.au [Centre for Water Management and Reuse, School of Natural and Built Environments, University of South Australia, Mawson Lakes, SA 5095 (Australia); Beecham, Simon, E-mail: simon.beecham@unisa.edu.au [Centre for Water Management and Reuse, School of Natural and Built Environments, University of South Australia, Mawson Lakes, SA 5095 (Australia); Chowdhury, Rezaul K., E-mail: rezaulkabir@uaeu.ac.ae [Centre for Water Management and Reuse, School of Natural and Built Environments, University of South Australia, Mawson Lakes, SA 5095 (Australia); Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, PO Box 15551 (United Arab Emirates)

    2015-10-15

    A generalized linear model was fitted to stochastically downscaled multi-site daily rainfall projections from CMIP5 General Circulation Models (GCMs) for the Onkaparinga catchment in South Australia to assess future changes to hydrologically relevant metrics. For this purpose three GCMs, two multi-model ensembles (one by averaging the predictors of GCMs and the other by regressing the predictors of GCMs against reanalysis datasets) and two scenarios (RCP4.5 and RCP8.5) were considered. The downscaling model was able to reasonably reproduce the observed historical rainfall statistics when the model was driven by NCEP reanalysis datasets. Significant bias was observed in the rainfall when downscaled from historical outputs of GCMs. Bias was corrected using the Frequency Adapted Quantile Mapping technique. Future changes in rainfall were computed from the bias corrected downscaled rainfall forced by GCM outputs for the period 2041–2060 and these were then compared to the base period 1961–2000. The results show that annual and seasonal rainfalls are likely to significantly decrease for all models and scenarios in the future. The number of dry days and maximum consecutive dry days will increase whereas the number of wet days and maximum consecutive wet days will decrease. Future changes of daily rainfall occurrence sequences combined with a reduction in rainfall amounts will lead to a drier catchment, thereby reducing the runoff potential. Because this is a catchment that is a significant source of Adelaide's water supply, irrigation water and water for maintaining environmental flows, an effective climate change adaptation strategy is needed in order to face future potential water shortages. - Highlights: • A generalized linear model was used for multi-site daily rainfall downscaling. • Rainfall was downscaled from CMIP5 GCM outputs. • Two multi-model ensemble approaches were used. • Bias was corrected using the Frequency Adapted Quantile Mapping

  19. Mathematical model of statistical identification of information support of road transport

    Directory of Open Access Journals (Sweden)

    V. G. Kozlov

    2016-01-01

    Full Text Available In this paper based on the statistical identification method using the theory of self-organizing systems, built multifactor model the relationship of road transport and training system. Background information for the model represented by a number of parameters of average annual road transport operations and information provision, including training complex system parameters (inputs, road management and output parameters. Ask two criteria: stability criterion model and test correlation. The program determines their minimum, and is the only model of optimal complexity. The predetermined number of parameters established mathematical relationship of each output parameter with the others. To improve the accuracy and regularity of the forecast of the interpolation nodes allocated in the test data sequence. Other data form the training sequence. Decision model based on the principle of selection. Running it with the gradual complication of the mathematical description and exhaustive search of all possible variants of the models on the specified criteria. Advantages of the proposed model: adequately reflects the actual process, allows you to enter any additional input parameters and determine their impact on the individual output parameters of the road transport, allows in turn change the values of key parameters in a certain ratio and to determine the appropriate changes the output parameters of the road transport, allows to predict the output parameters road transport operations.

  20. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    Science.gov (United States)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  1. Statistical shape model with random walks for inner ear segmentation

    DEFF Research Database (Denmark)

    Pujadas, Esmeralda Ruiz; Kjer, Hans Martin; Piella, Gemma

    2016-01-01

    is required. We propose a new framework for segmentation of micro-CT cochlear images using random walks combined with a statistical shape model (SSM). The SSM allows us to constrain the less contrasted areas and ensures valid inner ear shape outputs. Additionally, a topology preservation method is proposed...

  2. Multi-model MPC with output feedback

    Directory of Open Access Journals (Sweden)

    J. M. Perez

    2014-03-01

    Full Text Available In this work, a new formulation is presented for the model predictive control (MPC of a process system that is represented by a finite set of models, each one corresponding to a different operating point. The general case is considered of systems with stable and integrating outputs in closed-loop with output feedback. For this purpose, the controller is based on a non-minimal order model where the state is built with the measured outputs and the manipulated inputs of the control system. Therefore, the state can be considered as perfectly known and, consequently, there is no need to include a state observer in the control loop. This property of the proposed modeling approach is convenient to extend previous stability results of the closed loop system with robust MPC controllers based on state feedback. The controller proposed here is based on the solution of two optimization problems that are solved sequentially at the same time step. The method is illustrated with a simulated example of the process industry. The rigorous simulation of the control of an adiabatic flash of a multi-component hydrocarbon mixture illustrates the application of the robust controller. The dynamic simulation of this process is performed using EMSO - Environment Model Simulation and Optimization. Finally, a comparison with a linear MPC using a single model is presented.

  3. Reconstruction of an Input-Output Table for Germany in 1936 : Conceptual and Empirical-Statistical Problems

    NARCIS (Netherlands)

    Fremdling, Rainer; Staeglin, Reiner

    2012-01-01

    The objective of this contribution is to present the final results of a long-term research project which aimed at constructing an input-output table for Germany in 1936. Our research can be seen as follow-up of the activities of the German Imperial Statistical Office (Statistisches Reichsamt) which

  4. Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2

    Directory of Open Access Journals (Sweden)

    Gutmann Michael

    2005-02-01

    Full Text Available Abstract Background It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band. Results We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work. Conclusion We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing.

  5. 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

  6. A two stage data envelopment analysis model with undesirable output

    Science.gov (United States)

    Shariff Adli Aminuddin, Adam; Izzati Jaini, Nur; Mat Kasim, Maznah; Nawawi, Mohd Kamal Mohd

    2017-09-01

    The dependent relationship among the decision making units (DMU) is usually assumed to be non-existent in the development of Data Envelopment Analysis (DEA) model. The dependency can be represented by the multi-stage DEA model, where the outputs from the precedent stage will be the inputs for the latter stage. The multi-stage DEA model evaluate both the efficiency score for each stages and the overall efficiency of the whole process. The existing multi stage DEA models do not focus on the integration with the undesirable output, in which the higher input will generate lower output unlike the normal desirable output. This research attempts to address the inclusion of such undesirable output and investigate the theoretical implication and potential application towards the development of multi-stage DEA model.

  7. Global sensitivity analysis for models with spatially dependent outputs

    International Nuclear Information System (INIS)

    Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.

    2011-01-01

    The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)

  8. Statistical models of petrol engines vehicles dynamics

    Science.gov (United States)

    Ilie, C. O.; Marinescu, M.; Alexa, O.; Vilău, R.; Grosu, D.

    2017-10-01

    This paper focuses on studying statistical models of vehicles dynamics. It was design and perform a one year testing program. There were used many same type cars with gasoline engines and different mileage. Experimental data were collected of onboard sensors and those on the engine test stand. A database containing data of 64th tests was created. Several mathematical modelling were developed using database and the system identification method. Each modelling is a SISO or a MISO linear predictive ARMAX (AutoRegressive-Moving-Average with eXogenous inputs) model. It represents a differential equation with constant coefficients. It were made 64th equations for each dependency like engine torque as output and engine’s load and intake manifold pressure, as inputs. There were obtained strings with 64 values for each type of model. The final models were obtained using average values of the coefficients. The accuracy of models was assessed.

  9. 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.

  10. 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

  11. Output, renewable energy consumption and trade in Africa

    International Nuclear Information System (INIS)

    Ben Aïssa, Mohamed Safouane; Ben Jebli, Mehdi; Ben Youssef, Slim

    2014-01-01

    We use panel cointegration techniques to examine the relationship between renewable energy consumption, trade and output in a sample of 11 African countries covering the period 1980–2008. The results from panel error correction model reveal that there is evidence of a bidirectional causality between output and exports and between output and imports in both the short and long-run. However, in the short-run, there is no evidence of causality between output and renewable energy consumption and between trade (exports or imports) and renewable energy consumption. Also, in the long-run, there is no causality running from output or trade to renewable energy. In the long-run, our estimations show that renewable energy consumption and trade have a statistically significant and positive impact on output. Our energy policy recommendations are that national authorities should design appropriate fiscal incentives to encourage the use of renewable energies, create more regional economic integration for renewable energy technologies, and encourage trade openness because of its positive impact on technology transfer and on output. - Highlights: • We examine the relationship between renewable energy consumption, trade and output in African countries. • There is a bidirectional causality between output and trade in both the short and long-run. • In the short-run, there is no causality between renewable energy consumption and trade or output. • In the long-run, renewable energy consumption and trade have a statistically significant positive impact on output. • African authorities should encourage trade openness because of its positive impact on technology transfer and on output

  12. Statistical Downscaling Output GCM Modeling with Continuum Regression and Pre-Processing PCA Approach

    Directory of Open Access Journals (Sweden)

    Sutikno Sutikno

    2010-08-01

    Full Text Available One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM. GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD technique is used to bridge the large-scale GCM with a small scale (the study area. GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR and pre-processing with Principal Component Analysis (PCA methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990. This method is a generalization from Ordinary Least Square (OLS, Principal Component Regression (PCR and Partial Least Square method (PLS methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.

  13. Accessing National Water Model Output for Research and Application: An R package

    Science.gov (United States)

    Johnson, M.; Coll, J.

    2017-12-01

    With the National Water Model becoming operational in August of 2016, the need for a open source way to translate a huge amount of data into actionable intelligence and innovative research is apparent. The first step in doing this is to provide a package for accessing, managing, and writing data in a way that is both interpretable, portable, and useful to the end user in both the R environment, and other applications. This can be as simple as subsetting the outputs and writing to a CSV, but can also include converting discharge output to more meaningful statistics and measurements, and methods to visualize data in ways that are meaningful to a wider audience. The NWM R package presented here aims to serve this need through a suite of functions fit for researchers, first responders, and average citizens. A vignette of how this package can be applied to real-time flood mapping will be demonstrated.

  14. A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

    Science.gov (United States)

    2017-09-01

    application of statistical inference. Even when human forecasters leverage their professional experience, which is often gained through long periods of... application throughout statistics and Bayesian data analysis. The multivariate form of 2( , )  (e.g., Figure 12) is similarly analytically...data (i.e., no systematic manipulations with analytical functions), it is common in the statistical literature to apply mathematical transformations

  15. Investigating output and energy variations and their relationship to delivery QA results using Statistical Process Control for helical tomotherapy.

    Science.gov (United States)

    Binny, Diana; Mezzenga, Emilio; Lancaster, Craig M; Trapp, Jamie V; Kairn, Tanya; Crowe, Scott B

    2017-06-01

    The aims of this study were to investigate machine beam parameters using the TomoTherapy quality assurance (TQA) tool, establish a correlation to patient delivery quality assurance results and to evaluate the relationship between energy variations detected using different TQA modules. TQA daily measurement results from two treatment machines for periods of up to 4years were acquired. Analyses of beam quality, helical and static output variations were made. Variations from planned dose were also analysed using Statistical Process Control (SPC) technique and their relationship to output trends were studied. Energy variations appeared to be one of the contributing factors to delivery output dose seen in the analysis. Ion chamber measurements were reliable indicators of energy and output variations and were linear with patient dose verifications. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

  16. Statistical modeling of geopressured geothermal reservoirs

    Science.gov (United States)

    Ansari, Esmail; Hughes, Richard; White, Christopher D.

    2017-06-01

    Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

  17. Model output: fact or artefact?

    Science.gov (United States)

    Melsen, Lieke

    2015-04-01

    As a third-year PhD-student, I relatively recently entered the wonderful world of scientific Hydrology. A science that has many pillars that directly impact society, for example with the prediction of hydrological extremes (both floods and drought), climate change, applications in agriculture, nature conservation, drinking water supply, etcetera. Despite its demonstrable societal relevance, hydrology is often seen as a science between two stools. Like Klemeš (1986) stated: "By their academic background, hydrologists are foresters, geographers, electrical engineers, geologists, system analysts, physicists, mathematicians, botanists, and most often civil engineers." Sometimes it seems that the engineering genes are still present in current hydrological sciences, and this results in pragmatic rather than scientific approaches for some of the current problems and challenges we have in hydrology. Here, I refer to the uncertainty in hydrological modelling that is often neglected. For over thirty years, uncertainty in hydrological models has been extensively discussed and studied. But it is not difficult to find peer-reviewed articles in which it is implicitly assumed that model simulations represent the truth rather than a conceptualization of reality. For instance in trend studies, where data is extrapolated 100 years ahead. Of course one can use different forcing datasets to estimate the uncertainty of the input data, but how to prevent that the output is not a model artefact, caused by the model structure? Or how about impact studies, e.g. of a dam impacting river flow. Measurements are often available for the period after dam construction, so models are used to simulate river flow before dam construction. Both are compared in order to qualify the effect of the dam. But on what basis can we tell that the model tells us the truth? Model validation is common nowadays, but validation only (comparing observations with model output) is not sufficient to assume that a

  18. Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions

    Science.gov (United States)

    Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.

    2012-12-01

    General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.

  19. Semi-Automated Processing of Trajectory Simulator Output Files for Model Evaluation

    Science.gov (United States)

    2018-01-01

    ARL-TR-8284 ● JAN 2018 US Army Research Laboratory Semi-Automated Processing of Trajectory Simulator Output Files for Model...Semi-Automated Processing of Trajectory Simulator Output Files for Model Evaluation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...although some minor changes may be needed. The program processes a GTRAJ output text file that contains results from 2 or more simulations , where each

  20. Research Outputs of England's Hospital Episode Statistics (HES) Database: Bibliometric Analysis.

    Science.gov (United States)

    Chaudhry, Zain; Mannan, Fahmida; Gibson-White, Angela; Syed, Usama; Ahmed, Shirin; Majeed, Azeem

    2017-12-06

    Hospital administrative data, such as those provided by the Hospital Episode Statistics (HES) database in England, are increasingly being used for research and quality improvement. To date, no study has tried to quantify and examine trends in the use of HES for research purposes. To examine trends in the use of HES data for research. Publications generated from the use of HES data were extracted from PubMed and analysed. Publications from 1996 to 2014 were then examined further in the Science Citation Index (SCI) of the Thompson Scientific Institute for Science Information (Web of Science) for details of research specialty area. 520 studies, categorised into 44 specialty areas, were extracted from PubMed. The review showed an increase in publications over the 18-year period with an average of 27 publications per year, however with the majority of output observed in the latter part of the study period. The highest number of publications was in the Health Statistics specialty area. The use of HES data for research is becoming more common. Increase in publications over time shows that researchers are beginning to take advantage of the potential of HES data. Although HES is a valuable database, concerns exist over the accuracy and completeness of the data entered. Clinicians need to be more engaged with HES for the full potential of this database to be harnessed.

  1. Statistical mechanics of attractor neural network models with synaptic depression

    International Nuclear Information System (INIS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

    2009-01-01

    Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

  2. Investigation of solar photovoltaic module power output by various models

    International Nuclear Information System (INIS)

    Jakhrani, A.Q.; Othman, A.K.; Rigit, A.R.H.; Baini, R.

    2012-01-01

    This paper aims to investigate the power output of a solar photovoltaic module by various models and to formulate a suitable model for predicting the performance of solar photovoltaic modules. The model was used to correct the configurations of solar photovoltaic systems for sustainable power supply. Different types of models namely the efficiency, power, fill factor and current-voltage characteristic curve models have been reviewed. It was found that the examined models predicted a 40% yield of the rated power in cloudy weather conditions and up to 80% in clear skies. The models performed well in terms of electrical efficiency in cloudy days if the influence of low irradiance were incorporated. Both analytical and numerical methods were employed in the formulation of improved model which gave +- 2% error when compared with the rated power output of solar photovoltaic module. The proposed model is more practical in terms of number of variables used and acceptable performance in humid atmospheres. Therefore, it could be useful for the estimation of power output of the solar photovoltaic systems in Sarawak region. (author)

  3. 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.

  4. The value of risk: measuring the service output of U.S. commercial banks

    OpenAIRE

    Basu, Susanto; Inklaar, Robert; Wang, J. Christina

    2011-01-01

    Rather than charging direct fees, banks often charge implicitly for their services via interest spreads. As a result, much of bank output has to be estimated indirectly. In contrast to current statistical practice, dynamic optimizing models of banks argue that compensation for bearing systematic risk is not part of bank output. We apply these models and find that between 1997 and 2007, in the U.S. National Accounts, on average, bank output is overestimated by 21 percent and GDP is overestimat...

  5. Modeling the power output of piezoelectric energy harvesters

    KAUST Repository

    Al Ahmad, Mahmoud

    2011-04-30

    Design of experiments and multiphysics analyses were used to develop a parametric model for a d 33-based cantilever. The analysis revealed that the most significant parameters influencing the resonant frequency are the supporting layer thickness, piezoelectric layer thickness, and cantilever length. On the other hand, the most important factors affecting the charge output arethe piezoelectric thickness and the interdigitated electrode dimensions. The accuracy of the developed model was confirmed and showed less than 1% estimation error compared with a commercial simulation package. To estimate the power delivered to a load, the electric current output from the piezoelectric generator was calculated. A circuit model was built and used to estimate the power delivered to a load, which compared favorably to experimentally published power data on actual cantilevers of similar dimensions. © 2011 TMS.

  6. Modeling the power output of piezoelectric energy harvesters

    KAUST Repository

    Al Ahmad, Mahmoud; Alshareef, Husam N.

    2011-01-01

    Design of experiments and multiphysics analyses were used to develop a parametric model for a d 33-based cantilever. The analysis revealed that the most significant parameters influencing the resonant frequency are the supporting layer thickness, piezoelectric layer thickness, and cantilever length. On the other hand, the most important factors affecting the charge output arethe piezoelectric thickness and the interdigitated electrode dimensions. The accuracy of the developed model was confirmed and showed less than 1% estimation error compared with a commercial simulation package. To estimate the power delivered to a load, the electric current output from the piezoelectric generator was calculated. A circuit model was built and used to estimate the power delivered to a load, which compared favorably to experimentally published power data on actual cantilevers of similar dimensions. © 2011 TMS.

  7. Downscaling climate model output for water resources impacts assessment (Invited)

    Science.gov (United States)

    Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

    2013-12-01

    Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.

  8. Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

    Science.gov (United States)

    Clark, M.P.; Hay, L.E.

    2004-01-01

    This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases

  9. Adjustment of regional climate model output for modeling the climatic mass balance of all glaciers on Svalbard.

    NARCIS (Netherlands)

    Möller, M.; Obleitner, F.; Reijmer, C.H.; Pohjola, V.A.; Glowacki, P.; Kohler, J.

    2016-01-01

    Large-scale modeling of glacier mass balance relies often on the output from regional climate models (RCMs). However, the limited accuracy and spatial resolution of RCM output pose limitations on mass balance simulations at subregional or local scales. Moreover, RCM output is still rarely available

  10. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

    Directory of Open Access Journals (Sweden)

    Nam Do Hoai

    2011-01-01

    Full Text Available Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.

  11. Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano; Genton, Marc G.

    2015-01-01

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.

  12. Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2015-04-02

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.

  13. Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification

    Directory of Open Access Journals (Sweden)

    A. Sarri

    2012-06-01

    Full Text Available Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake. Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.

  14. Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical-Dynamical Downscaling for Wind Energy Applications

    DEFF Research Database (Denmark)

    Badger, Jake; Frank, Helmut; Hahmann, Andrea N.

    2014-01-01

    This paper demonstrates that a statistical dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind...

  15. Towards a Statistical Model of Tropical Cyclone Genesis

    Science.gov (United States)

    Fernandez, A.; Kashinath, K.; McAuliffe, J.; Prabhat, M.; Stark, P. B.; Wehner, M. F.

    2017-12-01

    Tropical Cyclones (TCs) are important extreme weather phenomena that have a strong impact on humans. TC forecasts are largely based on global numerical models that produce TC-like features. Aspects of Tropical Cyclones such as their formation/genesis, evolution, intensification and dissipation over land are important and challenging problems in climate science. This study investigates the environmental conditions associated with Tropical Cyclone Genesis (TCG) by testing how accurately a statistical model can predict TCG in the CAM5.1 climate model. TCG events are defined using TECA software @inproceedings{Prabhat2015teca, title={TECA: Petascale Pattern Recognition for Climate Science}, author={Prabhat and Byna, Surendra and Vishwanath, Venkatram and Dart, Eli and Wehner, Michael and Collins, William D}, booktitle={Computer Analysis of Images and Patterns}, pages={426-436}, year={2015}, organization={Springer}} to extract TC trajectories from CAM5.1. L1-regularized logistic regression (L1LR) is applied to the CAM5.1 output. The predictions have nearly perfect accuracy for data not associated with TC tracks and high accuracy differentiating between high vorticity and low vorticity systems. The model's active variables largely correspond to current hypotheses about important factors for TCG, such as wind field patterns and local pressure minima, and suggests new routes for investigation. Furthermore, our model's predictions of TC activity are competitive with the output of an instantaneous version of Emanuel and Nolan's Genesis Potential Index (GPI) @inproceedings{eman04, title = "Tropical cyclone activity and the global climate system", author = "Kerry Emanuel and Nolan, {David S.}", year = "2004", pages = "240-241", booktitle = "26th Conference on Hurricanes and Tropical Meteorology"}.

  16. Statistical surrogate models for prediction of high-consequence climate change.

    Energy Technology Data Exchange (ETDEWEB)

    Constantine, Paul; Field, Richard V., Jr.; Boslough, Mark Bruce Elrick

    2011-09-01

    In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest. A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.

  17. CO2 emissions, energy usage, and output in Central America

    International Nuclear Information System (INIS)

    Apergis, Nicholas; Payne, James E.

    2009-01-01

    This study extends the recent work of Ang (2007) [Ang, J.B., 2007. CO 2 emissions, energy consumption, and output in France. Energy Policy 35, 4772-4778] in examining the causal relationship between carbon dioxide emissions, energy consumption, and output within a panel vector error correction model for six Central American countries over the period 1971-2004. In long-run equilibrium energy consumption has a positive and statistically significant impact on emissions while real output exhibits the inverted U-shape pattern associated with the Environmental Kuznets Curve (EKC) hypothesis. The short-run dynamics indicate unidirectional causality from energy consumption and real output, respectively, to emissions along with bidirectional causality between energy consumption and real output. In the long-run there appears to be bidirectional causality between energy consumption and emissions.

  18. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

    Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun

    2008-01-01

    Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between

  19. Uniting statistical and individual-based approaches for animal movement modelling.

    Science.gov (United States)

    Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

    2014-01-01

    The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

  20. A Monte Carlo Study on Multiple Output Stochastic Frontiers

    DEFF Research Database (Denmark)

    Henningsen, Géraldine; Henningsen, Arne; Jensen, Uwe

    , dividing all other output quantities by the selected output quantity, and using these ratios as regressors (OD). Another approach is the stochastic ray production frontier (SR) which transforms the output quantities into their Euclidean distance as the dependent variable and their polar coordinates......In the estimation of multiple output technologies in a primal approach, the main question is how to handle the multiple outputs. Often an output distance function is used, where the classical approach is to exploit its homogeneity property by selecting one output quantity as the dependent variable...... of both specifications for the case of a Translog output distance function with respect to different common statistical problems as well as problems arising as a consequence of zero values in the output quantities. Although, our results partly show clear reactions to statistical misspecifications...

  1. Mathematical model of accelerator output characteristics and their calculation on a computer

    International Nuclear Information System (INIS)

    Mishulina, O.A.; Ul'yanina, M.N.; Kornilova, T.V.

    1975-01-01

    A mathematical model is described of output characteristics of a linear accelerator. The model is a system of differential equations. Presence of phase limitations is a specific feature of setting the problem which makes it possible to ensure higher simulation accuracy and determine a capture coefficient. An algorithm is elaborated of computing output characteristics based upon the mathematical model suggested. A capture coefficient, coordinate expectation characterizing an average phase value of the beam particles, coordinate expectation characterizing an average value of the reverse relative velocity of the beam particles as well as dispersion of these coordinates are output characteristics of the accelerator. Calculation methods of the accelerator output characteristics are described in detail. The computations have been performed on the BESM-6 computer, the characteristics computing time being 2 min 20 sec. Relative error of parameter computation averages 10 -2

  2. An Improved Mathematical Model for Computing Power Output of Solar Photovoltaic Modules

    Directory of Open Access Journals (Sweden)

    Abdul Qayoom Jakhrani

    2014-01-01

    Full Text Available It is difficult to determine the input parameters values for equivalent circuit models of photovoltaic modules through analytical methods. Thus, the previous researchers preferred to use numerical methods. Since, the numerical methods are time consuming and need long term time series data which is not available in most developing countries, an improved mathematical model was formulated by combination of analytical and numerical methods to overcome the limitations of existing methods. The values of required model input parameters were computed analytically. The expression for output current of photovoltaic module was determined explicitly by Lambert W function and voltage was determined numerically by Newton-Raphson method. Moreover, the algebraic equations were derived for the shape factor which involves the ideality factor and the series resistance of a single diode photovoltaic module power output model. The formulated model results were validated with rated power output of a photovoltaic module provided by manufacturers using local meteorological data, which gave ±2% error. It was found that the proposed model is more practical in terms of precise estimations of photovoltaic module power output for any required location and number of variables used.

  3. Investigation on the integral output power model of a large-scale wind farm

    Institute of Scientific and Technical Information of China (English)

    BAO Nengsheng; MA Xiuqian; NI Weidou

    2007-01-01

    The integral output power model of a large-scale wind farm is needed when estimating the wind farm's output over a period of time in the future.The actual wind speed power model and calculation method of a wind farm made up of many wind turbine units are discussed.After analyzing the incoming wind flow characteristics and their energy distributions,and after considering the multi-effects among the wind turbine units and certain assumptions,the incoming wind flow model of multi-units is built.The calculation algorithms and steps of the integral output power model of a large-scale wind farm are provided.Finally,an actual power output of the wind farm is calculated and analyzed by using the practical measurement wind speed data.The characteristics of a large-scale wind farm are also discussed.

  4. NACP Site: Terrestrial Biosphere Model Output Data in Original Format

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American...

  5. NACP Site: Terrestrial Biosphere Model Output Data in Original Format

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon...

  6. 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

  7. Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

    Directory of Open Access Journals (Sweden)

    Asma Foughali

    2015-07-01

    Full Text Available This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2 in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT from the European program ENSEMBLES, forced by two global climate models (GCM: ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to

  8. Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

    DEFF Research Database (Denmark)

    Thøgersen, Emil; Tranberg, Bo; Herp, Jürgen

    2017-01-01

    deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple...... wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using...... the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain...

  9. Statistical studies on the light output and energy resolution of small LSO single crystals with different surface treatments combined with various reflector materials

    CERN Document Server

    Heinrichs, U; Bussmann, N; Engels, R; Kemmerling, G; Weber, S; Ziemons, K

    2002-01-01

    The optimization of light output and energy resolution of scintillators is of special interest for the development of high resolution and high sensitivity PET. The aim of this work is to obtain statistically reliable results concerning optimal surface treatment of scintillation crystals and the selection of reflector material. For this purpose, raw, mechanically polished and etched LSO crystals (size 2x2x10 mm sup 3) were combined with various reflector materials (Teflon tape, Teflon matrix, BaSO sub 4) and exposed to a sup 2 sup 2 Na source. In order to ensure the statistical reliability of the results, groups of 10 LSO crystals each were measured for all combinations of surface treatment and reflector material. Using no reflector material the light output increased up to 551+-35% by mechanical polishing the surface compared to 100+-5% for raw crystals. Etching the surface increased the light output to 441+-29%. The untreated crystals had an energy resolution of 24.6+-4.0%. By mechanical polishing the surfac...

  10. Sampling, Probability Models and Statistical Reasoning Statistical

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...

  11. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  12. CO{sub 2} emissions, energy usage, and output in Central America

    Energy Technology Data Exchange (ETDEWEB)

    Apergis, Nicholas [Department of Banking and Financial Management, University of Piraeus, Karaoli and Dimitriou 80, Piraeus, ATTIKI 18534 (Greece); Payne, James E. [College of Arts and Sciences, Illinois State University, Campus Box 4100, Normal, IL 61790-4100 (United States)

    2009-08-15

    This study extends the recent work of Ang (2007) [Ang, J.B., 2007. CO{sub 2} emissions, energy consumption, and output in France. Energy Policy 35, 4772-4778] in examining the causal relationship between carbon dioxide emissions, energy consumption, and output within a panel vector error correction model for six Central American countries over the period 1971-2004. In long-run equilibrium energy consumption has a positive and statistically significant impact on emissions while real output exhibits the inverted U-shape pattern associated with the Environmental Kuznets Curve (EKC) hypothesis. The short-run dynamics indicate unidirectional causality from energy consumption and real output, respectively, to emissions along with bidirectional causality between energy consumption and real output. In the long-run there appears to be bidirectional causality between energy consumption and emissions. (author)

  13. Measuring power output intermittency and unsteady loading in a micro wind farm model

    OpenAIRE

    Bossuyt, Juliaan; Howland, Michael; Meneveau, Charles; Meyers, Johan

    2016-01-01

    In this study porous disc models are used as a turbine model for a wind-tunnel wind farm experiment, allowing the measurement of the power output, thrust force and spatially averaged incoming velocity for every turbine. The model's capabilities for studying the unsteady turbine loading, wind farm power output intermittency and spatio temporal correlations between wind turbines are demonstrated on an aligned wind farm, consisting of 100 wind turbine models.

  14. Statistical Model of Extreme Shear

    DEFF Research Database (Denmark)

    Larsen, Gunner Chr.; Hansen, Kurt Schaldemose

    2004-01-01

    In order to continue cost-optimisation of modern large wind turbines, it is important to continously increase the knowledge on wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describe the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of high-sampled full-scale time series measurements...... are consistent, given the inevitabel uncertainties associated with model as well as with the extreme value data analysis. Keywords: Statistical model, extreme wind conditions, statistical analysis, turbulence, wind loading, statistical analysis, turbulence, wind loading, wind shear, wind turbines....

  15. Statistical Language Modeling for Historical Documents using Weighted Finite-State Transducers and Long Short-Term Memory

    OpenAIRE

    Al Azawi, Mayce

    2015-01-01

    The goal of this work is to develop statistical natural language models and processing techniques based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short- Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more robust, and easier to train than traditional methods, i.e., words list and rule-based models. They improve the output of recognition systems and make them more accessible to users for browsing and reading...

  16. Principles for statistical inference on big spatio-temporal data from climate models

    KAUST Repository

    Castruccio, Stefano; Genton, Marc G.

    2018-01-01

    The vast increase in size of modern spatio-temporal datasets has prompted statisticians working in environmental applications to develop new and efficient methodologies that are still able to achieve inference for nontrivial models within an affordable time. Climate model outputs push the limits of inference for Gaussian processes, as their size can easily be larger than 10 billion data points. Drawing from our experience in a set of previous work, we provide three principles for the statistical analysis of such large datasets that leverage recent methodological and computational advances. These principles emphasize the need of embedding distributed and parallel computing in the inferential process.

  17. Principles for statistical inference on big spatio-temporal data from climate models

    KAUST Repository

    Castruccio, Stefano

    2018-02-24

    The vast increase in size of modern spatio-temporal datasets has prompted statisticians working in environmental applications to develop new and efficient methodologies that are still able to achieve inference for nontrivial models within an affordable time. Climate model outputs push the limits of inference for Gaussian processes, as their size can easily be larger than 10 billion data points. Drawing from our experience in a set of previous work, we provide three principles for the statistical analysis of such large datasets that leverage recent methodological and computational advances. These principles emphasize the need of embedding distributed and parallel computing in the inferential process.

  18. Robust Combining of Disparate Classifiers Through Order Statistics

    Science.gov (United States)

    Tumer, Kagan; Ghosh, Joydeep

    2001-01-01

    Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

  19. Statistical considerations on safety analysis

    International Nuclear Information System (INIS)

    Pal, L.; Makai, M.

    2004-01-01

    The authors have investigated the statistical methods applied to safety analysis of nuclear reactors and arrived at alarming conclusions: a series of calculations with the generally appreciated safety code ATHLET were carried out to ascertain the stability of the results against input uncertainties in a simple experimental situation. Scrutinizing those calculations, we came to the conclusion that the ATHLET results may exhibit chaotic behavior. A further conclusion is that the technological limits are incorrectly set when the output variables are correlated. Another formerly unnoticed conclusion of the previous ATHLET calculations that certain innocent looking parameters (like wall roughness factor, the number of bubbles per unit volume, the number of droplets per unit volume) can influence considerably such output parameters as water levels. The authors are concerned with the statistical foundation of present day safety analysis practices and can only hope that their own misjudgment will be dispelled. Until then, the authors suggest applying correct statistical methods in safety analysis even if it makes the analysis more expensive. It would be desirable to continue exploring the role of internal parameters (wall roughness factor, steam-water surface in thermal hydraulics codes, homogenization methods in neutronics codes) in system safety codes and to study their effects on the analysis. In the validation and verification process of a code one carries out a series of computations. The input data are not precisely determined because measured data have an error, calculated data are often obtained from a more or less accurate model. Some users of large codes are content with comparing the nominal output obtained from the nominal input, whereas all the possible inputs should be taken into account when judging safety. At the same time, any statement concerning safety must be aleatory, and its merit can be judged only when the probability is known with which the

  20. NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that various...

  1. NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: This data set contains the originally-submitted observation measurement data, terrestrial biosphere model output data, and inverse model simulations that...

  2. Alternative to Ritt's pseudodivision for finding the input-output equations of multi-output models.

    Science.gov (United States)

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

    2012-09-01

    Differential algebra approaches to structural identifiability analysis of a dynamic system model in many instances heavily depend upon Ritt's pseudodivision at an early step in analysis. The pseudodivision algorithm is used to find the characteristic set, of which a subset, the input-output equations, is used for identifiability analysis. A simpler algorithm is proposed for this step, using Gröbner Bases, along with a proof of the method that includes a reduced upper bound on derivative requirements. Efficacy of the new algorithm is illustrated with several biosystem model examples. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Enhanced DEA model with undesirable output and interval data for rice growing farmers performance assessment

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Sahubar Ali Mohd. Nadhar, E-mail: sahubar@uum.edu.my; Ramli, Razamin, E-mail: razamin@uum.edu.my; Baten, M. D. Azizul, E-mail: baten-math@yahoo.com [School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)

    2015-12-11

    Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers’ efficiency.

  4. Enhanced DEA model with undesirable output and interval data for rice growing farmers performance assessment

    International Nuclear Information System (INIS)

    Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul

    2015-01-01

    Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers’ efficiency

  5. Enhanced DEA model with undesirable output and interval data for rice growing farmers performance assessment

    Science.gov (United States)

    Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul

    2015-12-01

    Agricultural production process typically produces two types of outputs which are economic desirable as well as environmentally undesirable outputs (such as greenhouse gas emission, nitrate leaching, effects to human and organisms and water pollution). In efficiency analysis, this undesirable outputs cannot be ignored and need to be included in order to obtain the actual estimation of firms efficiency. Additionally, climatic factors as well as data uncertainty can significantly affect the efficiency analysis. There are a number of approaches that has been proposed in DEA literature to account for undesirable outputs. Many researchers has pointed that directional distance function (DDF) approach is the best as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, it has been found that interval data approach is the most suitable to account for data uncertainty as it is much simpler to model and need less information regarding its distribution and membership function. In this paper, an enhanced DEA model based on DDF approach that considers undesirable outputs as well as climatic factors and interval data is proposed. This model will be used to determine the efficiency of rice farmers who produces undesirable outputs and operates under uncertainty. It is hoped that the proposed model will provide a better estimate of rice farmers' efficiency.

  6. Modelling and study on the output flow characteristics of expansion energy used hydropneumatic transformer

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Yan; Wu, Tiecheng; Cai, Maolin; Liu, Chong [Beihang University, Beijing (China)

    2016-03-15

    Hydropneumatic transformer (short for HP transformer) is used to pump pressurized hydraulic oil. Whereas, due to its insufficient usage of energy and low efficiency, a new kind of HP transformer: EEUHP transformer (Expansion energy used hydropneumatic transformer) was proposed. To illustrate the characteristics of the EEUHP transformer, a mathematical model was built. To verify the mathematical model, an experimental prototype was setup and studied. Through simulation and experimental study on the EEUHP transformer, the influence of five key parameters on the output flow of the EEUHP transformer were obtained, and some conclusions can be drawn. Firstly, the mathematical model was proved to be valid. Furthermore, the EEUHP transformer costs fewer of compressed air than the normal HP transformer when the output flow of the two kinds of transformers are almost same. Moreover, with an increase in the output pressure, the output flow decreases sharply. Finally, with an increase in the effective area of hydraulic output port, the output flow increases distinctly. This research can be referred to in the performance and design optimization of the EEUHP transformers.

  7. Conditions for Model Matching of Switched Asynchronous Sequential Machines with Output Feedback

    OpenAIRE

    Jung–Min Yang

    2016-01-01

    Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the ...

  8. Exclusion statistics and integrable models

    International Nuclear Information System (INIS)

    Mashkevich, S.

    1998-01-01

    The definition of exclusion statistics that was given by Haldane admits a 'statistical interaction' between distinguishable particles (multispecies statistics). For such statistics, thermodynamic quantities can be evaluated exactly; explicit expressions are presented here for cluster coefficients. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models of the Calogero-Sutherland type. The interesting questions of generalizing this correspondence to the higher-dimensional and the multispecies cases remain essentially open; however, our results provide some hints as to searches for the models in question

  9. Spatial Statistical and Modeling Strategy for Inventorying and Monitoring Ecosystem Resources at Multiple Scales and Resolution Levels

    Science.gov (United States)

    Robin M. Reich; C. Aguirre-Bravo; M.S. Williams

    2006-01-01

    A statistical strategy for spatial estimation and modeling of natural and environmental resource variables and indicators is presented. This strategy is part of an inventory and monitoring pilot study that is being carried out in the Mexican states of Jalisco and Colima. Fine spatial resolution estimates of key variables and indicators are outputs that will allow the...

  10. Visuanimation in statistics

    KAUST Repository

    Genton, Marc G.

    2015-04-14

    This paper explores the use of visualization through animations, coined visuanimation, in the field of statistics. In particular, it illustrates the embedding of animations in the paper itself and the storage of larger movies in the online supplemental material. We present results from statistics research projects using a variety of visuanimations, ranging from exploratory data analysis of image data sets to spatio-temporal extreme event modelling; these include a multiscale analysis of classification methods, the study of the effects of a simulated explosive volcanic eruption and an emulation of climate model output. This paper serves as an illustration of visuanimation for future publications in Stat. Copyright © 2015 John Wiley & Sons, Ltd.

  11. The synthesis of the correlation function of pseudorandom binary numbers at the output shift register

    Science.gov (United States)

    Galustov, G. G.; Voronin, V. V.

    2017-05-01

    The sequence generator generates a sequence of pseudorandom binary numbers using a linear-feedback shift register (LFSR). This block implements LFSR using a simple shift register generator (SSRG, or Fibonacci) configuration. In this article we introduce the concept of probabilistic binary element provides requirements, which ensure compliance with the criterion of "uniformity" in the implementation of the basic physical generators uniformly distributed random number sequences. Based on these studies, we obtained an analytic relation between the parameters of the binary sequence and parameters of a numerical sequence with the shift register output. The received analytical dependencies can help in evaluating the statistical characteristics of the processes in solving problems of statistical modeling. It is supposed that the formation of the binary sequence output from the binary probabilistic element is produced using a physical noise process. It is shown that the observed errors in statistical modeling using pseudo-random numbers do not occur if the model examines linear systems with constant parameters, but in case models of nonlinear systems, higher order moments can have a Gaussian distribution.

  12. 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.

  13. Statistical Downscaling of Temperature with the Random Forest Model

    Directory of Open Access Journals (Sweden)

    Bo Pang

    2017-01-01

    Full Text Available The issues with downscaling the outputs of a global climate model (GCM to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA and partial correlation analysis (PAR were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

  14. System convergence in transport models: algorithms efficiency and output uncertainty

    DEFF Research Database (Denmark)

    Rich, Jeppe; Nielsen, Otto Anker

    2015-01-01

    of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed...... much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large......-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened...

  15. Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

    Energy Technology Data Exchange (ETDEWEB)

    Fox, Aaron; La Pointe, Paul [Golder Associates Inc (United States); Simeonov, Assen [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden); Hermanson, Jan; Oehman, Johan [Golder Associates AB, Stockholm (Sweden)

    2007-11-15

    . These models describe fracture intensity and size as a single range from borehole to outcrop scale; and - the combined outcrop scale and tectonic fault models (OSM+TFM), where separate distributions for size and intensity describe the fractures observed at outcrop scale (largely joints) and the features observed at regional scales (lineaments that are largely faults or deformation zones). Fracture intensity and fracture size are not rigidly coupled. The stochastic intensity model is build using power laws, and combines fracture intensity data from outcrops (P21) and boreholes (P10) to simultaneously match both data sets. Intensity statistics are presented for each fracture set in each domain, and the spatial variation of intensity described as a function of lithology or as a gamma distribution where possible. This report also describes the sources of uncertainty in the methodologies, data, and analyses used to build the version 2.2 geological DFN, and offers insight as to the potential magnitudes of their effects on downstream models. The outputs of the geological DFN modeling process are recommended parameters or statistical distributions describing fracture set orientations, radius sizes, volumetric intensities, spatial correlations and models, and other parameters necessary to build stochastic models (lithology and scaling corrections, termination matrices)

  16. Statistical geological discrete fracture network model. Forsmark modelling stage 2.2

    International Nuclear Information System (INIS)

    Fox, Aaron; La Pointe, Paul; Simeonov, Assen; Hermanson, Jan; Oehman, Johan

    2007-11-01

    . These models describe fracture intensity and size as a single range from borehole to outcrop scale; and - the combined outcrop scale and tectonic fault models (OSM+TFM), where separate distributions for size and intensity describe the fractures observed at outcrop scale (largely joints) and the features observed at regional scales (lineaments that are largely faults or deformation zones). Fracture intensity and fracture size are not rigidly coupled. The stochastic intensity model is build using power laws, and combines fracture intensity data from outcrops (P21) and boreholes (P10) to simultaneously match both data sets. Intensity statistics are presented for each fracture set in each domain, and the spatial variation of intensity described as a function of lithology or as a gamma distribution where possible. This report also describes the sources of uncertainty in the methodologies, data, and analyses used to build the version 2.2 geological DFN, and offers insight as to the potential magnitudes of their effects on downstream models. The outputs of the geological DFN modeling process are recommended parameters or statistical distributions describing fracture set orientations, radius sizes, volumetric intensities, spatial correlations and models, and other parameters necessary to build stochastic models (lithology and scaling corrections, termination matrices)

  17. Statistical modelling with quantile functions

    CERN Document Server

    Gilchrist, Warren

    2000-01-01

    Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics.Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data.Statistical Modelling with Quantile Functions adds a new dimension to the practice of stati...

  18. The assessment of different models to predict solar module temperature, output power and efficiency for Nis, Serbia

    International Nuclear Information System (INIS)

    Pantic, Lana S.; Pavlović, Tomislav M.; Milosavljević, Dragana D.; Radonjic, Ivana S.; Radovic, Miodrag K.; Sazhko, Galina

    2016-01-01

    Five different models for calculating solar module temperature, output power and efficiency for sunny days with different solar radiation intensities and ambient temperatures are assessed in this paper. Thereafter, modeled values are compared to the experimentally obtained values for the horizontal solar module in Nis, Serbia. The criterion for determining the best model was based on the statistical analysis and the agreement between the calculated and the experimental values. The calculated values of solar module temperature are in good agreement with the experimentally obtained ones, with some variations over and under the measured values. The best agreement between calculated and experimentally obtained values was for summer months with high solar radiation intensity. The nonlinear model for calculating the output power is much better than the linear model and at the same time predicts better the total electrical energy generated by the solar module during the day. The nonlinear model for calculating the solar module efficiency predicts the efficiency higher than the STC (Standard Test Conditions) value of solar module efficiency for all conditions, while the linear model predicts the solar module efficiency very well. This paper provides a simple and efficient guideline to estimate relevant parameters of a monocrystalline silicon solar module under the moderate-continental climate conditions. - Highlights: • Linear model for solar module temperature gives accurate predictions for August. • The nonlinear model better predicts the solar module power than the linear model. • For calculating solar module power for Nis we propose the nonlinear model. • For calculating solar model efficiency for Nis we propose adoption of linear model. • The adopted models can be used for calculations throughout the year.

  19. A Statistical Programme Assignment Model

    DEFF Research Database (Denmark)

    Rosholm, Michael; Staghøj, Jonas; Svarer, Michael

    When treatment effects of active labour market programmes are heterogeneous in an observable way  across the population, the allocation of the unemployed into different programmes becomes a particularly  important issue. In this paper, we present a statistical model designed to improve the present...... duration of unemployment spells may result if a statistical programme assignment model is introduced. We discuss several issues regarding the  plementation of such a system, especially the interplay between the statistical model and  case workers....

  20. Book Trade Research and Statistics. Prices of U.S. and Foreign Published Materials; Book Title Output and Average Prices: 2000 Final and 2001 Preliminary Figures; Book Sales Statistics, 2001: AAP Preliminary Estimates; U.S. Book Exports and Imports: 2001; Number of Book Outlets in the United States and Canada; Review Media Statistics.

    Science.gov (United States)

    Sullivan, Sharon G.; Barr, Catherine; Grabois, Andrew

    2002-01-01

    Includes six articles that report on prices of U.S. and foreign published materials; book title output and average prices; book sales statistics; book exports and imports; book outlets in the U.S. and Canada; and review media statistics. (LRW)

  1. Supplementary Material for: Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2016-01-01

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific datasets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 1018 entries. Supplementary materials for this article are available online.

  2. 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

  3. 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

  4. Frog Statistics

    Science.gov (United States)

    Whole Frog Project and Virtual Frog Dissection Statistics wwwstats output for January 1 through duplicate or extraneous accesses. For example, in these statistics, while a POST requesting an image is as well. Note that this under-represents the bytes requested. Starting date for following statistics

  5. Multi-model analysis of terrestrial carbon cycles in Japan: reducing uncertainties in model outputs among different terrestrial biosphere models using flux observations

    Science.gov (United States)

    Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

    2009-08-01

    Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales.

  6. 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.

  7. 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

  8. An extended environmental input-output lifecycle assessment model to study the urban food-energy-water nexus

    Science.gov (United States)

    Sherwood, John; Clabeaux, Raeanne; Carbajales-Dale, Michael

    2017-10-01

    We developed a physically-based environmental account of US food production systems and integrated these data into the environmental-input-output life cycle assessment (EIO-LCA) model. The extended model was used to characterize the food, energy, and water (FEW) intensities of every US economic sector. The model was then applied to every Bureau of Economic Analysis metropolitan statistical area (MSA) to determine their FEW usages. The extended EIO-LCA model can determine the water resource use (kGal), energy resource use (TJ), and food resource use in units of mass (kg) or energy content (kcal) of any economic activity within the United States. We analyzed every economic sector to determine its FEW intensities per dollar of economic output. This data was applied to each of the 382 MSAs to determine their total and per dollar of GDP FEW usages by allocating MSA economic production to the corresponding FEW intensities of US economic sectors. Additionally, a longitudinal study was performed for the Los Angeles-Long Beach-Anaheim, CA, metropolitan statistical area to examine trends from this singular MSA and compare it to the overall results. Results show a strong correlation between GDP and energy use, and between food and water use across MSAs. There is also a correlation between GDP and greenhouse gas emissions. The longitudinal study indicates that these correlations can shift alongside a shifting industrial composition. Comparing MSAs on a per GDP basis reveals that central and southern California tend to be more resource intensive than many other parts of the country, while much of Florida has abnormally low resource requirements. Results of this study enable a more complete understanding of food, energy, and water as key ingredients to a functioning economy. With the addition of the food data to the EIO-LCA framework, researchers will be able to better study the food-energy-water nexus and gain insight into how these three vital resources are interconnected

  9. The Value of Risk : Measuring the Service Output of U.S. Commercial Banks

    NARCIS (Netherlands)

    Basu, Susanto; Inklaar, Robert; Wang, J. Christina

    2008-01-01

    Rather than charging direct fees, banks often charge implicitly for their services via interest spreads. As a result, much of bank output has to be estimated indirectly. In contrast to current statistical practice, dynamic optimizing models of banks argue that compensation for bearing systematic

  10. Diffeomorphic Statistical Deformation Models

    DEFF Research Database (Denmark)

    Hansen, Michael Sass; Hansen, Mads/Fogtman; Larsen, Rasmus

    2007-01-01

    In this paper we present a new method for constructing diffeomorphic statistical deformation models in arbitrary dimensional images with a nonlinear generative model and a linear parameter space. Our deformation model is a modified version of the diffeomorphic model introduced by Cootes et al....... The modifications ensure that no boundary restriction has to be enforced on the parameter space to prevent folds or tears in the deformation field. For straightforward statistical analysis, principal component analysis and sparse methods, we assume that the parameters for a class of deformations lie on a linear...... with ground truth in form of manual expert annotations, and compared to Cootes's model. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation, registration or classification purposes....

  11. 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.

  12. Rice growing farmers efficiency measurement using a slack based interval DEA model with undesirable outputs

    Science.gov (United States)

    Khan, Sahubar Ali Mohd. Nadhar; Ramli, Razamin; Baten, M. D. Azizul

    2017-11-01

    In recent years eco-efficiency which considers the effect of production process on environment in determining the efficiency of firms have gained traction and a lot of attention. Rice farming is one of such production processes which typically produces two types of outputs which are economic desirable as well as environmentally undesirable. In efficiency analysis, these undesirable outputs cannot be ignored and need to be included in the model to obtain the actual estimation of firm's efficiency. There are numerous approaches that have been used in data envelopment analysis (DEA) literature to account for undesirable outputs of which directional distance function (DDF) approach is the most widely used as it allows for simultaneous increase in desirable outputs and reduction of undesirable outputs. Additionally, slack based DDF DEA approaches considers the output shortfalls and input excess in determining efficiency. In situations when data uncertainty is present, the deterministic DEA model is not suitable to be used as the effects of uncertain data will not be considered. In this case, it has been found that interval data approach is suitable to account for data uncertainty as it is much simpler to model and need less information regarding the underlying data distribution and membership function. The proposed model uses an enhanced DEA model which is based on DDF approach and incorporates slack based measure to determine efficiency in the presence of undesirable factors and data uncertainty. Interval data approach was used to estimate the values of inputs, undesirable outputs and desirable outputs. Two separate slack based interval DEA models were constructed for optimistic and pessimistic scenarios. The developed model was used to determine rice farmers efficiency from Kepala Batas, Kedah. The obtained results were later compared to the results obtained using a deterministic DDF DEA model. The study found that 15 out of 30 farmers are efficient in all cases. It

  13. 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

  14. Modeling of Output Characteristics of a UV Cu+ Ne-CuBr Laser

    Directory of Open Access Journals (Sweden)

    Snezhana Georgieva Gocheva-Ilieva

    2012-01-01

    Full Text Available This paper examines experiment data for a Ne-CuBr UV copper ion laser excited by longitudinal pulsed discharge emitting in multiline regime. The flexible multivariate adaptive regression splines (MARSs method has been used to develop nonparametric regression models describing the laser output power and service life of the devices. The models have been constructed as explicit functions of 9 basic input laser characteristics. The obtained models account for local nonlinearities of the relationships within the various multivariate subregions. The built best MARS models account for over 98% of data. The models are used to estimate the investigated output laser characteristics of existing UV lasers. The capabilities for using the models in predicting existing and future experiments have been demonstrated. Specific analyses have been presented comparing the models with actual experiments. The obtained results are applicable for guiding and planning the engineering experiment. The modeling methodology can be applied for a wide range of similar lasers and laser devices.

  15. Statistical modeling for degradation data

    CERN Document Server

    Lio, Yuhlong; Ng, Hon; Tsai, Tzong-Ru

    2017-01-01

    This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.

  16. Exclusion statistics and integrable models

    International Nuclear Information System (INIS)

    Mashkevich, S.

    1998-01-01

    The definition of exclusion statistics, as given by Haldane, allows for a statistical interaction between distinguishable particles (multi-species statistics). The thermodynamic quantities for such statistics ca be evaluated exactly. The explicit expressions for the cluster coefficients are presented. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models. The interesting questions of generalizing this correspondence onto the higher-dimensional and the multi-species cases remain essentially open

  17. The value of risk : measuring the service output of U.S. commercial banks

    NARCIS (Netherlands)

    Basu, Susanto; Inklaar, Robert; Wang, J. Christina

    Banks often charge implicitly for their services via interest spreads, instead of explicit fees. Much of bank output thus has to be estimated indirectly. In contrast to current statistical practice, dynamic optimizing models of banks argue that compensation for bearing systematic risk is not part of

  18. Including model uncertainty in the model predictive control with output feedback

    Directory of Open Access Journals (Sweden)

    Rodrigues M.A.

    2002-01-01

    Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.

  19. Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

    Science.gov (United States)

    Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

    2018-05-01

    Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed

  20. A conceptual guide to statistics using SPSS

    CERN Document Server

    Berkman, Elliot T

    2011-01-01

    Bridging an understanding of Statistics and SPSS. This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual u

  1. Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

    Science.gov (United States)

    White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

    2017-12-01

    As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

  2. Rweb:Web-based Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Jeff Banfield

    1999-03-01

    Full Text Available Rweb is a freely accessible statistical analysis environment that is delivered through the World Wide Web (WWW. It is based on R, a well known statistical analysis package. The only requirement to run the basic Rweb interface is a WWW browser that supports forms. If you want graphical output you must, of course, have a browser that supports graphics. The interface provides access to WWW accessible data sets, so you may run Rweb on your own data. Rweb can provide a four window statistical computing environment (code input, text output, graphical output, and error information through browsers that support Javascript. There is also a set of point and click modules under development for use in introductory statistics courses.

  3. A Monte Carlo Study on Multiple Output Stochastic Frontiers: Comparison of Two Approaches

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Jensen, Uwe

    , dividing all other output quantities by the selected output quantity, and using these ratios as regressors (OD). Another approach is the stochastic ray production frontier (SR) which transforms the output quantities into their Euclidean distance as the dependent variable and their polar coordinates......In the estimation of multiple output technologies in a primal approach, the main question is how to handle the multiple outputs. Often an output distance function is used, where the classical approach is to exploit its homogeneity property by selecting one output quantity as the dependent variable...... of both specifications for the case of a Translog output distance function with respect to different common statistical problems as well as problems arising as a consequence of zero values in the output quantities. Although, our results partly show clear reactions to statistical misspecifications...

  4. 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

  5. Book Trade Research and Statistics. Prices of U.S. and Foreign Published Materials; Book Title Output and Average Prices: 2001 Final and 2002 Preliminary Figures; Book Sales Statistics, 2002: AAP Preliminary Estimates; U.S. Book Exports and Imports:2002; Number of Book Outlets in the United States and Canada; Review Media Statistics.

    Science.gov (United States)

    Sullivan, Sharon G.; Grabois, Andrew; Greco, Albert N.

    2003-01-01

    Includes six reports related to book trade statistics, including prices of U.S. and foreign materials; book title output and average prices; book sales statistics; book exports and imports; book outlets in the U.S. and Canada; and numbers of books and other media reviewed by major reviewing publications. (LRW)

  6. Statistical Modeling of Large Wind Plant System's Generation - A Case Study

    International Nuclear Information System (INIS)

    Sabolic, D.

    2014-01-01

    This paper presents simplistic, yet very accurate, descriptive statistical models of various static and dynamic parameters of energy output from a large system of wind plants operated by Bonneville Power Administration (BPA), USA. The system's size at the end of 2013 was 4515 MW of installed capacity. The 5-minute readings from the beginning of 2007 to the end of 2013, recorded and published by BPA, were used to derive a number of experimental distributions, which were then used to devise theoretic statistical models with merely one or two parameters. In spite of the simplicity, they reproduced experimental data with great accuracy, which was checked by rigorous tests of goodness-of-fit. Statistical distribution functions were obtained for the following wind generation-related quantities: total generation as percentage of total installed capacity; change in total generation power in 5, 10, 15, 20, 25, 30, 45, and 60 minutes as percentage of total installed capacity; duration of intervals with total generated power, expressed as percentage of total installed capacity, lower than certain pre-specified level. Limitation of total installed wind plant capacity, when it is determined by regulation demand from wind plants, is discussed, too. The models presented here can be utilized in analyses related to power system economics/policy, which is also briefly discussed in the paper. (author).

  7. Early-Transition Output Decline Revisited

    Directory of Open Access Journals (Sweden)

    Crt Kostevc

    2016-05-01

    Full Text Available In this paper we revisit the issue of aggregate output decline that took place in the early transition period. We propose an alternative explanation of output decline that is applicable to Central- and Eastern-European countries. In the first part of the paper we develop a simple dynamic general equilibrium model that builds on work by Gomulka and Lane (2001. In particular, we consider price liberalization, interpreted as elimination of distortionary taxation, as a trigger of the output decline. We show that price liberalization in interaction with heterogeneous adjustment costs and non-employment benefits lead to aggregate output decline and surge in wage inequality. While these patterns are consistent with actual dynamics in CEE countries, this model cannot generate output decline in all sectors. Instead sectors that were initially taxed even exhibit output growth. Thus, in the second part we consider an alternative general equilibrium model with only one production sector and two types of labor and distortion in a form of wage compression during the socialist era. The trigger for labor mobility and consequently output decline is wage liberalization. Assuming heterogeneity of workers in terms of adjustment costs and non-employment benefits can explain output decline in all industries.

  8. Simulation of the output power of copper bromide lasers by the MARS method

    International Nuclear Information System (INIS)

    Iliev, I P; Voynikova, D S; Gocheva-Ilieva, S G

    2012-01-01

    The dependence of the output power of CuBr lasers (operating at wavelengths of 510.6 and 578.2 nm) on ten input physical parameters has been statistically analysed based on a large amount of experimental data accumulated for these lasers. Regression models have been built using the flexible nonparametric method of multivariate adaptive regression splines (MARS) to describe both linear and nonlinear local dependences. These models cover more than 97% initial data with an error comparable with the experimental error; they are applied to estimate and predict the output powers of both existing and future lasers. The advantage of the models constructed for estimating laser parameters over the standard parametric methods of multivariate factor and regression analysis is demonstrated.

  9. Online Statistical Modeling (Regression Analysis) for Independent Responses

    Science.gov (United States)

    Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

    2017-06-01

    Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

  10. A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

    Science.gov (United States)

    Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

    2018-03-01

    Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

  11. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  12. Modelling Analysis of Forestry Input-Output Elasticity in China

    Directory of Open Access Journals (Sweden)

    Guofeng Wang

    2016-01-01

    Full Text Available Based on an extended economic model and space econometrics, this essay analyzed the spatial distributions and interdependent relationships of the production of forestry in China; also the input-output elasticity of forestry production were calculated. Results figure out there exists significant spatial correlation in forestry production in China. Spatial distribution is mainly manifested as spatial agglomeration. The output elasticity of labor force is equal to 0.6649, and that of capital is equal to 0.8412. The contribution of land is significantly negative. Labor and capital are the main determinants for the province-level forestry production in China. Thus, research on the province-level forestry production should not ignore the spatial effect. The policy-making process should take into consideration the effects between provinces on the production of forestry. This study provides some scientific technical support for forestry production.

  13. Reactive Power Pricing Model Considering the Randomness of Wind Power Output

    Science.gov (United States)

    Dai, Zhong; Wu, Zhou

    2018-01-01

    With the increase of wind power capacity integrated into grid, the influence of the randomness of wind power output on the reactive power distribution of grid is gradually highlighted. Meanwhile, the power market reform puts forward higher requirements for reasonable pricing of reactive power service. Based on it, the article combined the optimal power flow model considering wind power randomness with integrated cost allocation method to price reactive power. Meanwhile, considering the advantages and disadvantages of the present cost allocation method and marginal cost pricing, an integrated cost allocation method based on optimal power flow tracing is proposed. The model realized the optimal power flow distribution of reactive power with the minimal integrated cost and wind power integration, under the premise of guaranteeing the balance of reactive power pricing. Finally, through the analysis of multi-scenario calculation examples and the stochastic simulation of wind power outputs, the article compared the results of the model pricing and the marginal cost pricing, which proved that the model is accurate and effective.

  14. Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs.

    Science.gov (United States)

    Vitolo, Claudia; Di Giuseppe, Francesca; D'Andrea, Mirko

    2018-01-01

    The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. In this paper we describe the functionalities of the package and give examples using publicly available datasets. Fire danger model outputs are taken from the modeling components of the European Forest Fire Information System (EFFIS) and observed burned areas from the Global Fire Emission Database (GFED). Complete documentation, including a vignette, is also available within the package.

  15. 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 ...

  16. On modelling the interaction between two rotating bodies with statistically distributed features: an application to dressing of grinding wheels

    Science.gov (United States)

    Spampinato, A.; Axinte, D. A.

    2017-12-01

    The mechanisms of interaction between bodies with statistically arranged features present characteristics common to different abrasive processes, such as dressing of abrasive tools. In contrast with the current empirical approach used to estimate the results of operations based on attritive interactions, the method we present in this paper allows us to predict the output forces and the topography of a simulated grinding wheel for a set of specific operational parameters (speed ratio and radial feed-rate), providing a thorough understanding of the complex mechanisms regulating these processes. In modelling the dressing mechanisms, the abrasive characteristics of both bodies (grain size, geometry, inter-space and protrusion) are first simulated; thus, their interaction is simulated in terms of grain collisions. Exploiting a specifically designed contact/impact evaluation algorithm, the model simulates the collisional effects of the dresser abrasives on the grinding wheel topography (grain fracture/break-out). The method has been tested for the case of a diamond rotary dresser, predicting output forces within less than 10% error and obtaining experimentally validated grinding wheel topographies. The study provides a fundamental understanding of the dressing operation, enabling the improvement of its performance in an industrial scenario, while being of general interest in modelling collision-based processes involving statistically distributed elements.

  17. 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,

  18. Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input-output analysis.

    Science.gov (United States)

    El Haimar, Amine; Santos, Joost R

    2014-03-01

    Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics. © 2013 Society for Risk Analysis.

  19. Validating an Air Traffic Management Concept of Operation Using Statistical Modeling

    Science.gov (United States)

    He, Yuning; Davies, Misty Dawn

    2013-01-01

    Validating a concept of operation for a complex, safety-critical system (like the National Airspace System) is challenging because of the high dimensionality of the controllable parameters and the infinite number of states of the system. In this paper, we use statistical modeling techniques to explore the behavior of a conflict detection and resolution algorithm designed for the terminal airspace. These techniques predict the robustness of the system simulation to both nominal and off-nominal behaviors within the overall airspace. They also can be used to evaluate the output of the simulation against recorded airspace data. Additionally, the techniques carry with them a mathematical value of the worth of each prediction-a statistical uncertainty for any robustness estimate. Uncertainty Quantification (UQ) is the process of quantitative characterization and ultimately a reduction of uncertainties in complex systems. UQ is important for understanding the influence of uncertainties on the behavior of a system and therefore is valuable for design, analysis, and verification and validation. In this paper, we apply advanced statistical modeling methodologies and techniques on an advanced air traffic management system, namely the Terminal Tactical Separation Assured Flight Environment (T-TSAFE). We show initial results for a parameter analysis and safety boundary (envelope) detection in the high-dimensional parameter space. For our boundary analysis, we developed a new sequential approach based upon the design of computer experiments, allowing us to incorporate knowledge from domain experts into our modeling and to determine the most likely boundary shapes and its parameters. We carried out the analysis on system parameters and describe an initial approach that will allow us to include time-series inputs, such as the radar track data, into the analysis

  20. 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.

  1. Classical model of intermediate statistics

    International Nuclear Information System (INIS)

    Kaniadakis, G.

    1994-01-01

    In this work we present a classical kinetic model of intermediate statistics. In the case of Brownian particles we show that the Fermi-Dirac (FD) and Bose-Einstein (BE) distributions can be obtained, just as the Maxwell-Boltzmann (MD) distribution, as steady states of a classical kinetic equation that intrinsically takes into account an exclusion-inclusion principle. In our model the intermediate statistics are obtained as steady states of a system of coupled nonlinear kinetic equations, where the coupling constants are the transmutational potentials η κκ' . We show that, besides the FD-BE intermediate statistics extensively studied from the quantum point of view, we can also study the MB-FD and MB-BE ones. Moreover, our model allows us to treat the three-state mixing FD-MB-BE intermediate statistics. For boson and fermion mixing in a D-dimensional space, we obtain a family of FD-BE intermediate statistics by varying the transmutational potential η BF . This family contains, as a particular case when η BF =0, the quantum statistics recently proposed by L. Wu, Z. Wu, and J. Sun [Phys. Lett. A 170, 280 (1992)]. When we consider the two-dimensional FD-BE statistics, we derive an analytic expression of the fraction of fermions. When the temperature T→∞, the system is composed by an equal number of bosons and fermions, regardless of the value of η BF . On the contrary, when T=0, η BF becomes important and, according to its value, the system can be completely bosonic or fermionic, or composed both by bosons and fermions

  2. Research output in medieval and crusades studies 1981-2011

    DEFF Research Database (Denmark)

    Nielsen, Torben Kjersgaard

    2017-01-01

    This article investigates the numerical research output of crusade studies over the past thirty years. The article compares its findings to the output of medieval studies in general in the same period. It shows in detail how the applied bibliometric statistics are generated and elaborates on some...... of the methodological considerations necessary in carrying out this kind of quantitative research. On the basis of bibliometric statistics generated from the International Medieval Bibliography (IMB) and Bibliographie de Civilisation Médiévale (BCM), the article identifies a numeric decrease in research output both...... in crusade studies in particular and in medieval studies in general. The article proposes further discussion on the “why” and “how” of this somewhat surprising result....

  3. An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

    Directory of Open Access Journals (Sweden)

    Juan Pablo Rivera

    2015-07-01

    Full Text Available Physically-based radiative transfer models (RTMs help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an “Emulator toolbox” that enables analysing multi-output machine learning regression algorithms (MO-MLRAs on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO’s MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR, kernel ridge regression (KRR and neural networks (NN. These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance. SCOPE generates, amongst others, sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced

  4. Robust Output Model Predictive Control of an Unstable Rijke Tube

    Directory of Open Access Journals (Sweden)

    Fabian Jarmolowitz

    2012-01-01

    Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

  5. Modeling and control of the output current of a Reformed Methanol Fuel Cell system

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Pasupathi, Sivakumar

    2015-01-01

    In this work, a dynamic Matlab SIMULINK model of the relationship between the fuel cell current set point of a Reformed Methanol Fuel Cell system and the output current of the system is developed. The model contains an estimated fuel cell model, based on a polarization curve and assumed first order...... dynamics, as well as a battery model based on an equivalent circuit model and a balance of plant power consumption model. The models are tuned with experimental data and verified using a verification data set. The model is used to develop an output current controller which can control the charge current...... of the battery. The controller is a PI controller with feedforward and anti-windup. The performance of the controller is tested and verified on the physical system....

  6. 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.

  7. Statistical Model of Extreme Shear

    DEFF Research Database (Denmark)

    Hansen, Kurt Schaldemose; Larsen, Gunner Chr.

    2005-01-01

    In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...

  8. Aspects of statistical model for multifragmentation

    International Nuclear Information System (INIS)

    Bhattacharyya, P.; Das Gupta, S.; Mekjian, A. Z.

    1999-01-01

    We deal with two different aspects of an exactly soluble statistical model of fragmentation. First we show, using zero range force and finite temperature Thomas-Fermi theory, that a common link can be found between finite temperature mean field theory and the statistical fragmentation model. We show the latter naturally arises in the spinodal region. Next we show that although the exact statistical model is a canonical model and uses temperature, microcanonical results which use constant energy rather than constant temperature can also be obtained from the canonical model using saddle-point approximation. The methodology is extremely simple to implement and at least in all the examples studied in this work is very accurate. (c) 1999 The American Physical Society

  9. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Auffhammer, M.; Hsiang, S. M.; Schlenker, W.; Sobel, A.

    2013-06-28

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  10. Automated statistical modeling of analytical measurement systems

    International Nuclear Information System (INIS)

    Jacobson, J.J.

    1992-01-01

    The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability

  11. Validation of models with multivariate output

    International Nuclear Information System (INIS)

    Rebba, Ramesh; Mahadevan, Sankaran

    2006-01-01

    This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading

  12. Statistical modelling for ship propulsion efficiency

    DEFF Research Database (Denmark)

    Petersen, Jóan Petur; Jacobsen, Daniel J.; Winther, Ole

    2012-01-01

    This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks...

  13. Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

    Science.gov (United States)

    Kumar, Sujay V.; Wang, Shugong; Mocko, David M.; Peters-Lidard, Christa D.; Xia, Youlong

    2017-11-01

    Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

  14. 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

  15. 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

  16. Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

    Energy Technology Data Exchange (ETDEWEB)

    Melin, Alexander M. [ORNL; Olama, Mohammed M. [ORNL; Dong, Jin [ORNL; Djouadi, Seddik M. [ORNL; Zhang, Yichen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

    2017-09-01

    The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.

  17. Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

    International Nuclear Information System (INIS)

    Thøgersen, E; Tranberg, B; Greiner, M; Herp, J

    2017-01-01

    The wake produced by a wind turbine is dynamically meandering and of rather narrow nature. Only when looking at large time averages, the wake appears to be static and rather broad, and is then well described by simple engineering models like the Jensen wake model (JWM). We generalise the latter deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain is calculated to be 7.5%. This outcome indicates the possible operational robustness of an optimised yaw control for real-life wind farms. (paper)

  18. Statistical meandering wake model and its application to yaw-angle optimisation of wind farms

    Science.gov (United States)

    Thøgersen, E.; Tranberg, B.; Herp, J.; Greiner, M.

    2017-05-01

    The wake produced by a wind turbine is dynamically meandering and of rather narrow nature. Only when looking at large time averages, the wake appears to be static and rather broad, and is then well described by simple engineering models like the Jensen wake model (JWM). We generalise the latter deterministic models to a statistical meandering wake model (SMWM), where a random directional deflection is assigned to a narrow wake in such a way that on average it resembles a broad Jensen wake. In a second step, the model is further generalised to wind-farm level, where the deflections of the multiple wakes are treated as independently and identically distributed random variables. When carefully calibrated to the Nysted wind farm, the ensemble average of the statistical model produces the same wind-direction dependence of the power efficiency as obtained from the standard Jensen model. Upon using the JWM to perform a yaw-angle optimisation of wind-farm power output, we find an optimisation gain of 6.7% for the Nysted wind farm when compared to zero yaw angles and averaged over all wind directions. When applying the obtained JWM-based optimised yaw angles to the SMWM, the ensemble-averaged gain is calculated to be 7.5%. This outcome indicates the possible operational robustness of an optimised yaw control for real-life wind farms.

  19. Statistical theory applications and associated computer codes

    International Nuclear Information System (INIS)

    Prince, A.

    1980-01-01

    The general format is along the same lines as that used in the O.M. Session, i.e. an introduction to the nature of the physical problems and methods of solution based on the statistical model of the nucleus. Both binary and higher multiple reactions are considered. The computer codes used in this session are a combination of optical model and statistical theory. As with the O.M. sessions, the preparation of input and analysis of output are thoroughly examined. Again, comparison with experimental data serves to demonstrate the validity of the results and possible areas for improvement. (author)

  20. Firm productivity, pollution, and output: theory and empirical evidence from China.

    Science.gov (United States)

    Tang, Erzi; Zhang, Jingjing; Haider, Zulfiqar

    2015-11-01

    Using a theoretical model, this paper argues that as firm productivity increases, there is a decrease in firm-level pollution intensity. However, as productivity increases, firms tend to increase their aggregate output, which requires the use of additional resources that increase pollution. Hence, an increase in productivity results in two opposing effects where increased productivity may in fact increase pollution created by a firm. We describe the joint effect of these two mechanisms on pollution emissions as the "productivity dilemma" of pollution emission. Based on firm-level data from China, we also empirically test this productivity dilemma hypothesis. Our empirical results suggest that, in general, firm productivity has a positive and statistically significant impact on pollution emission in China. However, the impact of productivity on pollution becomes negative when we control for increases in firm output. The empirical evidence also confirms the positive influence of productivity on output, which suggests that the main determinant of pollution is the firm's output. The empirical results provide evidence of the existence of, what we describe as, the productivity dilemma of pollution emission.

  1. Sensometrics: Thurstonian and Statistical Models

    DEFF Research Database (Denmark)

    Christensen, Rune Haubo Bojesen

    . sensR is a package for sensory discrimination testing with Thurstonian models and ordinal supports analysis of ordinal data with cumulative link (mixed) models. While sensR is closely connected to the sensometrics field, the ordinal package has developed into a generic statistical package applicable......This thesis is concerned with the development and bridging of Thurstonian and statistical models for sensory discrimination testing as applied in the scientific discipline of sensometrics. In sensory discrimination testing sensory differences between products are detected and quantified by the use...... and sensory discrimination testing in particular in a series of papers by advancing Thurstonian models for a range of sensory discrimination protocols in addition to facilitating their application by providing software for fitting these models. The main focus is on identifying Thurstonian models...

  2. Statistical modelling for social researchers principles and practice

    CERN Document Server

    Tarling, Roger

    2008-01-01

    This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...

  3. Topology for statistical modeling of petascale data.

    Energy Technology Data Exchange (ETDEWEB)

    Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)

    2011-07-01

    This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.

  4. Bayesian models: A statistical primer for ecologists

    Science.gov (United States)

    Hobbs, N. Thompson; Hooten, Mevin B.

    2015-01-01

    Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

  5. Statistical Model-Based Face Pose Estimation

    Institute of Scientific and Technical Information of China (English)

    GE Xinliang; YANG Jie; LI Feng; WANG Huahua

    2007-01-01

    A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.

  6. Output fields from the NOAA WAVEWATCH III® wave model monthly hindcasts

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA WAVEWATCH III® hindcast dataset comprises output fields from the monthly WAVEWATCH III® hindcast model runs conducted at the National Centers for...

  7. Simple statistical model for branched aggregates

    DEFF Research Database (Denmark)

    Lemarchand, Claire; Hansen, Jesper Schmidt

    2015-01-01

    , given that it already has bonds with others. The model is applied here to asphaltene nanoaggregates observed in molecular dynamics simulations of Cooee bitumen. The variation with temperature of the probabilities deduced from this model is discussed in terms of statistical mechanics arguments....... The relevance of the statistical model in the case of asphaltene nanoaggregates is checked by comparing the predicted value of the probability for one molecule to have exactly i bonds with the same probability directly measured in the molecular dynamics simulations. The agreement is satisfactory......We propose a statistical model that can reproduce the size distribution of any branched aggregate, including amylopectin, dendrimers, molecular clusters of monoalcohols, and asphaltene nanoaggregates. It is based on the conditional probability for one molecule to form a new bond with a molecule...

  8. Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

    Directory of Open Access Journals (Sweden)

    Sie Long Kek

    2015-01-01

    Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

  9. Matrix Tricks for Linear Statistical Models

    CERN Document Server

    Puntanen, Simo; Styan, George PH

    2011-01-01

    In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and

  10. Statistical Model Checking of Rich Models and Properties

    DEFF Research Database (Denmark)

    Poulsen, Danny Bøgsted

    in undecidability issues for the traditional model checking approaches. Statistical model checking has proven itself a valuable supplement to model checking and this thesis is concerned with extending this software validation technique to stochastic hybrid systems. The thesis consists of two parts: the first part...... motivates why existing model checking technology should be supplemented by new techniques. It also contains a brief introduction to probability theory and concepts covered by the six papers making up the second part. The first two papers are concerned with developing online monitoring techniques...... systems. The fifth paper shows how stochastic hybrid automata are useful for modelling biological systems and the final paper is concerned with showing how statistical model checking is efficiently distributed. In parallel with developing the theory contained in the papers, a substantial part of this work...

  11. SU-F-T-143: Implementation of a Correction-Based Output Model for a Compact Passively Scattered Proton Therapy System

    Energy Technology Data Exchange (ETDEWEB)

    Ferguson, S; Ahmad, S; Chen, Y; Ferreira, C; Islam, M; Lau, A; Jin, H [University of Oklahoma Health Sciences Center, Oklahoma City, OK (United States); Keeling, V [Carti, Inc., Little Rock, AR (United States)

    2016-06-15

    Purpose: To commission and investigate the accuracy of an output (cGy/MU) prediction model for a compact passively scattered proton therapy system. Methods: A previously published output prediction model (Sahoo et al, Med Phys, 35, 5088–5097, 2008) was commissioned for our Mevion S250 proton therapy system. This model is a correction-based model that multiplies correction factors (d/MUwnc=ROFxSOBPF xRSFxSOBPOCFxOCRxFSFxISF). These factors accounted for changes in output due to options (12 large, 5 deep, and 7 small), modulation width M, range R, off-center, off-axis, field-size, and off-isocenter. In this study, the model was modified to ROFxSOBPFxRSFxOCRxFSFxISF-OCFxGACF by merging SOBPOCF and ISF for simplicity and introducing a gantry angle correction factor (GACF). To commission the model, outputs over 1,000 data points were taken at the time of the system commissioning. The output was predicted by interpolation (1D for SOBPF, FSF, and GACF; 2D for RSF and OCR) with inverse-square calculation (ISF-OCR). The outputs of 273 combinations of R and M covering total 24 options were measured to test the model. To minimize fluence perturbation, scattered dose from range compensator and patient was not considered. The percent differences between the predicted (P) and measured (M) outputs were calculated to test the prediction accuracy ([P-M]/Mx100%). Results: GACF was required because of up to 3.5% output variation dependence on the gantry angle. A 2D interpolation was required for OCR because the dose distribution was not radially symmetric especially for the deep options. The average percent differences were −0.03±0.98% (mean±SD) and the differences of all the measurements fell within ±3%. Conclusion: It is concluded that the model can be clinically used for the compact passively scattered proton therapy system. However, great care should be taken when the field-size is less than 5×5 cm{sup 2} where a direct output measurement is required due to substantial

  12. Statistical Modelling of Wind Proles - Data Analysis and Modelling

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre

    The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....

  13. Search Databases and Statistics

    DEFF Research Database (Denmark)

    Refsgaard, Jan C; Munk, Stephanie; Jensen, Lars J

    2016-01-01

    having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database...... searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here....

  14. Statistical physics of pairwise probability models

    DEFF Research Database (Denmark)

    Roudi, Yasser; Aurell, Erik; Hertz, John

    2009-01-01

    (dansk abstrakt findes ikke) Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of  data......: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying...

  15. 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.

  16. Climatic change on the Gulf of Fonseca (Central America) using two-step statistical downscaling of CMIP5 model outputs

    Science.gov (United States)

    Ribalaygua, Jaime; Gaitán, Emma; Pórtoles, Javier; Monjo, Robert

    2018-05-01

    A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method's performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak).

  17. A Synergistic Approach for Evaluating Climate Model Output for Ecological Applications

    Directory of Open Access Journals (Sweden)

    Rachel D. Cavanagh

    2017-09-01

    Full Text Available Increasing concern about the impacts of climate change on ecosystems is prompting ecologists and ecosystem managers to seek reliable projections of physical drivers of change. The use of global climate models in ecology is growing, although drawing ecologically meaningful conclusions can be problematic. The expertise required to access and interpret output from climate and earth system models is hampering progress in utilizing them most effectively to determine the wider implications of climate change. To address this issue, we present a joint approach between climate scientists and ecologists that explores key challenges and opportunities for progress. As an exemplar, our focus is the Southern Ocean, notable for significant change with global implications, and on sea ice, given its crucial role in this dynamic ecosystem. We combined perspectives to evaluate the representation of sea ice in global climate models. With an emphasis on ecologically-relevant criteria (sea ice extent and seasonality we selected a subset of eight models that reliably reproduce extant sea ice distributions. While the model subset shows a similar mean change to the full ensemble in sea ice extent (approximately 50% decline in winter and 30% decline in summer, there is a marked reduction in the range. This improved the precision of projected future sea ice distributions by approximately one third, and means they are more amenable to ecological interpretation. We conclude that careful multidisciplinary evaluation of climate models, in conjunction with ongoing modeling advances, should form an integral part of utilizing model output.

  18. 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.

  19. a mathematical model for predicting output in an oilfield in the niger

    African Journals Online (AJOL)

    eobe

    resultant model was found to have greater utility in predicting oil field output as it produced less residual. The ... decision making by the oilfield manager is facilitated by reliable ... Scaling laws from percolation theory was used to predict oilfield ...

  20. Modelling health and output at business cycle horizons for the USA.

    Science.gov (United States)

    Narayan, Paresh Kumar

    2010-07-01

    In this paper we employ a theoretical framework - a simple macro model augmented with health - that draws guidance from the Keynesian view of business cycles to examine the relative importance of permanent and transitory shocks in explaining variations in health expenditure and output at business cycle horizons for the USA. The variance decomposition analysis of shocks reveals that at business cycle horizons permanent shocks explain the bulk of the variations in output, while transitory shocks explain the bulk of the variations in health expenditures. We undertake a shock decomposition analysis for private health expenditures versus public health expenditures and interestingly find that while transitory shocks are more important for private sector expenditures, permanent shocks dominate public health expenditures. Copyright (c) 2009 John Wiley & Sons, Ltd.

  1. Notes on the Implementation of Non-Parametric Statistics within the Westinghouse Realistic Large Break LOCA Evaluation Model (ASTRUM)

    International Nuclear Information System (INIS)

    Frepoli, Cesare; Oriani, Luca

    2006-01-01

    In recent years, non-parametric or order statistics methods have been widely used to assess the impact of the uncertainties within Best-Estimate LOCA evaluation models. The bounding of the uncertainties is achieved with a direct Monte Carlo sampling of the uncertainty attributes, with the minimum trial number selected to 'stabilize' the estimation of the critical output values (peak cladding temperature (PCT), local maximum oxidation (LMO), and core-wide oxidation (CWO A non-parametric order statistics uncertainty analysis was recently implemented within the Westinghouse Realistic Large Break LOCA evaluation model, also referred to as 'Automated Statistical Treatment of Uncertainty Method' (ASTRUM). The implementation or interpretation of order statistics in safety analysis is not fully consistent within the industry. This has led to an extensive public debate among regulators and researchers which can be found in the open literature. The USNRC-approved Westinghouse method follows a rigorous implementation of the order statistics theory, which leads to the execution of 124 simulations within a Large Break LOCA analysis. This is a solid approach which guarantees that a bounding value (at 95% probability) of the 95 th percentile for each of the three 10 CFR 50.46 ECCS design acceptance criteria (PCT, LMO and CWO) is obtained. The objective of this paper is to provide additional insights on the ASTRUM statistical approach, with a more in-depth analysis of pros and cons of the order statistics and of the Westinghouse approach in the implementation of this statistical methodology. (authors)

  2. Input-output model of regional environmental and economic impacts of nuclear power plants

    International Nuclear Information System (INIS)

    Johnson, M.H.; Bennett, J.T.

    1979-01-01

    The costs of delayed licensing of nuclear power plants calls for a more-comprehensive method of quantifying the economic and environmental impacts on a region. A traditional input-output (I-O) analysis approach is extended to assess the effects of changes in output, income, employment, pollution, water consumption, and the costs and revenues of local government disaggregated among 23 industry sectors during the construction and operating phases. Unlike earlier studies, this model uses nonlinear environmental interactions and specifies environmental feedbacks to the economic sector. 20 references

  3. Uncertainty the soul of modeling, probability & statistics

    CERN Document Server

    Briggs, William

    2016-01-01

    This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

  4. 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

  5. Changing statistics of storms in the North Atlantic?

    International Nuclear Information System (INIS)

    Storch, H. von; Guddal, J.; Iden, K.A.; Jonsson, T.; Perlwitz, J.; Reistad, M.; Ronde, J. de; Schmidt, H.; Zorita, E.

    1993-01-01

    Problems in the present discussion about increasing storminess in the North Atlantic area are discusesd. Observational data so far available do not indicate a change in the storm statistics. Output from climate models points to an itensified storm track in the North Atlantic, but because of the limited skill of present-day climate models in simulating high-frequency variability and regional details any such 'forecast' has to be considered with caution. A downscaling procedure which relates large-scale time-mean aspects of the state of the atmosphere and ocean to the local statistics of storms is proposed to reconstruct past variations of high-frequency variability in the atmosphere (storminess) and in the sea state (wave statistics). First results are presented. (orig.)

  6. Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory

    Science.gov (United States)

    Sundberg, R.; Moberg, A.; Hind, A.

    2012-08-01

    A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

  7. Statistical Models for Social Networks

    NARCIS (Netherlands)

    Snijders, Tom A. B.; Cook, KS; Massey, DS

    2011-01-01

    Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

  8. Evaluation of input output efficiency of oil field considering undesirable output —A case study of sandstone reservoir in Xinjiang oilfield

    Science.gov (United States)

    Zhang, Shuying; Wu, Xuquan; Li, Deshan; Xu, Yadong; Song, Shulin

    2017-06-01

    Based on the input and output data of sandstone reservoir in Xinjiang oilfield, the SBM-Undesirable model is used to study the technical efficiency of each block. Results show that: the model of SBM-undesirable to evaluate its efficiency and to avoid defects caused by traditional DEA model radial angle, improve the accuracy of the efficiency evaluation. by analyzing the projection of the oil blocks, we find that each block is in the negative external effects of input redundancy and output deficiency benefit and undesirable output, and there are greater differences in the production efficiency of each block; the way to improve the input-output efficiency of oilfield is to optimize the allocation of resources, reduce the undesirable output and increase the expected output.

  9. Using Economic Input/Output Tables to Predict a Country's Nuclear Status

    International Nuclear Information System (INIS)

    Weimar, Mark R.; Daly, Don S.; Wood, Thomas W.

    2010-01-01

    Both nuclear power and nuclear weapons programs should have (related) economic signatures which are detectible at some scale. We evaluated this premise in a series of studies using national economic input/output (IO) data. Statistical discrimination models using economic IO tables predict with a high probability whether a country with an unknown predilection for nuclear weapons proliferation is in fact engaged in nuclear power development or nuclear weapons proliferation. We analyzed 93 IO tables, spanning the years 1993 to 2005 for 37 countries that are either members or associates of the Organization for Economic Cooperation and Development (OECD). The 2009 OECD input/output tables featured 48 industrial sectors based on International Standard Industrial Classification (ISIC) Revision 3, and described the respective economies in current country-of-origin valued currency. We converted and transformed these reported values to US 2005 dollars using appropriate exchange rates and implicit price deflators, and addressed discrepancies in reported industrial sectors across tables. We then classified countries with Random Forest using either the adjusted or industry-normalized values. Random Forest, a classification tree technique, separates and categorizes countries using a very small, select subset of the 2304 individual cells in the IO table. A nation's efforts in nuclear power, be it for electricity or nuclear weapons, are an enterprise with a large economic footprint -- an effort so large that it should discernibly perturb coarse country-level economics data such as that found in yearly input-output economic tables. The neoclassical economic input-output model describes a country's or region's economy in terms of the requirements of industries to produce the current level of economic output. An IO table row shows the distribution of an industry's output to the industrial sectors while a table column shows the input required of each industrial sector by a given

  10. 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.

  11. Functional summary statistics for the Johnson-Mehl model

    DEFF Research Database (Denmark)

    Møller, Jesper; Ghorbani, Mohammad

    The Johnson-Mehl germination-growth model is a spatio-temporal point process model which among other things have been used for the description of neurotransmitters datasets. However, for such datasets parametric Johnson-Mehl models fitted by maximum likelihood have yet not been evaluated by means...... of functional summary statistics. This paper therefore invents four functional summary statistics adapted to the Johnson-Mehl model, with two of them based on the second-order properties and the other two on the nuclei-boundary distances for the associated Johnson-Mehl tessellation. The functional summary...... statistics theoretical properties are investigated, non-parametric estimators are suggested, and their usefulness for model checking is examined in a simulation study. The functional summary statistics are also used for checking fitted parametric Johnson-Mehl models for a neurotransmitters dataset....

  12. 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.

  13. Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

    Science.gov (United States)

    Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

    1999-01-01

    Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

  14. Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

    Science.gov (United States)

    Platnick, Steven E.

    2011-01-01

    The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed.

  15. Evacuation emergency response model coupling atmospheric release advisory capability output

    International Nuclear Information System (INIS)

    Rosen, L.C.; Lawver, B.S.; Buckley, D.W.; Finn, S.P.; Swenson, J.B.

    1983-01-01

    A Federal Emergency Management Agency (FEMA) sponsored project to develop a coupled set of models between those of the Lawrence Livermore National Laboratory (LLNL) Atmospheric Release Advisory Capability (ARAC) system and candidate evacuation models is discussed herein. This report describes the ARAC system and discusses the rapid computer code developed and the coupling with ARAC output. The computer code is adapted to the use of color graphics as a means to display and convey the dynamics of an emergency evacuation. The model is applied to a specific case of an emergency evacuation of individuals surrounding the Rancho Seco Nuclear Power Plant, located approximately 25 miles southeast of Sacramento, California. The graphics available to the model user for the Rancho Seco example are displayed and noted in detail. Suggestions for future, potential improvements to the emergency evacuation model are presented

  16. Distributions with given marginals and statistical modelling

    CERN Document Server

    Fortiana, Josep; Rodriguez-Lallena, José

    2002-01-01

    This book contains a selection of the papers presented at the meeting `Distributions with given marginals and statistical modelling', held in Barcelona (Spain), July 17-20, 2000. In 24 chapters, this book covers topics such as the theory of copulas and quasi-copulas, the theory and compatibility of distributions, models for survival distributions and other well-known distributions, time series, categorical models, definition and estimation of measures of dependence, monotonicity and stochastic ordering, shape and separability of distributions, hidden truncation models, diagonal families, orthogonal expansions, tests of independence, and goodness of fit assessment. These topics share the use and properties of distributions with given marginals, this being the fourth specialised text on this theme. The innovative aspect of the book is the inclusion of statistical aspects such as modelling, Bayesian statistics, estimation, and tests.

  17. Commissioning of output factors for uniform scanning proton beams

    International Nuclear Information System (INIS)

    Zheng Yuanshui; Ramirez, Eric; Mascia, Anthony; Ding Xiaoning; Okoth, Benny; Zeidan, Omar; Hsi Wen; Harris, Ben; Schreuder, Andries N.; Keole, Sameer

    2011-01-01

    Purpose: Current commercial treatment planning systems are not able to accurately predict output factors and calculate monitor units for proton fields. Patient-specific field output factors are thus determined by either measurements or empirical modeling based on commissioning data. The objective of this study is to commission output factors for uniform scanning beams utilized at the ProCure proton therapy centers. Methods: Using water phantoms and a plane parallel ionization chamber, the authors first measured output factors with a fixed 10 cm diameter aperture as a function of proton range and modulation width for clinically available proton beams with ranges between 4 and 31.5 cm and modulation widths between 2 and 15 cm. The authors then measured the output factor as a function of collimated field size at various calibration depths for proton beams of various ranges and modulation widths. The authors further examined the dependence of the output factor on the scanning area (i.e., uncollimated proton field), snout position, and phantom material. An empirical model was developed to calculate the output factor for patient-specific fields and the model-predicted output factors were compared to measurements. Results: The output factor increased with proton range and field size, and decreased with modulation width. The scanning area and snout position have a small but non-negligible effect on the output factors. The predicted output factors based on the empirical modeling agreed within 2% of measurements for all prostate treatment fields and within 3% for 98.5% of all treatment fields. Conclusions: Comprehensive measurements at a large subset of available beam conditions are needed to commission output factors for proton therapy beams. The empirical modeling agrees well with the measured output factor data. This investigation indicates that it is possible to accurately predict output factors and thus eliminate or reduce time-consuming patient-specific output

  18. Actuarial statistics with generalized linear mixed models

    NARCIS (Netherlands)

    Antonio, K.; Beirlant, J.

    2007-01-01

    Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics

  19. Analyses of gust fronts by means of limited area NWP model outputs

    Czech Academy of Sciences Publication Activity Database

    Kašpar, Marek

    67-68, - (2003), s. 559-572 ISSN 0169-8095 R&D Projects: GA ČR GA205/00/1451 Institutional research plan: CEZ:AV0Z3042911 Keywords : gust front * limited area NWP model * output Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.012, year: 2003

  20. The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV

    Science.gov (United States)

    Ho, Y.; Weber, J.

    2017-12-01

    WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.

  1. Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model

    Science.gov (United States)

    Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç

    2017-01-01

    This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.

  2. Structured statistical models of inductive reasoning.

    Science.gov (United States)

    Kemp, Charles; Tenenbaum, Joshua B

    2009-01-01

    Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

  3. Measuring nuclear power plant output by neutrino detection

    International Nuclear Information System (INIS)

    Korovkin, V.A.; Kodanev, S.A.; Panashchenko, N.S.; Sokolov, D.A.; Solov'yanov, O.M.; Tverdovskii, N.D.; Yarichin, A.D.; Ketov, S.N.; Kopeikin, V.I.; Machulin, I.N.; Mikaelyan, L.A.; Sinev, V.V.

    1989-01-01

    Neutrino emission from a reactor is inseparably linked with the fission process of heavy nuclei: each fission contributes a specific amount to the overall power output and gives rise to neutrinos which are emitted by the fission fragments created. Using a detector to record the neutrino flux gives a curve for the number of nuclei undergoing fission and the reactor power output. The question of whether it is practically possible to make use of neutrino emission from reactors was first posed in the mid-70s in connection with preparations for neutrino research at the Roven nuclear power plant (RAES) and in 1986 at an IAEA symposium on the topic of guarantees. Since 1982, research has been carried on at RAES on the fundamental properties and interactions of neutrinos. Based on this research and in parallel with it, in 1983 specialists from the Kurchatov Nuclear Power Institute and RAES jointly conducted an experiment which demonstrated in principle the possibility of remotely measuring reactor power output using the neutrino emission. This experiment had extremely limited statistics and is of interest today as the first demonstration of practical usage of neutrino emission from a reactor. At present the statistics for detecting neutrino events have increased tenfold and experience in lengthy measurements has been accumulated. This allows better analysis for the possibilities of the method. This paper reviews neutrino detection, theoretical bases of the method, determining the fission scale values for converting a number of neutrinos into power output, and measuring the power output

  4. Statistical modelling in biostatistics and bioinformatics selected papers

    CERN Document Server

    Peng, Defen

    2014-01-01

    This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...

  5. Multi-regional input–output model and ecological network analysis for regional embodied energy accounting in China

    International Nuclear Information System (INIS)

    Zhang, Yan; Zheng, Hongmei; Yang, Zhifeng; Su, Meirong; Liu, Gengyuan; Li, Yanxian

    2015-01-01

    Chinese regions frequently exchange materials, but regional differences in economic development create unbalanced flows of these resources. In this study, we examined energy by assessing embodied energy consumption to describe the energy-flow structure in China's seven regions. Based on multi-regional monetary input–output tables and energy statistical yearbooks for Chinese provinces in 2002 and 2007, we accounted for both direct and indirect energy consumption, respectively, and the integral input and output of the provinces. Most integral inputs of energy flowed from north to south or from east to west, whereas integral output flows were mainly from northeast to southwest. This differed from the direct flows, which were predominantly from north to south and west to east. This demonstrates the importance of calculating both direct and indirect energy flows. Analysis of the distance and direction traveled by the energy consumption centers of gravity showed that the centers for embodied energy consumption and inputs moved southeast because of the movements of the centers of the Eastern region. However, the center for outputs moved northeast because the movement of the Central region. These analyses provide a basis for identifying how regional economic development policies influence the embodied energy consumption and its flows among regions. - Highlights: • We integrated multi-regional input–output analysis with ecological network analysis. • We accounted for both direct and indirect energy consumption. • The centers of gravity for embodied energy flows moved southeast from 2002 to 2007. • The results support planning of energy consumption and energy flows among regions.

  6. Econometric model of the petroleum industry. [Determining crude supply and outputs/prices of refinery products

    Energy Technology Data Exchange (ETDEWEB)

    Rice, P [Oak Ridge National Lab., TN; Smith, V K

    1977-11-01

    This paper describes a forty-two nonlinear equation model of the U.S. petroleum industry estimated over the period 1946 to 1973. The model specifies refinery outputs and prices as being simultaneously determined by market forces while the domestic output of crude oil is determined in a block-recursive segment of the model. The simultaneous behavioral equations are estimated with nonlinear two-stage least-squares adjusted to reflect the implications of autocorrelation for those equations where it appears to be a problem. A multi-period sample simulation, together with forecasts for 1974 and 1975 are used to evaluate the model's performance. Finally, it is used to forecast to 1985 under two scenarios and compared with the Federal Energy Administration's forecast for the same period. 2 figures, 8 tables, 38 references.

  7. Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems

    International Nuclear Information System (INIS)

    Su, Yan; Chan, Lai-Cheong; Shu, Lianjie; Tsui, Kwok-Leung

    2012-01-01

    Highlights: ► We develop online prediction models for solar photovoltaic system performance. ► The proposed prediction models are simple but with reasonable accuracy. ► The maximum monthly average minutely efficiency varies 10.81–12.63%. ► The average efficiency tends to be slightly higher in winter months. - Abstract: This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R 2 . The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9 am to 4 pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months.

  8. Toward a more robust variance-based global sensitivity analysis of model outputs

    Energy Technology Data Exchange (ETDEWEB)

    Tong, C

    2007-10-15

    Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol' [25]. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay [13] proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', which have been shown to be equivalent to Sobol's first-order sensitivity indices. Practical issues with using these variance estimators are how to choose adequate sample sizes and how to assess the accuracy of the results. This paper proposes a modified McKay main effect method featuring an adaptive procedure for accuracy assessment and improvement. We also extend our adaptive technique to the computation of second-order sensitivity indices. Details of the proposed adaptive procedure as wells as numerical results are included in this paper.

  9. Social indicators and other income statistics using the EUROMOD baseline: a comparison with Eurostat and National Statistics

    OpenAIRE

    Mantovani, Daniela; Sutherland, Holly

    2003-01-01

    This paper reports an exercise to validate EUROMOD output for 1998 by comparing income statistics calculated from the baseline micro-output with comparable statistics from other sources, including the European Community Household Panel. The main potential reasons for discrepancies are identified. While there are some specific national issues that arise, there are two main general points to consider in interpreting EUROMOD estimates of social indicators across EU member States: (a) the method ...

  10. Governmentally amplified output volatility

    Science.gov (United States)

    Funashima, Yoshito

    2016-11-01

    Predominant government behavior is decomposed by frequency into several periodic components: updating cycles of infrastructure, Kuznets cycles, fiscal policy over business cycles, and election cycles. Little is known, however, about the theoretical impact of such cyclical behavior in public finance on output fluctuations. Based on a standard neoclassical growth model, this study intends to examine the frequency at which public investment cycles are relevant to output fluctuations. We find an inverted U-shaped relationship between output volatility and length of cycle in public investment. This implies that periodic behavior in public investment at a certain frequency range can cause aggravated output resonance. Moreover, we present an empirical analysis to test the theoretical implication, using the U.S. data in the period from 1968 to 2015. The empirical results suggest that such resonance phenomena change from low to high frequency.

  11. One output function: a misconception of students studying digital systems - a case study

    Science.gov (United States)

    Trotskovsky, E.; Sabag, N.

    2015-05-01

    Background:Learning processes are usually characterized by students' misunderstandings and misconceptions. Engineering educators intend to help their students overcome their misconceptions and achieve correct understanding of the concept. This paper describes a misconception in digital systems held by many students who believe that combinational logic circuits should have only one output. Purpose:The current study aims to investigate the roots of the misconception about one-output function and the pedagogical methods that can help students overcome the misconception. Sample:Three hundred and eighty-one students in the Departments of Electrical and Electronics and Mechanical Engineering at an academic engineering college, who learned the same topics of a digital combinational system, participated in the research. Design and method:In the initial research stage, students were taught according to traditional method - first to design a one-output combinational logic system, and then to implement a system with a number of output functions. In the main stage, an experimental group was taught using a new method whereby they were shown how to implement a system with several output functions, prior to learning about one-output systems. A control group was taught using the traditional method. In the replication stage (the third stage), an experimental group was taught using the new method. A mixed research methodology was used to examine the results of the new learning method. Results:Quantitative research showed that the new teaching approach resulted in a statistically significant decrease in student errors, and qualitative research revealed students' erroneous thinking patterns. Conclusions:It can be assumed that the traditional teaching method generates an incorrect mental model of the one-output function among students. The new pedagogical approach prevented the creation of an erroneous mental model and helped students develop the correct conceptual understanding.

  12. Investigation on the Effects of Process Parameters on Laser Percussion Drilling Using Finite Element Methodology; Statistical Modelling and Optimization

    Directory of Open Access Journals (Sweden)

    Mahmoud Moradi

    Full Text Available Abstract In the present research, the simulation of the Nickel-base superalloy Inconel 718 fiber-laser drilling process with the thickness of 1mm is investigated through the Finite Element Method. In order to specify the appropriate Gaussian distribution of laser beam, the results of an experimental research on glass laser drilling were simulated using three types of Gaussian distribution. The DFLUX subroutine was used to implement the laser heat sources of the models using the Fortran language. After the appropriate Gaussian distribution was chosen, the model was validated with the experimental results of the Nickel-base superalloy Inconel 718 laser drilling process. The negligible error percentage among the experimental and simulation results demonstrates the high accuracy of this model. The experiments were performed based on the Response Surface Methodology (RSM as a statistical design of experiment (DOE approach to investigate the influence of process parameters on the responses, obtaining the mathematical regressions and predicting the new results. Four parameters i.e. laser pulse frequency (150 to 550 Hz, laser power (200 to 500 watts, laser focal plane position (-0.5 to +0.5 mm and the duty cycle (30 to 70% were considered to be the input variables in 5 levels and four external parameters i.e. the hole's entrance and exit diameters, hole taper angle and the weight of mass removed from the hole, were observed to be the process output responses of this central composite design. By performing the statistical analysis, the input and output parameters were found to have a direct relation with each other. By an increase in each of the input variables, the entrance and exit hole diameters, the hole taper angel, and the weight of mass removed from the hole increase. Finally, the results of the conducted simulations and statistical analyses having been used, the laser drilling process was optimized by means of the desire ability approach. Good

  13. Eigenvalue and Entropy Statistics for Products of Conjugate Random Quantum Channels

    Directory of Open Access Journals (Sweden)

    Benoît Collins

    2010-06-01

    Full Text Available Using the graphical calculus and integration techniques introduced by the authors, we study the statistical properties of outputs of products of random quantum channels for entangled inputs. In particular, we revisit and generalize models of relevance for the recent counterexamples to the minimum output entropy additivity problems. Our main result is a classification of regimes for which the von Neumann entropy is lower on average than the elementary bounds that can be obtained with linear algebra techniques.

  14. Statistical mechanical analysis of the linear vector channel in digital communication

    International Nuclear Information System (INIS)

    Takeda, Koujin; Hatabu, Atsushi; Kabashima, Yoshiyuki

    2007-01-01

    A statistical mechanical framework to analyze linear vector channel models in digital wireless communication is proposed for a large system. The framework is a generalization of that proposed for code-division multiple-access systems in Takeda et al (2006 Europhys. Lett. 76 1193) and enables the analysis of the system in which the elements of the channel transfer matrix are statistically correlated with each other. The significance of the proposed scheme is demonstrated by assessing the performance of an existing model of multi-input multi-output communication systems

  15. A Stochastic Fractional Dynamics Model of Rainfall Statistics

    Science.gov (United States)

    Kundu, Prasun; Travis, James

    2013-04-01

    Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is designed to faithfully reflect the scale dependence and is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. The main restriction is the assumption that the statistics of the precipitation field is spatially homogeneous and isotropic and stationary in time. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of the radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment. Some data sets containing periods of non-stationary behavior that involves occasional anomalously correlated rain events, present a challenge for the model.

  16. The efficiency of the agricultural sector in Poland in the light output-input model1

    Directory of Open Access Journals (Sweden)

    Czyżewski Andrzej

    2015-05-01

    Full Text Available The study turns attention to the use of the input-output model (account of interbranch flows in macroeconomic assessments of the effectiveness of the agricultural sector. In the introductory part the essence of the account of interbranch flows has been specified, pointing to its historical origin and place in the economic theory, and the morphological structure of the individual parts (quarters of the model has been presented. Then the study discusses the application of the account of interbranch flows in macroeconomic assessments of the effectiveness of the agricultural sector, defining and characterizing a number of indicators which allow to conclude on the effectiveness of the agricultural sector on the basis of the account of interbranch flows. The last, empirical part of the study assesses the effectiveness of the agricultural sector in Poland on the basis of interbranch flows statistics for the years 2000 and 2005. The analyses allowed to demonstrate increased efficiency of the agricultural sector in Poland after Poland joined the EU, and also to say that the account of interbranch flows is an important tool enabling comprehensive assessment of the effectiveness of the agricultural sector in the macro-scale, through the prism of the effect - disbursement, which accounts for its exceptional suitability in this kind of analyses.

  17. 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

  18. Statistical Models and Methods for Lifetime Data

    CERN Document Server

    Lawless, Jerald F

    2011-01-01

    Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,

  19. Topology for Statistical Modeling of Petascale Data

    Energy Technology Data Exchange (ETDEWEB)

    Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)

    2013-10-31

    Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.

  20. Statistical models and methods for reliability and survival analysis

    CERN Document Server

    Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

    2013-01-01

    Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

  1. UFO - The Universal FEYNRULES Output

    Science.gov (United States)

    Degrande, Céline; Duhr, Claude; Fuks, Benjamin; Grellscheid, David; Mattelaer, Olivier; Reiter, Thomas

    2012-06-01

    We present a new model format for automatized matrix-element generators, the so-called Universal FEYNRULES Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is designed to be flexible, modular and agnostic of any assumption such as the number of particles or the color and Lorentz structures appearing in the interaction vertices. Unlike other model formats where text files need to be parsed, the information on the model is encoded into a PYTHON module that can easily be linked to other computer codes. We then describe an interface for the MATHEMATICA package FEYNRULES that allows for an automatic output of models in the UFO format.

  2. 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...

  3. Prediction model and treatment of high-output ileostomy in colorectal cancer surgery.

    Science.gov (United States)

    Fujino, Shiki; Miyoshi, Norikatsu; Ohue, Masayuki; Takahashi, Yuske; Yasui, Masayoshi; Sugimura, Keijiro; Akita, Hirohumi; Takahashi, Hidenori; Kobayashi, Shogo; Yano, Masahiko; Sakon, Masato

    2017-09-01

    The aim of the present study was to examine the risk factors of high-output ileostomy (HOI), which is associated with electrolyte abnormalities and/or stoma complications, and to create a prediction model. The medical records of 68 patients who underwent colorectal cancer surgery with ileostomy between 2011 and 2016 were retrospectively investigated. All the patients underwent surgical resection for colorectal cancer at the Osaka Medical Center for Cancer and Cardiovascular Diseases (Osaka, Japan). A total of 7 patients with inadequate data on ileostomy output were excluded. Using a group of 50 patients who underwent surgery between 2011 and 2013, the risk of HOI was classified by a decision tree model using a partition platform. The HOI prediction model was validated in an additional group of 11 patients who underwent surgery between 2014 and 2016. Univariate analysis of clinical factors demonstrated that young age (P=0.003) and high white blood cell (WBC) count (Pmodel, three factors (gender, age and WBC on postoperative day 1) were generated for the prediction of HOI. The patients were classified into five groups, and HOI was observed in 0-88% of patients in each group. The area under the curve (AUC) was 0.838. The model was validated by an external dataset in an independent patient group, for which the AUC was 0.792. In conclusion, HOI patients were classified and an HOI prediction model was developed that may help clinicians in postoperative care.

  4. Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic.

    Science.gov (United States)

    Satorra, Albert; Bentler, Peter M

    2010-06-01

    A scaled difference test statistic [Formula: see text] that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (2001). The statistic [Formula: see text] is asymptotically equivalent to the scaled difference test statistic T̄(d) introduced in Satorra (2000), which requires more involved computations beyond standard output of SEM software. The test statistic [Formula: see text] has been widely used in practice, but in some applications it is negative due to negativity of its associated scaling correction. Using the implicit function theorem, this note develops an improved scaling correction leading to a new scaled difference statistic T̄(d) that avoids negative chi-square values.

  5. A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

    Energy Technology Data Exchange (ETDEWEB)

    Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-11

    This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.

  6. Grouping influences output interference in short-term memory: a mixture modeling study

    Directory of Open Access Journals (Sweden)

    Min-Suk eKang

    2016-05-01

    Full Text Available Output interference is a source of forgetting induced by recalling. We investigated how grouping influences output interference in short-term memory. In Experiment 1, the participants were asked to remember four colored items. Those items were grouped by temporal coincidence as well as spatial alignment: two items were presented in the first memory array and two were presented in the second, and the items in both arrays were either vertically or horizontally aligned as well. The participants then performed two recall tasks in sequence by selecting a color presented at a cued location from a color wheel. In the same-group condition, the participants reported both items from the same memory array; however, in the different-group condition, the participants reported one item from each memory array. We analyzed participant responses with a mixture model, which yielded two measures: guess rate and precision of recalled memories. The guess rate in the second recall was higher for the different-group condition than for the same-group condition; however, the memory precisions obtained for both conditions were similarly degraded in the second recall. In Experiment 2, we varied the probability of the same- and different-group conditions with a ratio of 3 to 7. We expected output interference to be higher in the same-group condition than in the different-group condition. This is because items of the other group are more likely to be probed in the second recall phase and, thus, protecting those items during the first recall phase leads to a better performance. Nevertheless, the same pattern of results was robustly reproduced, suggesting grouping shields the grouped items from output interference because of the secured accessibility. We discussed how grouping influences output interference.

  7. Energy and output dynamics in Bangladesh

    International Nuclear Information System (INIS)

    Paul, Biru Paksha; Uddin, Gazi Salah

    2011-01-01

    The relationship between energy consumption and output is still ambiguous in the existing literature. The economy of Bangladesh, having spectacular output growth and rising energy demand as well as energy efficiency in recent decades, can be an ideal case for examining energy-output dynamics. We find that while fluctuations in energy consumption do not affect output fluctuations, movements in output inversely affect movements in energy use. The results of Granger causality tests in this respect are consistent with those of innovative accounting that includes variance decompositions and impulse responses. Autoregressive distributed lag models also suggest a role of output in Bangladesh's energy use. Hence, the findings of this study have policy implications for other developing nations where measures for energy conservation and efficiency can be relevant in policymaking.

  8. Model-generated air quality statistics for application in vegetation response models in Alberta

    International Nuclear Information System (INIS)

    McVehil, G.E.; Nosal, M.

    1990-01-01

    To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models

  9. Performance modeling, loss networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi

    2009-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

  10. Monte Carlo and Lambertian light guide models of the light output from scintillation crystals at megavoltage energies

    International Nuclear Information System (INIS)

    Evans, Philip M.; Mosleh-Shirazi, M. Amin; Harris, Emma J.; Seco, Joao

    2006-01-01

    A new model of the light output from single-crystal scintillators in megavoltage energy x-ray beams has been developed, based on the concept of a Lambertian light guide model (LLG). This was evaluated in comparison with a Monte Carlo (MC) model of optical photon transport, previously developed and reported in the literature, which was used as a gold standard. The LLG model was developed to enable optimization of scintillator detector design. In both models the dose deposition and light propagation were decoupled, the scintillators were cuboids, split into a series of cells as a function of depth, with Lambertian side and entrance faces, and a specular exit face. The signal in a sensor placed 1 and 1000 mm beyond the exit face was calculated. Cesium iodide (CSI) crystals of 1.5 and 3 mm square cross section and 1, 5, and 10 mm depth were modeled. Both models were also used to determine detector signal and optical gain factor as a function of CsI scintillator thickness, from 2 to 10 mm. Results showed a variation in light output with position of dose deposition of a factor of up to approximately 5, for long, thin scintillators (such as 10x1.5x1.5 mm 3 ). For short, fat scintillators (such as 1x3x3 mm 3 ) the light output was more uniform with depth. MC and LLG generally agreed to within 5%. Results for a sensor distance of 1 mm showed an increase in light output the closer the light originates to the exit face, while a distance of 1000 mm showed a decrease in light output the closer the light originates to the exit face. For a sensor distance of 1 mm, the ratio of signal for a 10 mm scintillator to that for a 2 mm scintillator was 1.98, whereas for the 1000 mm distance the ratio was 3.00. The ratio of quantum efficiency (QE) between 10 and 2 mm thicknesses was 4.62. We conclude that these models may be used for detector optimization, with the light guide model suitable for parametric study

  11. Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

    Science.gov (United States)

    Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

    2017-12-01

    Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

  12. 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.

  13. Statistical Models of Adaptive Immune populations

    Science.gov (United States)

    Sethna, Zachary; Callan, Curtis; Walczak, Aleksandra; Mora, Thierry

    The availability of large (104-106 sequences) datasets of B or T cell populations from a single individual allows reliable fitting of complex statistical models for naïve generation, somatic selection, and hypermutation. It is crucial to utilize a probabilistic/informational approach when modeling these populations. The inferred probability distributions allow for population characterization, calculation of probability distributions of various hidden variables (e.g. number of insertions), as well as statistical properties of the distribution itself (e.g. entropy). In particular, the differences between the T cell populations of embryonic and mature mice will be examined as a case study. Comparing these populations, as well as proposed mixed populations, provides a concrete exercise in model creation, comparison, choice, and validation.

  14. Statistical criteria for characterizing irradiance time series.

    Energy Technology Data Exchange (ETDEWEB)

    Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

    2010-10-01

    We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

  15. Tropical geometry of statistical models.

    Science.gov (United States)

    Pachter, Lior; Sturmfels, Bernd

    2004-11-16

    This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

  16. 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.

  17. 12th Workshop on Stochastic Models, Statistics and Their Applications

    CERN Document Server

    Rafajłowicz, Ewaryst; Szajowski, Krzysztof

    2015-01-01

    This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

  18. Statistical Validation of Engineering and Scientific Models: Background

    International Nuclear Information System (INIS)

    Hills, Richard G.; Trucano, Timothy G.

    1999-01-01

    A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made

  19. Importance of Statistical Evidence in Estimating Valid DEA Scores.

    Science.gov (United States)

    Barnum, Darold T; Johnson, Matthew; Gleason, John M

    2016-03-01

    Data Envelopment Analysis (DEA) allows healthcare scholars to measure productivity in a holistic manner. It combines a production unit's multiple outputs and multiple inputs into a single measure of its overall performance relative to other units in the sample being analyzed. It accomplishes this task by aggregating a unit's weighted outputs and dividing the output sum by the unit's aggregated weighted inputs, choosing output and input weights that maximize its output/input ratio when the same weights are applied to other units in the sample. Conventional DEA assumes that inputs and outputs are used in different proportions by the units in the sample. So, for the sample as a whole, inputs have been substituted for each other and outputs have been transformed into each other. Variables are assigned different weights based on their marginal rates of substitution and marginal rates of transformation. If in truth inputs have not been substituted nor outputs transformed, then there will be no marginal rates and therefore no valid basis for differential weights. This paper explains how to statistically test for the presence of substitutions among inputs and transformations among outputs. Then, it applies these tests to the input and output data from three healthcare DEA articles, in order to identify the effects on DEA scores when input substitutions and output transformations are absent in the sample data. It finds that DEA scores are badly biased when substitution and transformation are absent and conventional DEA models are used.

  20. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  1. Light output measurements and computational models of microcolumnar CsI scintillators for x-ray imaging

    Energy Technology Data Exchange (ETDEWEB)

    Nillius, Peter, E-mail: nillius@mi.physics.kth.se; Klamra, Wlodek; Danielsson, Mats [Royal Institute of Technology (KTH), Stockholm SE-100 44 (Sweden); Sibczynski, Pawel [National Centre for Nuclear Research, Otwock 05-400 (Poland); Sharma, Diksha; Badano, Aldo [Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, Maryland 20993 (United States)

    2015-02-15

    Purpose: The authors report on measurements of light output and spatial resolution of microcolumnar CsI:Tl scintillator detectors for x-ray imaging. In addition, the authors discuss the results of simulations aimed at analyzing the results of synchrotron and sealed-source exposures with respect to the contributions of light transport to the total light output. Methods: The authors measured light output from a 490-μm CsI:Tl scintillator screen using two setups. First, the authors used a photomultiplier tube (PMT) to measure the response of the scintillator to sealed-source exposures. Second, the authors performed imaging experiments with a 27-keV monoenergetic synchrotron beam and a slit to calculate the total signal generated in terms of optical photons per keV. The results of both methods are compared to simulations obtained with hybridMANTIS, a coupled x-ray, electron, and optical photon Monte Carlo transport package. The authors report line response (LR) and light output for a range of linear absorption coefficients and describe a model that fits at the same time the light output and the blur measurements. Comparing the experimental results with the simulations, the authors obtained an estimate of the absorption coefficient for the model that provides good agreement with the experimentally measured LR. Finally, the authors report light output simulation results and their dependence on scintillator thickness and reflectivity of the backing surface. Results: The slit images from the synchrotron were analyzed to obtain a total light output of 48 keV{sup −1} while measurements using the fast PMT instrument setup and sealed-sources reported a light output of 28 keV{sup −1}. The authors attribute the difference in light output estimates between the two methods to the difference in time constants between the camera and PMT measurements. Simulation structures were designed to match the light output measured with the camera while providing good agreement with the

  2. Multiple commodities in statistical microeconomics: Model and market

    Science.gov (United States)

    Baaquie, Belal E.; Yu, Miao; Du, Xin

    2016-11-01

    A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

  3. Statistical models for optimizing mineral exploration

    International Nuclear Information System (INIS)

    Wignall, T.K.; DeGeoffroy, J.

    1987-01-01

    The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)

  4. Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation

    International Nuclear Information System (INIS)

    Carta, Jose A.; Ramirez, Penelope; Velazquez, Sergio

    2008-01-01

    Static methods which are based on statistical techniques to estimate the mean power output of a WECS (wind energy conversion system) have been widely employed in the scientific literature related to wind energy. In the static method which we use in this paper, for a given wind regime probability distribution function and a known WECS power curve, the mean power output of a WECS is obtained by resolving the integral, usually using numerical evaluation techniques, of the product of these two functions. In this paper an analysis is made of the influence of the level of fit between an empirical probability density function of a sample of wind speeds and the probability density function of the adjusted theoretical model on the relative error ε made in the estimation of the mean annual power output of a WECS. The mean power output calculated through the use of a quasi-dynamic or chronological method, that is to say using time-series of wind speed data and the power versus wind speed characteristic of the wind turbine, serves as the reference. The suitability of the distributions is judged from the adjusted R 2 statistic (R a 2 ). Hourly mean wind speeds recorded at 16 weather stations located in the Canarian Archipelago, an extensive catalogue of wind-speed probability models and two wind turbines of 330 and 800 kW rated power are used in this paper. Among the general conclusions obtained, the following can be pointed out: (a) that the R a 2 statistic might be useful as an initial gross indicator of the relative error made in the mean annual power output estimation of a WECS when a probabilistic method is employed; (b) the relative errors tend to decrease, in accordance with a trend line defined by a second-order polynomial, as R a 2 increases

  5. Statistical physics of pairwise probability models

    Directory of Open Access Journals (Sweden)

    Yasser Roudi

    2009-11-01

    Full Text Available Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.

  6. The quantum theory of statistical multistep nucleus reactions

    CERN Document Server

    Zhivopistsev, F A

    2002-01-01

    The phenomenological models and quantum approaches to the description of the statistical multistep nuclear reactions are discussed. The basic advantages and deficiencies of various modifications of the quantum theory of the statistical multistep direct reactions: Feshbach-Kerman-Koonin formalism, the generalized model of the statistical multistep reactions (GMSMR) are considered in detail. The possibility of obtaining the consistent description of the experimental spectra for the reactions with nucleons is shown by the particular examples. Further improvement and development of the quantum formalism for the more complete and consecutive description of various mechanisms of the component particle formalism in the output channel, the correct of the unbound state densities of the intermediate and finite nuclei are needed for the analysis of the inclusive reactions with participation of the component particles, (and with an account of the contributions to the cross sections of the nucleus cluster and shell areas)...

  7. Robust Model Predictive Control Using Linear Matrix Inequalities for the Treatment of Asymmetric Output Constraints

    Directory of Open Access Journals (Sweden)

    Mariana Santos Matos Cavalca

    2012-01-01

    Full Text Available One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC strategies based on linear matrix inequalities (LMIs. However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.

  8. Multi-level emulation of complex climate model responses to boundary forcing data

    Science.gov (United States)

    Tran, Giang T.; Oliver, Kevin I. C.; Holden, Philip B.; Edwards, Neil R.; Sóbester, András; Challenor, Peter

    2018-04-01

    Climate model components involve both high-dimensional input and output fields. It is desirable to efficiently generate spatio-temporal outputs of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for efficiency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1's energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM's spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of different types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components.

  9. Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modeling.

    Science.gov (United States)

    Hagenlocher, Michael; Delmelle, Eric; Casas, Irene; Kienberger, Stefan

    2013-08-14

    As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population are needed to adequately plan targeted preventive intervention. We propose a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, a tropical urban environment of Colombia. Based on a set of socioeconomic and demographic indicators derived from census data and ancillary geospatial datasets, we develop a spatial approach for both expert-based and purely statistical-based modeling of current vulnerability levels across 340 neighborhoods of the city using a Geographic Information System (GIS). The results of both approaches are comparatively evaluated by means of spatial statistics. A web-based approach is proposed to facilitate the visualization and the dissemination of the output vulnerability index to the community. The statistical and the expert-based modeling approach exhibit a high concordance, globally, and spatially. The expert-based approach indicates a slightly higher vulnerability mean (0.53) and vulnerability median (0.56) across all neighborhoods, compared to the purely statistical approach (mean = 0.48; median = 0.49). Both approaches reveal that high values of vulnerability tend to cluster in the eastern, north-eastern, and western part of the city. These are poor neighborhoods with high percentages of young (i.e., local expertise, statistical approaches could be used, with caution. By decomposing identified vulnerability "hotspots" into their underlying factors, our approach provides valuable information on both (1) the location of neighborhoods, and (2) vulnerability factors that should be given priority in the context of targeted intervention

  10. 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).

  11. Production statistics of gas turbines and superchargers in Japan in 1991

    Energy Technology Data Exchange (ETDEWEB)

    Honma, T [Toshiba Corp., Tokyo (Japan). Principal Office

    1992-01-01

    In 1991, total production of land and marine gas turbines has made a new record of 416 units with a power output of 2,771MW(54% increase compared to previous year). Production of small units have decreased by 7% where as medium units production has increased 18% and 14% in number of units and power output respectively. Compared to previous year, the production of large units has increased by 50% in number of units and 70% in power output. The units for export shared 10% and 70% of grand total number of units and power output respectively. Gas turbines to be burnt gaseous fuel and liquid fuel have been 74% and 26% in power output respectively. Production statistics of turbojet and turbofan engines has been almost same to the previous year with slight increase and decrease of small turbojet engine and medium size turbojet engine respectively. Production of turboshaft and turboprop engines along with the models of superchargers, have increased. 10 figs., 9 tabs.

  12. Statistical transmutation in doped quantum dimer models.

    Science.gov (United States)

    Lamas, C A; Ralko, A; Cabra, D C; Poilblanc, D; Pujol, P

    2012-07-06

    We prove a "statistical transmutation" symmetry of doped quantum dimer models on the square, triangular, and kagome lattices: the energy spectrum is invariant under a simultaneous change of statistics (i.e., bosonic into fermionic or vice versa) of the holes and of the signs of all the dimer resonance loops. This exact transformation enables us to define the duality equivalence between doped quantum dimer Hamiltonians and provides the analytic framework to analyze dynamical statistical transmutations. We investigate numerically the doping of the triangular quantum dimer model with special focus on the topological Z(2) dimer liquid. Doping leads to four (instead of two for the square lattice) inequivalent families of Hamiltonians. Competition between phase separation, superfluidity, supersolidity, and fermionic phases is investigated in the four families.

  13. The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code

    International Nuclear Information System (INIS)

    Sutton, T.M.; Brown, F.B.; Bischoff, F.G.; MacMillan, D.B.; Ellis, C.L.; Ward, J.T.; Ballinger, C.T.; Kelly, D.J.; Schindler, L.

    1999-01-01

    capability of performing iterated-source (criticality), multiplied-fixed-source, and fixed-source calculations. MCV uses a highly detailed continuous-energy (as opposed to multigroup) representation of neutron histories and cross section data. The spatial modeling is fully three-dimensional (3-D), and any geometrical region that can be described by quadric surfaces may be represented. The primary results are region-wise reaction rates, neutron production rates, slowing-down-densities, fluxes, leakages, and when appropriate the eigenvalue or multiplication factor. Region-wise nuclidic reaction rates are also computed, which may then be used by other modules in the system to determine time-dependent nuclide inventories so that RACER can perform depletion calculations. Furthermore, derived quantities such as ratios and sums of primary quantities and/or other derived quantities may also be calculated. MCV performs statistical analyses on output quantities, computing estimates of the 95% confidence intervals as well as indicators as to the reliability of these estimates. The remainder of this chapter provides an overview of the MCV algorithm. The following three chapters describe the MCV mathematical, physical, and statistical treatments in more detail. Specifically, Chapter 2 discusses topics related to tracking the histories including: geometry modeling, how histories are moved through the geometry, and variance reduction techniques related to the tracking process. Chapter 3 describes the nuclear data and physical models employed by MCV. Chapter 4 discusses the tallies, statistical analyses, and edits. Chapter 5 provides some guidance as to how to run the code, and Chapter 6 is a list of the code input options

  14. Stochastic Modeling and Performance Analysis of Multimedia SoCs

    DEFF Research Database (Denmark)

    Raman, Balaji; Nouri, Ayoub; Gangadharan, Deepak

    2013-01-01

    solutions where each modeling technique has both the above mentioned characteristics. We present a probabilistic analytical framework and a statistical model checking approach to design system-on-chips for low-cost multimedia systems. We apply the modeling techniques to size the output buffer in a video......Reliability and flexibility are among the key required features of a framework used to model a system. Existing approaches to design resource-constrained, soft-real time systems either provide guarantees for output quality or account for loss in the system, but not both. We propose two independent...... decoder. The results shows that, for our stochastic design metric, the analytical framework upper bounds (and relatively accurate) compare to the statistical model checking technique. Also, we observed significant reduction in resource usage (such as output buffer size) with tolerable loss in output...

  15. Textual information access statistical models

    CERN Document Server

    Gaussier, Eric

    2013-01-01

    This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization).In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications

  16. Statistical analysis of arsenic contamination in drinking water in a city of Iran and its modeling using GIS.

    Science.gov (United States)

    Sadeghi, Fatemeh; Nasseri, Simin; Mosaferi, Mohammad; Nabizadeh, Ramin; Yunesian, Masud; Mesdaghinia, Alireza

    2017-05-01

    In this research, probable arsenic contamination in drinking water in the city of Ardabil was studied in 163 samples during four seasons. In each season, sampling was carried out randomly in the study area. Results were analyzed statistically applying SPSS 19 software, and the data was also modeled by Arc GIS 10.1 software. The maximum permissible arsenic concentration in drinking water defined by the World Health Organization and Iranian national standard is 10 μg/L. Statistical analysis showed 75, 88, 47, and 69% of samples in autumn, winter, spring, and summer, respectively, had concentrations higher than the national standard. The mean concentrations of arsenic in autumn, winter, spring, and summer were 19.89, 15.9, 10.87, and 14.6 μg/L, respectively, and the overall average in all samples through the year was 15.32 μg/L. Although GIS outputs indicated that the concentration distribution profiles changed in four consecutive seasons, variance analysis of the results showed that statistically there is no significant difference in arsenic levels in four seasons.

  17. Model for neural signaling leap statistics

    International Nuclear Information System (INIS)

    Chevrollier, Martine; Oria, Marcos

    2011-01-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.

  18. Model for neural signaling leap statistics

    Science.gov (United States)

    Chevrollier, Martine; Oriá, Marcos

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

  19. WE-A-201-02: Modern Statistical Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Niemierko, A.

    2016-06-15

    Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear

  20. WE-A-201-02: Modern Statistical Modeling

    International Nuclear Information System (INIS)

    Niemierko, A.

    2016-01-01

    Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear

  1. A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

    Science.gov (United States)

    Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

    2017-04-01

    Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the

  2. Bayesian models a statistical primer for ecologists

    CERN Document Server

    Hobbs, N Thompson

    2015-01-01

    Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili

  3. 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

  4. GPCC - A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

    Science.gov (United States)

    Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge th...

  5. 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.

  6. Total output operation chart optimization of cascade reservoirs and its application

    International Nuclear Information System (INIS)

    Jiang, Zhiqiang; Ji, Changming; Sun, Ping; Wang, Liping; Zhang, Yanke

    2014-01-01

    Highlights: • We propose a new double nested model for cascade reservoirs operation optimization. • We use two methods to extract the output distribution ratio. • The adopted two methods perform better than the widely used methods at present. • Stepwise regression method performs better than mean value method on the whole. - Abstract: With the rapid development of cascade hydropower stations in recent decades, the cascade system composed of multiple reservoirs needs unified operation and management. However, the output distribution problem has not yet been solved reasonably when the total output of cascade system obtained, which makes the full utilization of hydropower resources in cascade reservoirs very difficult. Discriminant criterion method is a traditional and common method to solve the output distribution problem at present, but some shortcomings cannot be ignored in the practical application. In response to the above concern, this paper proposes a new total output operation chart optimization model and a new optimal output distribution model, the two models constitute to a double nested model with the goal of maximizing power generation. This paper takes the cascade reservoirs of Li Xianjiang River in China as an instance to obtain the optimal total output operation chart by the proposed double nested model and the 43 years historical runoff data, progressive searching method and progressive optimality algorithm are used in solving the model. In order to take the obtained total output operation chart into practical operation, mean value method and stepwise regression method are adopted to extract the output distribution ratios on the basis of the optimal simulation intermediate data. By comparing with discriminant criterion method and conventional method, the combined utilization of total output operation chart and output distribution ratios presents better performance in terms of power generation and assurance rate, which proves it is an effective

  7. Wind gust estimation by combining numerical weather prediction model and statistical post-processing

    Science.gov (United States)

    Patlakas, Platon; Drakaki, Eleni; Galanis, George; Spyrou, Christos; Kallos, George

    2017-04-01

    The continuous rise of off-shore and near-shore activities as well as the development of structures, such as wind farms and various offshore platforms, requires the employment of state-of-the-art risk assessment techniques. Such analysis is used to set the safety standards and can be characterized as a climatologically oriented approach. Nevertheless, a reliable operational support is also needed in order to minimize cost drawbacks and human danger during the construction and the functioning stage as well as during maintenance activities. One of the most important parameters for this kind of analysis is the wind speed intensity and variability. A critical measure associated with this variability is the presence and magnitude of wind gusts as estimated in the reference level of 10m. The latter can be attributed to different processes that vary among boundary-layer turbulence, convection activities, mountain waves and wake phenomena. The purpose of this work is the development of a wind gust forecasting methodology combining a Numerical Weather Prediction model and a dynamical statistical tool based on Kalman filtering. To this end, the parameterization of Wind Gust Estimate method was implemented to function within the framework of the atmospheric model SKIRON/Dust. The new modeling tool combines the atmospheric model with a statistical local adaptation methodology based on Kalman filters. This has been tested over the offshore west coastline of the United States. The main purpose is to provide a useful tool for wind analysis and prediction and applications related to offshore wind energy (power prediction, operation and maintenance). The results have been evaluated by using observational data from the NOAA's buoy network. As it was found, the predicted output shows a good behavior that is further improved after the local adjustment post-process.

  8. Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

    Science.gov (United States)

    Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

    2016-01-01

    Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

  9. Equilibrium statistical mechanics of lattice models

    CERN Document Server

    Lavis, David A

    2015-01-01

    Most interesting and difficult problems in equilibrium statistical mechanics concern models which exhibit phase transitions. For graduate students and more experienced researchers this book provides an invaluable reference source of approximate and exact solutions for a comprehensive range of such models. Part I contains background material on classical thermodynamics and statistical mechanics, together with a classification and survey of lattice models. The geometry of phase transitions is described and scaling theory is used to introduce critical exponents and scaling laws. An introduction is given to finite-size scaling, conformal invariance and Schramm—Loewner evolution. Part II contains accounts of classical mean-field methods. The parallels between Landau expansions and catastrophe theory are discussed and Ginzburg—Landau theory is introduced. The extension of mean-field theory to higher-orders is explored using the Kikuchi—Hijmans—De Boer hierarchy of approximations. In Part III the use of alge...

  10. The Association Between Urine Output, Creatinine Elevation, and Death.

    Science.gov (United States)

    Engoren, Milo; Maile, Michael D; Heung, Michael; Jewell, Elizabeth S; Vahabzadeh, Christie; Haft, Jonathan W; Kheterpal, Sachin

    2017-04-01

    Acute kidney injury can be defined by a fall in urine output, and urine output criteria may be more sensitive in identifying acute kidney injury than traditional serum creatinine criteria. However, as pointed out in the Kidney Disease Improving Global Outcome guidelines, the association of urine output with subsequent creatinine elevations and death is poorly characterized. The purpose of this study was to determine what degrees of reduced urine output are associated with subsequent creatinine elevation and death. This was a retrospective cohort study of adult patients (age ≥18 years) cared for in a cardiovascular intensive care unit after undergoing cardiac operations in a tertiary care university medical center. All adult patients who underwent cardiac operations and were not receiving dialysis preoperatively were studied. The development of acute kidney injury was defined as an increase in creatinine of more than 0.3 mg/dL or by more than 50% above baseline by postoperative day 3. Acute kidney injury developed in 1,061 of 4,195 patients (25%). Urine output had moderate discrimination in predicting subsequent acute kidney injury (C statistic = .637 ± .054). Lower urine output and longer duration of low urine output were associated with greater odds of developing acute kidney injury and death. We found that there is similar accuracy in using urine output corrected for actual, ideal, or adjusted weight to discriminate future acute kidney injury by creatinine elevation and recommend using actual weight for its simplicity. We also found that low urine output is associated with subsequent acute kidney injury and that the association is greater for lower urine output and for low urine output of longer durations. Low urine output (creatinine elevation, is independently associated with mortality. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  11. Spherical Process Models for Global Spatial Statistics

    KAUST Repository

    Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.

    2017-01-01

    Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture

  12. Model for neural signaling leap statistics

    Energy Technology Data Exchange (ETDEWEB)

    Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.

  13. Financial Development and Output Growth: A Panel Study for Asian Countries

    Directory of Open Access Journals (Sweden)

    Sangjoon Jun

    2012-03-01

    Full Text Available This paper investigates the relationship between financial markets and output growth for a panel of 27 Asian countries over 1960-2009. It utilizes the recently-developed panel cointegration techniques to test and estimate the long-run equilibrium relationship between real GDP and financial development proxies. Real GDP and financial development variables are found to have unit roots and to be cointegrated, based on various panel unit root tests and panel cointegration tests. We find that there is a statistically significant positive bi-directional cointegrating relationship between financial development and output growth by three distinct methods of panel cointegration estimation. Empirical findings suggest that financial market development promotes output growth and in turn output growth stimulates further financial development.

  14. Output power fluctuations due to different weights of macro particles used in particle-in-cell simulations of Cerenkov devices

    International Nuclear Information System (INIS)

    Bao, Rong; Li, Yongdong; Liu, Chunliang; Wang, Hongguang

    2016-01-01

    The output power fluctuations caused by weights of macro particles used in particle-in-cell (PIC) simulations of a backward wave oscillator and a travelling wave tube are statistically analyzed. It is found that the velocities of electrons passed a specific slow-wave structure form a specific electron velocity distribution. The electron velocity distribution obtained in PIC simulation with a relative small weight of macro particles is considered as an initial distribution. By analyzing this initial distribution with a statistical method, the estimations of the output power fluctuations caused by different weights of macro particles are obtained. The statistical method is verified by comparing the estimations with the simulation results. The fluctuations become stronger with increasing weight of macro particles, which can also be determined reversely from estimations of the output power fluctuations. With the weights of macro particles optimized by the statistical method, the output power fluctuations in PIC simulations are relatively small and acceptable.

  15. Analysis and Evaluation of Statistical Models for Integrated Circuits Design

    Directory of Open Access Journals (Sweden)

    Sáenz-Noval J.J.

    2011-10-01

    Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.

  16. The issue of statistical power for overall model fit in evaluating structural equation models

    Directory of Open Access Journals (Sweden)

    Richard HERMIDA

    2015-06-01

    Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.

  17. Understanding and forecasting polar stratospheric variability with statistical models

    Directory of Open Access Journals (Sweden)

    C. Blume

    2012-07-01

    Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.

  18. Improved model for statistical alignment

    Energy Technology Data Exchange (ETDEWEB)

    Miklos, I.; Toroczkai, Z. (Zoltan)

    2001-01-01

    The statistical approach to molecular sequence evolution involves the stochastic modeling of the substitution, insertion and deletion processes. Substitution has been modeled in a reliable way for more than three decades by using finite Markov-processes. Insertion and deletion, however, seem to be more difficult to model, and thc recent approaches cannot acceptably deal with multiple insertions and deletions. A new method based on a generating function approach is introduced to describe the multiple insertion process. The presented algorithm computes the approximate joint probability of two sequences in 0(13) running time where 1 is the geometric mean of the sequence lengths.

  19. Characterizing the effects of cell settling on bioprinter output

    International Nuclear Information System (INIS)

    Pepper, Matthew E; Burg, Timothy C; Burg, Karen J L; Groff, Richard E; Seshadri, Vidya

    2012-01-01

    The time variation in bioprinter output, i.e. the number of cells per printed drop, was studied over the length of a typical printing experiment. This variation impacts the cell population size of bioprinted samples, which should ideally be consistent. The variation in output was specifically studied in the context of cell settling. The bioprinter studied is based on the thermal inkjet HP26A cartridge; however, the results are relevant to other cell delivery systems that draw fluid from a reservoir. A simple mathematical model suggests that the cell concentration in the bottom of the reservoir should increase linearly over time, up to some maximum, and that the cell output should be proportional to this concentration. Two studies were performed in which D1 murine stem cells and similarly sized polystyrene latex beads were printed. The bead output profiles were consistent with the model. The cell output profiles initially followed the increasing trend predicted by the settling model, but after several minutes the cell output peaked and then decreased. The decrease in cell output was found to be associated with the number of use cycles the cartridge had experienced. The differing results for beads and cells suggest that a biological process, such as adhesion, causes the decrease in cell output. Further work will be required to identify the exact process. (communication)

  20. EVALUATION OF CONTINUOUS THERMODILUTION METHOD FOR CARDIAC OUTPUT MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Roman Parežnik

    2001-12-01

    Full Text Available Background. Continuous monitoring of haemodynamic variables is often necessary for detection of rapid changes in critically ill patients. In our patients recently introduced continuous thermodilution technique (CTD for cardiac output measurement was compared to bolus thermodilution technique (BTD which is a »golden standard« method for cardiac output (CO measurement in intensive care medicine.Methods. Ten critically ill patients were included in a retrospective observational study. Using CTD method cardiac output was measured continuously. BTD measurements using the same equipment were performed intermittently. The data obtained by BTD were compared to those obtained by CTD just before the BTD (CTD-before and 2–3 minutes after the BTD (CTD-after. The CO values were divided into three groups: all CO values, CO > 4.5 L/min, CO < 4.5 L/min. The bias (mean difference between values obtained by two methods, standard deviation, 95% confidence limits and relative error were calculated and the linear regression analysis was performed. t-test for pared data was used to compare the biases for CTD-before and CTD-after for an individual group. The p value of less than 0.05 was considered statistically significant.Results. A total of 60 data triplets were obtained. CTD-before ranged from 1.9 L/min to 12.6 L/min, CTD-after from 2.0 to 13.2 L/min and BTD from 1.9 to 12.0 L/min. For all CO values the bias for CTD-before was 0.13 ± 0.52 L/min (95% confidence limits 1.17–0.91 L/min, relative error was 3.52 ± 15.20%, linear regression equation was CTD-before = 0.96 × BTD + 0.01 and Pearson’s correlation coefficient was 0.95. The values for CTD-after were 0.08 ± 0.46 L/min (1.0–0.84 L/min, 2.22 ± 9.05%, CTD-after = 0.98 × BTD + 0.01 and 0.98 respectively. For all CO values there was no statistically significant difference between biases for CTD-before and CTD-after (p = 0,51. There was no statistically significant difference between biases for CTD

  1. A utility piezoelectric energy harvester with low frequency and high-output voltage: Theoretical model, experimental verification and energy storage

    Directory of Open Access Journals (Sweden)

    Guangyi Zhang

    2016-09-01

    Full Text Available In this paper, a utility piezoelectric energy harvester with low frequency and high-output voltage is presented. Firstly, the harvester’s three theoretical models are presented, namely the static model, the quasi static model and the dynamic vibration model. By analyzing the influence of the mass ratio of the mass block to the beam on output characteristics of the harvester, we compare the quasi static model and the dynamic vibration model and then define their applicable ranges. Secondly, simulation and experiments are done to verify the models, using the harvester with PZT-5H piezoelectric material, which are proved to be consistent with each other. The experimental results show that the output open-circuit voltage and the output power can reach up to 86.36V and 27.5mW respectively. The experiments are conducted when this harvester system is excited by the first modal frequency (58.90Hz with the acceleration 10m/s2. In this low frequency vibration case, it is easy to capture the energy in the daily environment. In addition, LTC 3588-1 chip (Linear Technology Corporation is used as the medium energy circuit to transfer charges from the PZT-5H electrode to the 0.22F 5V super capacitor and ML621 rechargeable button battery. For this super-capacitor, it takes about 100min for the capacitor voltage to rise from 0V to 3.6V. For this button battery, it takes about 200min to increase the battery voltage from 2.5V to 3.48V.

  2. A statistical approach to nuclear fuel design and performance

    Science.gov (United States)

    Cunning, Travis Andrew

    As CANDU fuel failures can have significant economic and operational consequences on the Canadian nuclear power industry, it is essential that factors impacting fuel performance are adequately understood. Current industrial practice relies on deterministic safety analysis and the highly conservative "limit of operating envelope" approach, where all parameters are assumed to be at their limits simultaneously. This results in a conservative prediction of event consequences with little consideration given to the high quality and precision of current manufacturing processes. This study employs a novel approach to the prediction of CANDU fuel reliability. Probability distributions are fitted to actual fuel manufacturing datasets provided by Cameco Fuel Manufacturing, Inc. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case---a hypothesized 80% reactor outlet header break loss of coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of less than 0.3% when predicting the first statistical moment, and a relative discrepancy of less than 5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance

  3. Choice of theoretical model for beam scattering at accelerator output foil for particle energy determination

    International Nuclear Information System (INIS)

    Balagyra, V.S.; Ryabka, P.M.

    1999-01-01

    For measuring the charged particle energy calculations of mean square angles of electron beam multiple Coulomb scattering at output combined accelerator target were undertaken according to seven theoretical models. Mollier method showed the best agreement with experiments

  4. Daily precipitation statistics in regional climate models

    DEFF Research Database (Denmark)

    Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel

    2003-01-01

    An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...

  5. Infinite Random Graphs as Statistical Mechanical Models

    DEFF Research Database (Denmark)

    Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria

    2011-01-01

    We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...

  6. An R2 statistic for fixed effects in the linear mixed model.

    Science.gov (United States)

    Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

    2008-12-20

    Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

  7. Statistical benchmark for BosonSampling

    International Nuclear Information System (INIS)

    Walschaers, Mattia; Mayer, Klaus; Buchleitner, Andreas; Kuipers, Jack; Urbina, Juan-Diego; Richter, Klaus; Tichy, Malte Christopher

    2016-01-01

    Boson samplers—set-ups that generate complex many-particle output states through the transmission of elementary many-particle input states across a multitude of mutually coupled modes—promise the efficient quantum simulation of a classically intractable computational task, and challenge the extended Church–Turing thesis, one of the fundamental dogmas of computer science. However, as in all experimental quantum simulations of truly complex systems, one crucial problem remains: how to certify that a given experimental measurement record unambiguously results from enforcing the claimed dynamics, on bosons, fermions or distinguishable particles? Here we offer a statistical solution to the certification problem, identifying an unambiguous statistical signature of many-body quantum interference upon transmission across a multimode, random scattering device. We show that statistical analysis of only partial information on the output state allows to characterise the imparted dynamics through particle type-specific features of the emerging interference patterns. The relevant statistical quantifiers are classically computable, define a falsifiable benchmark for BosonSampling, and reveal distinctive features of many-particle quantum dynamics, which go much beyond mere bunching or anti-bunching effects. (fast track communication)

  8. Chains, Shops and Networks: Official Statistics and the Creation of Public Value

    Directory of Open Access Journals (Sweden)

    Asle Rolland

    2015-06-01

    Full Text Available The paper concerns offi cial statistics, particularly as produced by the NSIs. Their contribution to the society is considered well captured by the concept of public value. Official statistics create value for the democracy as foundation for evidence-based politics. Democracies and autocracies alike need statistics to govern the public. Unique for the democracy is the need of statistics to govern the governors, for which the independence of the NSI is crucial. Three ways of creating public value are the value chain, the value shop and the value network. The chain is appropriate for the production, the shop for the interpretation and the network for the dissemination of statistics. Automation reduces the need to rely on the value chain as core business model. Thereto automation increases the statistical output, which in turn increases the need of shop and network activities. Replacing the chain with the shop as core model will elevate the NSIs from commodity producers to a processing industry.

  9. Greenhouse gas footprinting for small businesses - The use of input-output data

    International Nuclear Information System (INIS)

    Berners-Lee, M.; Howard, D.C.; Moss, J.; Kaivanto, K.; Scott, W.A.

    2011-01-01

    To mitigate anthropogenic climate change greenhouse gas emissions (GHG) must be reduced; their major source is man's use of energy. A key way to manage emissions is for the energy consumer to understand their impact and the consequences of changing their activities. This paper addresses the challenge of delivering relevant, practical and reliable greenhouse gas 'footprint' information for small and medium sized businesses. The tool we describe is capable of ascribing parts of the total footprint to specific actions to which the business can relate and is sensitive enough to reflect the consequences of change. It provides a comprehensive description of all emissions for each business and sets them in the context of local, national and global statistics. It includes the GHG costs of all goods and services irrespective of their origin and without double accounting. We describe the development and use of the tool, which draws upon both national input-output data and process-based life cycle analysis techniques; a hybrid model. The use of national data sets the output in context and makes the results consistent with national and global targets, while the life cycle techniques provide a means of reflecting the dynamics of actions. The model is described in some detail along with a rationale and a short discussion of validity. As the tool is designed for small commercial users, we have taken care to combine rigour with practicality; parameterising from readily available client data whilst being clear about uncertainties. As an additional incentive, we also report on the potential costs or savings of switching activities. For users to benefit from the tool, they need to understand the output and know how much confidence they should place in the results. We not only describe an application of non-parametric statistics to generate confidence intervals, but also offer users the option of and guidance on adjusting figures to examine the sensitivity of the model to its

  10. Earth System Model Development and Analysis using FRE-Curator and Live Access Servers: On-demand analysis of climate model output with data provenance.

    Science.gov (United States)

    Radhakrishnan, A.; Balaji, V.; Schweitzer, R.; Nikonov, S.; O'Brien, K.; Vahlenkamp, H.; Burger, E. F.

    2016-12-01

    There are distinct phases in the development cycle of an Earth system model. During the model development phase, scientists make changes to code and parameters and require rapid access to results for evaluation. During the production phase, scientists may make an ensemble of runs with different settings, and produce large quantities of output, that must be further analyzed and quality controlled for scientific papers and submission to international projects such as the Climate Model Intercomparison Project (CMIP). During this phase, provenance is a key concern:being able to track back from outputs to inputs. We will discuss one of the paths taken at GFDL in delivering tools across this lifecycle, offering on-demand analysis of data by integrating the use of GFDL's in-house FRE-Curator, Unidata's THREDDS and NOAA PMEL's Live Access Servers (LAS).Experience over this lifecycle suggests that a major difficulty in developing analysis capabilities is only partially the scientific content, but often devoted to answering the questions "where is the data?" and "how do I get to it?". "FRE-Curator" is the name of a database-centric paradigm used at NOAA GFDL to ingest information about the model runs into an RDBMS (Curator database). The components of FRE-Curator are integrated into Flexible Runtime Environment workflow and can be invoked during climate model simulation. The front end to FRE-Curator, known as the Model Development Database Interface (MDBI) provides an in-house web-based access to GFDL experiments: metadata, analysis output and more. In order to provide on-demand visualization, MDBI uses Live Access Servers which is a highly configurable web server designed to provide flexible access to geo-referenced scientific data, that makes use of OPeNDAP. Model output saved in GFDL's tape archive, the size of the database and experiments, continuous model development initiatives with more dynamic configurations add complexity and challenges in providing an on

  11. Mixed deterministic statistical modelling of regional ozone air pollution

    KAUST Repository

    Kalenderski, Stoitchko

    2011-03-17

    We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..

  12. Adaptive Maneuvering Frequency Method of Current Statistical Model

    Institute of Scientific and Technical Information of China (English)

    Wei Sun; Yongjian Yang

    2017-01-01

    Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

  13. Speech emotion recognition based on statistical pitch model

    Institute of Scientific and Technical Information of China (English)

    WANG Zhiping; ZHAO Li; ZOU Cairong

    2006-01-01

    A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.

  14. Statistical modelling of citation exchange between statistics journals.

    Science.gov (United States)

    Varin, Cristiano; Cattelan, Manuela; Firth, David

    2016-01-01

    Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.

  15. 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-...

  16. Statistical validation of normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis

    2012-09-01

    To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Statistical Validation of Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schilstra, Cornelis [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Radiotherapy Institute Friesland, Leeuwarden (Netherlands)

    2012-09-01

    Purpose: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. Methods and Materials: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Results: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Conclusion: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.

  18. Shell model in large spaces and statistical spectroscopy

    International Nuclear Information System (INIS)

    Kota, V.K.B.

    1996-01-01

    For many nuclear structure problems of current interest it is essential to deal with shell model in large spaces. For this, three different approaches are now in use and two of them are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the shell model Monte Carlo method. A brief overview of these two methods is given. Large space shell model studies raise fundamental questions regarding the information content of the shell model spectrum of complex nuclei. This led to the third approach- the statistical spectroscopy methods. The principles of statistical spectroscopy have their basis in nuclear quantum chaos and they are described (which are substantiated by large scale shell model calculations) in some detail. (author)

  19. Advances in statistical models for data analysis

    CERN Document Server

    Minerva, Tommaso; Vichi, Maurizio

    2015-01-01

    This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

  20. The embodied energy and environmental emissions of construction projects in China: An economic input-output LCA model

    International Nuclear Information System (INIS)

    Chang Yuan; Ries, Robert J.; Wang Yaowu

    2010-01-01

    A complete understanding of the resource consumption, embodied energy, and environmental emissions of civil projects in China is difficult due to the lack of comprehensive national statistics. To quantitatively assess the energy and environmental impacts of civil construction at a macro-level, this study developed a 24 sector environmental input-output life-cycle assessment model (I-O LCA) based on 2002 Chinese national economic and environmental data. The model generates an economy-wide inventory of energy use and environmental emissions. Estimates based on the level of economic activity related to planned future civil works in 2015 are made. Results indicate that the embodied energy of construction projects accounts for nearly one-sixth of the total economy's energy consumption in 2007, and may account for approximately one-fifth of the total energy use by 2015. This energy consumption is dominated by coal and oil consumptions. Energy-related emissions are the main polluters of the country's atmosphere and environment. If the industry's energy use and manufacturing techniques remain the same as in 2002, challenges to the goals for total energy consumption in China will appear in the next decade. Thus, effective implementation of efficient energy technologies and regulations are indispensable for achieving China's energy and environmental quality goals.

  1. A Simplified Algorithm for Statistical Investigation of Damage Spreading

    International Nuclear Information System (INIS)

    Gecow, Andrzej

    2009-01-01

    On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method--function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

  2. Computationally efficient statistical differential equation modeling using homogenization

    Science.gov (United States)

    Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.

    2013-01-01

    Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

  3. Analytical approach for modeling and performance analysis of microring resonators as optical filters with multiple output bus waveguides

    Science.gov (United States)

    Lakra, Suchita; Mandal, Sanjoy

    2017-06-01

    A quadruple micro-optical ring resonator (QMORR) with multiple output bus waveguides is mathematically modeled and analyzed by making use of the delay-line signal processing approach in Z-domain and Mason's gain formula. The performances of QMORR with two output bus waveguides with vertical coupling are analyzed. This proposed structure is capable of providing wider free spectral response from both the output buses with appreciable cross talk. Thus, this configuration could provide increased capacity to insert a large number of communication channels. The simulated frequency response characteristic and its dispersion and group delay characteristics are graphically presented using the MATLAB environment.

  4. Bioclim Deliverable D8b: development of the physical/statistical down-scaling methodology and application to climate model Climber for BIOCLIM Work-package 3

    International Nuclear Information System (INIS)

    2003-01-01

    The overall aim of BIOCLIM is to assess the possible long term impacts due to climate change on the safety of radioactive waste repositories in deep formations. The main aim of this deliverable is to provide time series of climatic variables at the high resolution as needed by performance assessment (PA) of radioactive waste repositories, on the basis of coarse output from the CLIMBER-GREMLINS climate model. The climatological variables studied here are long-term (monthly) mean temperature and precipitation, as these are the main variables of interest for performance assessment. CLIMBER-GREMLINS is an earth-system model of intermediate complexity (EMIC), designed for long climate simulations (glacial cycles). Thus, this model has a coarse resolution (about 50 degrees in longitude) and other limitations which are sketched in this report. For the purpose of performance assessment, the climatological variables are required at scales pertinent for the knowledge of the conditions at the depository site. In this work, the final resolution is that of the best available global gridded present-day climatology, which is 1/6 degree in both longitude and latitude. To obtain climate-change information at this high resolution on the basis of the climate model outputs, a 2-step down-scaling method is designed. First, physical considerations are used to define variables which are expected to have links which climatological values; secondly a statistical model is used to find the links between these variables and the high-resolution climatology of temperature and precipitation. Thus the method is termed as 'physical/statistical': it involves physically based assumptions to compute predictors from model variables and then relies on statistics to find empirical links between these predictors and the climatology. The simple connection of coarse model results to regional values can not be done on a purely empirical way because the model does not provide enough information - it is both

  5. Models for probability and statistical inference theory and applications

    CERN Document Server

    Stapleton, James H

    2007-01-01

    This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...

  6. Fluctuations and correlations in statistical models of hadron production

    International Nuclear Information System (INIS)

    Gorenstein, M. I.

    2012-01-01

    An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

  7. Growth curve models and statistical diagnostics

    CERN Document Server

    Pan, Jian-Xin

    2002-01-01

    Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.

  8. Monetary Policy and Industrial Output in the BRICS Countries: A Markov-Switching Model

    Directory of Open Access Journals (Sweden)

    Kutu Adebayo Augustine

    2017-12-01

    Full Text Available This paper examines whether the five BRICS countries share similar business cycles and determines the probability of any of the countries moving from a contractionary regime to an expansionary regime. The study further examines the extent to which changes in monetary policy affect industrial output in expansions relative to contractions. Employing the Peersman and Smets (2001 Markov-Switching Model (MSM and monthly data from 1994.01–2013.12, the study reveals that the five BRICS countries have similar business cycles. The results further demonstrate that the BRICS countries’ business cycles are characterized by two distinct growth rate phases: a contractionary regime and an expansionary regime. It can also be observed that the area-wide monetary policy has significantly large effects on industrial output in recessions as well as in booms. It has also been established that there is a high probability of moving from state one (recession to state two (expansion and that on average, the probabilities of staying in state 2 (expansion are high for each of the five countries. It is, therefore, recommended that the BRICS countries should sustain uniform policy consistency (monetary policy, especially as they formulate and implement economic policies to stimulate industrial output.

  9. Advanced data analysis in neuroscience integrating statistical and computational models

    CERN Document Server

    Durstewitz, Daniel

    2017-01-01

    This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering.  Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...

  10. Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

    Science.gov (United States)

    Wilby, Robert L.

    2013-04-01

    Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to

  11. Input-output and energy demand models for Ireland: Data collection report. Part 1: EXPLOR

    Energy Technology Data Exchange (ETDEWEB)

    Henry, E W; Scott, S

    1981-01-01

    Data are presented in support of EXPLOR, an input-output economic model for Ireland. The data follow the listing of exogenous data-sets used by Batelle in document X11/515/77. Data are given for 1974, 1980, and 1985 and consist of household consumption, final demand-production, and commodity prices. (ACR)

  12. How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

    NARCIS (Netherlands)

    Borsboom, D.; Haig, B.D.

    2013-01-01

    Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

  13. Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation

    Energy Technology Data Exchange (ETDEWEB)

    Carta, Jose A. [Department of Mechanical Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Ramirez, Penelope; Velazquez, Sergio [Department of Renewable Energies, Technological Institute of the Canary Islands, Pozo Izquierdo Beach s/n, 35119 Santa Lucia, Gran Canaria, Canary Islands (Spain)

    2008-10-15

    Static methods which are based on statistical techniques to estimate the mean power output of a WECS (wind energy conversion system) have been widely employed in the scientific literature related to wind energy. In the static method which we use in this paper, for a given wind regime probability distribution function and a known WECS power curve, the mean power output of a WECS is obtained by resolving the integral, usually using numerical evaluation techniques, of the product of these two functions. In this paper an analysis is made of the influence of the level of fit between an empirical probability density function of a sample of wind speeds and the probability density function of the adjusted theoretical model on the relative error {epsilon} made in the estimation of the mean annual power output of a WECS. The mean power output calculated through the use of a quasi-dynamic or chronological method, that is to say using time-series of wind speed data and the power versus wind speed characteristic of the wind turbine, serves as the reference. The suitability of the distributions is judged from the adjusted R{sup 2} statistic (R{sub a}{sup 2}). Hourly mean wind speeds recorded at 16 weather stations located in the Canarian Archipelago, an extensive catalogue of wind-speed probability models and two wind turbines of 330 and 800 kW rated power are used in this paper. Among the general conclusions obtained, the following can be pointed out: (a) that the R{sub a}{sup 2} statistic might be useful as an initial gross indicator of the relative error made in the mean annual power output estimation of a WECS when a probabilistic method is employed; (b) the relative errors tend to decrease, in accordance with a trend line defined by a second-order polynomial, as R{sub a}{sup 2} increases. (author)

  14. Output power distributions of mobile radio base stations based on network measurements

    International Nuclear Information System (INIS)

    Colombi, D; Thors, B; Persson, T; Törnevik, C; Wirén, N; Larsson, L-E

    2013-01-01

    In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

  15. Output power distributions of mobile radio base stations based on network measurements

    Science.gov (United States)

    Colombi, D.; Thors, B.; Persson, T.; Wirén, N.; Larsson, L.-E.; Törnevik, C.

    2013-04-01

    In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

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

    Science.gov (United States)

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

    2011-09-01

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

  17. Cellular automata and statistical mechanical models

    International Nuclear Information System (INIS)

    Rujan, P.

    1987-01-01

    The authors elaborate on the analogy between the transfer matrix of usual lattice models and the master equation describing the time development of cellular automata. Transient and stationary properties of probabilistic automata are linked to surface and bulk properties, respectively, of restricted statistical mechanical systems. It is demonstrated that methods of statistical physics can be successfully used to describe the dynamic and the stationary behavior of such automata. Some exact results are derived, including duality transformations, exact mappings, disorder, and linear solutions. Many examples are worked out in detail to demonstrate how to use statistical physics in order to construct cellular automata with desired properties. This approach is considered to be a first step toward the design of fully parallel, probabilistic systems whose computational abilities rely on the cooperative behavior of their components

  18. Growth Curve Models and Applications : Indian Statistical Institute

    CERN Document Server

    2017-01-01

    Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.   There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...

  19. Improving statistical reasoning theoretical models and practical implications

    CERN Document Server

    Sedlmeier, Peter

    1999-01-01

    This book focuses on how statistical reasoning works and on training programs that can exploit people''s natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

  20. Solar radiation data - statistical analysis and simulation models

    Energy Technology Data Exchange (ETDEWEB)

    Mustacchi, C; Cena, V; Rocchi, M; Haghigat, F

    1984-01-01

    The activities consisted in collecting meteorological data on magnetic tape for ten european locations (with latitudes ranging from 42/sup 0/ to 56/sup 0/ N), analysing the multi-year sequences, developing mathematical models to generate synthetic sequences having the same statistical properties of the original data sets, and producing one or more Short Reference Years (SRY's) for each location. The meteorological parameters examinated were (for all the locations) global + diffuse radiation on horizontal surface, dry bulb temperature, sunshine duration. For some of the locations additional parameters were available, namely, global, beam and diffuse radiation on surfaces other than horizontal, wet bulb temperature, wind velocity, cloud type, cloud cover. The statistical properties investigated were mean, variance, autocorrelation, crosscorrelation with selected parameters, probability density function. For all the meteorological parameters, various mathematical models were built: linear regression, stochastic models of the AR and the DAR type. In each case, the model with the best statistical behaviour was selected for the production of a SRY for the relevant parameter/location.

  1. Explaining output volatility: The case of taxation

    DEFF Research Database (Denmark)

    Posch, Olaf

    the second moment of output growth rates without (long-run) effects on the first moment. Taking the model to the data, we exploit observed heterogeneity patterns to estimate effects of tax rates on macro volatility using panel estimation, explicitly modeling the unobserved variance process. We find a strong......This paper studies the effects of taxation on output volatility in OECD countries to shed light on the sources of observed heterogeneity over time and across countries. To this end, we derive tax effects on macro aggregates in a stochastic neoclassical model. As a result, taxes are shown to affect...... positive effects....

  2. Statistical Model Checking for Biological Systems

    DEFF Research Database (Denmark)

    David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

    2014-01-01

    Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...

  3. A hybrid input–output multi-objective model to assess economic–energy–environment trade-offs in Brazil

    International Nuclear Information System (INIS)

    Carvalho, Ariovaldo Lopes de; Antunes, Carlos Henggeler; Freire, Fausto; Henriques, Carla Oliveira

    2015-01-01

    A multi-objective linear programming (MOLP) model based on a hybrid Input–Output (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with Input–Output Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. - Highlights: • A hybrid Input–Output multi-objective model is applied to the Brazilian economy. • Objective functions are GDP, employment level, energy consumption and GHG emissions. • Interactive search process identifies trade-offs between the competing objectives. • Positive correlations between GDP growth and employment. • Positive correlations between energy consumption and GHG emissions

  4. Right-sizing statistical models for longitudinal data.

    Science.gov (United States)

    Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

    2015-12-01

    Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

  5. STATISTICAL DOWNSCALING DENGAN PERGESERAN WAKTU BERDASARKAN KORELASI SILANG

    Directory of Open Access Journals (Sweden)

    Aji Hamim Wigena

    2015-09-01

    Full Text Available Pergeseran waktu (time lag dalam analisis data deret waktu diperlukan terutama untuk analisis hubungan dua peubah (variable, seperti dalam statistical downscaling. Pergeseran waktu ini ditentukan berdasarkan korelasi silang tinggi yang setara dengan hubungan yang kuat antar kedua peubah tersebut sehingga dapat digunakan dalam pemodelan untuk prakiraan yang lebih akurat. Makalah ini mengenai statistical downscaling dengan memperhatikan korelasi silang antara data curah hujan dengan data presipitasi Global Circulation Model (GCM dari Climate Model Inter Comparison Project (CMIP5. Salah satu syarat dalam statistical downscaling adalah peubah  skala lokal dan global berkorelasi tinggi. Kedua tipe peubah tersebut berupa data deret waktu sehingga fungsi korelasi silang diterapkan untuk memperoleh pergeseran waktu. Korelasi silang yang tinggi menentukan pergeseran waktu pada luaran GCM yang menghasilkan hubungan fungsional lebih kuat antara kedua tipe peubah. Model regresi komponen utama dan regresi kuadrat terkecil parsial digunakan dalam makalah ini. Model-model dengan pergeseran waktu menduga curah hujan lebih baik daripada model-model tanpa pergeseran waktu.   Time lag in time series data analysis is required especially to analyze the relationship of two variables, such as in statistical downscaling. Time lag is determined based on high cross correlation which is equivalent to strong relationship between the two variables and can be used in modeling for a more accurate forecast. This paper is about  statistical downscaling by considering the cross correlation between rainfall data and precipitation data from Global Circulation Model (GCM of Climate Model Inter Comparison Project (CMIP5. One of the conditions in statistical downscaling is that local scale and global scale variables are highly correlated. Both types of variables are time series data, thus cross correlation function is applied to find time lags. High cross correlation determines

  6. Greenhouse gas footprinting for small businesses--the use of input-output data.

    Science.gov (United States)

    Berners-Lee, M; Howard, D C; Moss, J; Kaivanto, K; Scott, W A

    2011-02-01

    To mitigate anthropogenic climate change greenhouse gas emissions (GHG) must be reduced; their major source is man's use of energy. A key way to manage emissions is for the energy consumer to understand their impact and the consequences of changing their activities. This paper addresses the challenge of delivering relevant, practical and reliable greenhouse gas 'footprint' information for small and medium sized businesses. The tool we describe is capable of ascribing parts of the total footprint to specific actions to which the business can relate and is sensitive enough to reflect the consequences of change. It provides a comprehensive description of all emissions for each business and sets them in the context of local, national and global statistics. It includes the GHG costs of all goods and services irrespective of their origin and without double accounting. We describe the development and use of the tool, which draws upon both national input-output data and process-based life cycle analysis techniques; a hybrid model. The use of national data sets the output in context and makes the results consistent with national and global targets, while the life cycle techniques provide a means of reflecting the dynamics of actions. The model is described in some detail along with a rationale and a short discussion of validity. As the tool is designed for small commercial users, we have taken care to combine rigour with practicality; parameterising from readily available client data whilst being clear about uncertainties. As an additional incentive, we also report on the potential costs or savings of switching activities. For users to benefit from the tool, they need to understand the output and know how much confidence they should place in the results. We not only describe an application of non-parametric statistics to generate confidence intervals, but also offer users the option of and guidance on adjusting figures to examine the sensitivity of the model to its

  7. Output Information Based Fault-Tolerant Iterative Learning Control for Dual-Rate Sampling Process with Disturbances and Output Delay

    Directory of Open Access Journals (Sweden)

    Hongfeng Tao

    2018-01-01

    Full Text Available For a class of single-input single-output (SISO dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method.

  8. Towards a standard model for research in agent-based modeling and simulation

    Directory of Open Access Journals (Sweden)

    Nuno Fachada

    2015-11-01

    Full Text Available Agent-based modeling (ABM is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present PPHPC, a model which aims to serve as a standard in agent based modeling research, namely, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and parallelization studies. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output, also providing a canonical implementation of PPHPC. The paper serves as a complete reference to the presented model, and can be used as a tutorial for simulation practitioners who wish to improve the way they communicate their ABMs.

  9. Statistical models based on conditional probability distributions

    International Nuclear Information System (INIS)

    Narayanan, R.S.

    1991-10-01

    We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)

  10. A statistical model for mapping morphological shape

    Directory of Open Access Journals (Sweden)

    Li Jiahan

    2010-07-01

    Full Text Available Abstract Background Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine. Results We derived a statistical model for mapping specific genes or quantitative trait loci (QTLs that control morphological shape. The model was formulated within the mixture framework, in which different types of shape are thought to result from genotypic discrepancies at a QTL. The EM algorithm was implemented to estimate QTL genotype-specific shapes based on a shape correspondence analysis. Computer simulation was used to investigate the statistical property of the model. Conclusion By identifying specific QTLs for morphological shape, the model developed will help to ask, disseminate and address many major integrative biological and genetic questions and challenges in the genetic control of biological shape and function.

  11. Data Envelopment Analysis with Fixed Inputs, Undesirable Outputs and Negative Data

    Directory of Open Access Journals (Sweden)

    F. Seyed Esmaeili

    2017-03-01

    Full Text Available In Data Envelopment Analysis (DEA, different models have been measured to evaluate the performance of decision making units with multiple inputs and outputs. Revised model of Slack-based measures known as MBSM of collective models family has been introduced by Sharp et al. Slack-based measure has been introduced by Ton. In this study, a model is proposed that is able to estimate the efficiency when a number of outputs of decision making units are undesirable, inputs are fixed and some of outputs and inputs are negative. So that, level of undesirable output is reduced at the constant level of inputs in the evaluation unit and by conserving the efficiency.

  12. Statistical model selection with “Big Data”

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2015-12-01

    Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

  13. Statistical shape and appearance models of bones.

    Science.gov (United States)

    Sarkalkan, Nazli; Weinans, Harrie; Zadpoor, Amir A

    2014-03-01

    When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone. Copyright © 2013 Elsevier Inc. All rights reserved.

  14. Statistical models and NMR analysis of polymer microstructure

    Science.gov (United States)

    Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...

  15. Workshop on Model Uncertainty and its Statistical Implications

    CERN Document Server

    1988-01-01

    In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

  16. Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

    Energy Technology Data Exchange (ETDEWEB)

    Alwan, Aravind; Aluru, N.R.

    2013-12-15

    This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.

  17. Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

    International Nuclear Information System (INIS)

    Alwan, Aravind; Aluru, N.R.

    2013-01-01

    This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems

  18. Output performance analyses of solar array on stratospheric airship with thermal effect

    International Nuclear Information System (INIS)

    Li, Jun; Lv, Mingyun; Tan, Dongjie; Zhu, Weiyu; Sun, Kangwen; Zhang, Yuanyuan

    2016-01-01

    Highlights: • A model investigating the output power of solar array is proposed. • The output power in the cruise condition with thermal effect is researched. • The effect of some factors on output performance is discussed in detail. • A suitable transmissivity of external layer is crucial in preliminary design step. - Abstract: Output performance analyses of the solar array are very critical for solving the energy problem of a long endurance stratospheric airship, and the solar cell efficiency is very sensitive to temperature of the solar cell. But the research about output performance of solar array with thermal effect is rare. This paper outlines a numerical model including the thermal model of airship and solar cells, the incident solar radiation model on the solar array, and the power output model. Based on this numerical model, a MATLAB computer program is developed. In the course of the investigation, the comparisons of the simulation results with and without considering thermal effect are reported. Furthermore, effects of the transmissivity of external encapsulation layer of solar array and wind speed on the thermal performance and output power of solar array are discussed in detail. The results indicate that this method is helpful for planning energy management.

  19. Kolmogorov complexity, pseudorandom generators and statistical models testing

    Czech Academy of Sciences Publication Activity Database

    Šindelář, Jan; Boček, Pavel

    2002-01-01

    Roč. 38, č. 6 (2002), s. 747-759 ISSN 0023-5954 R&D Projects: GA ČR GA102/99/1564 Institutional research plan: CEZ:AV0Z1075907 Keywords : Kolmogorov complexity * pseudorandom generators * statistical models testing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.341, year: 2002

  20. Two-dimensional modeling of x-ray output from switched foil implosions on Procyon

    Science.gov (United States)

    Bowers, R. L.; Nakafuji, G.; Greene, A. E.; McLenithan, K. D.; Peterson, D. L.; Roderick, N. F.

    1996-09-01

    A series of two-dimensional radiation magnetohydrodynamic calculations are presented of a Z-pinch implosion using a plasma flow switch. Results from a recent experiment using the high explosive driven generator Procyon, which delivered 16.5 MA to a plasma flow switch and switched about 15 MA into a static load, are used to study the implosion of a 29 mg load foil [J. H. Goforth et al., ``Review of the Procyon Explosive Pulsed Power System,'' in Ninth IEEE Pulsed Power Conference, June 1993, Albuquerque, edited by K. R. Prestwich and W. L. Baker (Institute of Electrical and Electronics Engineers, Piscataway, NJ, 1993), p. 36]. The interaction of the switch with the load plasma and the effects of background plasma on the total radiation output is examined. Models which assume ideal switching are also included. Also included are the effects of perturbations in the load plasma which may be associated with initial vaporization of the load foil. If the background plasma density in the switch region and in the load region does not affect the dynamics, the pinch is predicted to produce a total radiation output of about 4 MJ. Including perturbations of the load plasma associated with switching and assuming a background plasma density after switching in excess of 10-7 g/cm3 results in a total output from the pinch of about 0.6 MJ.

  1. Visualization of the variability of 3D statistical shape models by animation.

    Science.gov (United States)

    Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter

    2004-01-01

    Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

  2. Applied systems ecology: models, data, and statistical methods

    Energy Technology Data Exchange (ETDEWEB)

    Eberhardt, L L

    1976-01-01

    In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.

  3. Prioritizing Interdependent Production Processes using Leontief Input-Output Model

    Directory of Open Access Journals (Sweden)

    Masbad Jesah Grace

    2016-03-01

    Full Text Available This paper proposes a methodology in identifying key production processes in an interdependent production system. Previous approaches on this domain have drawbacks that may potentially affect the reliability of decision-making. The proposed approach adopts the Leontief input-output model (L-IOM which was proven successful in analyzing interdependent economic systems. The motivation behind such adoption lies in the strength of L-IOM in providing a rigorous quantitative framework in identifying key components of interdependent systems. In this proposed approach, the consumption and production flows of each process are represented respectively by the material inventory produced by the prior process and the material inventory produced by the current process, both in monetary values. A case study in a furniture production system located in central Philippines was carried out to elucidate the proposed approach. Results of the case were reported in this work

  4. Amplitude and Phase Characteristics of Signals at the Output of Spatially Separated Antennas for Paths with Scattering

    Science.gov (United States)

    Anikin, A. S.

    2018-06-01

    Conditional statistical characteristics of the phase difference are considered depending on the ratio of instantaneous output signal amplitudes of spatially separated weakly directional antennas for the normal field model for paths with radio-wave scattering. The dependences obtained are related to the physical processes on the radio-wave propagation path. The normal model parameters are established at which the statistical characteristics of the phase difference depend on the ratio of the instantaneous amplitudes and hence can be used to measure the phase difference. Using Shannon's formula, the amount of information on the phase difference of signals contained in the ratio of their amplitudes is calculated depending on the parameters of the normal field model. Approaches are suggested to reduce the shift of phase difference measured for paths with radio-wave scattering. A comparison with results of computer simulation by the Monte Carlo method is performed.

  5. Measurement needs guided by synthetic radar scans in high-resolution model output

    Science.gov (United States)

    Varble, A.; Nesbitt, S. W.; Borque, P.

    2017-12-01

    Microphysical and dynamical process interactions within deep convective clouds are not well understood, partly because measurement strategies often focus on statistics of cloud state rather than cloud processes. While processes cannot be directly measured, they can be inferred with sufficiently frequent and detailed scanning radar measurements focused on the life cycleof individual cloud regions. This is a primary goal of the 2018-19 DOE ARM Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and NSF Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaigns in central Argentina, where orographic deep convective initiation is frequent with some high-impact systems growing into the tallest and largest in the world. An array of fixed and mobile scanning multi-wavelength dual-polarization radars will be coupled with surface observations, sounding systems, multi-wavelength vertical profilers, and aircraft in situ measurements to characterize convective cloud life cycles and their relationship with environmental conditions. While detailed cloud processes are an observational target, the radar scan patterns that are most ideal for observing them are unclear. They depend on the locations and scales of key microphysical and dynamical processes operating within the cloud. High-resolution simulations of clouds, while imperfect, can provide information on these locations and scales that guide radar measurement needs. Radar locations are set in the model domain based on planned experiment locations, and simulatedorographic deep convective initiation and upscale growth are sampled using a number of different scans involving RHIs or PPIs with predefined elevation and azimuthal angles that approximately conform with radar range and beam width specifications. Each full scan pattern is applied to output atsingle model time steps with time step intervals that depend on the length of time

  6. CO{sub 2} emissions, electricity consumption and output in ASEAN

    Energy Technology Data Exchange (ETDEWEB)

    Lean, Hooi Hooi [Economics Program, School of Social Sciences, Universiti Sains Malaysia (Malaysia); Smyth, Russell [Department of Economics, Monash University, Clayton 3800 (Australia)

    2010-06-15

    This study examines the causal relationship between carbon dioxide emissions, electricity consumption and economic growth within a panel vector error correction model for five ASEAN countries over the period 1980-2006. The long-run estimates indicate that there is a statistically significant positive association between electricity consumption and emissions and a non-linear relationship between emissions and real output, consistent with the environmental Kuznets curve. The long-run estimates, however, do not indicate the direction of causality between the variables. The results from the Granger causality tests suggest that in the long-run there is unidirectional Granger causality running from electricity consumption and emissions to economic growth. The results also point to unidirectional Granger causality running from emissions to electricity consumption in the short-run. (author)

  7. Mechanisms Regulating the Cardiac Output Response to Cyanide Infusion, a Model of Hypoxia

    Science.gov (United States)

    Liang, Chang-seng; Huckabee, William E.

    1973-01-01

    When tissue metabolic changes like those of hypoxia were induced by intra-aortic infusion of cyanide in dogs, cardiac output began to increase after 3 to 5 min, reached a peak (220% of the control value) at 15 min, and returned to control in 40 min. This pattern of cardiac output rise was not altered by vagotomy with or without atropine pretreatment. However, this cardiac output response could be differentiated into three phases by pretreating the animals with agents that block specific activities of the sympatho-adrenal system. First, ganglionic blockade produced by mecamylamine or sympathetic nerve blockade by bretylium abolished the middle phase of the cardiac output seen in the untreated animal, but early and late phases still could be discerned. Second, beta-adrenergic receptor blockade produced by propranolol shortened the total duration of the cardiac output rise by abolishing the late phase. Third, when given together, propranolol and mecamylamine (or bretylium) prevented most of the cardiac output rise that follows the early phase. When cyanide was given to splenectomized dogs, the duration of the cardiac output response was not shortened, but the response became biphasic, resembling that seen after chemical sympathectomy. A similar biphasic response of the cardiac output also resulted from splenic denervation; sham operation or nephrectomy had no effect on the monophasic pattern of the normal response. Splenic venous blood obtained from cyanide-treated dogs, when infused intraportally, caused an increase in cardiac output in recipient dogs; similar infusion of arterial blood had no effects. These results suggest that the cardiac output response to cyanide infusion consists of three components: an early phase, related neither to the autonomic nervous system nor to circulating catecholamines; a middle phase, caused by a nonadrenergic humoral substance released from the spleen by sympathetic stimulation; and a late phase, dependent upon adrenergic receptors

  8. The use of statistical models in heavy-ion reactions studies

    International Nuclear Information System (INIS)

    Stokstad, R.G.

    1984-01-01

    This chapter reviews the use of statistical models to describe nuclear level densities and the decay of equilibrated nuclei. The statistical models of nuclear structure and nuclear reactions presented here have wide application in the analysis of heavy-ion reaction data. Applications are illustrated with examples of gamma-ray decay, the emission of light particles and heavier clusters of nucleons, and fission. In addition to the compound nucleus, the treatment of equilibrated fragments formed in binary reactions is discussed. The statistical model is shown to be an important tool for the identification of products from nonequilibrium decay

  9. Multivariate statistical modelling based on generalized linear models

    CERN Document Server

    Fahrmeir, Ludwig

    1994-01-01

    This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

  10. Linear mixed models a practical guide using statistical software

    CERN Document Server

    West, Brady T; Galecki, Andrzej T

    2006-01-01

    Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo

  11. 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...

  12. Active Learning with Statistical Models.

    Science.gov (United States)

    1995-01-01

    Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

  13. Thin disk laser with unstable resonator and reduced output coupler

    Science.gov (United States)

    Gavili, Anwar; Shayganmanesh, Mahdi

    2018-05-01

    In this paper, feasibility of using unstable resonator with reduced output coupling in a thin disk laser is studied theoretically. Unstable resonator is modeled by wave-optics using Collins integral and iterative method. An Yb:YAG crystal with 250 micron thickness is considered as a quasi-three level active medium and modeled by solving rate equations of energy levels populations. The amplification of laser beam in the active medium is calculated based on the Beer-Lambert law and Rigrod method. Using generalized beam parameters method, laser beam parameters like, width, divergence, M2 factor, output power as well as near and far-field beam profiles are calculated for unstable resonator. It is demonstrated that for thin disk laser (with single disk) in spite of the low thickness of the disk which leads to low gain factor, it is possible to use unstable resonator (with reduced output coupling) and achieve good output power with appropriate beam quality. Also, the behavior of output power and beam quality versus equivalent Fresnel number is investigated and optimized value of output coupling for maximum output power is achieved.

  14. Parametric analysis of the statistical model of the stick-slip process

    Science.gov (United States)

    Lima, Roberta; Sampaio, Rubens

    2017-06-01

    In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.

  15. Public Investment and Output Performance: Evidence from Nigeria

    Directory of Open Access Journals (Sweden)

    Aregbeyen Omo

    2016-05-01

    Full Text Available This study examined the direct/indirect long-run relationships and dynamic interactions between public investment (PI and output performance in Nigeria using annual data spanning 1970-2010. A macro-econometric model derived from Keynes’ income-expenditure framework was employed. The model was disaggregated into demand and supply sides to trace the direct and indirect effects of PI on aggregate output. The direct supply side effect was assessed using the magnitude of PI multiplier coefficient, while the indirect effect of PI on the demand side was evaluated with marginal propensity to consume, accelerator coefficient and import multiplier. The results showed relatively less strong direct effect of PI on aggregate output, while the indirect effects were stronger with the import multiplier being the most pronounced. This is attributed to declining capital expenditure, poor implementation and low quality of PI projects due to widespread corruption. By and large, we concluded that PI exerted considerable influence on aggregate output.

  16. Short-term prediction of windfarm power output - from theory to practice

    International Nuclear Information System (INIS)

    Landberg, L.

    1998-01-01

    From the very complicated and evolved theories of boundary-layer meteorology encompassing the equations of turbulence and mean flow, a model has been derived to predict the power output from wind farms. For practical dispatching purposes the predictions must reach as far into the future as 36 hours. The model has been put into an operation frame-work where the predictions for a number of wind farms scattered all over Europe are available on-line on the World Wide Web. The system is very versatile and new wind farms can be included within a few days. The system is made up of predictions from the Danish Meteorological Institute HIRLAM model which are refined using the WASP model from Risoe National Laboratory. The paper will describe this operation set-up, give examples of the performance of the model of wind farms in the UK, Denmark, Greece and the US. An analysis of the error for a one-year period will also be presented. Finally, possible improvements will be discussed. These include Kalman filtering and other statistical methods. (Author)

  17. Industrial output restriction and the Kyoto protocol. An input-output approach with application to Canada

    International Nuclear Information System (INIS)

    Lixon, Benoit; Thomassin, Paul J.; Hamaide, Bertrand

    2008-01-01

    The objective of this paper is to assess the economic impacts of reducing greenhouse gas emissions by decreasing industrial output in Canada to a level that will meet the target set out in the Kyoto Protocol. The study uses an ecological-economic Input-Output model combining economic components valued in monetary terms with ecologic components - GHG emissions - expressed in physical terms. Economic and greenhouse gas emissions data for Canada are computed in the same sectoral disaggregation. Three policy scenarios are considered: the first one uses the direct emission coefficients to allocate the reduction in industrial output, while the other two use the direct plus indirect emission coefficients. In the first two scenarios, the reduction in industrial sector output is allocated uniformly across sectors while it is allocated to the 12 largest emitting industries in the last one. The estimated impacts indicate that the results vary with the different allocation methods. The third policy scenario, allocation to the 12 largest emitting sectors, is the most cost effective of the three as the impacts of the Kyoto Protocol reduces Gross Domestic Product by 3.1% compared to 24% and 8.1% in the first two scenarios. Computed economic costs should be considered as upper-bounds because the model assumes immediate adjustment to the Kyoto Protocol and because flexibility mechanisms are not incorporated. The resulting upper-bound impact of the third scenario may seem to contradict those who claim that the Kyoto Protocol would place an unbearable burden on the Canadian economy. (author)

  18. Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams

    Directory of Open Access Journals (Sweden)

    Chongshi Gu

    2013-01-01

    Full Text Available Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in expressing the objective model. Previous projects show that the semi-parametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semi-parametric statistical model is equivalent to that of the parametric statistical model. The modeling of the semi-parametric statistical model is simple, has a reasonable principle, and has a strong practicality, with a good application prospect in the actual project.

  19. Statistical models for competing risk analysis

    International Nuclear Information System (INIS)

    Sather, H.N.

    1976-08-01

    Research results on three new models for potential applications in competing risks problems. One section covers the basic statistical relationships underlying the subsequent competing risks model development. Another discusses the problem of comparing cause-specific risk structure by competing risks theory in two homogeneous populations, P1 and P2. Weibull models which allow more generality than the Berkson and Elveback models are studied for the effect of time on the hazard function. The use of concomitant information for modeling single-risk survival is extended to the multiple failure mode domain of competing risks. The model used to illustrate the use of this methodology is a life table model which has constant hazards within pre-designated intervals of the time scale. Two parametric models for bivariate dependent competing risks, which provide interesting alternatives, are proposed and examined

  20. Money and Output: A Test of Reverse Causation.

    OpenAIRE

    Coleman, Wilbur John, II

    1996-01-01

    This paper attempts to explain the correlation between money and output at various leads and lags with a model in which money is largely neutral and endogenously responds to output. Money is endogenous because both monetary policy and deposit creation are endogenous. Parameters are selected according to the simulated moments estimation technique. While the estimated model succeeds along some dimensions in matching properties of postwar U.S. data, its failure to match key patterns of lead-lag ...

  1. SoS contract verification using statistical model checking

    Directory of Open Access Journals (Sweden)

    Alessandro Mignogna

    2013-11-01

    Full Text Available Exhaustive formal verification for systems of systems (SoS is impractical and cannot be applied on a large scale. In this paper we propose to use statistical model checking for efficient verification of SoS. We address three relevant aspects for systems of systems: 1 the model of the SoS, which includes stochastic aspects; 2 the formalization of the SoS requirements in the form of contracts; 3 the tool-chain to support statistical model checking for SoS. We adapt the SMC technique for application to heterogeneous SoS. We extend the UPDM/SysML specification language to express the SoS requirements that the implemented strategies over the SoS must satisfy. The requirements are specified with a new contract language specifically designed for SoS, targeting a high-level English- pattern language, but relying on an accurate semantics given by the standard temporal logics. The contracts are verified against the UPDM/SysML specification using the Statistical Model Checker (SMC PLASMA combined with the simulation engine DESYRE, which integrates heterogeneous behavioral models through the functional mock-up interface (FMI standard. The tool-chain allows computing an estimation of the satisfiability of the contracts by the SoS. The results help the system architect to trade-off different solutions to guide the evolution of the SoS.

  2. Delay signatures in the chaotic intensity output of a quantum dot ...

    Indian Academy of Sciences (India)

    Delay identification from the chaotic intensity output of a quantum dot laser with optical feedback is done using numerical and information theoretic techniques. Four quantifiers, namely autocorrelation function, delayed mutual information, permutation entropy and permutation statistical complexity, are employed in delay ...

  3. Complex Data Modeling and Computationally Intensive Statistical Methods

    CERN Document Server

    Mantovan, Pietro

    2010-01-01

    The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici

  4. Does Black’s Hypothesis for Output Variability Hold for Mexico?

    OpenAIRE

    Macri, Joseph; Sinha, Dipendra

    2007-01-01

    Using two data series, namely GDP and the index of industrial production, we study the relationship between output variability and the growth rate of output. Ng-Perron unit root test shows that the growth rate of GDP is non-stationary but the growth rate of industrial output is stationary. Thus, we use the ARCH-M model for the monthly data of industrial output. A number of specifications (with and without a dummy variable) are used. In all cases, the results show that output variability has a...

  5. MCNP output data analysis with ROOT (MODAR)

    Science.gov (United States)

    Carasco, C.

    2010-12-01

    file into Microsoft Excel. Such an approach is satisfactory when the quantity of data is small but is not efficient when the size of the simulated data is large, for example when time-energy correlations are studied in detail such as in problems involving the associated particle technique. In addition, since the finite time resolution of the simulated detector cannot be modeled with MCNP, systems in which time-energy correlation is crucial cannot be described in a satisfactory way. Finally, realistic particle energy deposit in detectors is calculated with MCNP in a two step process involving type-5 then type-8 tallies. In the first step, the photon flux energy spectrum associated to a time region is selected and serves as a source energy distribution for the second step. Thus, several files must be manipulated before getting the result, which can be time consuming if one needs to study several time regions or different detectors performances. In the same way, modeling counting statistics obtained in a limited acquisition time requires several steps and can also be time consuming. Solution method: In order to overcome the previous limitations, the MODAR C++ code has been written to make use of CERN's ROOT data analysis software. MCNP output data are read from the MCNP output file with dedicated routines. Two dimensional histograms are filled and can be handled efficiently within the ROOT framework. To keep a user friendly analysis tool, all processing and data display can be done by means of ROOT Graphical User Interface. Specific routines have been written to include detectors finite time resolution and energy response function as well as counting statistics in a straightforward way. Reasons for new version: For applications involving the Associate Particle Technique, a large number of gamma rays are produced by the fast neutrons interactions. To study the energy spectra, it is useful to identify the gamma-ray energy peaks in a straightforward way. Therefore, the

  6. Multiscale Analysis of the Water Content Output the NWP Model COSMO Over Switzerland and Comparison With Radar Data

    Science.gov (United States)

    Wolfensberger, D.; Gires, A.; Berne, A.; Tchiguirinskaia, I.; Schertzer, D. J. M.

    2015-12-01

    The resolution of operational numerical prediction models is typically of the order of a few kilometres meaning that small-scale features of precipitation can not be resolved explicitly. This creates the need for representative parametrizations of microphysical processes whose properties should be carefully analysed. In this study we will focus on the COSMO model which is a non-hydrostatic limited-area model, initially developed as the Lokal Model and used operationally in Switzerland and Germany. In its operational version, cloud microphysical processes are simulated with a one-moment bulk scheme where five hydrometeor classes are considered: cloud droplets, rain, ice crystals, snow, and graupel. A more sophisticated two-moment scheme is also available. The study will focus on two case studies: one in Payerne in western Switzerland in a relatively flat region and one in Davos in the eastern Swiss Alps in a more complex terrain.The objective of this work is to characterize the ability of the COSMO NWP model to reproduce the microphysics of precipitation across temporal and spatial scales as well as scaling variability. The characterization of COSMO outputs will rely on the Universal Multifractals framework, which allows to analyse and simulate geophysical fields extremely variabile over a wide range of scales with the help of a reduced number of parameters. First COSMO outputs are analysed; spatial multifractal analysis of 2D maps at various altitudes for each time steps are carried out for simulated solid, liquid, vapour and total water content. In general the fields exhibit a good quality of scaling on the whole range of available scales (2 km - 250 km), but some loss of scaling quality corresponding to the emergence of a scaling break are sometimes visible. This behaviour is not found at the same time or at the same altitude according to the water state and does not necessarily spread to the total water content. It is interpreted with the help of the underlying

  7. A statistical model for porous structure of rocks

    Institute of Scientific and Technical Information of China (English)

    JU Yang; YANG YongMing; SONG ZhenDuo; XU WenJing

    2008-01-01

    The geometric features and the distribution properties of pores in rocks were In-vestigated by means of CT scanning tests of sandstones. The centroidal coordl-nares of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob-ability density functions upon which the random distribution of pore position, dis-tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex-amine the stress distribution, the pattern of element failure and the inoaculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

  8. A statistical model for porous structure of rocks

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The geometric features and the distribution properties of pores in rocks were in- vestigated by means of CT scanning tests of sandstones. The centroidal coordi- nates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob- ability density functions upon which the random distribution of pore position, dis- tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex- amine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.

  9. A Climate Statistics Tool and Data Repository

    Science.gov (United States)

    Wang, J.; Kotamarthi, V. R.; Kuiper, J. A.; Orr, A.

    2017-12-01

    Researchers at Argonne National Laboratory and collaborating organizations have generated regional scale, dynamically downscaled climate model output using Weather Research and Forecasting (WRF) version 3.3.1 at a 12km horizontal spatial resolution over much of North America. The WRF model is driven by boundary conditions obtained from three independent global scale climate models and two different future greenhouse gas emission scenarios, named representative concentration pathways (RCPs). The repository of results has a temporal resolution of three hours for all the simulations, includes more than 50 variables, is stored in Network Common Data Form (NetCDF) files, and the data volume is nearly 600Tb. A condensed 800Gb set of NetCDF files were made for selected variables most useful for climate-related planning, including daily precipitation, relative humidity, solar radiation, maximum temperature, minimum temperature, and wind. The WRF model simulations are conducted for three 10-year time periods (1995-2004, 2045-2054, and 2085-2094), and two future scenarios RCP4.5 and RCP8.5). An open-source tool was coded using Python 2.7.8 and ESRI ArcGIS 10.3.1 programming libraries to parse the NetCDF files, compute summary statistics, and output results as GIS layers. Eight sets of summary statistics were generated as examples for the contiguous U.S. states and much of Alaska, including number of days over 90°F, number of days with a heat index over 90°F, heat waves, monthly and annual precipitation, drought, extreme precipitation, multi-model averages, and model bias. This paper will provide an overview of the project to generate the main and condensed data repositories, describe the Python tool and how to use it, present the GIS results of the computed examples, and discuss some of the ways they can be used for planning. The condensed climate data, Python tool, computed GIS results, and documentation of the work are shared on the Internet.

  10. Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

    Science.gov (United States)

    Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

    2018-02-01

    Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

  11. Data Envelopment Analysis with Uncertain Inputs and Outputs

    Directory of Open Access Journals (Sweden)

    Meilin Wen

    2014-01-01

    Full Text Available Data envelopment analysis (DEA, as a useful management and decision tool, has been widely used since it was first invented by Charnes et al. in 1978. On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. This paper will consider DEA in uncertain environment, thus producing a new model based on uncertain measure. Due to the complexity of the new uncertain DEA model, an equivalent deterministic model is presented. Finally, a numerical example is presented to illustrate the effectiveness of the uncertain DEA model.

  12. (ajst) statistical mechanics model for orientational

    African Journals Online (AJOL)

    Science and Engineering Series Vol. 6, No. 2, pp. 94 - 101. STATISTICAL MECHANICS MODEL FOR ORIENTATIONAL. MOTION OF TWO-DIMENSIONAL RIGID ROTATOR. Malo, J.O. ... there is no translational motion and that they are well separated so .... constant and I is the moment of inertia of a linear rotator. Thus, the ...

  13. Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles

    Science.gov (United States)

    Mini, C.; Hogue, T. S.; Pincetl, S.

    2012-04-01

    Modeling water demand is a complex exercise in the choice of the functional form, techniques and variables to integrate in the model. The goal of the current research is to identify the determinants that control total and outdoor residential water use in semi-arid cities and to utilize that information in the development of statistical and biophysical models that can forecast spatial and temporal urban water use. The City of Los Angeles is unique in its highly diverse socio-demographic, economic and cultural characteristics across neighborhoods, which introduces significant challenges in modeling water use. Increasing climate variability also contributes to uncertainties in water use predictions in urban areas. Monthly individual water use records were acquired from the Los Angeles Department of Water and Power (LADWP) for the 2000 to 2010 period. Study predictors of residential water use include socio-demographic, economic, climate and landscaping variables at the zip code level collected from US Census database. Climate variables are estimated from ground-based observations and calculated at the centroid of each zip code by inverse-distance weighting method. Remotely-sensed products of vegetation biomass and landscape land cover are also utilized. Two linear regression models were developed based on the panel data and variables described: a pooled-OLS regression model and a linear mixed effects model. Both models show income per capita and the percentage of landscape areas in each zip code as being statistically significant predictors. The pooled-OLS model tends to over-estimate higher water use zip codes and both models provide similar RMSE values.Outdoor water use was estimated at the census tract level as the residual between total water use and indoor use. This residual is being compared with the output from a biophysical model including tree and grass cover areas, climate variables and estimates of evapotranspiration at very high spatial resolution. A

  14. Performance modeling, stochastic networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi R

    2013-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

  15. A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

    Directory of Open Access Journals (Sweden)

    Shuai Luo

    2016-02-01

    Full Text Available Bioelectrochemical systems (BES are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

  16. Two statistics for evaluating parameter identifiability and error reduction

    Science.gov (United States)

    Doherty, John; Hunt, Randall J.

    2009-01-01

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

  17. Motor output variability, deafferentation, and putative deficits in kinesthetic reafference in Parkinson's disease.

    Science.gov (United States)

    Torres, Elizabeth B; Cole, Jonathan; Poizner, Howard

    2014-01-01

    Parkinson's disease (PD) is a neurodegenerative disorder defined by motor impairments that include rigidity, systemic slowdown of movement (bradykinesia), postural problems, and tremor. While the progressive decline in motor output functions is well documented, less understood are impairments linked to the continuous kinesthetic sensation emerging from the flow of motions. There is growing evidence in recent years that kinesthetic problems are also part of the symptoms of PD, but objective methods to readily quantify continuously unfolding motions across different contexts have been lacking. Here we present evidence from a deafferented subject (IW) and a new statistical platform that enables new analyses of motor output variability measured as a continuous flow of kinesthetic reafferent input. Systematic increasing similarities between the patterns of motor output variability in IW and the participants with increasing degrees of PD severity suggest potential deficits in kinesthetic sensing in PD. We propose that these deficits may result from persistent, noisy, and random motor patterns as the disorder progresses. The stochastic signatures from the unfolding motions revealed levels of noise in the motor output fluctuations of these patients bound to decrease the kinesthetic signal's bandwidth. The results are interpreted in light of the concept of kinesthetic reafference ( Von Holst and Mittelstaedt, 1950). In this context, noisy motor output variability from voluntary movements in PD leads to a returning stream of noisy afference caused, in turn, by those faulty movements themselves. Faulty efferent output re-enters the CNS as corrupted sensory motor input. We find here that severity level in PD leads to the persistence of such patterns, thus bringing the statistical signatures of the subjects with PD systematically closer to those of the subject without proprioception.

  18. Validation of statistical models for creep rupture by parametric analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)

    2012-01-15

    Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).

  19. Statistical modelling of transcript profiles of differentially regulated genes

    Directory of Open Access Journals (Sweden)

    Sergeant Martin J

    2008-07-01

    Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data

  20. Classification of Specialized Farms Applying Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Zuzana Hloušková

    2017-01-01

    Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

  1. Acceleration transforms and statistical kinetic models

    International Nuclear Information System (INIS)

    LuValle, M.J.; Welsher, T.L.; Svoboda, K.

    1988-01-01

    For a restricted class of problems a mathematical model of microscopic degradation processes, statistical kinetics, is developed and linked through acceleration transforms to the information which can be obtained from a system in which the only observable sign of degradation is sudden and catastrophic failure. The acceleration transforms were developed in accelerated life testing applications as a tool for extrapolating from the observable results of an accelerated life test to the dynamics of the underlying degradation processes. A particular concern of a physicist attempting to interpreted the results of an analysis based on acceleration transforms is determining the physical species involved in the degradation process. These species may be (a) relatively abundant or (b) relatively rare. The main results of this paper are a theorem showing that for an important subclass of statistical kinetic models, acceleration transforms cannot be used to distinguish between cases a and b, and an example showing that in some cases falling outside the restrictions of the theorem, cases a and b can be distinguished by their acceleration transforms

  2. Statistical models describing the energy signature of buildings

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Thavlov, Anders

    2010-01-01

    Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...

  3. Steady State Analysis of LCLC Resonant Converter with Capacitive Output Filter

    Directory of Open Access Journals (Sweden)

    Navid Shafiyi

    2010-01-01

    Full Text Available This paper presents the mathematical analysis and modeling of a 4th order LCLC resonant converter with capacitive output filter in steady-state condition. Due to the nonlinearity of the LCLC resonant circuit with capacitive output filter, the conventional modeling procedure cannot thoroughly describe the behavior of the converter. In this paper, a mathematical model is proposed that can accommodate the absence of the output inductor and predict the converter performance for a wide range of operating conditions. A 2.25KW prototype converter is provided to evaluate the accuracy of the proposed model. Experimental results show that the proposed model can precisely predict the behavior of the converter for a wide range of operating conditions.

  4. STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL

    Directory of Open Access Journals (Sweden)

    Andreea Feraru

    2016-06-01

    Full Text Available This article entitled Statistical Models of Representing Intellectual Capital approaches and analyses the concept of intellectual capital, as well as the main models which can support enterprisers/managers in evaluating and quantifying the advantages of intellectual capital. Most authors examine intellectual capital from a static perspective and focus on the development of its various evaluation models. In this chapter we surveyed the classical static models: Sveiby, Edvisson, Balanced Scorecard, as well as the canonical model of intellectual capital. Among the group of static models for evaluating organisational intellectual capital the canonical model stands out. This model enables the structuring of organisational intellectual capital in: human capital, structural capital and relational capital. Although the model is widely spread, it is a static one and can thus create a series of errors in the process of evaluation, because all the three entities mentioned above are not independent from the viewpoint of their contents, as any logic of structuring complex entities requires.

  5. Statistical mechanics of directed models of polymers in the square lattice

    CERN Document Server

    Rensburg, J V

    2003-01-01

    Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce...

  6. Wavelet transform-vector quantization compression of supercomputer ocean model simulation output

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J N; Brislawn, C M

    1992-11-12

    We describe a new procedure for efficient compression of digital information for storage and transmission purposes. The algorithm involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The new vector quantizer design procedure optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The wavelet-vector quantization method, which originates in digital image compression. is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. In this paper we discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research and at the Los Alamos Advanced Computing Laboratory.

  7. The epistemology of mathematical and statistical modeling: a quiet methodological revolution.

    Science.gov (United States)

    Rodgers, Joseph Lee

    2010-01-01

    A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. Copyrigiht 2009 APA, all rights reserved.

  8. Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions

    Science.gov (United States)

    Santos, João A.; Malheiro, Aureliano C.; Karremann, Melanie K.; Pinto, Joaquim G.

    2011-03-01

    The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO2 may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley.

  9. Establishing statistical models of manufacturing parameters

    International Nuclear Information System (INIS)

    Senevat, J.; Pape, J.L.; Deshayes, J.F.

    1991-01-01

    This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature

  10. CM-DataONE: A Framework for collaborative analysis of climate model output

    Science.gov (United States)

    Xu, Hao; Bai, Yuqi; Li, Sha; Dong, Wenhao; Huang, Wenyu; Xu, Shiming; Lin, Yanluan; Wang, Bin

    2015-04-01

    CM-DataONE is a distributed collaborative analysis framework for climate model data which aims to break through the data access barriers of increasing file size and to accelerate research process. As data size involved in project such as the fifth Coupled Model Intercomparison Project (CMIP5) has reached petabytes, conventional methods for analysis and diagnosis of model outputs have been rather time-consuming and redundant. CM-DataONE is developed for data publishers and researchers from relevant areas. It can enable easy access to distributed data and provide extensible analysis functions based on tools such as NCAR Command Language, NetCDF Operators (NCO) and Climate Data Operators (CDO). CM-DataONE can be easily installed, configured, and maintained. The main web application has two separate parts which communicate with each other through APIs based on HTTP protocol. The analytic server is designed to be installed in each data node while a data portal can be configured anywhere and connect to a nearest node. Functions such as data query, analytic task submission, status monitoring, visualization and product downloading are provided to end users by data portal. Data conform to CMIP5 Model Output Format in each peer node can be scanned by the server and mapped to a global information database. A scheduler included in the server is responsible for task decomposition, distribution and consolidation. Analysis functions are always executed where data locate. Analysis function package included in the server has provided commonly used functions such as EOF analysis, trend analysis and time series. Functions are coupled with data by XML descriptions and can be easily extended. Various types of results can be obtained by users for further studies. This framework has significantly decreased the amount of data to be transmitted and improved efficiency in model intercomparison jobs by supporting online analysis and multi-node collaboration. To end users, data query is

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

    Science.gov (United States)

    Tsaur, Ruey-Chyn

    2015-02-01

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

  12. Output power distributions of terminals in a 3G mobile communication network.

    Science.gov (United States)

    Persson, Tomas; Törnevik, Christer; Larsson, Lars-Eric; Lovén, Jan

    2012-05-01

    The objective of this study was to examine the distribution of the output power of mobile phones and other terminals connected to a 3G network in Sweden. It is well known that 3G terminals can operate with very low output power, particularly for voice calls. Measurements of terminal output power were conducted in the Swedish TeliaSonera 3G network in November 2008 by recording network statistics. In the analysis, discrimination was made between rural, suburban, urban, and dedicated indoor networks. In addition, information about terminal output power was possible to collect separately for voice and data traffic. Information from six different Radio Network Controllers (RNCs) was collected during at least 1 week. In total, more than 800000 h of voice calls were collected and in addition to that a substantial amount of data traffic. The average terminal output power for 3G voice calls was below 1 mW for any environment including rural, urban, and dedicated indoor networks. This is <1% of the maximum available output power. For data applications the average output power was about 6-8 dB higher than for voice calls. For rural areas the output power was about 2 dB higher, on average, than in urban areas. Copyright © 2011 Wiley Periodicals, Inc.

  13. Study of cumulative fatigue damage detection for used parts with nonlinear output frequency response functions based on NARMAX modelling

    Science.gov (United States)

    Huang, Honglan; Mao, Hanying; Mao, Hanling; Zheng, Weixue; Huang, Zhenfeng; Li, Xinxin; Wang, Xianghong

    2017-12-01

    Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlinear output frequency response functions under rectangular pulse excitation, and a based nonlinear output frequency response functions index is introduced to detect the cumulative fatigue damage in used parts. Then, a novel damage detection approach that integrates the NARMAX model and the rectangular pulse is proposed for nonlinear output frequency response functions identification and cumulative fatigue damage detection of used parts. Finally, experimental studies of fatigued plate specimens and used connecting rod parts are conducted to verify the validity of the novel approach. The obtained results reveal that the new approach can detect cumulative fatigue damages of used parts effectively and efficiently and that the various values of the based nonlinear output frequency response functions index can be used to detect the different fatigue damages or working time. Since the proposed new approach can extract nonlinear properties of systems by only a single excitation of the inspected system, it shows great promise for use in remanufacturing engineering applications.

  14. Multi-decadal Variability of the Wind Power Output

    Science.gov (United States)

    Kirchner Bossi, Nicolas; García-Herrera, Ricardo; Prieto, Luis; Trigo, Ricardo M.

    2014-05-01

    The knowledge of the long-term wind power variability is essential to provide a realistic outlook on the power output during the lifetime of a planned wind power project. In this work, the Power Output (Po) of a market wind turbine is simulated with a daily resolution for the period 1871-2009 at two different locations in Spain, one at the Central Iberian Plateau and another at the Gibraltar Strait Area. This is attained through a statistical downscaling of the daily wind conditions. It implements a Greedy Algorithm as classificator of a geostrophic-based wind predictor, which is derived by considering the SLP daily field from the 56 ensemble members of the longest homogeneous reanalysis available (20CR, 1871-2009). For calibration and validation purposes we use 10 years of wind observations (the predictand) at both sites. As a result, a series of 139 annual wind speed Probability Density Functions (PDF) are obtained, with a good performance in terms of wind speed uncertainty reduction (average daily wind speed MAE=1.48 m/s). The obtained centennial series allow to investigate the multi-decadal variability of wind power from different points of view. Significant periodicities around the 25-yr frequency band, as well as long-term linear trends are detected at both locations. In addition, a negative correlation is found between annual Po at both locations, evidencing the differences in the dynamical mechanisms ruling them (and possible complementary behavior). Furthermore, the impact that the three leading large-scale circulation patterns over Iberia (NAO, EA and SCAND) exert over wind power output is evaluated. Results show distinct (and non-stationary) couplings to these forcings depending on the geographical position and season or month. Moreover, significant non-stationary correlations are observed with the slow varying Atlantic Multidecadal Oscillation (AMO) index for both case studies. Finally, an empirical relationship is explored between the annual Po and the

  15. Statistical Modelling of the Soil Dielectric Constant

    Science.gov (United States)

    Usowicz, Boguslaw; Marczewski, Wojciech; Bogdan Usowicz, Jerzy; Lipiec, Jerzy

    2010-05-01

    The dielectric constant of soil is the physical property being very sensitive on water content. It funds several electrical measurement techniques for determining the water content by means of direct (TDR, FDR, and others related to effects of electrical conductance and/or capacitance) and indirect RS (Remote Sensing) methods. The work is devoted to a particular statistical manner of modelling the dielectric constant as the property accounting a wide range of specific soil composition, porosity, and mass density, within the unsaturated water content. Usually, similar models are determined for few particular soil types, and changing the soil type one needs switching the model on another type or to adjust it by parametrization of soil compounds. Therefore, it is difficult comparing and referring results between models. The presented model was developed for a generic representation of soil being a hypothetical mixture of spheres, each representing a soil fraction, in its proper phase state. The model generates a serial-parallel mesh of conductive and capacitive paths, which is analysed for a total conductive or capacitive property. The model was firstly developed to determine the thermal conductivity property, and now it is extended on the dielectric constant by analysing the capacitive mesh. The analysis is provided by statistical means obeying physical laws related to the serial-parallel branching of the representative electrical mesh. Physical relevance of the analysis is established electrically, but the definition of the electrical mesh is controlled statistically by parametrization of compound fractions, by determining the number of representative spheres per unitary volume per fraction, and by determining the number of fractions. That way the model is capable covering properties of nearly all possible soil types, all phase states within recognition of the Lorenz and Knudsen conditions. In effect the model allows on generating a hypothetical representative of

  16. Simulation of Distributed PV Power Output in Oahu Hawaii

    Energy Technology Data Exchange (ETDEWEB)

    Lave, Matthew Samuel [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2016-08-01

    Distributed solar photovoltaic (PV) power generation in Oahu has grown rapidly since 2008. For applications such as determining the value of energy storage, it is important to have PV power output timeseries. Since these timeseries of not typically measured, here we produce simulated distributed PV power output for Oahu. Simulated power output is based on (a) satellite-derived solar irradiance, (b) PV permit data by neighborhood, and (c) population data by census block. Permit and population data was used to model locations of distributed PV, and irradiance data was then used to simulate power output. PV power output simulations are presented by sub-neighborhood polygons, neighborhoods, and for the whole island of Oahu. Summary plots of annual PV energy and a sample week timeseries of power output are shown, and a the files containing the entire timeseries are described.

  17. Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

    Science.gov (United States)

    Qi, D.; Majda, A.

    2017-12-01

    A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

  18. Bayesian models based on test statistics for multiple hypothesis testing problems.

    Science.gov (United States)

    Ji, Yuan; Lu, Yiling; Mills, Gordon B

    2008-04-01

    We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

  19. On-the-fly confluence detection for statistical model checking (extended version)

    NARCIS (Netherlands)

    Hartmanns, Arnd; Timmer, Mark

    Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the

  20. Automatic Derivation of Statistical Data Analysis Algorithms: Planetary Nebulae and Beyond

    OpenAIRE

    Fischer, Bernd; Knuth, Kevin; Hajian, Arsen; Schumann, Johann

    2004-01-01

    AUTOBAYES is a fully automatic program synthesis system for the data analysis domain. Its input is a declarative problem description in form of a statistical model; its output is documented and optimized C/C++ code. The synthesis process relies on the combination of three key techniques. Bayesian networks are used as a compact internal representation mechanism which enables problem decompositions and guides the algorithm derivation. Program schemas are used as independently composable buildin...

  1. The R Commander: A Basic-Statistics Graphical User Interface to R

    Directory of Open Access Journals (Sweden)

    John Fox

    2005-08-01

    Full Text Available Unlike S-PLUS, R does not incorporate a statistical graphical user interface (GUI, but it does include tools for building GUIs. Based on the tcltk package (which furnishes an interface to the Tcl/Tk GUI toolkit, the Rcmdr package provides a basic-statistics graphical user interface to R called the "R Commander." The design objectives of the R Commander were as follows: to support, through an easy-to-use, extensible, cross-platform GUI, the statistical functionality required for a basic-statistics course (though its current functionality has grown to include support for linear and generalized-linear models, and other more advanced features; to make it relatively difficult to do unreasonable things; and to render visible the relationship between choices made in the GUI and the R commands that they generate. The R Commander uses a simple and familiar menu/dialog-box interface. Top-level menus include File, Edit, Data, Statistics, Graphs, Models, Distributions, Tools, and Help, with the complete menu tree given in the paper. Each dialog box includes a Help button, which leads to a relevant help page. Menu and dialog-box selections generate R commands, which are recorded in a script window and are echoed, along with output, to an output window. The script window also provides the ability to edit, enter, and re-execute commands. Error messages, warnings, and some other information appear in a separate messages window. Data sets in the R Commander are simply R data frames, and can be read from attached packages or imported from files. Although several data frames may reside in memory, only one is "active" at any given time. There may also be an active statistical model (e.g., an R lm or glm ob ject. The purpose of this paper is to introduce and describe the use of the R Commander GUI; to describe the design and development of the R Commander; and to explain how the R Commander GUI can be extended. The second part of the paper (following a brief

  2. 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.

  3. SyntEyes KTC: higher order statistical eye model for developing keratoconus.

    Science.gov (United States)

    Rozema, Jos J; Rodriguez, Pablo; Ruiz Hidalgo, Irene; Navarro, Rafael; Tassignon, Marie-José; Koppen, Carina

    2017-05-01

    To present and validate a stochastic eye model for developing keratoconus to e.g. improve optical corrective strategies. This could be particularly useful for researchers that do not have access to original keratoconic data. The Scheimpflug tomography, ocular biometry and wavefront of 145 keratoconic right eyes were collected. These data were processed using principal component analysis for parameter reduction, followed by a multivariate Gaussian fit that produces a stochastic model for keratoconus (SyntEyes KTC). The output of this model is filtered to remove the occasional incorrect topography patterns by either an automatic or manual procedure. Finally, the output of this keratoconus model is matched to that of the original model for normal eyes using the non-corneal biometry to obtain a description of keratoconus development. The synthetic data generated by the model were found to be significantly equal to the original data (non-parametric Mann-Whitney equivalence test; 145/154 passed). The variability of the synthetic data, however, was often significantly less than that of the original data, especially for the higher order Zernike terms of corneal elevation (non-parametric Levene test; p eyes with incorrect topographies. Interpolation between matched pairs of normal and keratoconic SyntEyes appears to provide an adequate model for keratoconus progression. The synthetic data provided by the proposed keratoconus model closely resembles actual clinical data and may be used for a range of research applications when (sufficient) real data is not available. © 2017 The Authors Ophthalmic & Physiological Optics © 2017 The College of Optometrists.

  4. Robust output synchronization of heterogeneous nonlinear agents in uncertain networks.

    Science.gov (United States)

    Yang, Xi; Wan, Fuhua; Tu, Mengchuan; Shen, Guojiang

    2017-11-01

    This paper investigates the global robust output synchronization problem for a class of nonlinear multi-agent systems. In the considered setup, the controlled agents are heterogeneous and with both dynamic and parametric uncertainties, the controllers are incapable of exchanging their internal states with the neighbors, and the communication network among agents is defined by an uncertain simple digraph. The problem is pursued via nonlinear output regulation theory and internal model based design. For each agent, the input-driven filter and the internal model compose the controller, and the decentralized dynamic output feedback control law is derived by using backstepping method and the modified dynamic high-gain technique. The theoretical result is applied to output synchronization problem for uncertain network of Lorenz-type agents. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Topology for Statistical Modeling of Petascale Data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Rojas, Maurice [Texas A & M Univ., College Station, TX (United States)

    2014-07-01

    This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled "Topology for Statistical Modeling of Petascale Data", funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program.

  6. Input-Output Economics : Theory and Applications - Featuring Asian Economies

    NARCIS (Netherlands)

    Ten Raa, T.

    2009-01-01

    Thijs ten Raa, author of the acclaimed text The Economics of Input–Output Analysis, now takes the reader to the forefront of the field. This volume collects and unifies his and his co-authors' research papers on national accounting, Input–Output coefficients, economic theory, dynamic models,

  7. Unit 16 - Output

    OpenAIRE

    Unit 16, CC in GIS; Star, Jeffrey L.

    1990-01-01

    This unit discusses issues related to GIS output, including the different types of output possible and the hardware for producing each. It describes text, graphic and digital data that can be generated by a GIS as well as line printers, dot matrix printers/plotters, pen plotters, optical scanners and cathode ray tubes (CRTs) as technologies for generating the output.

  8. Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

    Science.gov (United States)

    Lam, H K

    2012-02-01

    This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

  9. Analyzing sickness absence with statistical models for survival data

    DEFF Research Database (Denmark)

    Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars

    2007-01-01

    OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...

  10. Towards systematic evaluation of crop model outputs for global land-use models

    Science.gov (United States)

    Leclere, David; Azevedo, Ligia B.; Skalský, Rastislav; Balkovič, Juraj; Havlík, Petr

    2016-04-01

    Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include

  11. A Markovian model of evolving world input-output network.

    Directory of Open Access Journals (Sweden)

    Vahid Moosavi

    Full Text Available The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money.

  12. A Markovian model of evolving world input-output network.

    Science.gov (United States)

    Moosavi, Vahid; Isacchini, Giulio

    2017-01-01

    The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money.

  13. Physics-based statistical model and simulation method of RF propagation in urban environments

    Science.gov (United States)

    Pao, Hsueh-Yuan; Dvorak, Steven L.

    2010-09-14

    A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

  14. Encoding Dissimilarity Data for Statistical Model Building.

    Science.gov (United States)

    Wahba, Grace

    2010-12-01

    We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.

  15. Advanced surrogate model and sensitivity analysis methods for sodium fast reactor accident assessment

    International Nuclear Information System (INIS)

    Marrel, A.; Marie, N.; De Lozzo, M.

    2015-01-01

    Within the framework of the generation IV Sodium Fast Reactors, the safety in case of severe accidents is assessed. From this statement, CEA has developed a new physical tool to model the accident initiated by the Total Instantaneous Blockage (TIB) of a sub-assembly. This TIB simulator depends on many uncertain input parameters. This paper aims at proposing a global methodology combining several advanced statistical techniques in order to perform a global sensitivity analysis of this TIB simulator. The objective is to identify the most influential uncertain inputs for the various TIB outputs involved in the safety analysis. The proposed statistical methodology combining several advanced statistical techniques enables to take into account the constraints on the TIB simulator outputs (positivity constraints) and to deal simultaneously with various outputs. To do this, a space-filling design is used and the corresponding TIB model simulations are performed. Based on this learning sample, an efficient constrained Gaussian process metamodel is fitted on each TIB model outputs. Then, using the metamodels, classical sensitivity analyses are made for each TIB output. Multivariate global sensitivity analyses based on aggregated indices are also performed, providing additional valuable information. Main conclusions on the influence of each uncertain input are derived. - Highlights: • Physical-statistical tool for Sodium Fast Reactors TIB accident. • 27 uncertain parameters (core state, lack of physical knowledge) are highlighted. • Constrained Gaussian process efficiently predicts TIB outputs (safety criteria). • Multivariate sensitivity analyses reveal that three inputs are mainly influential. • The type of corium propagation (thermal or hydrodynamic) is the most influential

  16. Simple classical model for Fano statistics in radiation detectors

    Energy Technology Data Exchange (ETDEWEB)

    Jordan, David V. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)], E-mail: David.Jordan@pnl.gov; Renholds, Andrea S.; Jaffe, John E.; Anderson, Kevin K.; Rene Corrales, L.; Peurrung, Anthony J. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)

    2008-02-01

    A simple classical model that captures the essential statistics of energy partitioning processes involved in the creation of information carriers (ICs) in radiation detectors is presented. The model pictures IC formation from a fixed amount of deposited energy in terms of the statistically analogous process of successively sampling water from a large, finite-volume container ('bathtub') with a small dipping implement ('shot or whiskey glass'). The model exhibits sub-Poisson variance in the distribution of the number of ICs generated (the 'Fano effect'). Elementary statistical analysis of the model clarifies the role of energy conservation in producing the Fano effect and yields Fano's prescription for computing the relative variance of the IC number distribution in terms of the mean and variance of the underlying, single-IC energy distribution. The partitioning model is applied to the development of the impact ionization cascade in semiconductor radiation detectors. It is shown that, in tandem with simple assumptions regarding the distribution of energies required to create an (electron, hole) pair, the model yields an energy-independent Fano factor of 0.083, in accord with the lower end of the range of literature values reported for silicon and high-purity germanium. The utility of this simple picture as a diagnostic tool for guiding or constraining more detailed, 'microscopic' physical models of detector material response to ionizing radiation is discussed.

  17. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  18. Inflation, inflation uncertainty and output growth in the USA

    Science.gov (United States)

    Bhar, Ramprasad; Mallik, Girijasankar

    2010-12-01

    Employing a multivariate EGARCH-M model, this study investigates the effects of inflation uncertainty and growth uncertainty on inflation and output growth in the United States. Our results show that inflation uncertainty has a positive and significant effect on the level of inflation and a negative and significant effect on the output growth. However, output uncertainty has no significant effect on output growth or inflation. The oil price also has a positive and significant effect on inflation. These findings are robust and have been corroborated by use of an impulse response function. These results have important implications for inflation-targeting monetary policy, and the aim of stabilization policy in general.

  19. Study and modeling of changes in volumetric efficiency of helix conveyors at different rotational speeds and inclination angels by ANFIS and statistical methods

    Directory of Open Access Journals (Sweden)

    A Zareei

    2017-05-01

    Full Text Available Introduction Spiral conveyors effectively carry solid masses as free or partly free flow of materials. They create good throughput and they are the perfect solution to solve the problems of transport, due to their simple structure, high efficiency and low maintenance costs. This study aims to investigate the performance characteristics of conveyors as function of auger diameter, rotational speed and handling inclination angle. The performance characteristic was investigated according to volumetric efficiency. In another words, the purpose of this study was obtaining a suitable model for volumetric efficiency changes of steep auger to transfer agricultural products. Three different diameters of auger, five levels of rotational speed and three slope angles were used to investigate the effects of changes in these parameters on volumetric efficiency of auger. The used method is novel in this area and the results show that performance by ANFIS models is much better than common statistical models. Materials and Methods The experiments were conducted in Department of Mechanical Engineering of Agricultural Machinery in Urmia University. In this study, SAYOS cultivar of wheat was used. This cultivar of wheat had hard seeds and the humidity was 12% (based on wet. Before testing, all foreign material was separated from the wheat such as stone, dust, plant residues and green seeds. Bulk density of wheat was 790 kg m-3. The auger shaft of the spiral conveyor was received its rotational force through belt and electric motor and its rotation leading to transfer the product to the output. In this study, three conveyors at diameters of 13, 17.5, and 22.5 cm, five levels of rotational speed at 100, 200, 300, 400, and 500 rpm and three handling angles of 10, 20, and 30º were tested. Adaptive Nero-fuzzy inference system (ANFIS is the combination of fuzzy systems and artificial neural network, so it has both benefits. This system is useful to solve the complex non

  20. Statistical learning modeling method for space debris photometric measurement

    Science.gov (United States)

    Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

    2016-03-01

    Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

  1. A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa

    Directory of Open Access Journals (Sweden)

    Xi Chen

    2015-04-01

    Full Text Available The present study analyses the new Visible Infrared Imaging Radiometer Suite (VIIRS lights data to determine whether it can provide more accurate proxies for socioeconomic data in areas with poor quality data than proxies based on stable lights. Our analysis indicates that VIIRS lights are a promising supplementary source for standard measures on population and economic output at a small scale, especially for low population and economic density areas in Africa. The current analysis also suggests that in comparison to stable lights generated by the DMSP-OLS system, data generated by the VIIRS system provide more information to estimate population than output index. However, further analysis and formal statistical models are needed to evaluate the usefulness of VIIRS lights versus other lights products. With more advanced methods, there is also a potential to generate a synthetic index by combining different lights products to produce a better proxy measure for other indexes.

  2. Predicting Output Power for Nearshore Wave Energy Harvesting

    Directory of Open Access Journals (Sweden)

    Henock Mamo Deberneh

    2018-04-01

    Full Text Available Energy harvested from a Wave Energy Converter (WEC varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them.

  3. GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science

    Science.gov (United States)

    Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.

    2018-03-01

    We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.

  4. Experimental, statistical, and biological models of radon carcinogenesis

    International Nuclear Information System (INIS)

    Cross, F.T.

    1991-09-01

    Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig

  5. Nonlinear observer output-feedback MPC treatment scheduling for HIV

    Directory of Open Access Journals (Sweden)

    Zurakowski Ryan

    2011-05-01

    Full Text Available Abstract Background Mathematical models of the immune response to the Human Immunodeficiency Virus demonstrate the potential for dynamic schedules of Highly Active Anti-Retroviral Therapy to enhance Cytotoxic Lymphocyte-mediated control of HIV infection. Methods In previous work we have developed a model predictive control (MPC based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated output-feedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We use Monte-Carlo approaches to test robustness of the algorithm. Results The nonlinear observer shows robust state tracking while preserving state positivity both for continuous and discrete measurements. The integrated output-feedback MPC algorithm stabilizes the desired steady-state. Monte-Carlo testing shows significant robustness to modeling error, with 90% success rates in stabilizing the desired steady-state with 15% variance from nominal on all model parameters. Conclusions The possibility of enhancing immune responsiveness to HIV through dynamic scheduling of treatment is exciting. Output-feedback Model Predictive Control is uniquely well-suited to solutions of these types of problems. The unique constraints of state positivity and very slow sampling are addressable by using a special-purpose nonlinear state estimator, as described in this paper. This shows the possibility of using output-feedback MPC-based algorithms for this purpose.

  6. The novel application of Benford's second order analysis for monitoring radiation output in interventional radiology.

    Science.gov (United States)

    Cournane, S; Sheehy, N; Cooke, J

    2014-06-01

    Benford's law is an empirical observation which predicts the expected frequency of digits in naturally occurring datasets spanning multiple orders of magnitude, with the law having been most successfully applied as an audit tool in accountancy. This study investigated the sensitivity of the technique in identifying system output changes using simulated changes in interventional radiology Dose-Area-Product (DAP) data, with any deviations from Benford's distribution identified using z-statistics. The radiation output for interventional radiology X-ray equipment is monitored annually during quality control testing; however, for a considerable portion of the year an increased output of the system, potentially caused by engineering adjustments or spontaneous system faults may go unnoticed, leading to a potential increase in the radiation dose to patients. In normal operation recorded examination radiation outputs vary over multiple orders of magnitude rendering the application of normal statistics ineffective for detecting systematic changes in the output. In this work, the annual DAP datasets complied with Benford's first order law for first, second and combinations of the first and second digits. Further, a continuous 'rolling' second order technique was devised for trending simulated changes over shorter timescales. This distribution analysis, the first employment of the method for radiation output trending, detected significant changes simulated on the original data, proving the technique useful in this case. The potential is demonstrated for implementation of this novel analysis for monitoring and identifying change in suitable datasets for the purpose of system process control. Copyright © 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  7. A statistical model for instable thermodynamical systems

    International Nuclear Information System (INIS)

    Sommer, Jens-Uwe

    2003-01-01

    A generic model is presented for statistical systems which display thermodynamic features in contrast to our everyday experience, such as infinite and negative heat capacities. Such system are instable in terms of classical equilibrium thermodynamics. Using our statistical model, we are able to investigate states of instable systems which are undefined in the framework of equilibrium thermodynamics. We show that a region of negative heat capacity in the adiabatic environment, leads to a first order like phase transition when the system is coupled to a heat reservoir. This phase transition takes place without a phase coexistence. Nevertheless, all intermediate states are stable due to fluctuations. When two instable system are brought in thermal contact, the temperature of the composed system is lower than the minimum temperature of the individual systems. Generally, the equilibrium states of instable system cannot be simply decomposed into equilibrium states of the individual systems. The properties of instable system depend on the environment, ensemble equivalence is broken

  8. Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

    Science.gov (United States)

    Zhang, Yue; Fu, Kun; Sun, Xian; Xu, Guangluan; Wang, Hongqi

    2016-06-01

    The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

  9. MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2016-06-01

    Full Text Available The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

  10. Statistical Modelling of Resonant Cross Section Structure in URR, Model of the Characteristic Function

    International Nuclear Information System (INIS)

    Koyumdjieva, N.

    2006-01-01

    A statistical model for the resonant cross section structure in the Unresolved Resonance Region has been developed in the framework of the R-matrix formalism in Reich Moore approach with effective accounting of the resonance parameters fluctuations. The model uses only the average resonance parameters and can be effectively applied for analyses of cross sections functional, averaged over many resonances. Those are cross section moments, transmission and self-indication functions measured through thick sample. In this statistical model the resonant cross sections structure is accepted to be periodic and the R-matrix is a function of ε=E/D with period 0≤ε≤N; R nc (ε)=π/2√(S n *S c )1/NΣ(i=1,N)(β in *β ic *ctg[π(ε i - = ε-iS i )/N]; Here S n ,S c ,S i is respectively neutron strength function, strength function for fission or inelastic channel and strength function for radiative capture, N is the number of resonances (ε i ,β i ) that obey the statistic of Porter-Thomas and Wigner's one. The simple case of this statistical model concerns the resonant cross section structure for non-fissile nuclei under the threshold for inelastic scattering - the model of the characteristic function with HARFOR program. In the above model some improvements of calculation of the phases and logarithmic derivatives of neutron channels have been done. In the parameterization we use the free parameter R l ∞ , which accounts the influence of long-distant resonances. The above scheme for statistical modelling of the resonant cross section structure has been applied for evaluation of experimental data for total, capture and inelastic cross sections for 232 Th in the URR (4-150) keV and also the transmission and self-indication functions in (4-175) keV. The set of evaluated average resonance parameters have been obtained. The evaluated average resonance parameters in the URR are consistent with those in the Resolved Resonance Region (CRP for Th-U cycle, Vienna, 2006

  11. Statistical fluctuations of an ocean surface inferred from shoes and ships

    Science.gov (United States)

    Lerche, Ian; Maubeuge, Frédéric

    1995-12-01

    This paper shows that it is possible to roughly estimate some ocean properties using simple time-dependent statistical models of ocean fluctuations. Based on a real incident, the loss by a vessel of a Nike shoes container in the North Pacific Ocean, a statistical model was tested on data sets consisting of the Nike shoes found by beachcombers a few months later. This statistical treatment of the shoes' motion allows one to infer velocity trends of the Pacific Ocean, together with their fluctuation strengths. The idea is to suppose that there is a mean bulk flow speed that can depend on location on the ocean surface and time. The fluctuations of the surface flow speed are then treated as statistically random. The distribution of shoes is described in space and time using Markov probability processes related to the mean and fluctuating ocean properties. The aim of the exercise is to provide some of the properties of the Pacific Ocean that are otherwise calculated using a sophisticated numerical model, OSCURS, where numerous data are needed. Relevant quantities are sharply estimated, which can be useful to (1) constrain output results from OSCURS computations, and (2) elucidate the behavior patterns of ocean flow characteristics on long time scales.

  12. Statistical models for expert judgement and wear prediction

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1994-01-01

    This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)

  13. Security of statistical data bases: invasion of privacy through attribute correlational modeling

    Energy Technology Data Exchange (ETDEWEB)

    Palley, M.A.

    1985-01-01

    This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.

  14. A no extensive statistical model for the nucleon structure function

    International Nuclear Information System (INIS)

    Trevisan, Luis A.; Mirez, Carlos

    2013-01-01

    We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.

  15. PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool

    KAUST Repository

    AlTurki, Musab

    2011-01-01

    Statistical model checking is an attractive formal analysis method for probabilistic systems such as, for example, cyber-physical systems which are often probabilistic in nature. This paper is about drastically increasing the scalability of statistical model checking, and making such scalability of analysis available to tools like Maude, where probabilistic systems can be specified at a high level as probabilistic rewrite theories. It presents PVeStA, an extension and parallelization of the VeStA statistical model checking tool [10]. PVeStA supports statistical model checking of probabilistic real-time systems specified as either: (i) discrete or continuous Markov Chains; or (ii) probabilistic rewrite theories in Maude. Furthermore, the properties that it can model check can be expressed in either: (i) PCTL/CSL, or (ii) the QuaTEx quantitative temporal logic. As our experiments show, the performance gains obtained from parallelization can be very high. © 2011 Springer-Verlag.

  16. Input-output supervisor

    International Nuclear Information System (INIS)

    Dupuy, R.

    1970-01-01

    The input-output supervisor is the program which monitors the flow of informations between core storage and peripheral equipments of a computer. This work is composed of three parts: 1 - Study of a generalized input-output supervisor. With sample modifications it looks like most of input-output supervisors which are running now on computers. 2 - Application of this theory on a magnetic drum. 3 - Hardware requirement for time-sharing. (author) [fr

  17. User manual for Blossom statistical package for R

    Science.gov (United States)

    Talbert, Marian; Cade, Brian S.

    2005-01-01

    Blossom is an R package with functions for making statistical comparisons with distance-function based permutation tests developed by P.W. Mielke, Jr. and colleagues at Colorado State University (Mielke and Berry, 2001) and for testing parameters estimated in linear models with permutation procedures developed by B. S. Cade and colleagues at the Fort Collins Science Center, U.S. Geological Survey. This manual is intended to provide identical documentation of the statistical methods and interpretations as the manual by Cade and Richards (2005) does for the original Fortran program, but with changes made with respect to command inputs and outputs to reflect the new implementation as a package for R (R Development Core Team, 2012). This implementation in R has allowed for numerous improvements not supported by the Cade and Richards (2005) Fortran implementation, including use of categorical predictor variables in most routines.

  18. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors

    International Nuclear Information System (INIS)

    Liu, Xiuli; Moreno, Blanca; García, Ana Salomé

    2016-01-01

    A combined forecast of Grey forecasting method and neural network back propagation model, which is called Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO model), is proposed. A real case of energy consumption forecast is used to validate the effectiveness of the proposed model. The GNF-IO model predicts coal, crude oil, natural gas, renewable and nuclear primary energy consumption volumes by Spain's 36 sub-sectors from 2010 to 2015 according to three different GDP growth scenarios (optimistic, baseline and pessimistic). Model test shows that the proposed model has higher simulation and forecasting accuracy on energy consumption than Grey models separately and other combination methods. The forecasts indicate that the primary energies as coal, crude oil and natural gas will represent on average the 83.6% percent of the total of primary energy consumption, raising concerns about security of supply and energy cost and adding risk for some industrial production processes. Thus, Spanish industry must speed up its transition to an energy-efficiency economy, achieving a cost reduction and increase in the level of self-supply. - Highlights: • Forecasting System Using Grey Models combined with Input-Output Models is proposed. • Primary energy consumption in Spain is used to validate the model. • The grey-based combined model has good forecasting performance. • Natural gas will represent the majority of the total of primary energy consumption. • Concerns about security of supply, energy cost and industry competitiveness are raised.

  19. Estimation of international output-energy relation. Effects of alternative output measures

    International Nuclear Information System (INIS)

    Shrestha, R.M.

    2000-01-01

    This paper analyzes the output-energy relationship with alternative measures of output and energy. Our analysis rejects the hypothesis of non-diminishing returns to energy consumption when GDP at purchasing power parities is used as the output measure unlike the case with GNP at market exchange rates. This finding also holds when energy input includes the usage of both commercial and traditional fuels. 13 refs

  20. Inclusion of temperature dependence of fission barriers in statistical model calculations

    International Nuclear Information System (INIS)

    Newton, J.O.; Popescu, D.G.; Leigh, J.R.

    1990-08-01

    The temperature dependence of fission barriers has been interpolated from the results of recent theoretical calculations and included in the statistical model code PACE2. It is shown that the inclusion of temperature dependence causes significant changes to the values of the statistical model parameters deduced from fits to experimental data. 21 refs., 2 figs

  1. A Census of Statistics Requirements at U.S. Journalism Programs and a Model for a "Statistics for Journalism" Course

    Science.gov (United States)

    Martin, Justin D.

    2017-01-01

    This essay presents data from a census of statistics requirements and offerings at all 4-year journalism programs in the United States (N = 369) and proposes a model of a potential course in statistics for journalism majors. The author proposes that three philosophies underlie a statistics course for journalism students. Such a course should (a)…

  2. Geochemical modelling of CO2-water-rock interactions for carbon storage : data requirements and outputs

    International Nuclear Information System (INIS)

    Kirste, D.

    2008-01-01

    A geochemical model was used to predict the short-term and long-term behaviour of carbon dioxide (CO 2 ), formation water, and reservoir mineralogy at a carbon sequestration site. Data requirements for the geochemical model included detailed mineral petrography; formation water chemistry; thermodynamic and kinetic data for mineral phases; and rock and reservoir physical characteristics. The model was used to determine the types of outputs expected for potential CO 2 storage sites and natural analogues. Reaction path modelling was conducted to determine the total reactivity or CO 2 storage capability of the rock by applying static equilibrium and kinetic simulations. Potential product phases were identified using the modelling technique, which also enabled the identification of the chemical evolution of the system. Results of the modelling study demonstrated that changes in porosity and permeability over time should be considered during the site selection process.

  3. Central Limit Theorem for Exponentially Quasi-local Statistics of Spin Models on Cayley Graphs

    Science.gov (United States)

    Reddy, Tulasi Ram; Vadlamani, Sreekar; Yogeshwaran, D.

    2018-04-01

    Central limit theorems for linear statistics of lattice random fields (including spin models) are usually proven under suitable mixing conditions or quasi-associativity. Many interesting examples of spin models do not satisfy mixing conditions, and on the other hand, it does not seem easy to show central limit theorem for local statistics via quasi-associativity. In this work, we prove general central limit theorems for local statistics and exponentially quasi-local statistics of spin models on discrete Cayley graphs with polynomial growth. Further, we supplement these results by proving similar central limit theorems for random fields on discrete Cayley graphs taking values in a countable space, but under the stronger assumptions of α -mixing (for local statistics) and exponential α -mixing (for exponentially quasi-local statistics). All our central limit theorems assume a suitable variance lower bound like many others in the literature. We illustrate our general central limit theorem with specific examples of lattice spin models and statistics arising in computational topology, statistical physics and random networks. Examples of clustering spin models include quasi-associated spin models with fast decaying covariances like the off-critical Ising model, level sets of Gaussian random fields with fast decaying covariances like the massive Gaussian free field and determinantal point processes with fast decaying kernels. Examples of local statistics include intrinsic volumes, face counts, component counts of random cubical complexes while exponentially quasi-local statistics include nearest neighbour distances in spin models and Betti numbers of sub-critical random cubical complexes.

  4. Structural reliability in context of statistical uncertainties and modelling discrepancies

    International Nuclear Information System (INIS)

    Pendola, Maurice

    2000-01-01

    Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr

  5. Artificial Neural Networks to Predict the Power Output of a PV Panel

    Directory of Open Access Journals (Sweden)

    Valerio Lo Brano

    2014-01-01

    Full Text Available The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs for the power energy output forecasting of photovoltaic (PV modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP, a recursive neural network (RNN, and a gamma memory (GM trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

  6. Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

    Science.gov (United States)

    Nishino, Ko; Lombardi, Stephen

    2011-01-01

    We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

  7. Statistical identification of effective input variables

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1982-09-01

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

  8. Statistical approach for selection of regression model during validation of bioanalytical method

    Directory of Open Access Journals (Sweden)

    Natalija Nakov

    2014-06-01

    Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

  9. ROBUST CONTROL ALGORITHM FOR MULTIVARIABLE PLANTS WITH QUANTIZED OUTPUT

    Directory of Open Access Journals (Sweden)

    A. A. Margun

    2017-01-01

    Full Text Available The paper deals with robust output control algorithm for multivariable plants under disturbances. A plant is described by the system of linear differential equations with known relative degrees. Plant parameters are unknown but belong to the known closed bounded set. Plant state vector is unmeasured. Plant output is measured only via static quantizer. Control system algorithm is based on the high gain feedback method. Developed controller provides exponential convergence of tracking error to the bounded area. The area bounds depend on quantizer parameters and the value of external disturbances. Experimental approbation of the proposed control algorithm is performed with the use of Twin Rotor MIMO System laboratory bench. This bench is a helicopter like model with two degrees of freedom (pitch and yaw. DC motors are used as actuators. The output signals are measured via optical encoders. Mathematical model of laboratory bench is obtained. Proposed algorithm was compared with proportional - integral – differential controller in conditions of output quantization. Obtained results have confirmed the efficiency of proposed controller.

  10. Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

    Science.gov (United States)

    Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

    2017-07-01

    In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

  11. SIMON. A computer program for reliability and statistical analysis using Monte Carlo simulation. Program description and manual

    International Nuclear Information System (INIS)

    Kongsoe, H.E.; Lauridsen, K.

    1993-09-01

    SIMON is a program for calculation of reliability and statistical analysis. The program is of the Monte Carlo type, and it is designed with high flexibility, and has a large potential for application to complex problems like reliability analyses of very large systems and of systems, where complex modelling or knowledge of special details are required. Examples of application of the program, including input and output, for reliability and statistical analysis are presented. (au) (3 tabs., 3 ills., 5 refs.)

  12. Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.

    Science.gov (United States)

    MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C

    2018-03-29

    This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  13. Simulation model structure numerically robust to changes in magnitude and combination of input and output variables

    DEFF Research Database (Denmark)

    Rasmussen, Bjarne D.; Jakobsen, Arne

    1999-01-01

    Mathematical models of refrigeration systems are often based on a coupling of component models forming a “closed loop” type of system model. In these models the coupling structure of the component models represents the actual flow path of refrigerant in the system. Very often numerical...... instabilities prevent the practical use of such a system model for more than one input/output combination and for other magnitudes of refrigerating capacities.A higher numerical robustness of system models can be achieved by making a model for the refrigeration cycle the core of the system model and by using...... variables with narrow definition intervals for the exchange of information between the cycle model and the component models.The advantages of the cycle-oriented method are illustrated by an example showing the refrigeration cycle similarities between two very different refrigeration systems....

  14. Application of statistical and dynamics models for snow avalanche hazard assessment in mountain regions of Russia

    Science.gov (United States)

    Turchaninova, A.

    2012-04-01

    The estimation of extreme avalanche runout distances, flow velocities, impact pressures and volumes is an essential part of snow engineering in mountain regions of Russia. It implies the avalanche hazard assessment and mapping. Russian guidelines accept the application of different avalanche models as well as approaches for the estimation of model input parameters. Consequently different teams of engineers in Russia apply various dynamics and statistical models for engineering practice. However it gives more freedom to avalanche practitioners and experts but causes lots of uncertainties in case of serious limitations of avalanche models. We discuss these problems by presenting the application results of different well known and widely used statistical (developed in Russia) and avalanche dynamics models for several avalanche test sites in the Khibini Mountains (The Kola Peninsula) and the Caucasus. The most accurate and well-documented data from different powder and wet, big rare and small frequent snow avalanche events is collected from 1960th till today in the Khibini Mountains by the Avalanche Safety Center of "Apatit". This data was digitized and is available for use and analysis. Then the detailed digital avalanche database (GIS) was created for the first time. It contains contours of observed avalanches (ESRI shapes, more than 50 years of observations), DEMs, remote sensing data, description of snow pits, photos etc. Thus, the Russian avalanche data is a unique source of information for understanding of an avalanche flow rheology and the future development and calibration of the avalanche dynamics models. GIS database was used to analyze model input parameters and to calibrate and verify avalanche models. Regarding extreme dynamic parameters the outputs using different models can differ significantly. This is unacceptable for the engineering purposes in case of the absence of the well-defined guidelines in Russia. The frequency curves for the runout distance

  15. Motor output variability, deafferentation, and putative deficits in kinesthetic reafference in Parkinson’s disease

    Science.gov (United States)

    Torres, Elizabeth B.; Cole, Jonathan; Poizner, Howard

    2014-01-01

    Parkinson’s disease (PD) is a neurodegenerative disorder defined by motor impairments that include rigidity, systemic slowdown of movement (bradykinesia), postural problems, and tremor. While the progressive decline in motor output functions is well documented, less understood are impairments linked to the continuous kinesthetic sensation emerging from the flow of motions. There is growing evidence in recent years that kinesthetic problems are also part of the symptoms of PD, but objective methods to readily quantify continuously unfolding motions across different contexts have been lacking. Here we present evidence from a deafferented subject (IW) and a new statistical platform that enables new analyses of motor output variability measured as a continuous flow of kinesthetic reafferent input. Systematic increasing similarities between the patterns of motor output variability in IW and the participants with increasing degrees of PD severity suggest potential deficits in kinesthetic sensing in PD. We propose that these deficits may result from persistent, noisy, and random motor patterns as the disorder progresses. The stochastic signatures from the unfolding motions revealed levels of noise in the motor output fluctuations of these patients bound to decrease the kinesthetic signal’s bandwidth. The results are interpreted in light of the concept of kinesthetic reafference ( Von Holst and Mittelstaedt, 1950). In this context, noisy motor output variability from voluntary movements in PD leads to a returning stream of noisy afference caused, in turn, by those faulty movements themselves. Faulty efferent output re-enters the CNS as corrupted sensory motor input. We find here that severity level in PD leads to the persistence of such patterns, thus bringing the statistical signatures of the subjects with PD systematically closer to those of the subject without proprioception. PMID:25374524

  16. Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

    DEFF Research Database (Denmark)

    Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

    2006-01-01

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

  17. Bayesian statistic methods and theri application in probabilistic simulation models

    Directory of Open Access Journals (Sweden)

    Sergio Iannazzo

    2007-03-01

    Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

  18. Statistical modelling for recurrent events: an application to sports injuries.

    Science.gov (United States)

    Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F

    2014-09-01

    Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  19. The GNASH preequilibrium-statistical nuclear model code

    International Nuclear Information System (INIS)

    Arthur, E. D.

    1988-01-01

    The following report is based on materials presented in a series of lectures at the International Center for Theoretical Physics, Trieste, which were designed to describe the GNASH preequilibrium statistical model code and its use. An overview is provided of the code with emphasis upon code's calculational capabilities and the theoretical models that have been implemented in it. Two sample problems are discussed, the first dealing with neutron reactions on 58 Ni. the second illustrates the fission model capabilities implemented in the code and involves n + 235 U reactions. Finally a description is provided of current theoretical model and code development underway. Examples of calculated results using these new capabilities are also given. 19 refs., 17 figs., 3 tabs

  20. Development of a statistical model for cervical cancer cell death with irreversible electroporation in vitro.

    Science.gov (United States)

    Yang, Yongji; Moser, Michael A J; Zhang, Edwin; Zhang, Wenjun; Zhang, Bing

    2018-01-01

    The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric

  1. Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model

    International Nuclear Information System (INIS)

    Zhu Qin; Peng Xizhe; Wu Kaiya

    2012-01-01

    Based on the input–output model and the comparable price input–output tables, the current paper investigates the indirect carbon emissions from residential consumption in China in 1992–2005, and examines the impacts on the emissions using the structural decomposition method. The results demonstrate that the rise of the residential consumption level played a dominant role in the growth of residential indirect emissions. The persistent decline of the carbon emission intensity of industrial sectors presented a significant negative effect on the emissions. The change in the intermediate demand of industrial sectors resulted in an overall positive effect, except in the initial years. The increase in population prompted the indirect emissions to a certain extent; however, population size is no longer the main reason for the growth of the emissions. The change in the consumption structure showed a weak positive effect, demonstrating the importance for China to control and slow down the increase in the emissions while in the process of optimizing the residential consumption structure. The results imply that the means for restructuring the economy and improving efficiency, rather than for lowering the consumption scale, should be adopted by China to achieve the targets of energy conservation and emission reduction. - Highlights: ► We build the input–output model of indirect carbon emissions from residential consumption. ► We calculate the indirect emissions using the comparable price input–output tables. ► We examine the impacts on the indirect emissions using the structural decomposition method. ► The change in the consumption structure showed a weak positive effect on the emissions. ► China's population size is no longer the main reason for the growth of the emissions.

  2. Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

    DEFF Research Database (Denmark)

    ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto

    2015-01-01

    We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...

  3. 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)

  4. Computing Models of M-type Host Stars and their Panchromatic Spectral Output

    Science.gov (United States)

    Linsky, Jeffrey; Tilipman, Dennis; France, Kevin

    2018-06-01

    We have begun a program of computing state-of-the-art model atmospheres from the photospheres to the coronae of M stars that are the host stars of known exoplanets. For each model we are computing the emergent radiation at all wavelengths that are critical for assessingphotochemistry and mass-loss from exoplanet atmospheres. In particular, we are computing the stellar extreme ultraviolet radiation that drives hydrodynamic mass loss from exoplanet atmospheres and is essential for determing whether an exoplanet is habitable. The model atmospheres are computed with the SSRPM radiative transfer/statistical equilibrium code developed by Dr. Juan Fontenla. The code solves for the non-LTE statistical equilibrium populations of 18,538 levels of 52 atomic and ion species and computes the radiation from all species (435,986 spectral lines) and about 20,000,000 spectral lines of 20 diatomic species.The first model computed in this program was for the modestly active M1.5 V star GJ 832 by Fontenla et al. (ApJ 830, 152 (2016)). We will report on a preliminary model for the more active M5 V star GJ 876 and compare this model and its emergent spectrum with GJ 832. In the future, we will compute and intercompare semi-empirical models and spectra for all of the stars observed with the HST MUSCLES Treasury Survey, the Mega-MUSCLES Treasury Survey, and additional stars including Proxima Cen and Trappist-1.This multiyear theory program is supported by a grant from the Space Telescope Science Institute.

  5. Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

    Science.gov (United States)

    Bagherpoor, H M; Salmasi, Farzad R

    2015-07-01

    In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  6. The Development of On-Line Statistics Program for Radiation Oncology

    International Nuclear Information System (INIS)

    Kim, Yoon Jong; Lee, Dong Hoon; Ji, Young Hoon; Lee, Dong Han; Jo, Chul Ku; Kim, Mi Sook; Ru, Sung Rul; Hong, Seung Hong

    2001-01-01

    Purpose : By developing on-line statistics program to record the information of radiation oncology to share the information with internet. It is possible to supply basic reference data for administrative plans to improve radiation oncology. Materials and methods : The information of radiation oncology statistics had been collected by paper forms about 52 hospitals in the past. Now, we can input the data by internet web browsers. The statistics program used windows NT 4.0 operation system, Internet Information Server 4.0 (IIS4.0) as a web server and the Microsoft Access MDB. We used Structured Query Language (SQL), Visual Basic, VBScript and JAVAScript to display the statistics according to years and hospitals. Results : This program shows present conditions about man power, research, therapy machines, technic, brachytherapy, clinic statistics, radiation safety management, institution, quality assurance and radioisotopes in radiation oncology department. The database consists of 38 inputs and 6 outputs windows. Statistical output windows can be increased continuously according to user need. Conclusion : We have developed statistics program to process all of the data in department of radiation oncology for reference information. Users easily could input the data by internet web browsers and share the information

  7. The Statistical Modeling of the Trends Concerning the Romanian Population

    Directory of Open Access Journals (Sweden)

    Gabriela OPAIT

    2014-11-01

    Full Text Available This paper reflects the statistical modeling concerning the resident population in Romania, respectively the total of the romanian population, through by means of the „Least Squares Method”. Any country it develops by increasing of the population, respectively of the workforce, which is a factor of influence for the growth of the Gross Domestic Product (G.D.P.. The „Least Squares Method” represents a statistical technique for to determine the trend line of the best fit concerning a model.

  8. Research and development statistics 2001

    CERN Document Server

    2002-01-01

    This publication provides recent basic statistics on the resources devoted to R&D in OECD countries. The statistical series are presented for the last seven years for which data are available and cover expenditure by source of funds and type of costs; personnel by occupation and/or level of qualification; both at the national level by performance sector, for enterprises by industry, and for higher education by field of science. The publication also provides information on the output of science and technology (S&T) activities relating to the technology balance of payments.

  9. Sound statistical model checking for MDP using partial order and confluence reduction

    NARCIS (Netherlands)

    Hartmanns, Arnd; Timmer, Mark

    Statistical model checking (SMC) is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can in general only provide sound

  10. Statistical modeling to support power system planning

    Science.gov (United States)

    Staid, Andrea

    This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today's power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate

  11. Output Error Method for Tiltrotor Unstable in Hover

    Directory of Open Access Journals (Sweden)

    Lichota Piotr

    2017-03-01

    Full Text Available This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

  12. Efficient Parallel Statistical Model Checking of Biochemical Networks

    Directory of Open Access Journals (Sweden)

    Paolo Ballarini

    2009-12-01

    Full Text Available We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.

  13. A Model of Statistics Performance Based on Achievement Goal Theory.

    Science.gov (United States)

    Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

    2003-01-01

    Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

  14. Auxetic piezoelectric energy harvesters for increased electric power output

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available This letter presents a piezoelectric bimorph with auxetic (negative Poisson’s ratio behaviors for increased power output in vibration energy harvesting. The piezoelectric bimorph comprises a 2D auxetic substrate sandwiched between two piezoelectric layers. The auxetic substrate is capable of introducing auxetic behaviors and thus increasing the transverse stress in the piezoelectric layers when the bimorph is subjected to a longitudinal stretching load. As a result, both 31- and 32-modes are simultaneously exploited to generate electric power, leading to an increased power output. The increasing power output principle was theoretically analyzed and verified by finite element (FE modelling. The FE modelling results showed that the auxetic substrate can increase the transverse stress of a bimorph by 16.7 times. The average power generated by the auxetic bimorph is 2.76 times of that generated by a conventional bimorph.

  15. Sensitivity analysis of model output - a step towards robust safety indicators?

    International Nuclear Information System (INIS)

    Broed, R.; Pereira, A.; Moberg, L.

    2004-01-01

    The protection of the environment from ionising radiation challenges the radioecological community with the issue of harmonising disparate safety indicators. These indicators should preferably cover the whole spectrum of model predictions on chemo-toxic and radiation impact of contaminants. In question is not only the protection of man and biota but also of abiotic systems. In many cases modelling will constitute the basis for an evaluation of potential impact. It is recognised that uncertainty and sensitivity analysis of model output will play an important role in the 'construction' of safety indicators that are robust, reliable and easy to explain to all groups of stakeholders including the general public. However, environmental models of transport of radionuclides have some extreme characteristics. They are, a) complex, b) non-linear, c) include a huge number of input parameters, d) input parameters are associated with large or very large uncertainties, e) parameters are often correlated to each other, f) uncertainties other than parameter-driven may be present in the modelling system, g) space variability and time-dependence of parameters are present, h) model predictions may cover geological time scales. Consequently, uncertainty and sensitivity analysis are non-trivial tasks, challenging the decision-maker when it comes to the interpretation of safety indicators or the application of regulatory criteria. In this work we use the IAEA model ISAM, to make a set of Monte Carlo calculations. The ISAM model includes several nuclides and decay chains, many compartments and variable parameters covering the range of nuclide migration pathways from the near field to the biosphere. The goal of our calculations is to make a global sensitivity analysis. After extracting the non-influential parameters, the M.C. calculations are repeated with those parameters frozen. Reducing the number of parameters to a few ones will simplify the interpretation of the results and the use

  16. A new method to determine the number of experimental data using statistical modeling methods

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Jung-Ho; Kang, Young-Jin; Lim, O-Kaung; Noh, Yoojeong [Pusan National University, Busan (Korea, Republic of)

    2017-06-15

    For analyzing the statistical performance of physical systems, statistical characteristics of physical parameters such as material properties need to be estimated by collecting experimental data. For accurate statistical modeling, many such experiments may be required, but data are usually quite limited owing to the cost and time constraints of experiments. In this study, a new method for determining a rea- sonable number of experimental data is proposed using an area metric, after obtaining statistical models using the information on the underlying distribution, the Sequential statistical modeling (SSM) approach, and the Kernel density estimation (KDE) approach. The area metric is used as a convergence criterion to determine the necessary and sufficient number of experimental data to be acquired. The pro- posed method is validated in simulations, using different statistical modeling methods, different true models, and different convergence criteria. An example data set with 29 data describing the fatigue strength coefficient of SAE 950X is used for demonstrating the performance of the obtained statistical models that use a pre-determined number of experimental data in predicting the probability of failure for a target fatigue life.

  17. Output hardcopy devices

    CERN Document Server

    Durbeck, Robert

    1988-01-01

    Output Hardcopy Devices provides a technical summary of computer output hardcopy devices such as plotters, computer output printers, and CRT generated hardcopy. Important related technical areas such as papers, ribbons and inks, color techniques, controllers, and character fonts are also covered. Emphasis is on techniques primarily associated with printing, as well as the plotting capabilities of printing devices that can be effectively used for computer graphics in addition to their various printing functions. Comprised of 19 chapters, this volume begins with an introduction to vector and ras

  18. Forecasting the Romanian sectoral economy using the input-output method

    Directory of Open Access Journals (Sweden)

    Liliana DUGULEANĂ

    2017-07-01

    Full Text Available The purpose of this paper is to forecast the sectoral output in 2013 based on the input-output structure of Romanian economy in 2010. Considering that the economic linkage mechanisms do not easily change during certain time periods, the forecasting is possible, even if not in the sequence of the time passing. Using the technical matrix of the sectoral structure described for year 2010 and some known indicators of the economic sectors, as the value added for each sector in 2013, the sectoral output is projected for 2013. The Romanian GDP in 2013 is estimated based on the input-output model. From a managerial perspective, this study is useful to forecast the sectoral output and to understand the sectoral behaviour, based on the input-output analysis of the value added, the compensation for employees and the final demand, which were considered here.

  19. Logarithmic transformed statistical models in calibration

    International Nuclear Information System (INIS)

    Zeis, C.D.

    1975-01-01

    A general type of statistical model used for calibration of instruments having the property that the standard deviations of the observed values increase as a function of the mean value is described. The application to the Helix Counter at the Rocky Flats Plant is primarily from a theoretical point of view. The Helix Counter measures the amount of plutonium in certain types of chemicals. The method described can be used also for other calibrations. (U.S.)

  20. An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

    Directory of Open Access Journals (Sweden)

    Zheng-Xin Wang

    2014-01-01

    Full Text Available The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n, n interpolation coefficients (taken as unknown parameters are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n model. The modelling results can assist the government in developing future policies regarding high-tech industry management.