Fuzzy Uncertainty Evaluation for Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)

2015-05-15

This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.

The fuzzy set theory application to the analysis of accident progression event trees with phenomenological uncertainty issues

International Nuclear Information System (INIS)

Chun, Moon-Hyun; Ahn, Kwang-Il

1991-01-01

Fuzzy set theory provides a formal framework for dealing with the imprecision and vagueness inherent in the expert judgement, and therefore it can be used for more effective analysis of accident progression of PRA where experts opinion is a major means for quantifying some event probabilities and uncertainties. In this paper, an example application of the fuzzy set theory is first made to a simple portion of a given accident progression event tree with typical qualitative fuzzy input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated with example applications. Then the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e., a typical plant damage state 'SEC' of the Zion Nuclear Power Plant risk assessment. The results show that the fuzzy averages of the fuzzy outcomes are very close to the mean values obtained by current methods. The main purpose of this paper is to provide a formal procedure for application of the fuzzy set theory to accident progression event trees with imprecise and qualitative branch probabilities and/or with a number of phenomenological uncertainty issues. (author)

Evidence-based quantification of uncertainties induced via simulation-based modeling

International Nuclear Information System (INIS)

Riley, Matthew E.

2015-01-01

The quantification of uncertainties in simulation-based modeling traditionally focuses upon quantifying uncertainties in the parameters input into the model, referred to as parametric uncertainties. Often neglected in such an approach are the uncertainties induced by the modeling process itself. This deficiency is often due to a lack of information regarding the problem or the models considered, which could theoretically be reduced through the introduction of additional data. Because of the nature of this epistemic uncertainty, traditional probabilistic frameworks utilized for the quantification of uncertainties are not necessarily applicable to quantify the uncertainties induced in the modeling process itself. This work develops and utilizes a methodology – incorporating aspects of Dempster–Shafer Theory and Bayesian model averaging – to quantify uncertainties of all forms for simulation-based modeling problems. The approach expands upon classical parametric uncertainty approaches, allowing for the quantification of modeling-induced uncertainties as well, ultimately providing bounds on classical probability without the loss of epistemic generality. The approach is demonstrated on two different simulation-based modeling problems: the computation of the natural frequency of a simple two degree of freedom non-linear spring mass system and the calculation of the flutter velocity coefficient for the AGARD 445.6 wing given a subset of commercially available modeling choices. - Highlights: • Modeling-induced uncertainties are often mishandled or ignored in the literature. • Modeling-induced uncertainties are epistemic in nature. • Probabilistic representations of modeling-induced uncertainties are restrictive. • Evidence theory and Bayesian model averaging are integrated. • Developed approach is applicable for simulation-based modeling problems

Uncertainty and conservatism in safety evaluations based on a BEPU approach

International Nuclear Information System (INIS)

Yamaguchi, A.; Mizokami, S.; Kudo, Y.; Hotta, A.

2009-01-01

Atomic Energy Society of Japan has published 'Standard Method for Safety Evaluation using Best Estimate Code Based on Uncertainty and Scaling Analyses with Statistical Approach' to be applied to accidents and AOOs in the safety evaluation of LWRs. In this method, hereafter named as the AESJ-SSE (Statistical Safety Evaluation) method, identification and quantification of uncertainties will be performed and then a combination of the best estimate code and the evaluation of uncertainty propagation will be performed. Uncertainties are categorized into bias and variability. In general, bias is related to our state-of-knowledge on uncertainty objects (modeling, scaling, input data, etc.) while variability reflects stochastic features involved in these objects. Considering many kinds of uncertainties in thermal-hydraulics models and experimental databases show variabilities that will be strongly influenced by our state of knowledge, it seems reasonable that these variabilities are also related to state-of-knowledge. The design basis events (DBEs) that are employed for licensing analyses form a main part of the given or prior conservatism. The regulatory acceptance criterion is also regarded as the prior conservatism. In addition to these prior conservatisms, a certain amount of the posterior conservatism is added with maintaining intimate relationships with state-of-knowledge. In the AESJ-SSE method, this posterior conservatism can be incorporated into the safety evaluation in a combination of the following three ways, (1) broadening ranges of variability relevant to uncertainty objects, (2) employing more disadvantageous biases relevant to uncertainty objects and (3) adding an extra bias to the safety evaluation results. Knowing implemented quantitative bases of uncertainties and conservatism, the AESJ-SSE method provides a useful ground for rational decision-making. In order to seek for 'the best estimation' as well as reasonably setting the analytical margin, a degree

Adjoint-Based Uncertainty Quantification with MCNP

Energy Technology Data Exchange (ETDEWEB)

Seifried, Jeffrey E. [Univ. of California, Berkeley, CA (United States)

2011-09-01

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.

Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling

OpenAIRE

Tramblay, Yves; Bouvier, Christophe; Martin, C.; Didon-Lescot, J. F.; Todorovik, D.; Domergue, J. M.

2010-01-01

Flash floods are the most destructive natural hazards that occur in the Mediterranean region. Rainfall-runoff models can be very useful for flash flood forecasting and prediction. Event-based models are very popular for operational purposes, but there is a need to reduce the uncertainties related to the initial moisture conditions estimation prior to a flood event. This paper aims to compare several soil moisture indicators: local Time Domain Reflectometry (TDR) measurements of soil moisture,...

Quantum uncertainty relation based on the mean deviation

OpenAIRE

Sharma, Gautam; Mukhopadhyay, Chiranjib; Sazim, Sk; Pati, Arun Kumar

2018-01-01

Traditional forms of quantum uncertainty relations are invariably based on the standard deviation. This can be understood in the historical context of simultaneous development of quantum theory and mathematical statistics. Here, we present alternative forms of uncertainty relations, in both state dependent and state independent forms, based on the mean deviation. We illustrate the robustness of this formulation in situations where the standard deviation based uncertainty relation is inapplica...

Using Real-time Event Tracking Sensitivity Analysis to Overcome Sensor Measurement Uncertainties of Geo-Information Management in Drilling Disasters

Science.gov (United States)

Tavakoli, S.; Poslad, S.; Fruhwirth, R.; Winter, M.

2012-04-01

This paper introduces an application of a novel EventTracker platform for instantaneous Sensitivity Analysis (SA) of large scale real-time geo-information. Earth disaster management systems demand high quality information to aid a quick and timely response to their evolving environments. The idea behind the proposed EventTracker platform is the assumption that modern information management systems are able to capture data in real-time and have the technological flexibility to adjust their services to work with specific sources of data/information. However, to assure this adaptation in real time, the online data should be collected, interpreted, and translated into corrective actions in a concise and timely manner. This can hardly be handled by existing sensitivity analysis methods because they rely on historical data and lazy processing algorithms. In event-driven systems, the effect of system inputs on its state is of value, as events could cause this state to change. This 'event triggering' situation underpins the logic of the proposed approach. Event tracking sensitivity analysis method describes the system variables and states as a collection of events. The higher the occurrence of an input variable during the trigger of event, the greater its potential impact will be on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis with existing Entropy-based sensitivity analysis methods. The results have shown a 10% improvement in a computational efficiency with no compromise for accuracy. It has also shown that the computational time to perform the sensitivity analysis is 0.5% of the time required compared to using the Entropy-based method. The proposed method has been applied to real world data in the context of preventing emerging crises at drilling rigs. One of the major purposes of such rigs is to drill boreholes to explore oil or gas reservoirs with the final scope of recovering the content

A terrestrial lidar-based workflow for determining three-dimensional slip vectors and associated uncertainties

Science.gov (United States)

Gold, Peter O.; Cowgill, Eric; Kreylos, Oliver; Gold, Ryan D.

2012-01-01

Three-dimensional (3D) slip vectors recorded by displaced landforms are difficult to constrain across complex fault zones, and the uncertainties associated with such measurements become increasingly challenging to assess as landforms degrade over time. We approach this problem from a remote sensing perspective by using terrestrial laser scanning (TLS) and 3D structural analysis. We have developed an integrated TLS data collection and point-based analysis workflow that incorporates accurate assessments of aleatoric and epistemic uncertainties using experimental surveys, Monte Carlo simulations, and iterative site reconstructions. Our scanning workflow and equipment requirements are optimized for single-operator surveying, and our data analysis process is largely completed using new point-based computing tools in an immersive 3D virtual reality environment. In a case study, we measured slip vector orientations at two sites along the rupture trace of the 1954 Dixie Valley earthquake (central Nevada, United States), yielding measurements that are the first direct constraints on the 3D slip vector for this event. These observations are consistent with a previous approximation of net extension direction for this event. We find that errors introduced by variables in our survey method result in <2.5 cm of variability in components of displacement, and are eclipsed by the 10–60 cm epistemic errors introduced by reconstructing the field sites to their pre-erosion geometries. Although the higher resolution TLS data sets enabled visualization and data interactivity critical for reconstructing the 3D slip vector and for assessing uncertainties, dense topographic constraints alone were not sufficient to significantly narrow the wide (<26°) range of allowable slip vector orientations that resulted from accounting for epistemic uncertainties.

Managing wildfire events: risk-based decision making among a group of federal fire managers

Science.gov (United States)

Robyn S. Wilson; Patricia L. Winter; Lynn A. Maguire; Timothy. Ascher

2011-01-01

Managing wildfire events to achieve multiple management objectives involves a high degree of decision complexity and uncertainty, increasing the likelihood that decisions will be informed by experience-based heuristics triggered by available cues at the time of the decision. The research reported here tests the prevalence of three risk-based biases among 206...

Information-theoretic approach to uncertainty importance

International Nuclear Information System (INIS)

Park, C.K.; Bari, R.A.

1985-01-01

A method is presented for importance analysis in probabilistic risk assessments (PRA) for which the results of interest are characterized by full uncertainty distributions and not just point estimates. The method is based on information theory in which entropy is a measure of uncertainty of a probability density function. We define the relative uncertainty importance between two events as the ratio of the two exponents of the entropies. For the log-normal and log-uniform distributions the importance measure is comprised of the median (central tendency) and of the logarithm of the error factor (uncertainty). Thus, if accident sequences are ranked this way, and the error factors are not all equal, then a different rank order would result than if the sequences were ranked by the central tendency measure alone. As an illustration, the relative importance of internal events and in-plant fires was computed on the basis of existing PRA results

Global and Regional 3D Tomography for Improved Seismic Event Location and Uncertainty in Explosion Monitoring

Science.gov (United States)

Downey, N.; Begnaud, M. L.; Hipp, J. R.; Ballard, S.; Young, C. S.; Encarnacao, A. V.

2017-12-01

The SALSA3D global 3D velocity model of the Earth was developed to improve the accuracy and precision of seismic travel time predictions for a wide suite of regional and teleseismic phases. Recently, the global SALSA3D model was updated to include additional body wave phases including mantle phases, core phases, reflections off the core-mantle boundary and underside reflections off the surface of the Earth. We show that this update improves travel time predictions and leads directly to significant improvements in the accuracy and precision of seismic event locations as compared to locations computed using standard 1D velocity models like ak135, or 2½D models like RSTT. A key feature of our inversions is that path-specific model uncertainty of travel time predictions are calculated using the full 3D model covariance matrix computed during tomography, which results in more realistic uncertainty ellipses that directly reflect tomographic data coverage. Application of this method can also be done at a regional scale: we present a velocity model with uncertainty obtained using data obtained from the University of Utah Seismograph Stations. These results show a reduction in travel-time residuals for re-located events compared with those obtained using previously published models.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

Science.gov (United States)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

Science.gov (United States)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.

Aleatoric and epistemic uncertainties in sampling based nuclear data uncertainty and sensitivity analyses

International Nuclear Information System (INIS)

Zwermann, W.; Krzykacz-Hausmann, B.; Gallner, L.; Klein, M.; Pautz, A.; Velkov, K.

2012-01-01

Sampling based uncertainty and sensitivity analyses due to epistemic input uncertainties, i.e. to an incomplete knowledge of uncertain input parameters, can be performed with arbitrary application programs to solve the physical problem under consideration. For the description of steady-state particle transport, direct simulations of the microscopic processes with Monte Carlo codes are often used. This introduces an additional source of uncertainty, the aleatoric sampling uncertainty, which is due to the randomness of the simulation process performed by sampling, and which adds to the total combined output sampling uncertainty. So far, this aleatoric part of uncertainty is minimized by running a sufficiently large number of Monte Carlo histories for each sample calculation, thus making its impact negligible as compared to the impact from sampling the epistemic uncertainties. Obviously, this process may cause high computational costs. The present paper shows that in many applications reliable epistemic uncertainty results can also be obtained with substantially lower computational effort by performing and analyzing two appropriately generated series of samples with much smaller number of Monte Carlo histories each. The method is applied along with the nuclear data uncertainty and sensitivity code package XSUSA in combination with the Monte Carlo transport code KENO-Va to various critical assemblies and a full scale reactor calculation. It is shown that the proposed method yields output uncertainties and sensitivities equivalent to the traditional approach, with a high reduction of computing time by factors of the magnitude of 100. (authors)

Government policy uncertainty and stock prices: The case of Australia's uranium industry

International Nuclear Information System (INIS)

Ferguson, Andrew; Lam, Peter

2016-01-01

We investigate effects of government policy uncertainty on stock prices, reflecting tension between ‘private interest’ (economic benefits) and ‘public interest’ arguments over uranium mining. Using a sample of Australian-listed uranium firms from January 2005 through June 2008, we document a positive contemporaneous correlation between stock returns and volatility and two measures of government policy uncertainty, proxied by the spread in voters' opinion polls between the two major political parties and a news-based sentiment index. Event-study results show significant stock price reactions to key uranium-related policy events, with cross-sectional variation in event returns predicted by models incorporating firm- and project-level characteristics. Our research design and findings may inform future research on the capital market effects of government policy uncertainty in other regulated industries. - Highlights: • Government policy uncertainty has direct effects on stock prices of uranium explorers. • Stock returns are positively related to the spread in two-party-preferred voting intention. • Stock volatility is positively related to a uranium news-based sentiment index. • Event-study results show significant market reaction to key uranium policy events.

Improved Monte Carlo Method for PSA Uncertainty Analysis

International Nuclear Information System (INIS)

Choi, Jongsoo

2016-01-01

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard

Improved Monte Carlo Method for PSA Uncertainty Analysis

Energy Technology Data Exchange (ETDEWEB)

Choi, Jongsoo [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

2016-10-15

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard.

TEMAC, Top Event Sensitivity Analysis

International Nuclear Information System (INIS)

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

1988-01-01

1 - Description of program or function: TEMAC is designed to permit the user to easily estimate risk and to perform sensitivity and uncertainty analyses with a Boolean expression such as produced by the SETS computer program. SETS produces a mathematical representation of a fault tree used to model system unavailability. In the terminology of the TEMAC program, such a mathematical representation is referred to as a top event. The analysis of risk involves the estimation of the magnitude of risk, the sensitivity of risk estimates to base event probabilities and initiating event frequencies, and the quantification of the uncertainty in the risk estimates. 2 - Method of solution: Sensitivity and uncertainty analyses associated with top events involve mathematical operations on the corresponding Boolean expression for the top event, as well as repeated evaluations of the top event in a Monte Carlo fashion. TEMAC employs a general matrix approach which provides a convenient general form for Boolean expressions, is computationally efficient, and allows large problems to be analyzed. 3 - Restrictions on the complexity of the problem - Maxima of: 4000 cut sets, 500 events, 500 values in a Monte Carlo sample, 16 characters in an event name. These restrictions are implemented through the FORTRAN 77 PARAMATER statement

Event-Based Conceptual Modeling

DEFF Research Database (Denmark)

Bækgaard, Lars

The paper demonstrates that a wide variety of event-based modeling approaches are based on special cases of the same general event concept, and that the general event concept can be used to unify the otherwise unrelated fields of information modeling and process modeling. A set of event......-based modeling approaches are analyzed and the results are used to formulate a general event concept that can be used for unifying the seemingly unrelated event concepts. Events are characterized as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms...... of information structures. The general event concept can be used to guide systems analysis and design and to improve modeling approaches....

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-10-15

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

An Intelligent Complex Event Processing with D Numbers under Fuzzy Environment

Directory of Open Access Journals (Sweden)

Fuyuan Xiao

2016-01-01

Full Text Available Efficient matching of incoming mass events to persistent queries is fundamental to complex event processing systems. Event matching based on pattern rule is an important feature of complex event processing engine. However, the intrinsic uncertainty in pattern rules which are predecided by experts increases the difficulties of effective complex event processing. It inevitably involves various types of the intrinsic uncertainty, such as imprecision, fuzziness, and incompleteness, due to the inability of human beings subjective judgment. Nevertheless, D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. To address the above issues, an intelligent complex event processing method with D numbers under fuzzy environment is proposed based on the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS method. The novel method can fully support decision making in complex event processing systems. Finally, a numerical example is provided to evaluate the efficiency of the proposed method.

Methodologies for uncertainty analysis in the level 2 PSA and their implementation procedures

International Nuclear Information System (INIS)

Ahn, Kwang Il; Yang, Joon Eun; Kim, Dong Ha

2002-04-01

Main purpose of this report to present standardized methodologies for uncertainty analysis in the Level 2 Probabilistic Safety Assessment (PSA) and their implementation procedures, based on results obtained through a critical review of the existing methodologies for the analysis of uncertainties employed in the Level 2 PSA, especially Accident Progression Event Tree (APET). Uncertainties employed in the Level 2 PSA, quantitative expressions of overall knowledge of analysts' and experts' participating in the probabilistic quantification process of phenomenological accident progressions ranging from core melt to containment failure, their numerical values are directly related to the degree of confidence that the analyst has that a given phenomenological event or accident process will or will not occur, or analyst's subjective probabilities of occurrence. These results that are obtained from Level 2 PSA uncertainty analysis, become an essential contributor to the plant risk, in addition to the Level 1 PSA and Level 3 PSA uncertainties. Uncertainty analysis methodologies and their implementation procedures presented in this report was prepared based on the following criteria: 'uncertainty quantification process must be logical, scrutable, complete, consistent and in an appropriate level of detail, as mandated by the Level 2 PSA objectives'. For the aforementioned purpose, this report deals mainly with (1) summary of general or Level 2 PSA specific uncertainty analysis methodologies, (2) selection of phenomenological branch events for uncertainty analysis in the APET, methodology for quantification of APET uncertainty inputs and its implementation procedure, (3) statistical propagation of uncertainty inputs through APET and its implementation procedure, and (4) formal procedure for quantification of APET uncertainties and source term categories (STCs) through the Level 2 PSA quantification codes

Uncertainty analysis and validation of environmental models. The empirically based uncertainty analysis

International Nuclear Information System (INIS)

Monte, Luigi; Hakanson, Lars; Bergstroem, Ulla; Brittain, John; Heling, Rudie

1996-01-01

The principles of Empirically Based Uncertainty Analysis (EBUA) are described. EBUA is based on the evaluation of 'performance indices' that express the level of agreement between the model and sets of empirical independent data collected in different experimental circumstances. Some of these indices may be used to evaluate the confidence limits of the model output. The method is based on the statistical analysis of the distribution of the index values and on the quantitative relationship of these values with the ratio 'experimental data/model output'. Some performance indices are described in the present paper. Among these, the so-called 'functional distance' (d) between the logarithm of model output and the logarithm of the experimental data, defined as d 2 =Σ n 1 ( ln M i - ln O i ) 2 /n where M i is the i-th experimental value, O i the corresponding model evaluation and n the number of the couplets 'experimental value, predicted value', is an important tool for the EBUA method. From the statistical distribution of this performance index, it is possible to infer the characteristics of the distribution of the ratio 'experimental data/model output' and, consequently to evaluate the confidence limits for the model predictions. This method was applied to calculate the uncertainty level of a model developed to predict the migration of radiocaesium in lacustrine systems. Unfortunately, performance indices are affected by the uncertainty of the experimental data used in validation. Indeed, measurement results of environmental levels of contamination are generally associated with large uncertainty due to the measurement and sampling techniques and to the large variability in space and time of the measured quantities. It is demonstrated that this non-desired effect, in some circumstances, may be corrected by means of simple formulae

Accounting for sensor calibration, data validation, measurement and sampling uncertainties in monitoring urban drainage systems.

Science.gov (United States)

Bertrand-Krajewski, J L; Bardin, J P; Mourad, M; Béranger, Y

2003-01-01

Assessing the functioning and the performance of urban drainage systems on both rainfall event and yearly time scales is usually based on online measurements of flow rates and on samples of influent effluent for some rainfall events per year. In order to draw pertinent scientific and operational conclusions from the measurement results, it is absolutely necessary to use appropriate methods and techniques in order to i) calibrate sensors and analytical methods, ii) validate raw data, iii) evaluate measurement uncertainties, iv) evaluate the number of rainfall events to sample per year in order to determine performance indicator with a given uncertainty. Based an previous work, the paper gives a synthetic review of required and techniques, and illustrates their application to storage and settling tanks. Experiments show that, controlled and careful experimental conditions, relative uncertainties are about 20% for flow rates in sewer pipes, 6-10% for volumes, 25-35% for TSS concentrations and loads, and 18-276% for TSS removal rates. In order to evaluate the annual pollutant interception efficiency of storage and settling tanks with a given uncertainty, efforts should first be devoted to decrease the sampling uncertainty by increasing the number of sampled events.

Uncertainty, joint uncertainty, and the quantum uncertainty principle

International Nuclear Information System (INIS)

Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad

2016-01-01

Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found. (paper)

Experimental data bases useful for quantification of model uncertainties in best estimate codes

International Nuclear Information System (INIS)

Wilson, G.E.; Katsma, K.R.; Jacobson, J.L.; Boodry, K.S.

1988-01-01

A data base is necessary for assessment of thermal hydraulic codes within the context of the new NRC ECCS Rule. Separate effect tests examine particular phenomena that may be used to develop and/or verify models and constitutive relationships in the code. Integral tests are used to demonstrate the capability of codes to model global characteristics and sequence of events for real or hypothetical transients. The nuclear industry has developed a large experimental data base of fundamental nuclear, thermal-hydraulic phenomena for code validation. Given a particular scenario, and recognizing the scenario's important phenomena, selected information from this data base may be used to demonstrate applicability of a particular code to simulate the scenario and to determine code model uncertainties. LBLOCA experimental data bases useful to this objective are identified in this paper. 2 tabs

Fuzzy uncertainty modeling applied to AP1000 nuclear power plant LOCA

International Nuclear Information System (INIS)

Ferreira Guimaraes, Antonio Cesar; Franklin Lapa, Celso Marcelo; Lamego Simoes Filho, Francisco Fernando; Cabral, Denise Cunha

2011-01-01

Research highlights: → This article presents an uncertainty modelling study using a fuzzy approach. → The AP1000 Westinghouse NPP was used and it is provided of passive safety systems. → The use of advanced passive safety systems in NPP has limited operational experience. → Failure rates and basic events probabilities used on the fault tree analysis. → Fuzzy uncertainty approach was employed to reliability of the AP1000 large LOCA. - Abstract: This article presents an uncertainty modeling study using a fuzzy approach applied to the Westinghouse advanced nuclear reactor. The AP1000 Westinghouse Nuclear Power Plant (NPP) is provided of passive safety systems, based on thermo physics phenomenon, that require no operating actions, soon after an incident has been detected. The use of advanced passive safety systems in NPP has limited operational experience. As it occurs in any reliability study, statistically non-significant events report introduces a significant uncertainty level about the failure rates and basic events probabilities used on the fault tree analysis (FTA). In order to model this uncertainty, a fuzzy approach was employed to reliability analysis of the AP1000 large break Loss of Coolant Accident (LOCA). The final results have revealed that the proposed approach may be successfully applied to modeling of uncertainties in safety studies.

Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning

International Nuclear Information System (INIS)

Chetty, Indrin J.; Rosu, Mihaela; Kessler, Marc L.; Fraass, Benedick A.; Haken, Randall K. ten; Kong, Feng-Ming; McShan, Daniel L.

2006-01-01

Purpose: To investigate methods of reporting and analyzing statistical uncertainties in doses to targets and normal tissues in Monte Carlo (MC)-based treatment planning. Methods and Materials: Methods for quantifying statistical uncertainties in dose, such as uncertainty specification to specific dose points, or to volume-based regions, were analyzed in MC-based treatment planning for 5 lung cancer patients. The effect of statistical uncertainties on target and normal tissue dose indices was evaluated. The concept of uncertainty volume histograms for targets and organs at risk was examined, along with its utility, in conjunction with dose volume histograms, in assessing the acceptability of the statistical precision in dose distributions. The uncertainty evaluation tools were extended to four-dimensional planning for application on multiple instances of the patient geometry. All calculations were performed using the Dose Planning Method MC code. Results: For targets, generalized equivalent uniform doses and mean target doses converged at 150 million simulated histories, corresponding to relative uncertainties of less than 2% in the mean target doses. For the normal lung tissue (a volume-effect organ), mean lung dose and normal tissue complication probability converged at 150 million histories despite the large range in the relative organ uncertainty volume histograms. For 'serial' normal tissues such as the spinal cord, large fluctuations exist in point dose relative uncertainties. Conclusions: The tools presented here provide useful means for evaluating statistical precision in MC-based dose distributions. Tradeoffs between uncertainties in doses to targets, volume-effect organs, and 'serial' normal tissues must be considered carefully in determining acceptable levels of statistical precision in MC-computed dose distributions

Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

Science.gov (United States)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in

Uncertainties in model-based outcome predictions for treatment planning

International Nuclear Information System (INIS)

Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

Including model uncertainty in risk-informed decision making

International Nuclear Information System (INIS)

Reinert, Joshua M.; Apostolakis, George E.

2006-01-01

Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the risk achievement worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ΔCDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important. The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study

Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Hui He

2016-01-01

Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.

A decision-oriented measure of uncertainty importance for use in PSA

International Nuclear Information System (INIS)

Poern, Kurt

1997-01-01

For the interpretation of the results of probabilistic risk assessments it is important to have measures which identify the basic events that contribute most to the frequency of the top event but also to identify basic events that are the main contributors to the uncertainty in this frequency. Both types of measures, often called Importance Measure and Measure of Uncertainty Importance, respectively, have been the subject of interest for many researchers in the reliability field. The most frequent mode of uncertainty analysis in connection with probabilistic risk assessment has been to propagate the uncertainty of all model parameters up to an uncertainty distribution for the top event frequency. Various uncertainty importance measures have been proposed in order to point out the parameters that in some sense are the main contributors to the top event distribution. The new measure of uncertainty importance suggested here goes a step further in that it has been developed within a decision theory framework, thereby providing an indication of on what basic event it would be most valuable, from the decision-making point of view, to procure more information

Uncertainty Assessment in Long Term Urban Drainage Modelling

DEFF Research Database (Denmark)

Thorndahl, Søren

the probability of system failures (defined as either flooding or surcharge of manholes or combined sewer overflow); (2) an application of the Generalized Likelihood Uncertainty Estimation methodology in which an event based stochastic calibration is performed; and (3) long term Monte Carlo simulations...

Synthesis of Optimal Processing Pathway for Microalgae-based Biorefinery under Uncertainty

DEFF Research Database (Denmark)

Rizwan, Muhammad; Lee, Jay H.; Gani, Rafiqul

2015-01-01

decision making, we propose a systematic framework for the synthesis and optimal design of microalgae-based processing network under uncertainty. By incorporating major uncertainties into the biorefinery superstructure model we developed previously, a stochastic mixed integer nonlinear programming (s......The research in the field of microalgae-based biofuels and chemicals is in early phase of the development, and therefore a wide range of uncertainties exist due to inconsistencies among and shortage of technical information. In order to handle and address these uncertainties to ensure robust......MINLP) problem is formulated for determining the optimal biorefinery structure under given parameter uncertainties modelled as sampled scenarios. The solution to the sMINLP problem determines the optimal decisions with respect to processing technologies, material flows, and product portfolio in the presence...

Survey of sampling-based methods for uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; Sallaberry, C.J.; Storlie, C.B.

2006-01-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs (ii) generation of samples from uncertain analysis inputs (iii) propagation of sampled inputs through an analysis (iv) presentation of uncertainty analysis results, and (v) determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two-dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)

2006-06-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

Uncertainty related to Environmental Data and Estimated Extreme Events

DEFF Research Database (Denmark)

Burcharth, H. F.

The design loads on rubble mound breakwaters are almost entirely determined by the environmental conditions, i.e. sea state, water levels, sea bed characteristics, etc. It is the objective of sub-group B to identify the most important environmental parameters and evaluate the related uncertainties...... including those corresponding to extreme estimates typically used for design purposes. Basically a design condition is made up of a set of parameter values stemming from several environmental parameters. To be able to evaluate the uncertainty related to design states one must know the corresponding joint....... Consequently this report deals mainly with each parameter separately. Multi parameter problems are briefly discussed in section 9. It is important to notice that the quantified uncertainties reported in section 7.7 represent what might be regarded as typical figures to be used only when no more qualified...

Uncertainty Analysis of Few Group Cross Sections Based on Generalized Perturbation Theory

International Nuclear Information System (INIS)

Han, Tae Young; Lee, Hyun Chul; Noh, Jae Man

2014-01-01

In this paper, the methodology of the sensitivity and uncertainty analysis code based on GPT was described and the preliminary verification calculations on the PMR200 pin cell problem were carried out. As a result, they are in a good agreement when compared with the results by TSUNAMI. From this study, it is expected that MUSAD code based on GPT can produce the uncertainty of the homogenized few group microscopic cross sections for a core simulator. For sensitivity and uncertainty analyses for general core responses, a two-step method is available and it utilizes the generalized perturbation theory (GPT) for homogenized few group cross sections in the first step and stochastic sampling method for general core responses in the second step. The uncertainty analysis procedure based on GPT in the first step needs the generalized adjoint solution from a cell or lattice code. For this, the generalized adjoint solver has been integrated into DeCART in our previous work. In this paper, MUSAD (Modues of Uncertainty and Sensitivity Analysis for DeCART) code based on the classical perturbation theory was expanded to the function of the sensitivity and uncertainty analysis for few group cross sections based on GPT. First, the uncertainty analysis method based on GPT was described and, in the next section, the preliminary results of the verification calculation on a VHTR pin cell problem were compared with the results by TSUNAMI of SCALE 6.1

Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

Energy Technology Data Exchange (ETDEWEB)

Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

2015-01-15

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

Science.gov (United States)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Uncertainty theory

CERN Document Server

Liu, Baoding

2015-01-01

When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...

Predictive uncertainty in auditory sequence processing

Directory of Open Access Journals (Sweden)

Niels Chr. eHansen

2014-09-01

Full Text Available Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty - a property of listeners’ prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure.Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex. Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty. We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty. Finally, we simulate listeners’ perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature.The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.

Predictive uncertainty in auditory sequence processing.

Science.gov (United States)

Hansen, Niels Chr; Pearce, Marcus T

2014-01-01

Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty-a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.

Development of a Prototype Model-Form Uncertainty Knowledge Base

Science.gov (United States)

Green, Lawrence L.

2016-01-01

Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.

Optimal processing pathway selection for microalgae-based biorefinery under uncertainty

DEFF Research Database (Denmark)

Rizwan, Muhammad; Zaman, Muhammad; Lee, Jay H.

2015-01-01

We propose a systematic framework for the selection of optimal processing pathways for a microalgaebased biorefinery under techno-economic uncertainty. The proposed framework promotes robust decision making by taking into account the uncertainties that arise due to inconsistencies among...... and shortage in the available technical information. A stochastic mixed integer nonlinear programming (sMINLP) problem is formulated for determining the optimal biorefinery configurations based on a superstructure model where parameter uncertainties are modeled and included as sampled scenarios. The solution...... the accounting of uncertainty are compared with respect to different objectives. (C) 2015 Elsevier Ltd. All rights reserved....

Analytical propagation of uncertainties through fault trees

International Nuclear Information System (INIS)

Hauptmanns, Ulrich

2002-01-01

A method is presented which enables one to propagate uncertainties described by uniform probability density functions through fault trees. The approach is analytical. It is based on calculating the expected value and the variance of the top event probability. These two parameters are then equated with the corresponding ones of a beta-distribution. An example calculation comparing the analytically calculated beta-pdf (probability density function) with the top event pdf obtained using the Monte-Carlo method shows excellent agreement at a much lower expense of computing time

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

International Nuclear Information System (INIS)

Townsend, Lawrence W.; Zapp, E. Neal

1999-01-01

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

Antineutrinos from Earth: A reference model and its uncertainties

International Nuclear Information System (INIS)

Mantovani, Fabio; Carmignani, Luigi; Fiorentini, Gianni; Lissia, Marcello

2004-01-01

We predict geoneutrino fluxes in a reference model based on a detailed description of Earth's crust and mantle and using the best available information on the abundances of uranium, thorium, and potassium inside Earth's layers. We estimate the uncertainties of fluxes corresponding to the uncertainties of the element abundances. In addition to distance integrated fluxes, we also provide the differential fluxes as a function of distance from several sites of experimental interest. Event yields at several locations are estimated and their dependence on the neutrino oscillation parameters is discussed. At Kamioka we predict N(U+Th)=35±6 events for 10 32 proton yr and 100% efficiency assuming sin 2 (2θ)=0.863 and δm 2 =7.3x10 -5 eV 2 . The maximal prediction is 55 events, obtained in a model with fully radiogenic production of the terrestrial heat flow

Uncertainties in Nuclear Proliferation Modeling

International Nuclear Information System (INIS)

Kim, Chul Min; Yim, Man-Sung; Park, Hyeon Seok

2015-01-01

There have been various efforts in the research community to understand the determinants of nuclear proliferation and develop quantitative tools to predict nuclear proliferation events. Such systematic approaches have shown the possibility to provide warning for the international community to prevent nuclear proliferation activities. However, there are still large debates for the robustness of the actual effect of determinants and projection results. Some studies have shown that several factors can cause uncertainties in previous quantitative nuclear proliferation modeling works. This paper analyzes the uncertainties in the past approaches and suggests future works in the view of proliferation history, analysis methods, and variable selection. The research community still lacks the knowledge for the source of uncertainty in current models. Fundamental problems in modeling will remain even other advanced modeling method is developed. Before starting to develop fancy model based on the time dependent proliferation determinants' hypothesis, using graph theory, etc., it is important to analyze the uncertainty of current model to solve the fundamental problems of nuclear proliferation modeling. The uncertainty from different proliferation history coding is small. Serious problems are from limited analysis methods and correlation among the variables. Problems in regression analysis and survival analysis cause huge uncertainties when using the same dataset, which decreases the robustness of the result. Inaccurate variables for nuclear proliferation also increase the uncertainty. To overcome these problems, further quantitative research should focus on analyzing the knowledge suggested on the qualitative nuclear proliferation studies

Uncertainty analysis for results of thermal hydraulic codes of best-estimate-type

International Nuclear Information System (INIS)

Alva N, J.

2010-01-01

In this thesis, some fundamental knowledge is presented about uncertainty analysis and about diverse methodologies applied in the study of nuclear power plant transient event analysis, particularly related to thermal hydraulics phenomena. These concepts and methodologies mentioned in this work come from a wide bibliographical research in the nuclear power subject. Methodologies for uncertainty analysis have been developed by quite diverse institutions, and they have been widely used worldwide for application to results from best-estimate-type computer codes in nuclear reactor thermal hydraulics and safety analysis. Also, the main uncertainty sources, types of uncertainties, and aspects related to best estimate modeling and methods are introduced. Once the main bases of uncertainty analysis have been set, and some of the known methodologies have been introduced, it is presented in detail the CSAU methodology, which will be applied in the analyses. The main objective of this thesis is to compare the results of an uncertainty and sensibility analysis by using the Response Surface Technique to the application of W ilks formula, apply through a loss coolant experiment and an event of rise in a BWR. Both techniques are options in the part of uncertainty and sensibility analysis of the CSAU methodology, which was developed for the analysis of transients and accidents at nuclear power plants, and it is the base of most of the methodologies used in licensing of nuclear power plants practically everywhere. Finally, the results of applying both techniques are compared and discussed. (Author)

Event-based model diagnosis of rainfall-runoff model structures

International Nuclear Information System (INIS)

Stanzel, P.

2012-01-01

The objective of this research is a comparative evaluation of different rainfall-runoff model structures. Comparative model diagnostics facilitate the assessment of strengths and weaknesses of each model. The application of multiple models allows an analysis of simulation uncertainties arising from the selection of model structure, as compared with effects of uncertain parameters and precipitation input. Four different model structures, including conceptual and physically based approaches, are compared. In addition to runoff simulations, results for soil moisture and the runoff components of overland flow, interflow and base flow are analysed. Catchment runoff is simulated satisfactorily by all four model structures and shows only minor differences. Systematic deviations from runoff observations provide insight into model structural deficiencies. While physically based model structures capture some single runoff events better, they do not generally outperform conceptual model structures. Contributions to uncertainty in runoff simulations stemming from the choice of model structure show similar dimensions to those arising from parameter selection and the representation of precipitation input. Variations in precipitation mainly affect the general level and peaks of runoff, while different model structures lead to different simulated runoff dynamics. Large differences between the four analysed models are detected for simulations of soil moisture and, even more pronounced, runoff components. Soil moisture changes are more dynamical in the physically based model structures, which is in better agreement with observations. Streamflow contributions of overland flow are considerably lower in these models than in the more conceptual approaches. Observations of runoff components are rarely made and are not available in this study, but are shown to have high potential for an effective selection of appropriate model structures (author) [de

Bayesian analysis of rare events

Energy Technology Data Exchange (ETDEWEB)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Uncertainty quantification in flood risk assessment

Science.gov (United States)

Blöschl, Günter; Hall, Julia; Kiss, Andrea; Parajka, Juraj; Perdigão, Rui A. P.; Rogger, Magdalena; Salinas, José Luis; Viglione, Alberto

2017-04-01

Uncertainty is inherent to flood risk assessments because of the complexity of the human-water system, which is characterised by nonlinearities and interdependencies, because of limited knowledge about system properties and because of cognitive biases in human perception and decision-making. On top of the uncertainty associated with the assessment of the existing risk to extreme events, additional uncertainty arises because of temporal changes in the system due to climate change, modifications of the environment, population growth and the associated increase in assets. Novel risk assessment concepts are needed that take into account all these sources of uncertainty. They should be based on the understanding of how flood extremes are generated and how they change over time. They should also account for the dynamics of risk perception of decision makers and population in the floodplains. In this talk we discuss these novel risk assessment concepts through examples from Flood Frequency Hydrology, Socio-Hydrology and Predictions Under Change. We believe that uncertainty quantification in flood risk assessment should lead to a robust approach of integrated flood risk management aiming at enhancing resilience rather than searching for optimal defense strategies.

Pharmacological Fingerprints of Contextual Uncertainty.

Directory of Open Access Journals (Sweden)

Louise Marshall

2016-11-01

Full Text Available Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses.

Uncertainty visualization in HARDI based on ensembles of ODFs

KAUST Repository

Jiao, Fangxiang; Phillips, Jeff M.; Gur, Yaniv; Johnson, Chris R.

2012-01-01

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.

Uncertainty visualization in HARDI based on ensembles of ODFs

KAUST Repository

Jiao, Fangxiang

2012-02-01

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

Directory of Open Access Journals (Sweden)

Wooyeon Sunwoo

2017-01-01

Full Text Available Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5, ground-based soil moisture (SSMinsitu, remotely sensed surface soil moisture (SSM, and soil water index (SWI provided by the advanced scatterometer (ASCAT. To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI and ground based soil moisture data (SSMinsitu. In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with

Parton-shower uncertainties with Herwig 7: benchmarks at leading order

Energy Technology Data Exchange (ETDEWEB)

Bellm, Johannes; Schichtel, Peter [Durham University, Department of Physics, IPPP, Durham (United Kingdom); Nail, Graeme [University of Manchester, Particle Physics Group, School of Physics and Astronomy, Manchester (United Kingdom); Karlsruhe Institute of Technology, Institute for Theoretical Physics, Karlsruhe (Germany); Plaetzer, Simon [Durham University, Department of Physics, IPPP, Durham (United Kingdom); University of Manchester, Particle Physics Group, School of Physics and Astronomy, Manchester (United Kingdom); Siodmok, Andrzej [CERN, TH Department, Geneva (Switzerland); Polish Academy of Sciences, The Henryk Niewodniczanski Institute of Nuclear Physics in Cracow, Krakow (Poland)

2016-12-15

We perform a detailed study of the sources of perturbative uncertainty in parton-shower predictions within the Herwig 7 event generator. We benchmark two rather different parton-shower algorithms, based on angular-ordered and dipole-type evolution, against each other. We deliberately choose leading order plus parton shower as the benchmark setting to identify a controllable set of uncertainties. This will enable us to reliably assess improvements by higher-order contributions in a follow-up work. (orig.)

Parton Shower Uncertainties with Herwig 7: Benchmarks at Leading Order

CERN Document Server

Bellm, Johannes; Plätzer, Simon; Schichtel, Peter; Siódmok, Andrzej

2016-01-01

We perform a detailed study of the sources of perturbative uncertainty in parton shower predictions within the Herwig 7 event generator. We benchmark two rather different parton shower algorithms, based on angular-ordered and dipole-type evolution, against each other. We deliberately choose leading order plus parton shower as the benchmark setting to identify a controllable set of uncertainties. This will enable us to reliably assess improvements by higher-order contributions in a follow-up work.

Communicating Climate Uncertainties: Challenges and Opportunities Related to Spatial Scales, Extreme Events, and the Warming 'Hiatus'

Science.gov (United States)

Casola, J. H.; Huber, D.

2013-12-01

Many media, academic, government, and advocacy organizations have achieved sophistication in developing effective messages based on scientific information, and can quickly translate salient aspects of emerging climate research and evolving observations. However, there are several ways in which valid messages can be misconstrued by decision makers, leading them to inaccurate conclusions about the risks associated with climate impacts. Three cases will be discussed: 1) Issues of spatial scale in interpreting climate observations: Local climate observations may contradict summary statements about the effects of climate change on larger regional or global spatial scales. Effectively addressing these differences often requires communicators to understand local and regional climate drivers, and the distinction between a 'signal' associated with climate change and local climate 'noise.' Hydrological statistics in Missouri and California are shown to illustrate this case. 2) Issues of complexity related to extreme events: Climate change is typically invoked following a wide range of damaging meteorological events (e.g., heat waves, landfalling hurricanes, tornadoes), regardless of the strength of the relationship between anthropogenic climate change and the frequency or severity of that type of event. Examples are drawn from media coverage of several recent events, contrasting useful and potentially confusing word choices and frames. 3) Issues revolving around climate sensitivity: The so-called 'pause' or 'hiatus' in global warming has reverberated strongly through political and business discussions of climate change. Addressing the recent slowdown in warming yields an important opportunity to raise climate literacy in these communities. Attempts to use recent observations as a wedge between climate 'believers' and 'deniers' is likely to be counterproductive. Examples are drawn from Congressional testimony and media stories. All three cases illustrate ways that decision

Event-Based Conceptual Modeling

DEFF Research Database (Denmark)

Bækgaard, Lars

2009-01-01

The purpose of the paper is to obtain insight into and provide practical advice for event-based conceptual modeling. We analyze a set of event concepts and use the results to formulate a conceptual event model that is used to identify guidelines for creation of dynamic process models and static...... information models. We characterize events as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms of information structures. The conceptual event model is used to characterize a variety of event concepts and it is used to illustrate how events can...... be used to integrate dynamic modeling of processes and static modeling of information structures. The results are unique in the sense that no other general event concept has been used to unify a similar broad variety of seemingly incompatible event concepts. The general event concept can be used...

Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method

Directory of Open Access Journals (Sweden)

Kyu-Hyung Jo

2018-05-01

Full Text Available In light of the dissemination of renewable energy connected to the power grid, it has become necessary to consider the uncertainty in the generation of renewable energy as a unit commitment (UC problem. A methodology for solving the UC problem is presented by considering various uncertainties, which are assumed to have a normal distribution, by using a Monte Carlo simulation. Based on the constructed scenarios for load, wind, solar, and generator outages, a combination of scenarios is found that meets the reserve requirement to secure the power balance of the power grid. In those scenarios, the uncertainty integration method (UIM identifies the best combination by minimizing the additional reserve requirements caused by the uncertainty of power sources. An integration process for uncertainties is formulated for stochastic unit commitment (SUC problems and optimized by the improved genetic algorithm (IGA. The IGA is composed of five procedures and finds the optimal combination of unit status at the scheduled time, based on the determined source data. According to the number of unit systems, the IGA demonstrates better performance than the other optimization methods by applying reserve repairing and an approximation process. To account for the result of the proposed method, various UC strategies are tested with a modified 24-h UC test system and compared.

Uncertainty in hydrological signatures

Science.gov (United States)

McMillan, Hilary; Westerberg, Ida

2015-04-01

Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty

Awe, uncertainty, and agency detection.

Science.gov (United States)

Valdesolo, Piercarlo; Graham, Jesse

2014-01-01

Across five studies, we found that awe increases both supernatural belief (Studies 1, 2, and 5) and intentional-pattern perception (Studies 3 and 4)-two phenomena that have been linked to agency detection, or the tendency to interpret events as the consequence of intentional and purpose-driven agents. Effects were both directly and conceptually replicated, and mediational analyses revealed that these effects were driven by the influence of awe on tolerance for uncertainty. Experiences of awe decreased tolerance for uncertainty, which, in turn, increased the tendency to believe in nonhuman agents and to perceive human agency in random events.

Search for gamma-ray events in the BATSE data base

Science.gov (United States)

Lewin, Walter

1994-01-01

We find large location errors and error radii in the locations of channel 1 Cygnus X-1 events. These errors and their associated uncertainties are a result of low signal-to-noise ratios (a few sigma) in the two brightest detectors for each event. The untriggered events suffer from similarly low signal-to-noise ratios, and their location errors are expected to be at least as large as those found for Cygnus X-1 with a given signal-to-noise ratio. The statistical error radii are consistent with those found for Cygnus X-1 and with the published estimates. We therefore expect approximately 20 - 30 deg location errors for the untriggered events. Hence, many of the untriggered events occurring within a few months of the triggered activity from SGR 1900 plus 14 are indeed consistent with the SGR source location, although Cygnus X-1 is also a good candidate.

One Approach to the Fire PSA Uncertainty Analysis

International Nuclear Information System (INIS)

Simic, Z.; Mikulicic, V.; Vukovic, I.

2002-01-01

Experienced practical events and findings from the number of fire probabilistic safety assessment (PSA) studies show that fire has high relative importance for nuclear power plant safety. Fire PSA is a very challenging phenomenon and a number of issues are still in the area of research and development. This has a major impact on the conservatism of fire PSA findings. One way to reduce the level of conservatism is to conduct uncertainty analysis. At the top-level, uncertainty of the fire PSA can be separated in to three segments. The first segment is related to fire initiating events frequencies. The second uncertainty segment is connected to the uncertainty of fire damage. Finally, there is uncertainty related to the PSA model, which propagates this fire-initiated damage to the core damage or other analyzed risk. This paper discusses all three segments of uncertainty. Some recent experience with fire PSA study uncertainty analysis, usage of fire analysis code COMPBRN IIIe, and uncertainty evaluation importance to the final result is presented.(author)

Uncertainties for seismic moment tensors and applications to nuclear explosions, volcanic events, and earthquakes

Science.gov (United States)

Tape, C.; Alvizuri, C. R.; Silwal, V.; Tape, W.

2017-12-01

When considered as a point source, a seismic source can be characterized in terms of its origin time, hypocenter, moment tensor, and source time function. The seismologist's task is to estimate these parameters--and their uncertainties--from three-component ground motion recorded at irregularly spaced stations. We will focus on one portion of this problem: the estimation of the moment tensor and its uncertainties. With magnitude estimated separately, we are left with five parameters describing the normalized moment tensor. A lune of normalized eigenvalue triples can be used to visualize the two parameters (lune longitude and lune latitude) describing the source type, while the conventional strike, dip, and rake angles can be used to characterize the orientation. Slight modifications of these five parameters lead to a uniform parameterization of moment tensors--uniform in the sense that equal volumes in the coordinate domain of the parameterization correspond to equal volumes of moment tensors. For a moment tensor m that we have inferred from seismic data for an earthquake, we define P(V) to be the probability that the true moment tensor for the earthquake lies in the neighborhood of m that has fractional volume V. The average value of P(V) is then a measure of our confidence in our inference of m. The calculation of P(V) requires knowing both the probability P(w) and the fractional volume V(w) of the set of moment tensors within a given angular radius w of m. We apply this approach to several different data sets, including nuclear explosions from the Nevada Test Site, volcanic events from Uturuncu (Bolivia), and earthquakes. Several challenges remain: choosing an appropriate misfit function, handling time shifts between data and synthetic waveforms, and extending the uncertainty estimation to include more source parameters (e.g., hypocenter and source time function).

Dynamic Event Tree advancements and control logic improvements

Energy Technology Data Exchange (ETDEWEB)

Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Sen, Ramazan Sonat [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the

Dynamic Event Tree advancements and control logic improvements

International Nuclear Information System (INIS)

Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Sen, Ramazan Sonat; Cogliati, Joshua Joseph

2015-01-01

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named 'Hybrid Dynamic Event Tree' (HDET) and its Adaptive variant 'Adaptive Hybrid Dynamic Event Tree' (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre

Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes

International Nuclear Information System (INIS)

Zhu, T.

2015-01-01

Research on the uncertainty of nuclear data is motivated by practical necessity. Nuclear data uncertainties can propagate through nuclear system simulations into operation and safety related parameters. The tolerance for uncertainties in nuclear reactor design and operation can affect the economic efficiency of nuclear power, and essentially its sustainability. The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of MCNPX makes it the choice of routine criticality safety calculations at PSI/LRS, but also raises challenges for NDUQ by conventional sensitivity/uncertainty (S/U) methods. For example, only recently in 2011, the capability of calculating continuous energy κ_e_f_f sensitivity to nuclear data was demonstrated in certain M-C codes by using the method of iterated fission probability. The methodology developed during this PhD research is fundamentally different from the conventional S/U approach: nuclear data are treated as random variables and sampled in accordance to presumed probability distributions. When sampled nuclear data are used in repeated model calculations, the output variance is attributed to the collective uncertainties of nuclear data. The NUSS (Nuclear data Uncertainty Stochastic Sampling) tool is based on this sampling approach and implemented to work with MCNPX’s ACE format of nuclear data, which also gives NUSS compatibility with MCNP and SERPENT M-C codes. In contrast, multigroup uncertainties are used for the sampling of ACE-formatted pointwise-energy nuclear data in a groupwise manner due to the more limited quantity and quality of nuclear data uncertainties. Conveniently, the usage of multigroup nuclear data uncertainties allows consistent comparison between NUSS and other methods (both S/U and sampling-based) that employ the same

Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes

Energy Technology Data Exchange (ETDEWEB)

Zhu, T.

2015-07-01

Research on the uncertainty of nuclear data is motivated by practical necessity. Nuclear data uncertainties can propagate through nuclear system simulations into operation and safety related parameters. The tolerance for uncertainties in nuclear reactor design and operation can affect the economic efficiency of nuclear power, and essentially its sustainability. The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of MCNPX makes it the choice of routine criticality safety calculations at PSI/LRS, but also raises challenges for NDUQ by conventional sensitivity/uncertainty (S/U) methods. For example, only recently in 2011, the capability of calculating continuous energy κ{sub eff} sensitivity to nuclear data was demonstrated in certain M-C codes by using the method of iterated fission probability. The methodology developed during this PhD research is fundamentally different from the conventional S/U approach: nuclear data are treated as random variables and sampled in accordance to presumed probability distributions. When sampled nuclear data are used in repeated model calculations, the output variance is attributed to the collective uncertainties of nuclear data. The NUSS (Nuclear data Uncertainty Stochastic Sampling) tool is based on this sampling approach and implemented to work with MCNPX’s ACE format of nuclear data, which also gives NUSS compatibility with MCNP and SERPENT M-C codes. In contrast, multigroup uncertainties are used for the sampling of ACE-formatted pointwise-energy nuclear data in a groupwise manner due to the more limited quantity and quality of nuclear data uncertainties. Conveniently, the usage of multigroup nuclear data uncertainties allows consistent comparison between NUSS and other methods (both S/U and sampling-based) that employ the same

Centralizing Data Management with Considerations of Uncertainty and Information-Based Flexibility

OpenAIRE

Velu, Chander K.; Madnick, Stuart E.; Van Alstyne, Marshall W.

2013-01-01

This paper applies the theory of real options to analyze how the value of information-based flexibility should affect the decision to centralize or decentralize data management under low and high uncertainty. This study makes two main contributions. First, we show that in the presence of low uncertainty, centralization of data management decisions creates more total surplus for the firm as the similarity of business units increases. In contrast, in the presence of high uncertainty, centraliza...

A Proposal on the Advanced Sampling Based Sensitivity and Uncertainty Analysis Method for the Eigenvalue Uncertainty Analysis

International Nuclear Information System (INIS)

Kim, Song Hyun; Song, Myung Sub; Shin, Chang Ho; Noh, Jae Man

2014-01-01

In using the perturbation theory, the uncertainty of the response can be estimated by a single transport simulation, and therefore it requires small computational load. However, it has a disadvantage that the computation methodology must be modified whenever estimating different response type such as multiplication factor, flux, or power distribution. Hence, it is suitable for analyzing few responses with lots of perturbed parameters. Statistical approach is a sampling based method which uses randomly sampled cross sections from covariance data for analyzing the uncertainty of the response. XSUSA is a code based on the statistical approach. The cross sections are only modified with the sampling based method; thus, general transport codes can be directly utilized for the S/U analysis without any code modifications. However, to calculate the uncertainty distribution from the result, code simulation should be enough repeated with randomly sampled cross sections. Therefore, this inefficiency is known as a disadvantage of the stochastic method. In this study, an advanced sampling method of the cross sections is proposed and verified to increase the estimation efficiency of the sampling based method. In this study, to increase the estimation efficiency of the sampling based S/U method, an advanced sampling and estimation method was proposed. The main feature of the proposed method is that the cross section averaged from each single sampled cross section is used. For the use of the proposed method, the validation was performed using the perturbation theory

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

Science.gov (United States)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

OpenTURNS, an open source uncertainty engineering software

International Nuclear Information System (INIS)

Popelin, A.L.; Dufoy, A.

2013-01-01

The needs to assess robust performances for complex systems have lead to the emergence of a new industrial simulation challenge: to take into account uncertainties when dealing with complex numerical simulation frameworks. EDF has taken part in the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk and Statistics. OpenTURNS includes a large variety of qualified algorithms in order to manage uncertainties in industrial studies, from the uncertainty quantification step (with possibilities to model stochastic dependence thanks to the copula theory and stochastic processes), to the uncertainty propagation step (with some innovative simulation algorithms as the ziggurat method for normal variables) and the sensitivity analysis one (with some sensitivity index based on the evaluation of means conditioned to the realization of a particular event). It also enables to build some response surfaces that can include the stochastic modeling (with the chaos polynomial method for example). Generic wrappers to link OpenTURNS to the modeling software are proposed. At last, OpenTURNS is largely documented to provide rules to help use and contribution

Addendum to ‘Understanding risks in the light of uncertainty: low-probability, high-impact coastal events in cities’

Science.gov (United States)

Galarraga, Ibon; Sainz de Murieta, Elisa; Markandya, Anil; María Abadie, Luis

2018-02-01

This addendum adds to the analysis presented in ‘Understanding risks in the light of uncertainty: low-probability, high-impact coastal events in cities’ Abadie et al (2017 Environ. Res. Lett. 12 014017). We propose to use the framework developed earlier to enhance communication and understanding of risks, with the aim of bridging the gap between highly technical risk management discussion to the public risk aversion debate. We also propose that the framework could be used for stress-testing resilience.

Visualizing Uncertainty of Point Phenomena by Redesigned Error Ellipses

Science.gov (United States)

Murphy, Christian E.

2018-05-01

Visualizing uncertainty remains one of the great challenges in modern cartography. There is no overarching strategy to display the nature of uncertainty, as an effective and efficient visualization depends, besides on the spatial data feature type, heavily on the type of uncertainty. This work presents a design strategy to visualize uncertainty con-nected to point features. The error ellipse, well-known from mathematical statistics, is adapted to display the uncer-tainty of point information originating from spatial generalization. Modified designs of the error ellipse show the po-tential of quantitative and qualitative symbolization and simultaneous point based uncertainty symbolization. The user can intuitively depict the centers of gravity, the major orientation of the point arrays as well as estimate the ex-tents and possible spatial distributions of multiple point phenomena. The error ellipse represents uncertainty in an intuitive way, particularly suitable for laymen. Furthermore it is shown how applicable an adapted design of the er-ror ellipse is to display the uncertainty of point features originating from incomplete data. The suitability of the error ellipse to display the uncertainty of point information is demonstrated within two showcases: (1) the analysis of formations of association football players, and (2) uncertain positioning of events on maps for the media.

Jet Energy Scale Uncertainties in ATLAS

International Nuclear Information System (INIS)

Barillari, Teresa

2012-01-01

The first proton-proton collisions at a centre of mass energy of √s = 7TeV have been used by the ATLAS experiment to achieve an accuracy of the jet energy measurement between 2% and 4% for jets transverse momenta between 20 GeV and 2TeV and in the absolute pseudorapidity range up to 4.5. The jet energy scale uncertainty is derived from measurements in situ of the calorimeter single response to hadrons together with systematic variations in the Monte Carlo simulation. The transverse momentum balance between a central and a forward jet in events with two high transverse momenta jets is used to set the jet energy uncertainty in the forward region. The obtained uncertainty is confirmed by in-situ measurements. Jets in the TeV energy range have been tested using a system of well calibrated jets at low transverse momenta against high transverse momenta jets. A further reduction of the jet energy scale uncertainty between 1% and 2% for jets transverse momenta above 30 GeV has been achieved using data from the 2011 run based on an integrated luminosity of 5 fb −1 .

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

International Nuclear Information System (INIS)

Sanchez, Ana; Carlos, Sofia; Martorell, Sebastian; Villanueva, Jose F.

2009-01-01

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Energy Technology Data Exchange (ETDEWEB)

Sanchez, Ana [Department of Statistics and Operational Research, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Carlos, Sofia [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Martorell, Sebastian [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)], E-mail: smartore@iqn.upv.es; Villanueva, Jose F. [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)

2009-01-15

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.

Comparison between conservative perturbation and sampling based methods for propagation of Non-Neutronic uncertainties

International Nuclear Information System (INIS)

Campolina, Daniel de A.M.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2013-01-01

For all the physical components that comprise a nuclear system there is an uncertainty. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a best estimate calculation that has been replacing the conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using sampling based method is recent because of the huge computational effort required. In this work a sample space of MCNP calculations were used as a black box model to propagate the uncertainty of system parameters. The efficiency of the method was compared to a conservative method. Uncertainties in input parameters of the reactor considered non-neutronic uncertainties, including geometry dimensions and density. The effect of the uncertainties on the effective multiplication factor of the system was analyzed respect to the possibility of using many uncertainties in the same input. If the case includes more than 46 parameters with uncertainty in the same input, the sampling based method is proved to be more efficient than the conservative method. (author)

Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ketabchi, Hamed

2017-12-01

Combined simulation-optimization (S/O) schemes have long been recognized as a valuable tool in coastal groundwater management (CGM). However, previous applications have mostly relied on deterministic seawater intrusion (SWI) simulations. This is a questionable simplification, knowing that SWI models are inevitably prone to epistemic and aleatory uncertainty, and hence a management strategy obtained through S/O without consideration of uncertainty may result in significantly different real-world outcomes than expected. However, two key issues have hindered the use of uncertainty-based S/O schemes in CGM, which are addressed in this paper. The first issue is how to solve the computational challenges resulting from the need to perform massive numbers of simulations. The second issue is how the management problem is formulated in presence of uncertainty. We propose the use of Gaussian process (GP) emulation as a valuable tool in solving the computational challenges of uncertainty-based S/O in CGM. We apply GP emulation to the case study of Kish Island (located in the Persian Gulf) using an uncertainty-based S/O algorithm which relies on continuous ant colony optimization and Monte Carlo simulation. In doing so, we show that GP emulation can provide an acceptable level of accuracy, with no bias and low statistical dispersion, while tremendously reducing the computational time. Moreover, five new formulations for uncertainty-based S/O are presented based on concepts such as energy distances, prediction intervals and probabilities of SWI occurrence. We analyze the proposed formulations with respect to their resulting optimized solutions, the sensitivity of the solutions to the intended reliability levels, and the variations resulting from repeated optimization runs.

Event group importance measures for top event frequency analyses

International Nuclear Information System (INIS)

1995-01-01

Three traditional importance measures, risk reduction, partial derivative, nd variance reduction, have been extended to permit analyses of the relative importance of groups of underlying failure rates to the frequencies of resulting top events. The partial derivative importance measure was extended by assessing the contribution of a group of events to the gradient of the top event frequency. Given the moments of the distributions that characterize the uncertainties in the underlying failure rates, the expectation values of the top event frequency, its variance, and all of the new group importance measures can be quantified exactly for two familiar cases: (1) when all underlying failure rates are presumed independent, and (2) when pairs of failure rates based on common data are treated as being equal (totally correlated). In these cases, the new importance measures, which can also be applied to assess the importance of individual events, obviate the need for Monte Carlo sampling. The event group importance measures are illustrated using a small example problem and demonstrated by applications made as part of a major reactor facility risk assessment. These illustrations and applications indicate both the utility and the versatility of the event group importance measures

Event group importance measures for top event frequency analyses

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-31

Three traditional importance measures, risk reduction, partial derivative, nd variance reduction, have been extended to permit analyses of the relative importance of groups of underlying failure rates to the frequencies of resulting top events. The partial derivative importance measure was extended by assessing the contribution of a group of events to the gradient of the top event frequency. Given the moments of the distributions that characterize the uncertainties in the underlying failure rates, the expectation values of the top event frequency, its variance, and all of the new group importance measures can be quantified exactly for two familiar cases: (1) when all underlying failure rates are presumed independent, and (2) when pairs of failure rates based on common data are treated as being equal (totally correlated). In these cases, the new importance measures, which can also be applied to assess the importance of individual events, obviate the need for Monte Carlo sampling. The event group importance measures are illustrated using a small example problem and demonstrated by applications made as part of a major reactor facility risk assessment. These illustrations and applications indicate both the utility and the versatility of the event group importance measures.

Measure of uncertainty in regional grade variability

NARCIS (Netherlands)

Tutmez, B.; Kaymak, U.; Melin, P.; Castillo, O.; Gomez Ramirez, E.; Kacprzyk, J.; Pedrycz, W.

2007-01-01

Because the geological events are neither homogeneous nor isotropic, the geological investigations are characterized by particularly high uncertainties. This paper presents a hybrid methodology for measuring of uncertainty in regional grade variability. In order to evaluate the fuzziness in grade

Sparse grid-based polynomial chaos expansion for aerodynamics of an airfoil with uncertainties

Directory of Open Access Journals (Sweden)

Xiaojing WU

2018-05-01

Full Text Available The uncertainties can generate fluctuations with aerodynamic characteristics. Uncertainty Quantification (UQ is applied to compute its impact on the aerodynamic characteristics. In addition, the contribution of each uncertainty to aerodynamic characteristics should be computed by uncertainty sensitivity analysis. Non-Intrusive Polynomial Chaos (NIPC has been successfully applied to uncertainty quantification and uncertainty sensitivity analysis. However, the non-intrusive polynomial chaos method becomes inefficient as the number of random variables adopted to describe uncertainties increases. This deficiency becomes significant in stochastic aerodynamic analysis considering the geometric uncertainty because the description of geometric uncertainty generally needs many parameters. To solve the deficiency, a Sparse Grid-based Polynomial Chaos (SGPC expansion is used to do uncertainty quantification and sensitivity analysis for stochastic aerodynamic analysis considering geometric and operational uncertainties. It is proved that the method is more efficient than non-intrusive polynomial chaos and Monte Carlo Simulation (MSC method for the stochastic aerodynamic analysis. By uncertainty quantification, it can be learnt that the flow characteristics of shock wave and boundary layer separation are sensitive to the geometric uncertainty in transonic region. The uncertainty sensitivity analysis reveals the individual and coupled effects among the uncertainty parameters. Keywords: Non-intrusive polynomial chaos, Sparse grid, Stochastic aerodynamic analysis, Uncertainty sensitivity analysis, Uncertainty quantification

Uncertainty analysis for secondary energy distributions

International Nuclear Information System (INIS)

Gerstl, S.A.W.

1978-01-01

In many transport calculations the integral design parameter of interest (response) is determined mainly by secondary particles such as gamma rays from (n,γ) reactions or secondary neutrons from inelastic scattering events or (n,2n) reactions. Standard sensitivity analysis usually allows to calculate the sensitivities to the production cross sections of such secondaries, but an extended formalism is needed to also obtain the sensitivities to the energy distribution of the generated secondary particles. For a 30-group standard cross-section set 84% of all non-zero table positions pertain to the description of secondary energy distributions (SED's) and only 16% to the actual reaction cross sections. Therefore, any sensitivity/uncertainty analysis which does not consider the effects of SED's is incomplete and neglects most of the input data. This paper describes the methods of how sensitivity profiles for SED's are obtained and used to estimate the uncertainty of an integral response due to uncertainties in these SED's. The detailed theory is documented elsewhere and implemented in the LASL sensitivity code SENSIT. SED sensitivity profiles have proven particularly valuable in cross-section uncertainty analyses for fusion reactors. Even when the production cross sections for secondary neutrons were assumed to be without error, the uncertainties in the energy distribution of these secondaries produced appreciable uncertainties in the calculated tritium breeding rate. However, complete error files for SED's are presently nonexistent. Therefore, methods will be described that allow rough error estimates due to estimated SED uncertainties based on integral SED sensitivities

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

OpenAIRE

Wooyeon Sunwoo; Minha Choi

2017-01-01

Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall even...

The characterisation and evaluation of uncertainty in probabilistic risk analysis

International Nuclear Information System (INIS)

Parry, G.W.; Winter, P.W.

1980-10-01

The sources of uncertainty in probabilistic risk analysis are discussed using the event/fault tree methodology as an example. The role of statistics in quantifying these uncertainties is investigated. A class of uncertainties is identified which is, at present, unquantifiable, using either classical or Bayesian statistics. It is argued that Bayesian statistics is the more appropriate vehicle for the probabilistic analysis of rare events and a short review is given with some discussion on the representation of ignorance. (author)

Probabilistic Models for Solar Particle Events

Science.gov (United States)

Adams, James H., Jr.; Dietrich, W. F.; Xapsos, M. A.; Welton, A. M.

2009-01-01

Probabilistic Models of Solar Particle Events (SPEs) are used in space mission design studies to provide a description of the worst-case radiation environment that the mission must be designed to tolerate.The models determine the worst-case environment using a description of the mission and a user-specified confidence level that the provided environment will not be exceeded. This poster will focus on completing the existing suite of models by developing models for peak flux and event-integrated fluence elemental spectra for the Z>2 elements. It will also discuss methods to take into account uncertainties in the data base and the uncertainties resulting from the limited number of solar particle events in the database. These new probabilistic models are based on an extensive survey of SPE measurements of peak and event-integrated elemental differential energy spectra. Attempts are made to fit the measured spectra with eight different published models. The model giving the best fit to each spectrum is chosen and used to represent that spectrum for any energy in the energy range covered by the measurements. The set of all such spectral representations for each element is then used to determine the worst case spectrum as a function of confidence level. The spectral representation that best fits these worst case spectra is found and its dependence on confidence level is parameterized. This procedure creates probabilistic models for the peak and event-integrated spectra.

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

Science.gov (United States)

Wahl, N.; Hennig, P.; Wieser, H. P.; Bangert, M.

2017-07-01

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ≤slant {5} min). The resulting standard deviation (expectation value) of dose show average global γ{3% / {3}~mm} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity

Uncertainty in dispersion forecasts using meteorological ensembles

International Nuclear Information System (INIS)

Chin, H N; Leach, M J

1999-01-01

The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes

Impact of advanced fuel cycles on uncertainty associated with geologic repositories

International Nuclear Information System (INIS)

Rechard, Rob P.; Lee, Joon; Sutton, Mark; Greenberg, Harris R.; Robinson, Bruce A.; Nutt, W. Mark

2013-01-01

This paper provides a qualitative evaluation of the impact of advanced fuel cycles, particularly partition and transmutation of actinides, on the uncertainty associated with geologic disposal. Based on the discussion, advanced fuel cycles, will not materially alter (1) the repository performance (2) the spread in dose results around the mean (3) the modeling effort to include significant features, events, and processes in the performance assessment, or (4) the characterization of uncertainty associated with a geologic disposal system in the regulatory environment of the United States. (authors)

Application of best estimate and uncertainty safety analysis methodology to loss of flow events at Ontario's Power Generation's Darlington Nuclear Generating Station

International Nuclear Information System (INIS)

Huget, R.G.; Lau, D.K.; Luxat, J.C.

2001-01-01

Ontario Power Generation (OPG) is currently developing a new safety analysis methodology based on best estimate and uncertainty (BEAU) analysis. The framework and elements of the new safety analysis methodology are defined. The evolution of safety analysis technology at OPG has been thoroughly documented. Over the years, the use of conservative limiting assumptions in OPG safety analyses has led to gradual erosion of predicted safety margins. The main purpose of the new methodology is to provide a more realistic quantification of safety margins within a probabilistic framework, using best estimate results, with an integrated accounting of the underlying uncertainties. Another objective of the new methodology is to provide a cost-effective means for on-going safety analysis support of OPG's nuclear generating stations. Discovery issues and plant aging effects require that the safety analyses be periodically revised and, in the past, the cost of reanalysis at OPG has been significant. As OPG enters the new competitive marketplace for electricity, there is a strong need to conduct safety analysis in a less cumbersome manner. This paper presents the results of the first licensing application of the new methodology in support of planned design modifications to the shutdown systems (SDSs) at Darlington Nuclear Generating Station (NGS). The design modifications restore dual trip parameter coverage over the full range of reactor power for certain postulated loss-of-flow (LOF) events. The application of BEAU analysis to the single heat transport pump trip event provides a realistic estimation of the safety margins for the primary and backup trip parameters. These margins are significantly larger than those predicted by conventional limit of the operating envelope (LOE) analysis techniques. (author)

Uncertainty analysis with statistically correlated failure data

International Nuclear Information System (INIS)

Modarres, M.; Dezfuli, H.; Roush, M.L.

1987-01-01

Likelihood of occurrence of the top event of a fault tree or sequences of an event tree is estimated from the failure probability of components that constitute the events of the fault/event tree. Component failure probabilities are subject to statistical uncertainties. In addition, there are cases where the failure data are statistically correlated. At present most fault tree calculations are based on uncorrelated component failure data. This chapter describes a methodology for assessing the probability intervals for the top event failure probability of fault trees or frequency of occurrence of event tree sequences when event failure data are statistically correlated. To estimate mean and variance of the top event, a second-order system moment method is presented through Taylor series expansion, which provides an alternative to the normally used Monte Carlo method. For cases where component failure probabilities are statistically correlated, the Taylor expansion terms are treated properly. Moment matching technique is used to obtain the probability distribution function of the top event through fitting the Johnson Ssub(B) distribution. The computer program, CORRELATE, was developed to perform the calculations necessary for the implementation of the method developed. (author)

Model uncertainties in top-quark physics

CERN Document Server

Seidel, Markus

2014-01-01

The ATLAS and CMS collaborations at the Large Hadron Collider (LHC) are studying the top quark in pp collisions at 7 and 8 TeV. Due to the large integrated luminosity, precision measurements of production cross-sections and properties are often limited by systematic uncertainties. An overview of the modeling uncertainties for simulated events is given in this report.

Can agent based models effectively reduce fisheries management implementation uncertainty?

Science.gov (United States)

Drexler, M.

2016-02-01

Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

Application of a new importance measure for parametric uncertainty in PSA

International Nuclear Information System (INIS)

Poern, K.

1997-04-01

The traditional approach to uncertainty analysis in PSA, with propagation of basic event uncertainties through the PSA model, generates as an end product the uncertainty distribution of the top event frequency. This distribution, however, is not of much value for the decision maker. Most decisions are made under uncertainty. What the decision maker needs, to enhance the decision-making quality, is an adequate uncertainty importance measure that provides the decision maker with an indication of on what basic parameters it would be most valuable - as to the quality of the decision making in the specific situation - to procure more information. This paper will describe an application of a new measure of uncertainty importance that has been developed in the ongoing joint Nordic project NKS/RAK-1:3. The measure is called ''decision oriented'' because it is defined within a decision theoretic framework. It is defined as the expected value of a certain additional information about each basic parameter, and utilizes both the system structure and the complete uncertainty distributions of the basic parameters. The measure provides the analyst and the decision maker with a diagnostic information pointing to parameters on which more information would be most valuable to procure in order to enhance the decision-making quality. This uncertainty importance measure must not be confused with the more well-known, traditional importance measures of various kinds that are used to depict the contributions of each basic event or parameter (represented by point values) to the top event frequency. In this study the new measure is practically demonstrated through a real application on the top event: Water overflow through steam generator safety valves caused by steam generator tube rupture. This application object is one of the event sequences that the fore mentioned Nordic project has analysed with an integrated approach. The project has been funded by the Swedish Nuclear Power

Articulating uncertainty as part of scientific argumentation during model-based exoplanet detection tasks

Science.gov (United States)

Lee, Hee-Sun; Pallant, Amy; Pryputniewicz, Sarah

2015-08-01

Teaching scientific argumentation has emerged as an important goal for K-12 science education. In scientific argumentation, students are actively involved in coordinating evidence with theory based on their understanding of the scientific content and thinking critically about the strengths and weaknesses of the cited evidence in the context of the investigation. We developed a one-week-long online curriculum module called "Is there life in space?" where students conduct a series of four model-based tasks to learn how scientists detect extrasolar planets through the “wobble” and transit methods. The simulation model allows students to manipulate various parameters of an imaginary star and planet system such as planet size, orbit size, planet-orbiting-plane angle, and sensitivity of telescope equipment, and to adjust the display settings for graphs illustrating the relative velocity and light intensity of the star. Students can use model-based evidence to formulate an argument on whether particular signals in the graphs guarantee the presence of a planet. Students' argumentation is facilitated by the four-part prompts consisting of multiple-choice claim, open-ended explanation, Likert-scale uncertainty rating, and open-ended uncertainty rationale. We analyzed 1,013 scientific arguments formulated by 302 high school student groups taught by 7 teachers. We coded these arguments in terms of the accuracy of their claim, the sophistication of explanation connecting evidence to the established knowledge base, the uncertainty rating, and the scientific validity of uncertainty. We found that (1) only 18% of the students' uncertainty rationale involved critical reflection on limitations inherent in data and concepts, (2) 35% of students' uncertainty rationale reflected their assessment of personal ability and knowledge, rather than scientific sources of uncertainty related to the evidence, and (3) the nature of task such as the use of noisy data or the framing of

Uncertainty dimensions of information behaviour in a group based problem solving context

DEFF Research Database (Denmark)

Hyldegård, Jette

2009-01-01

This paper presents a study of uncertainty dimensions of information behaviour in a group based problem solving context. After a presentation of the cognitive uncertainty dimension underlying Kuhlthau's ISP-model, uncertainty factors associated with personality, the work task situation and social...... members' experiences of uncertainty differ from the individual information seeker in Kuhlthau's ISP-model, and how this experience may be related to personal, work task and social factors. A number of methods have been employed to collect data on each group member during the assignment process......: a demographic survey, a personality test, 3 process surveys, 3 diaries and 3 interviews. It was found that group members' experiences of uncertainty did not correspond with the ISP-model in that other factors beyond the mere information searching process seemed to intermingle with the complex process...

Status of XSUSA for sampling based nuclear data uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Zwermann, W.; Gallner, L.; Klein, M.; Krzydacz-Hausmann; Pasichnyk, I.; Pautz, A.; Velkov, K.

2013-01-01

In the present contribution, an overview of the sampling based XSUSA method for sensitivity and uncertainty analysis with respect to nuclear data is given. The focus is on recent developments and applications of XSUSA. These applications include calculations for critical assemblies, fuel assembly depletion calculations, and steady state as well as transient reactor core calculations. The analyses are partially performed in the framework of international benchmark working groups (UACSA - Uncertainty Analyses for Criticality Safety Assessment, UAM - Uncertainty Analysis in Modelling). It is demonstrated that particularly for full-scale reactor calculations the influence of the nuclear data uncertainties on the results can be substantial. For instance, for the radial fission rate distributions of mixed UO 2 /MOX light water reactor cores, the 2σ uncertainties in the core centre and periphery can reach values exceeding 10%. For a fast transient, the resulting time behaviour of the reactor power was covered by a wide uncertainty band. Overall, the results confirm the necessity of adding systematic uncertainty analyses to best-estimate reactor calculations. (authors)

Uncertainty-based calibration and prediction with a stormwater surface accumulation-washoff model based on coverage of sampled Zn, Cu, Pb and Cd field data

DEFF Research Database (Denmark)

Lindblom, Erik Ulfson; Ahlman, S.; Mikkelsen, Peter Steen

2011-01-01

allows identifying a range of behavioral model parameter sets. The small catchment size and nearness of the rain gauge justified excluding the hydrological model parameters from the uncertainty assessment. Uniform, closed prior distributions were heuristically specified for the dry and wet removal...... of accumulated metal available on the conceptual catchment surface. Forward Monte Carlo analysis based on the posterior parameter sets covered 95% of the observed event mean concentrations, and 95% prediction quantiles for site mean concentrations were estimated to 470 μg/l ±20% for Zn, 295 μg/l ±40% for Cu, 20...

Uncertainty of Water-hammer Loads for Safety Related Systems

Energy Technology Data Exchange (ETDEWEB)

Lee, Seung Chan; Yoon, Duk Joo [Korea Hydro and Nuclear Power Co., LT., Daejeon (Korea, Republic of)

2013-10-15

In this study, the basic methodology is base on ISO GUM (Guide to the Expression of Uncertainty in Measurements). For a given gas void volumes in the discharge piping, the maximum pressure of water hammer is defined in equation. From equation, uncertainty parameter is selected as U{sub s} (superficial velocity for the specific pipe size and corresponding area) of equation. The main uncertainty parameter (U{sub s}) is estimated by measurement method and Monte Carlo simulation. Two methods are in good agreement with the extended uncertainty. Extended uncertainty of the measurement and Monte Carlo simulation is 1.30 and 1.34 respectively in 95% confidence interval. In 99% confidence interval, the uncertainties are 1.95 and 1.97 respectively. NRC Generic Letter 2008-01 requires nuclear power plant operators to evaluate the possibility of noncondensable gas accumulation for the Emergency Core Cooling System. Specially, gas accumulation can result in system pressure transient in pump discharge piping at a pump start. Consequently, this evolves into a gas water, a water-hammer event and the force imbalances on the piping segments. In this paper, MCS (Monte Carlo Simulation) method is introduced in estimating the uncertainty of water hammer. The aim is to evaluate the uncertainty of the water hammer estimation results carried out by KHNP CRI in 2013.

Assessing flood forecast uncertainty with fuzzy arithmetic

Directory of Open Access Journals (Sweden)

de Bruyn Bertrand

2016-01-01

Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

Energy Technology Data Exchange (ETDEWEB)

Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

Scenario-based approach for flexible resource loading under uncertainty

NARCIS (Netherlands)

Wullink, G.; Gademann, A.J.R.M.; Hans, E.W.; Harten, van A.

2004-01-01

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To quote reliable due dates in order processing, manage resource capacity adequately and take into account uncertainty, the paper presents and analyses models and tools for

Hydraulic description of a flood event with optical remote sensors: a constructive constraint on modelling uncertainties

Science.gov (United States)

Battiston, Stéphanie; Allenbach, Bernard

2010-05-01

compartments; high resolution optical imagery allow the exhaustive inventory of breaches and overflows; turbidity variations and draw-off give information on stream directions. These facts are of primary interest to help in deriving a firm understanding of the flooding processes, but also comprise a powerful source for the necessary parameterization and/or calibration of hydraulic models. Thus the accuracy of flood extents derived from remote sensing data could, on the one hand, be valuable inputs to historical flood info-bases within overall risk-linked databases, and on the other hand, test the validity of hydrological modelling, while helping to lift equifinality uncertainties. These first investigations highlight that space imagery of events constitutes an unrivalled tool for flood disaster observation. This 2D record is complementary to all field measurements and the integration of "space derived flood products" is valuable for all stages of risk management. This potential of EO optical sensors for flood monitoring is also confirmed in a detailed analysis making a qualitative and quantitative evaluation of the results, confronting ten optical and radar remote sensing platforms with field observations.

MODELS OF AIR TRAFFIC CONTROLLERS ERRORS PREVENTION IN TERMINAL CONTROL AREAS UNDER UNCERTAINTY CONDITIONS

Directory of Open Access Journals (Sweden)

Volodymyr Kharchenko

2017-03-01

Full Text Available Purpose: the aim of this study is to research applied models of air traffic controllers’ errors prevention in terminal control areas (TMA under uncertainty conditions. In this work the theoretical framework descripting safety events and errors of air traffic controllers connected with the operations in TMA is proposed. Methods: optimisation of terminal control area formal description based on the Threat and Error management model and the TMA network model of air traffic flows. Results: the human factors variables associated with safety events in work of air traffic controllers under uncertainty conditions were obtained. The Threat and Error management model application principles to air traffic controller operations and the TMA network model of air traffic flows were proposed. Discussion: Information processing context for preventing air traffic controller errors, examples of threats in work of air traffic controllers, which are relevant for TMA operations under uncertainty conditions.

DEVS representation of dynamical systems - Event-based intelligent control. [Discrete Event System Specification

Science.gov (United States)

Zeigler, Bernard P.

1989-01-01

It is shown how systems can be advantageously represented as discrete-event models by using DEVS (discrete-event system specification), a set-theoretic formalism. Such DEVS models provide a basis for the design of event-based logic control. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The event-based contral paradigm is applied in advanced robotic and intelligent automation, showing how classical process control can be readily interfaced with rule-based symbolic reasoning systems.

Analysis of manufacturing based on object oriented discrete event simulation

Directory of Open Access Journals (Sweden)

Eirik Borgen

1990-01-01

Full Text Available This paper describes SIMMEK, a computer-based tool for performing analysis of manufacturing systems, developed at the Production Engineering Laboratory, NTH-SINTEF. Its main use will be in analysis of job shop type of manufacturing. But certain facilities make it suitable for FMS as well as a production line manufacturing. This type of simulation is very useful in analysis of any types of changes that occur in a manufacturing system. These changes may be investments in new machines or equipment, a change in layout, a change in product mix, use of late shifts, etc. The effects these changes have on for instance the throughput, the amount of VIP, the costs or the net profit, can be analysed. And this can be done before the changes are made, and without disturbing the real system. Simulation takes into consideration, unlike other tools for analysis of manufacturing systems, uncertainty in arrival rates, process and operation times, and machine availability. It also shows the interaction effects a job which is late in one machine, has on the remaining machines in its route through the layout. It is these effects that cause every production plan not to be fulfilled completely. SIMMEK is based on discrete event simulation, and the modeling environment is object oriented. The object oriented models are transformed by an object linker into data structures executable by the simulation kernel. The processes of the entity objects, i.e. the products, are broken down to events and put into an event list. The user friendly graphical modeling environment makes it possible for end users to build models in a quick and reliable way, using terms from manufacturing. Various tests and a check of model logic are helpful functions when testing validity of the models. Integration with software packages, with business graphics and statistical functions, is convenient in the result presentation phase.

An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method

International Nuclear Information System (INIS)

Campolina, Daniel; Lima, Paulo Rubens I.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2015-01-01

Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k eff was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)

Robustness for slope stability modelling under deep uncertainty

Science.gov (United States)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2015-04-01

Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

Facing uncertainty in ecosystem services-based resource management.

Science.gov (United States)

Grêt-Regamey, Adrienne; Brunner, Sibyl H; Altwegg, Jürg; Bebi, Peter

2013-09-01

The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to human-environment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Host Event Based Network Monitoring

Energy Technology Data Exchange (ETDEWEB)

Jonathan Chugg

2013-01-01

The purpose of INL’s research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.

A new measure of uncertainty importance based on distributional sensitivity analysis for PSA

International Nuclear Information System (INIS)

Han, Seok Jung; Tak, Nam Il; Chun, Moon Hyun

1996-01-01

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance

Interval-based reconstruction for uncertainty quantification in PET

Science.gov (United States)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

Neutrino induced events in the MINOS detectors

International Nuclear Information System (INIS)

Litchfield, Reuben Phillip

2008-01-01

The MINOS experiment is designed to study neutrino oscillations. It uses an accelerator generated beam of neutrinos and two detectors, the smaller at a distance of 1km and the larger at 735 km. By comparing the spectrum and flavour composition of the beam at the two detectors precise determinations of the oscillation parameters are possible. This thesis concentrates on the analysis of data from the larger Far Detector. By studying the spectrum of neutral current events it is possible to look for evidence of non-interacting 'sterile' neutrinos. The thesis describes how events are selected for this analysis, and a method for discriminating between charged current and neutral current events. The systematic uncertainties resulting from these cuts are evaluated. Several techniques for using Near Detector data to eliminate systematic uncertainties in the predicted Far Detector spectrum are compared. An oscillation analysis, based on the first year of MINOS data, uses the selected events to make a measurement of f s , the fraction of unseen neutrinos that are sterile. The measured value is f s = 0.07 +0.32 at 68%C.L., and is consistent with the standard three-neutrino picture, which has no sterile neutrino

Application of intelligence based uncertainty analysis for HLW disposal

International Nuclear Information System (INIS)

Kato, Kazuyuki

2003-01-01

Safety assessment for geological disposal of high level radioactive waste inevitably involves factors that cannot be specified in a deterministic manner. These are namely: (1) 'variability' that arises from stochastic nature of the processes and features considered, e.g., distribution of canister corrosion times and spatial heterogeneity of a host geological formation; (2) 'ignorance' due to incomplete or imprecise knowledge of the processes and conditions expected in the future, e.g., uncertainty in the estimation of solubilities and sorption coefficients for important nuclides. In many cases, a decision in assessment, e.g., selection among model options or determination of a parameter value, is subjected to both variability and ignorance in a combined form. It is clearly important to evaluate both influences of variability and ignorance on the result of a safety assessment in a consistent manner. We developed a unified methodology to handle variability and ignorance by using probabilistic and possibilistic techniques respectively. The methodology has been applied to safety assessment of geological disposal of high level radioactive waste. Uncertainties associated with scenarios, models and parameters were defined in terms of fuzzy membership functions derived through a series of interviews to the experts while variability was formulated by means of probability density functions (pdfs) based on available data set. The exercise demonstrated applicability of the new methodology and, in particular, its advantage in quantifying uncertainties based on expert's opinion and in providing information on dependence of assessment result on the level of conservatism. In addition, it was also shown that sensitivity analysis could identify key parameters in reducing uncertainties associated with the overall assessment. The above information can be used to support the judgment process and guide the process of disposal system development in optimization of protection against

Event-based Sensing for Space Situational Awareness

Science.gov (United States)

Cohen, G.; Afshar, S.; van Schaik, A.; Wabnitz, A.; Bessell, T.; Rutten, M.; Morreale, B.

A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

Energy Technology Data Exchange (ETDEWEB)

Bruschewski, Martin; Schiffer, Heinz-Peter [Technische Universitaet Darmstadt, Institute of Gas Turbines and Aerospace Propulsion, Darmstadt (Germany); Freudenhammer, Daniel [Technische Universitaet Darmstadt, Institute of Fluid Mechanics and Aerodynamics, Center of Smart Interfaces, Darmstadt (Germany); Buchenberg, Waltraud B. [University Medical Center Freiburg, Medical Physics, Department of Radiology, Freiburg (Germany); Grundmann, Sven [University of Rostock, Institute of Fluid Mechanics, Rostock (Germany)

2016-05-15

Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75% is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented. (orig.)

Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

Science.gov (United States)

Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven

2016-05-01

Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.

A Belief Rule Based Expert System to Assess Mental Disorder under Uncertainty

DEFF Research Database (Denmark)

Hossain, Mohammad Shahadat; Afif Monrat, Ahmed; Hasan, Mamun

2016-01-01

to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema......Mental disorder is a change of mental or behavioral pattern that causes sufferings and impairs the ability to function in ordinary life. In psychopathology, the assessment methods of mental disorder contain various types of uncertainties associated with signs and symptoms. This study identifies...

Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk.

Science.gov (United States)

MacLeod, D A; Morse, A P

2014-12-02

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

Out of the black box: expansion of a theory-based intervention to self-manage the uncertainty associated with active surveillance (AS) for prostate cancer.

Science.gov (United States)

Kazer, Meredith Wallace; Bailey, Donald E; Whittemore, Robin

2010-01-01

Active surveillance (AS) (sometimes referred to as watchful waiting) is an alternative approach to managing low-risk forms of prostate cancer. This management approach allows men to avoid expensive prostate cancer treatments and their well-documented adverse events of erectile dysfunction and incontinence. However, AS is associated with illness uncertainty and reduced quality of life (QOL; Wallace, 2003). An uncertainty management intervention (UMI) was developed by Mishel et al. (2002) to manage uncertainty in women treated for breast cancer and men treated for prostate cancer. However, the UMI was not developed for men undergoing AS for prostate cancer and has not been adequately tested in this population. This article reports on the expansion of a theory-based intervention to manage the uncertainty associated with AS for prostate cancer. Intervention Theory (Sidani & Braden, 1998) is discussed as a framework for revising the UMI intervention for men undergoing AS for prostate cancer (UMI-AS). The article concludes with plans for testing of the expanded intervention and implications for the extended theory.

Qualitative processing of uncertainty, conflicts and redundancy in knowledge bases

International Nuclear Information System (INIS)

Zbytovsky, V.

1994-01-01

This paper describes two techniques, created and implemented in the course of development of the real-time on-line expert system Recon at the Nuclear Research Institute at Rez, Czech Republic. The first of them is the qualitative processing of uncertainty, which is based on the introduction of the third logic value to logic data objects, and the credibility flag to arithmetic data objects. The treatment of the third value and credibility flags during the inference, the explanation method based on the graphic representation and the uncertainty processing during the explanation are also mentioned. The second technique, is a semantic checking of knowledge bases, which enables us to recover parts of the bases, that are meaningless, either because of an error during their implementation into a base, or because they are redundant. The paper includes the explanation of basic terms of this method, such as so called conflicts, K-group and K-situation. The two types of the conflict (dead-end and bubble) are also discussed. The paper also offers the complete mathematical apparatus, which the checking method is based on. (author). 4 refs, tabs

Complementary contributions of basolateral amygdala and orbitofrontal cortex to value learning under uncertainty

Science.gov (United States)

Stolyarova, Alexandra; Izquierdo, Alicia

2017-01-01

We make choices based on the values of expected outcomes, informed by previous experience in similar settings. When the outcomes of our decisions consistently violate expectations, new learning is needed to maximize rewards. Yet not every surprising event indicates a meaningful change in the environment. Even when conditions are stable overall, outcomes of a single experience can still be unpredictable due to small fluctuations (i.e., expected uncertainty) in reward or costs. In the present work, we investigate causal contributions of the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) in rats to learning under expected outcome uncertainty in a novel delay-based task that incorporates both predictable fluctuations and directional shifts in outcome values. We demonstrate that OFC is required to accurately represent the distribution of wait times to stabilize choice preferences despite trial-by-trial fluctuations in outcomes, whereas BLA is necessary for the facilitation of learning in response to surprising events. DOI: http://dx.doi.org/10.7554/eLife.27483.001 PMID:28682238

Development of Property Models with Uncertainty Estimate for Process Design under Uncertainty

DEFF Research Database (Denmark)

Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

more reliable predictions with a new and improved set of model parameters for GC (group contribution) based and CI (atom connectivity index) based models and to quantify the uncertainties in the estimated property values from a process design point-of-view. This includes: (i) parameter estimation using....... The comparison of model prediction uncertainties with reported range of measurement uncertainties is presented for the properties with related available data. The application of the developed methodology to quantify the effect of these uncertainties on the design of different unit operations (distillation column......, the developed methodology can be used to quantify the sensitivity of process design to uncertainties in property estimates; obtain rationally the risk/safety factors in process design; and identify additional experimentation needs in order to reduce most critical uncertainties....

Characterizing Drought Events from a Hydrological Model Ensemble

Science.gov (United States)

Smith, Katie; Parry, Simon; Prudhomme, Christel; Hannaford, Jamie; Tanguy, Maliko; Barker, Lucy; Svensson, Cecilia

2017-04-01

Hydrological droughts are a slow onset natural hazard that can affect large areas. Within the United Kingdom there have been eight major drought events over the last 50 years, with several events acting at the continental scale, and covering the entire nation. Many of these events have lasted several years and had significant impacts on agriculture, the environment and the economy. Generally in the UK, due to a northwest-southeast gradient in rainfall and relief, as well as varying underlying geology, droughts tend to be most severe in the southeast, which can threaten water supplies to the capital in London. With the impacts of climate change likely to increase the severity and duration of drought events worldwide, it is crucial that we gain an understanding of the characteristics of some of the longer and more extreme droughts of the 19th and 20th centuries, so we may utilize this information in planning for the future. Hydrological models are essential both for reconstructing such events that predate streamflow records, and for use in drought forecasting. However, whilst the uncertainties involved in modelling hydrological extremes on the flooding end of the flow regime have been studied in depth over the past few decades, the uncertainties in simulating droughts and low flow events have not yet received such rigorous academic attention. The "Cascade of Uncertainty" approach has been applied to explore uncertainty and coherence across simulations of notable drought events from the past 50 years using the airGR family of daily lumped catchment models. Parameter uncertainty has been addressed using a Latin Hypercube sampled experiment of 500,000 parameter sets per model (GR4J, GR5J and GR6J), over more than 200 catchments across the UK. The best performing model parameterisations, determined using a multi-objective function approach, have then been taken forward for use in the assessment of the impact of model parameters and model structure on drought event

Sampling based uncertainty analysis of 10% hot leg break LOCA in large scale test facility

International Nuclear Information System (INIS)

Sengupta, Samiran; Kraina, V.; Dubey, S. K.; Rao, R. S.; Gupta, S. K.

2010-01-01

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between 5 th and 95 th percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure

Statistically based uncertainty assessments in nuclear risk analysis

International Nuclear Information System (INIS)

Spencer, F.W.; Diegert, K.V.; Easterling, R.G.

1987-01-01

Over the last decade, the problems of estimation and uncertainty assessment in probabilistics risk assessment (PRAs) have been addressed in a variety of NRC and industry-sponsored projects. These problems have received attention because of a recognition that major uncertainties in risk estimation exist, which can be reduced by collecting more and better data and other information, and because of a recognition that better methods for assessing these uncertainties are needed. In particular, a clear understanding of the nature and magnitude of various sources of uncertainty is needed to facilitate descision-making on possible plant changes and research options. Recent PRAs have employed methods of probability propagation, sometimes involving the use of Bayes Theorem, and intended to formalize the use of ''engineering judgment'' or ''expert opinion.'' All sources, or feelings, of uncertainty are expressed probabilistically, so that uncertainty analysis becomes simply a matter of probability propagation. Alternatives to forcing a probabilistic framework at all stages of a PRA are a major concern in this paper, however

Refinement of the concept of uncertainty.

Science.gov (United States)

Penrod, J

2001-04-01

To analyse the conceptual maturity of uncertainty; to develop an expanded theoretical definition of uncertainty; to advance the concept using methods of concept refinement; and to analyse congruency with the conceptualization of uncertainty presented in the theory of hope, enduring, and suffering. Uncertainty is of concern in nursing as people experience complex life events surrounding health. In an earlier nursing study that linked the concepts of hope, enduring, and suffering into a single theoretical scheme, a state best described as 'uncertainty' arose. This study was undertaken to explore how this conceptualization fit with the scientific literature on uncertainty and to refine the concept. Initially, a concept analysis using advanced methods described by Morse, Hupcey, Mitcham and colleagues was completed. The concept was determined to be partially mature. A theoretical definition was derived and techniques of concept refinement using the literature as data were applied. The refined concept was found to be congruent with the concept of uncertainty that had emerged in the model of hope, enduring and suffering. Further investigation is needed to explore the extent of probabilistic reasoning and the effects of confidence and control on feelings of uncertainty and certainty.

Uncertainty propagation in probabilistic risk assessment: A comparative study

International Nuclear Information System (INIS)

Ahmed, S.; Metcalf, D.R.; Pegram, J.W.

1982-01-01

Three uncertainty propagation techniques, namely method of moments, discrete probability distribution (DPD), and Monte Carlo simulation, generally used in probabilistic risk assessment, are compared and conclusions drawn in terms of the accuracy of the results. For small uncertainty in the basic event unavailabilities, the three methods give similar results. For large uncertainty, the method of moments is in error, and the appropriate method is to propagate uncertainty in the discrete form either by DPD method without sampling or by Monte Carlo. (orig.)

Learning Risk-Taking and Coping with Uncertainty through Experiential, Team-Based Entrepreneurship Education

Science.gov (United States)

Arpiainen, Riitta-Liisa; Kurczewska, Agnieszka

2017-01-01

This empirical study investigates how students' perceptions of risk-taking and coping with uncertainty change while they are exposed to experience-based entrepreneurship education. The aim of the study is twofold. First, the authors set out to identify the dynamics of entrepreneurial thinking among students experiencing risk and uncertainty while…

Sensitivity/uncertainty analysis for the Hiroshima dosimetry reevaluation effort

International Nuclear Information System (INIS)

Broadhead, B.L.; Lillie, R.A.; Pace, J.V. III; Cacuci, D.G.

1987-01-01

Uncertainty estimates and cross correlations by range/survivor location have been obtained for the free-in-air (FIA) tissue kerma for the Hiroshima atomic event. These uncertainties in the FIA kerma include contributions due to various modeling parameters and the basic cross section data and are given at three ground ranges, 700, 1000 and 1500 m. The estimated uncertainties are nearly constant over the given ground ranges and are approximately 27% for the prompt neutron kerma and secondary gamma kerma and 35% for the prompt gamma kerma. The total kerma uncertainty is dominated by the secondary gamma kerma uncertainties which are in turn largely due to the modeling parameter uncertainties

Jet Energy Scale Uncertainties in ATLAS

CERN Document Server

Barillari, T; The ATLAS collaboration

2012-01-01

About one year after the first proton-proton collisions at a centre of mass energy of $sqrt(s) = 7,TeV$, the ATLAS experiment has achieved an accuracy of the jet energy measurement between $2-4%$ for jet transverse momenta from $20,GeV$ to $2,TeV$ in the pseudorapidity range up to $4.5$. The jet energy scale uncertainty is derived from in-situ single hadron response measurement along with systematic variations in the Monte Carlo simulation. In addition, the transverse momentum balance between a central and a forward jet in events with only two jets at high transverse momentum is used to set the jet energy uncertainty in the forward region. The obtained uncertainty is confirmed by in-situ measurements exploiting the transverse momentum balance between a jet and a well measured reference object like the photon transverse momentum in photon-jet events. Jets in the TeV-energy regime were tested using a system of well calibrated jets at low transverse momenta against a high-pt jet. Preliminary results from the 201...

Uncertainty relation and probability. Numerical illustration

International Nuclear Information System (INIS)

Fujikawa, Kazuo; Umetsu, Koichiro

2011-01-01

The uncertainty relation and the probability interpretation of quantum mechanics are intrinsically connected, as is evidenced by the evaluation of standard deviations. It is thus natural to ask if one can associate a very small uncertainty product of suitably sampled events with a very small probability. We have shown elsewhere that some examples of the evasion of the uncertainty relation noted in the past are in fact understood in this way. We here numerically illustrate that a very small uncertainty product is realized if one performs a suitable sampling of measured data that occur with a very small probability. We introduce a notion of cyclic measurements. It is also shown that our analysis is consistent with the Landau-Pollak-type uncertainty relation. It is suggested that the present analysis may help reconcile the contradicting views about the 'standard quantum limit' in the detection of gravitational waves. (author)

Correction of harmonic motion and Kepler orbit based on the minimal momentum uncertainty

Energy Technology Data Exchange (ETDEWEB)

Chung, Won Sang, E-mail: mimip4444@hanmail.net [Department of Physics and Research Institute of Natural Science, College of Natural Science, Gyeongsang National University, Jinju 660-701 (Korea, Republic of); Hassanabadi, Hassan, E-mail: h.hasanabadi@shahroodut.ac.ir [Physics Department, Shahrood University of Technology, Shahrood (Iran, Islamic Republic of)

2017-03-18

In this paper we consider the deformed Heisenberg uncertainty principle with the minimal uncertainty in momentum which is called a minimal momentum uncertainty principle (MMUP). We consider MMUP in D-dimension and its classical analogue. Using these we investigate the MMUP effect for the harmonic motion and Kepler orbit. - Highlights: • We discussed minimal momentum uncertainty relation. • We considered MMUR in D-dimension and used the deformed Poisson bracket to find the classical mechanics based on the MMUR. • Using these we investigate the MMUR effect for the harmonic motion and Kepler orbit. • Especially, we computed the corrected precession angle for each case. • We found that the corrected precession angle is always positive.

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Energy Technology Data Exchange (ETDEWEB)

Gillingham, Kenneth; Nordhaus, William; Anthoff, David; Blanford, Geoffrey J.; Bosetti, Valentina; Christensen, Peter; McJeon, Haewon C.; Reilly, J. M.; Sztorc, Paul

2015-10-01

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO₂ concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

Problems in event based engine control

DEFF Research Database (Denmark)

Hendricks, Elbert; Jensen, Michael; Chevalier, Alain Marie Roger

1994-01-01

Physically a four cycle spark ignition engine operates on the basis of four engine processes or events: intake, compression, ignition (or expansion) and exhaust. These events each occupy approximately 180° of crank angle. In conventional engine controllers, it is an accepted practice to sample...... the engine variables synchronously with these events (or submultiples of them). Such engine controllers are often called event-based systems. Unfortunately the main system noise (or disturbance) is also synchronous with the engine events: the engine pumping fluctuations. Since many electronic engine...... problems on accurate air/fuel ratio control of a spark ignition (SI) engine....

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

NARCIS (Netherlands)

Pratomo, J.; Kuffer, M.; Martinez, Javier; Kohli, D.

2017-01-01

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our

Uncertainty and sensitivity analysis on probabilistic safety assessment of an experimental facility

International Nuclear Information System (INIS)

Burgazzi, L.

2000-01-01

The aim of this work is to perform an uncertainty and sensitivity analysis on the probabilistic safety assessment of the International Fusion Materials Irradiation Facility (IFMIF), in order to assess the effect on the final risk values of the uncertainties associated with the generic data used for the initiating events and component reliability and to identify the key quantities contributing to this uncertainty. The analysis is conducted on the expected frequency calculated for the accident sequences, defined through the event tree (ET) modeling. This is in order to increment credit to the ET model quantification, to calculate frequency distributions for the occurrence of events and, consequently, to assess if sequences have been correctly selected on the probability standpoint and finally to verify the fulfillment of the safety conditions. Uncertainty and sensitivity analysis are performed using respectively Monte Carlo sampling and an importance parameter technique. (author)

Crashworthiness uncertainty analysis of typical civil aircraft based on Box–Behnken method

OpenAIRE

Ren Yiru; Xiang Jinwu

2014-01-01

The crashworthiness is an important design factor of civil aircraft related with the safety of occupant during impact accident. It is a highly nonlinear transient dynamic problem and may be greatly influenced by the uncertainty factors. Crashworthiness uncertainty analysis is conducted to investigate the effects of initial conditions, structural dimensions and material properties. Simplified finite element model is built based on the geometrical model and basic physics phenomenon. Box–Behnken...

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

Science.gov (United States)

Ligmann-Zielinska, Arika; Kramer, Daniel B; Spence Cheruvelil, Kendra; Soranno, Patricia A

2014-01-01

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

Directory of Open Access Journals (Sweden)

Arika Ligmann-Zielinska

Full Text Available Agent-based models (ABMs have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1 efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2 conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

Uncertainties of flood frequency estimation approaches based on continuous simulation using data resampling

Science.gov (United States)

Arnaud, Patrick; Cantet, Philippe; Odry, Jean

2017-11-01

Flood frequency analyses (FFAs) are needed for flood risk management. Many methods exist ranging from classical purely statistical approaches to more complex approaches based on process simulation. The results of these methods are associated with uncertainties that are sometimes difficult to estimate due to the complexity of the approaches or the number of parameters, especially for process simulation. This is the case of the simulation-based FFA approach called SHYREG presented in this paper, in which a rainfall generator is coupled with a simple rainfall-runoff model in an attempt to estimate the uncertainties due to the estimation of the seven parameters needed to estimate flood frequencies. The six parameters of the rainfall generator are mean values, so their theoretical distribution is known and can be used to estimate the generator uncertainties. In contrast, the theoretical distribution of the single hydrological model parameter is unknown; consequently, a bootstrap method is applied to estimate the calibration uncertainties. The propagation of uncertainty from the rainfall generator to the hydrological model is also taken into account. This method is applied to 1112 basins throughout France. Uncertainties coming from the SHYREG method and from purely statistical approaches are compared, and the results are discussed according to the length of the recorded observations, basin size and basin location. Uncertainties of the SHYREG method decrease as the basin size increases or as the length of the recorded flow increases. Moreover, the results show that the confidence intervals of the SHYREG method are relatively small despite the complexity of the method and the number of parameters (seven). This is due to the stability of the parameters and takes into account the dependence of uncertainties due to the rainfall model and the hydrological calibration. Indeed, the uncertainties on the flow quantiles are on the same order of magnitude as those associated with

An enhanced unified uncertainty analysis approach based on first order reliability method with single-level optimization

International Nuclear Information System (INIS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; Tooren, Michel van

2013-01-01

In engineering, there exist both aleatory uncertainties due to the inherent variation of the physical system and its operational environment, and epistemic uncertainties due to lack of knowledge and which can be reduced with the collection of more data. To analyze the uncertain distribution of the system performance under both aleatory and epistemic uncertainties, combined probability and evidence theory can be employed to quantify the compound effects of the mixed uncertainties. The existing First Order Reliability Method (FORM) based Unified Uncertainty Analysis (UUA) approach nests the optimization based interval analysis in the improved Hasofer–Lind–Rackwitz–Fiessler (iHLRF) algorithm based Most Probable Point (MPP) searching procedure, which is computationally inhibitive for complex systems and may encounter convergence problem as well. Therefore, in this paper it is proposed to use general optimization solvers to search MPP in the outer loop and then reformulate the double-loop optimization problem into an equivalent single-level optimization (SLO) problem, so as to simplify the uncertainty analysis process, improve the robustness of the algorithm, and alleviate the computational complexity. The effectiveness and efficiency of the proposed method is demonstrated with two numerical examples and one practical satellite conceptual design problem. -- Highlights: ► Uncertainty analysis under mixed aleatory and epistemic uncertainties is studied. ► A unified uncertainty analysis method is proposed with combined probability and evidence theory. ► The traditional nested analysis method is converted to single level optimization for efficiency. ► The effectiveness and efficiency of the proposed method are testified with three examples

Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

Science.gov (United States)

DeChant, C. M.; Moradkhani, H.

2014-12-01

Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

Neutrino induced events in the MINOS detectors

Energy Technology Data Exchange (ETDEWEB)

Litchfield, Reuben Phillip [Univ. of Oxford (United Kingdom). Keble College

2008-01-01

The MINOS experiment is designed to study neutrino oscillations. It uses an accelerator generated beam of neutrinos and two detectors, the smaller at a distance of 1km and the larger at 735 km. By comparing the spectrum and flavour composition of the beam at the two detectors precise determinations of the oscillation parameters are possible. This thesis concentrates on the analysis of data from the larger Far Detector. By studying the spectrum of neutral current events it is possible to look for evidence of non-interacting 'sterile' neutrinos. The thesis describes how events are selected for this analysis, and a method for discriminating between charged current and neutral current events. The systematic uncertainties resulting from these cuts are evaluated. Several techniques for using Near Detector data to eliminate systematic uncertainties in the predicted Far Detector spectrum are compared. An oscillation analysis, based on the first year of MINOS data, uses the selected events to make a measurement of f{sub s}, the fraction of unseen neutrinos that are sterile. The measured value is f_s = 0.07^+0.32 at 68%C.L., and is consistent with the standard three-neutrino picture, which has no sterile neutrino.

Development of the Operational Events Groups Ranking Tool

International Nuclear Information System (INIS)

Simic, Zdenko; Banov, Reni

2014-01-01

Both because of complexity and ageing, facilities like nuclear power plants require feedback from the operating experience in order to further improve safety and operation performance. That is the reason why significant effort is dedicated to operating experience feedback. This paper contains description of the specification and development of the application for the operating events ranking software tool. Robust and consistent way of selecting most important events for detail investigation is important because it is not feasible or even useful to investigate all of them. Development of the tool is based on the comprehensive events characterisation and methodical prioritization. This includes rich set of events parameters which allow their top level preliminary analysis, different ways of groupings and even to evaluate uncertainty propagation to the ranking results. One distinct feature of the implemented method is that user (i.e., expert) could determine how important is particular ranking parameter based on their pairwise comparison. For tools demonstration and usability it is crucial that sample database is also created. For useful analysis the whole set of events for 5 years is selected and characterised. Based on the preliminary results this tool seems valuable for new preliminary prospective on data as whole, and especially for the identification of events groups which should have priority in the more detailed assessment. The results are consisting of different informative views on the events groups importance and related sensitivity and uncertainty results. This presents valuable tool for improving overall picture about specific operating experience and also for helping identify the most important events groups for further assessment. It is clear that completeness and consistency of the input data characterisation is very important to get full and valuable importance ranking. Method and tool development described in this paper is part of continuous effort of

Uncertainty evaluation for three-dimensional scanning electron microscope reconstructions based on the stereo-pair technique

International Nuclear Information System (INIS)

Carli, L; Cantatore, A; De Chiffre, L; Genta, G; Barbato, G; Levi, R

2011-01-01

3D-SEM is a method, based on the stereophotogrammetry technique, which obtains three-dimensional topographic reconstructions starting typically from two SEM images, called the stereo-pair. In this work, a theoretical uncertainty evaluation of the stereo-pair technique, according to GUM (Guide to the Expression of Uncertainty in Measurement), was carried out, considering 3D-SEM reconstructions of a wire gauge with a reference diameter of 250 µm. Starting from the more commonly used tilting strategy, one based on the item rotation inside the SEM chamber was also adopted. The latter enables multiple-view reconstructions of the cylindrical item under consideration. Uncertainty evaluation was performed starting from a modified version of the Piazzesi equation, enabling the calculation of the z-coordinate from a given stereo-pair. The metrological characteristics of each input variable have been taken into account and a SEM stage calibration has been performed. Uncertainty tables for the cases of tilt and rotation were then produced, leading to the calculation of expanded uncertainty. For the case of rotation, the largest uncertainty contribution resulted to be the rotational angle; however, for the case of tilt it resulted to be the pixel size. A relative expanded uncertainty equal to 5% and 4% was obtained for the case of rotation and tilt, respectively

Epistemic uncertainties and natural hazard risk assessment - Part 1: A review of the issues

Science.gov (United States)

Beven, K. J.; Aspinall, W. P.; Bates, P. D.; Borgomeo, E.; Goda, K.; Hall, J. W.; Page, T.; Phillips, J. C.; Rougier, J. T.; Simpson, M.; Stephenson, D. B.; Smith, P. J.; Wagener, T.; Watson, M.

2015-12-01

Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. There is a lack of knowledge about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions that are made for risk management, so it is important to communicate the meaning of an uncertainty estimate and to provide an audit trail of the assumptions on which it is based. Some suggestions for good practice in doing so are made.

Quantifying uncertainties in wind energy assessment

Science.gov (United States)

Patlakas, Platon; Galanis, George; Kallos, George

2015-04-01

The constant rise of wind energy production and the subsequent penetration in global energy markets during the last decades resulted in new sites selection with various types of problems. Such problems arise due to the variability and the uncertainty of wind speed. The study of the wind speed distribution lower and upper tail may support the quantification of these uncertainties. Such approaches focused on extreme wind conditions or periods below the energy production threshold are necessary for a better management of operations. Towards this direction, different methodologies are presented for the credible evaluation of potential non-frequent/extreme values for these environmental conditions. The approaches used, take into consideration the structural design of the wind turbines according to their lifespan, the turbine failures, the time needed for repairing as well as the energy production distribution. In this work, a multi-parametric approach for studying extreme wind speed values will be discussed based on tools of Extreme Value Theory. In particular, the study is focused on extreme wind speed return periods and the persistence of no energy production based on a weather modeling system/hind cast/10-year dataset. More specifically, two methods (Annual Maxima and Peaks Over Threshold) were used for the estimation of extreme wind speeds and their recurrence intervals. Additionally, two different methodologies (intensity given duration and duration given intensity, both based on Annual Maxima method) were implied to calculate the extreme events duration, combined with their intensity as well as the event frequency. The obtained results prove that the proposed approaches converge, at least on the main findings, for each case. It is also remarkable that, despite the moderate wind speed climate of the area, several consequent days of no energy production are observed.

Parton shower and NLO-matching uncertainties in Higgs boson pair production

Science.gov (United States)

Jones, Stephen; Kuttimalai, Silvan

2018-02-01

We perform a detailed study of NLO parton shower matching uncertainties in Higgs boson pair production through gluon fusion at the LHC based on a generic and process independent implementation of NLO subtraction and parton shower matching schemes for loop-induced processes in the Sherpa event generator. We take into account the full top-quark mass dependence in the two-loop virtual corrections and compare the results to an effective theory approximation. In the full calculation, our findings suggest large parton shower matching uncertainties that are absent in the effective theory approximation. We observe large uncertainties even in regions of phase space where fixed-order calculations are theoretically well motivated and parton shower effects expected to be small. We compare our results to NLO matched parton shower simulations and analytic resummation results that are available in the literature.

Propagation of hydro-meteorological uncertainty in a model cascade framework to inundation prediction

Science.gov (United States)

Rodríguez-Rincón, J. P.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.

2015-07-01

This investigation aims to study the propagation of meteorological uncertainty within a cascade modelling approach to flood prediction. The methodology was comprised of a numerical weather prediction (NWP) model, a distributed rainfall-runoff model and a 2-D hydrodynamic model. The uncertainty evaluation was carried out at the meteorological and hydrological levels of the model chain, which enabled the investigation of how errors that originated in the rainfall prediction interact at a catchment level and propagate to an estimated inundation area and depth. For this, a hindcast scenario is utilised removing non-behavioural ensemble members at each stage, based on the fit with observed data. At the hydrodynamic level, an uncertainty assessment was not incorporated; instead, the model was setup following guidelines for the best possible representation of the case study. The selected extreme event corresponds to a flood that took place in the southeast of Mexico during November 2009, for which field data (e.g. rain gauges; discharge) and satellite imagery were available. Uncertainty in the meteorological model was estimated by means of a multi-physics ensemble technique, which is designed to represent errors from our limited knowledge of the processes generating precipitation. In the hydrological model, a multi-response validation was implemented through the definition of six sets of plausible parameters from past flood events. Precipitation fields from the meteorological model were employed as input in a distributed hydrological model, and resulting flood hydrographs were used as forcing conditions in the 2-D hydrodynamic model. The evolution of skill within the model cascade shows a complex aggregation of errors between models, suggesting that in valley-filling events hydro-meteorological uncertainty has a larger effect on inundation depths than that observed in estimated flood inundation extents.

A risk measurement tool for an underground electricity distribution system considering the consequences and uncertainties of manhole events

International Nuclear Information System (INIS)

Garcez, Thalles Vitelli; Teixeira de Almeida, Adiel

2014-01-01

This paper explores a risk measure of underground vaults that considers the consequences of arc faults. The increasing use of underground systems, together with the aging of networks, the lack of maintenance and interference from other (third party) underground systems nearby have caused many accidents in urban areas, thus endangering human life. The involvement of a large number (hundreds or thousands) of underground vaults with different characteristics, the lack of historical data on modes of failure, the rarity of the occurrence of some faults, the magnitude of their consequences and the involvement of a complex environment surrounding the hazard zone make risk management even more complex and uncertain. Furthermore, given that the (monetary, time, staff, etc.) resources of an electrical power company are limited and scarce, it is necessary to use decision-making tools that aggregate the consequences and the uncertainties to assess the risks jointly with the preference structure of the company, thus solving the problem more realistically. Therefore, this paper puts forward the use of an additional risk analysis for manhole events in underground electrical distribution networks with a view to its being used as a decision aid tool in risk management. As an illustration of the use of the risk measurement tool proposed, a numerical application is presented. The result rather than showing a ranking of underground vaults, gives a measure of the risk used that can show the decision-maker (DM) how much better one group of alternatives (formed by alternatives with quite similar risk values) is than other groups, based on the DM’s attitude to risk and grounded on the axiomatic structure of utility theory. - Highlights: • The paper proposes a risk measure of underground vaults for manhole events. • It makes risk analysis in underground electrical distribution networks. • It makes more than show a risk ranking of underground vaults. • It can show to the DM how

A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties

International Nuclear Information System (INIS)

Lv, Y.; Yan, X.D.; Sun, W.; Gao, Z.Y.

2015-01-01

Emergencies involved in a bus–subway corridor system are associated with many processes and factors with social and economic implications. These processes and factors and their interactions are related to a variety of uncertainties. In this study, an interval chance-constrained integer programming (EICI) method is developed in response to such challenges for bus–subway corridor based evacuation planning. The method couples a chance-constrained programming with an interval integer programming model framework. It can thus deal with interval uncertainties that cannot be quantified with specified probability distribution functions. Meanwhile, it can also reflect stochastic features of traffic flow capacity, and thereby help examine the related violation risk of constraint. The EICI method is applied to a subway incident based evacuation case study. It is solved through an interactive algorithm that does not lead to more complicated intermediate submodels and has a relatively low computational requirement. A number of decision alternatives could be directly generated based on results from the EICI method. It is indicated that the solutions cannot only help decision makers identify desired population evacuation and vehicle dispatch schemes under hybrid uncertainties, but also provide bases for in-depth analyses of tradeoffs among evacuation plans, total evacuation time, and constraint-violation risks. - Highlights: • An inexact model is developed for the bus–subway corridor evacuation management. • It tackles stochastic and interval uncertainties in an integer programming problem. • It can examine violation risk of the roadway flow capacity related constraint. • It will help identify evacuation schemes under hybrid uncertainties

Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty

International Nuclear Information System (INIS)

Zhu, Shun-Peng; Huang, Hong-Zhong; Peng, Weiwen; Wang, Hai-Kun; Mahadevan, Sankaran

2016-01-01

A probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs operating under uncertainty is developed. The framework incorporates the overall uncertainties appearing in a structural integrity assessment. A comprehensive uncertainty quantification (UQ) procedure is presented to quantify multiple types of uncertainty using multiplicative and additive UQ methods. In addition, the factors that contribute the most to the resulting output uncertainty are investigated and identified for uncertainty reduction in decision-making. A high prediction accuracy of the proposed framework is validated through a comparison of model predictions to the experimental results of GH4133 superalloy and full-scale tests of aero engine high-pressure turbine discs. - Highlights: • A probabilistic PoF-based framework for fatigue life prediction is proposed. • A comprehensive procedure forquantifyingmultiple types of uncertaintyis presented. • The factors that contribute most to the resulting output uncertainty are identified. • The proposed frameworkdemonstrates high prediction accuracybyfull-scale tests.

Model Uncertainties for Valencia RPA Effect for MINERvA

Energy Technology Data Exchange (ETDEWEB)

Gran, Richard [Univ. of Minnesota, Duluth, MN (United States)

2017-05-08

This technical note describes the application of the Valencia RPA multi-nucleon effect and its uncertainty to QE reactions from the GENIE neutrino event generator. The analysis of MINERvA neutrino data in Rodrigues et al. PRL 116 071802 (2016) paper makes clear the need for an RPA suppression, especially at very low momentum and energy transfer. That published analysis does not constrain the magnitude of the effect; it only tests models with and without the effect against the data. Other MINERvA analyses need an expression of the model uncertainty in the RPA effect. A well-described uncertainty can be used for systematics for unfolding, for model errors in the analysis of non-QE samples, and as input for fitting exercises for model testing or constraining backgrounds. This prescription takes uncertainties on the parameters in the Valencia RPA model and adds a (not-as-tight) constraint from muon capture data. For MINERvA we apply it as a 2D ($q_0$,$q_3$) weight to GENIE events, in lieu of generating a full beyond-Fermi-gas quasielastic events. Because it is a weight, it can be applied to the generated and fully Geant4 simulated events used in analysis without a special GENIE sample. For some limited uses, it could be cast as a 1D $Q^2$ weight without much trouble. This procedure is a suitable starting point for NOvA and DUNE where the energy dependence is modest, but probably not adequate for T2K or MicroBooNE.

Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Newman, Jennifer F.; Clifton, Andrew

2017-03-08

Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

Qualification and application of nuclear reactor accident analysis code with the capability of internal assessment of uncertainty

International Nuclear Information System (INIS)

Borges, Ronaldo Celem

2001-10-01

This thesis presents an independent qualification of the CIAU code ('Code with the capability of - Internal Assessment of Uncertainty') which is part of the internal uncertainty evaluation process with a thermal hydraulic system code on a realistic basis. This is done by combining the uncertainty methodology UMAE ('Uncertainty Methodology based on Accuracy Extrapolation') with the RELAP5/Mod3.2 code. This allows associating uncertainty band estimates with the results obtained by the realistic calculation of the code, meeting licensing requirements of safety analysis. The independent qualification is supported by simulations with RELAP5/Mod3.2 related to accident condition tests of LOBI experimental facility and to an event which has occurred in Angra 1 nuclear power plant, by comparison with measured results and by establishing uncertainty bands on safety parameter calculated time trends. These bands have indeed enveloped the measured trends. Results from this independent qualification of CIAU have allowed to ascertain the adequate application of a systematic realistic code procedure to analyse accidents with uncertainties incorporated in the results, although there is an evident need of extending the uncertainty data base. It has been verified that use of the code with this internal assessment of uncertainty is feasible in the design and license stages of a NPP. (author)

Quantification of uncertainties in turbulence modeling: A comparison of physics-based and random matrix theoretic approaches

International Nuclear Information System (INIS)

Wang, Jian-Xun; Sun, Rui; Xiao, Heng

2016-01-01

Highlights: • Compared physics-based and random matrix methods to quantify RANS model uncertainty. • Demonstrated applications of both methods in channel ow over periodic hills. • Examined the amount of information introduced in the physics-based approach. • Discussed implications to modeling turbulence in both near-wall and separated regions. - Abstract: Numerical models based on Reynolds-Averaged Navier-Stokes (RANS) equations are widely used in engineering turbulence modeling. However, the RANS predictions have large model-form uncertainties for many complex flows, e.g., those with non-parallel shear layers or strong mean flow curvature. Quantification of these large uncertainties originating from the modeled Reynolds stresses has attracted attention in the turbulence modeling community. Recently, a physics-based Bayesian framework for quantifying model-form uncertainties has been proposed with successful applications to several flows. Nonetheless, how to specify proper priors without introducing unwarranted, artificial information remains challenging to the current form of the physics-based approach. Another recently proposed method based on random matrix theory provides the prior distributions with maximum entropy, which is an alternative for model-form uncertainty quantification in RANS simulations. This method has better mathematical rigorousness and provides the most non-committal prior distributions without introducing artificial constraints. On the other hand, the physics-based approach has the advantages of being more flexible to incorporate available physical insights. In this work, we compare and discuss the advantages and disadvantages of the two approaches on model-form uncertainty quantification. In addition, we utilize the random matrix theoretic approach to assess and possibly improve the specification of priors used in the physics-based approach. The comparison is conducted through a test case using a canonical flow, the flow past

Recent developments in predictive uncertainty assessment based on the model conditional processor approach

Directory of Open Access Journals (Sweden)

G. Coccia

2011-10-01

Full Text Available The work aims at discussing the role of predictive uncertainty in flood forecasting and flood emergency management, its relevance to improve the decision making process and the techniques to be used for its assessment.

Real time flood forecasting requires taking into account predictive uncertainty for a number of reasons. Deterministic hydrological/hydraulic forecasts give useful information about real future events, but their predictions, as usually done in practice, cannot be taken and used as real future occurrences but rather used as pseudo-measurements of future occurrences in order to reduce the uncertainty of decision makers. Predictive Uncertainty (PU is in fact defined as the probability of occurrence of a future value of a predictand (such as water level, discharge or water volume conditional upon prior observations and knowledge as well as on all the information we can obtain on that specific future value from model forecasts. When dealing with commensurable quantities, as in the case of floods, PU must be quantified in terms of a probability distribution function which will be used by the emergency managers in their decision process in order to improve the quality and reliability of their decisions.

After introducing the concept of PU, the presently available processors are introduced and discussed in terms of their benefits and limitations. In this work the Model Conditional Processor (MCP has been extended to the possibility of using two joint Truncated Normal Distributions (TNDs, in order to improve adaptation to low and high flows.

The paper concludes by showing the results of the application of the MCP on two case studies, the Po river in Italy and the Baron Fork river, OK, USA. In the Po river case the data provided by the Civil Protection of the Emilia Romagna region have been used to implement an operational example, where the predicted variable is the observed water level. In the Baron Fork River

Reliability analysis under epistemic uncertainty

International Nuclear Information System (INIS)

Nannapaneni, Saideep; Mahadevan, Sankaran

2016-01-01

This paper proposes a probabilistic framework to include both aleatory and epistemic uncertainty within model-based reliability estimation of engineering systems for individual limit states. Epistemic uncertainty is considered due to both data and model sources. Sparse point and/or interval data regarding the input random variables leads to uncertainty regarding their distribution types, distribution parameters, and correlations; this statistical uncertainty is included in the reliability analysis through a combination of likelihood-based representation, Bayesian hypothesis testing, and Bayesian model averaging techniques. Model errors, which include numerical solution errors and model form errors, are quantified through Gaussian process models and included in the reliability analysis. The probability integral transform is used to develop an auxiliary variable approach that facilitates a single-level representation of both aleatory and epistemic uncertainty. This strategy results in an efficient single-loop implementation of Monte Carlo simulation (MCS) and FORM/SORM techniques for reliability estimation under both aleatory and epistemic uncertainty. Two engineering examples are used to demonstrate the proposed methodology. - Highlights: • Epistemic uncertainty due to data and model included in reliability analysis. • A novel FORM-based approach proposed to include aleatory and epistemic uncertainty. • A single-loop Monte Carlo approach proposed to include both types of uncertainties. • Two engineering examples used for illustration.

Rule-Based Event Processing and Reaction Rules

Science.gov (United States)

Paschke, Adrian; Kozlenkov, Alexander

Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

Consideration of vertical uncertainty in elevation-based sea-level rise assessments: Mobile Bay, Alabama case study

Science.gov (United States)

Gesch, Dean B.

2013-01-01

The accuracy with which coastal topography has been mapped directly affects the reliability and usefulness of elevationbased sea-level rise vulnerability assessments. Recent research has shown that the qualities of the elevation data must be well understood to properly model potential impacts. The cumulative vertical uncertainty has contributions from elevation data error, water level data uncertainties, and vertical datum and transformation uncertainties. The concepts of minimum sealevel rise increment and minimum planning timeline, important parameters for an elevation-based sea-level rise assessment, are used in recognition of the inherent vertical uncertainty of the underlying data. These concepts were applied to conduct a sea-level rise vulnerability assessment of the Mobile Bay, Alabama, region based on high-quality lidar-derived elevation data. The results that detail the area and associated resources (land cover, population, and infrastructure) vulnerable to a 1.18-m sea-level rise by the year 2100 are reported as a range of values (at the 95% confidence level) to account for the vertical uncertainty in the base data. Examination of the tabulated statistics about land cover, population, and infrastructure in the minimum and maximum vulnerable areas shows that these resources are not uniformly distributed throughout the overall vulnerable zone. The methods demonstrated in the Mobile Bay analysis provide an example of how to consider and properly account for vertical uncertainty in elevation-based sea-level rise vulnerability assessments, and the advantages of doing so.

Uncertainty and Sensitivity Analyses of a Pebble Bed HTGR Loss of Cooling Event

Directory of Open Access Journals (Sweden)

Gerhard Strydom

2013-01-01

Full Text Available The Very High Temperature Reactor Methods Development group at the Idaho National Laboratory identified the need for a defensible and systematic uncertainty and sensitivity approach in 2009. This paper summarizes the results of an uncertainty and sensitivity quantification investigation performed with the SUSA code, utilizing the International Atomic Energy Agency CRP 5 Pebble Bed Modular Reactor benchmark and the INL code suite PEBBED-THERMIX. Eight model input parameters were selected for inclusion in this study, and after the input parameters variations and probability density functions were specified, a total of 800 steady state and depressurized loss of forced cooling (DLOFC transient PEBBED-THERMIX calculations were performed. The six data sets were statistically analyzed to determine the 5% and 95% DLOFC peak fuel temperature tolerance intervals with 95% confidence levels. It was found that the uncertainties in the decay heat and graphite thermal conductivities were the most significant contributors to the propagated DLOFC peak fuel temperature uncertainty. No significant differences were observed between the results of Simple Random Sampling (SRS or Latin Hypercube Sampling (LHS data sets, and use of uniform or normal input parameter distributions also did not lead to any significant differences between these data sets.

High cumulants of conserved charges and their statistical uncertainties

Science.gov (United States)

Li-Zhu, Chen; Ye-Yin, Zhao; Xue, Pan; Zhi-Ming, Li; Yuan-Fang, Wu

2017-10-01

We study the influence of measured high cumulants of conserved charges on their associated statistical uncertainties in relativistic heavy-ion collisions. With a given number of events, the measured cumulants randomly fluctuate with an approximately normal distribution, while the estimated statistical uncertainties are found to be correlated with corresponding values of the obtained cumulants. Generally, with a given number of events, the larger the cumulants we measure, the larger the statistical uncertainties that are estimated. The error-weighted averaged cumulants are dependent on statistics. Despite this effect, however, it is found that the three sigma rule of thumb is still applicable when the statistics are above one million. Supported by NSFC (11405088, 11521064, 11647093), Major State Basic Research Development Program of China (2014CB845402) and Ministry of Science and Technology (MoST) (2016YFE0104800)

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

Science.gov (United States)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

A Proposal of Estimation Methodology to Improve Calculation Efficiency of Sampling-based Method in Nuclear Data Sensitivity and Uncertainty Analysis

International Nuclear Information System (INIS)

Song, Myung Sub; Kim, Song Hyun; Kim, Jong Kyung; Noh, Jae Man

2014-01-01

The uncertainty with the sampling-based method is evaluated by repeating transport calculations with a number of cross section data sampled from the covariance uncertainty data. In the transport calculation with the sampling-based method, the transport equation is not modified; therefore, all uncertainties of the responses such as k eff , reaction rates, flux and power distribution can be directly obtained all at one time without code modification. However, a major drawback with the sampling-based method is that it requires expensive computational load for statistically reliable results (inside confidence level 0.95) in the uncertainty analysis. The purpose of this study is to develop a method for improving the computational efficiency and obtaining highly reliable uncertainty result in using the sampling-based method with Monte Carlo simulation. The proposed method is a method to reduce the convergence time of the response uncertainty by using the multiple sets of sampled group cross sections in a single Monte Carlo simulation. The proposed method was verified by estimating GODIVA benchmark problem and the results were compared with that of conventional sampling-based method. In this study, sampling-based method based on central limit theorem is proposed to improve calculation efficiency by reducing the number of repetitive Monte Carlo transport calculation required to obtain reliable uncertainty analysis results. Each set of sampled group cross sections is assigned to each active cycle group in a single Monte Carlo simulation. The criticality uncertainty for the GODIVA problem is evaluated by the proposed and previous method. The results show that the proposed sampling-based method can efficiently decrease the number of Monte Carlo simulation required for evaluate uncertainty of k eff . It is expected that the proposed method will improve computational efficiency of uncertainty analysis with sampling-based method

Uncertainty management in knowledge based systems for nondestructive testing-an example from ultrasonic testing

International Nuclear Information System (INIS)

Rajagopalan, C.; Kalyanasundaram, P.; Baldev Raj

1996-01-01

The use of fuzzy logic, as a framework for uncertainty management, in a knowledge-based system (KBS) for ultrasonic testing of austenitic stainless steels is described. Parameters that may contain uncertain values are identified. Methodologies to handle uncertainty in these parameters using fuzzy logic are detailed. The overall improvement in the performance of the knowledge-based system after incorporating fuzzy logic is discussed. The methodology developed being universal, its extension to other KBS for nondestructive testing and evaluation is highlighted. (author)

Modelling of plasma-based dry reforming: how do uncertainties in the input data affect the calculation results?

Science.gov (United States)

Wang, Weizong; Berthelot, Antonin; Zhang, Quanzhi; Bogaerts, Annemie

2018-05-01

One of the main issues in plasma chemistry modeling is that the cross sections and rate coefficients are subject to uncertainties, which yields uncertainties in the modeling results and hence hinders the predictive capabilities. In this paper, we reveal the impact of these uncertainties on the model predictions of plasma-based dry reforming in a dielectric barrier discharge. For this purpose, we performed a detailed uncertainty analysis and sensitivity study. 2000 different combinations of rate coefficients, based on the uncertainty from a log-normal distribution, are used to predict the uncertainties in the model output. The uncertainties in the electron density and electron temperature are around 11% and 8% at the maximum of the power deposition for a 70% confidence level. Still, this can have a major effect on the electron impact rates and hence on the calculated conversions of CO2 and CH4, as well as on the selectivities of CO and H2. For the CO2 and CH4 conversion, we obtain uncertainties of 24% and 33%, respectively. For the CO and H2 selectivity, the corresponding uncertainties are 28% and 14%, respectively. We also identify which reactions contribute most to the uncertainty in the model predictions. In order to improve the accuracy and reliability of plasma chemistry models, we recommend using only verified rate coefficients, and we point out the need for dedicated verification experiments.

PSA-based evaluation and rating of operational events

International Nuclear Information System (INIS)

Gomez Cobo, A.

1997-01-01

The presentation discusses the PSA-based evaluation and rating of operational events, including the following: historical background, procedures for event evaluation using PSA, use of PSA for event rating, current activities

Managing project risks and uncertainties

Directory of Open Access Journals (Sweden)

Mike Mentis

2015-01-01

Full Text Available This article considers threats to a project slipping on budget, schedule and fit-for-purpose. Threat is used here as the collective for risks (quantifiable bad things that can happen and uncertainties (poorly or not quantifiable bad possible events. Based on experience with projects in developing countries this review considers that (a project slippage is due to uncertainties rather than risks, (b while eventuation of some bad things is beyond control, managed execution and oversight are still the primary means to keeping within budget, on time and fit-for-purpose, (c improving project delivery is less about bigger and more complex and more about coordinated focus, effectiveness and developing thought-out heuristics, and (d projects take longer and cost more partly because threat identification is inaccurate, the scope of identified threats is too narrow, and the threat assessment product is not integrated into overall project decision-making and execution. Almost by definition, what is poorly known is likely to cause problems. Yet it is not just the unquantifiability and intangibility of uncertainties causing project slippage, but that they are insufficiently taken into account in project planning and execution that cause budget and time overruns. Improving project performance requires purpose-driven and managed deployment of scarce seasoned professionals. This can be aided with independent oversight by deeply experienced panelists who contribute technical insights and can potentially show that diligence is seen to be done.

An Oracle-based Event Index for ATLAS

CERN Document Server

Gallas, Elizabeth; The ATLAS collaboration; Petrova, Petya Tsvetanova; Baranowski, Zbigniew; Canali, Luca; Formica, Andrea; Dumitru, Andrei

2016-01-01

The ATLAS EventIndex System has amassed a set of key quantities for a large number of ATLAS events into a Hadoop based infrastructure for the purpose of providing the experiment with a number of event-wise services. Collecting this data in one place provides the opportunity to investigate various storage formats and technologies and assess which best serve the various use cases as well as consider what other benefits alternative storage systems provide. In this presentation we describe how the data are imported into an Oracle RDBMS, the services we have built based on this architecture, and our experience with it. We've indexed about 15 billion real data events and about 25 billion simulated events thus far and have designed the system to accommodate future data which has expected rates of 5 and 20 billion events per year for real data and simulation, respectively. We have found this system offers outstanding performance for some fundamental use cases. In addition, profiting from the co-location of this data ...

Systematisation of spatial uncertainties for comparison between a MR and a CT-based radiotherapy workflow for prostate treatments

International Nuclear Information System (INIS)

Nyholm, Tufve; Nyberg, Morgan; Karlsson, Magnus G; Karlsson, Mikael

2009-01-01

In the present work we compared the spatial uncertainties associated with a MR-based workflow for external radiotherapy of prostate cancer to a standard CT-based workflow. The MR-based workflow relies on target definition and patient positioning based on MR imaging. A solution for patient transport between the MR scanner and the treatment units has been developed. For the CT-based workflow, the target is defined on a MR series but then transferred to a CT study through image registration before treatment planning, and a patient positioning using portal imaging and fiducial markers. An 'open bore' 1.5T MRI scanner, Siemens Espree, has been installed in the radiotherapy department in near proximity to a treatment unit to enable patient transport between the two installations, and hence use the MRI for patient positioning. The spatial uncertainty caused by the transport was added to the uncertainty originating from the target definition process, estimated through a review of the scientific literature. The uncertainty in the CT-based workflow was estimated through a literature review. The systematic uncertainties, affecting all treatment fractions, are reduced from 3-4 mm (1Sd) with a CT based workflow to 2-3 mm with a MR based workflow. The main contributing factor to this improvement is the exclusion of registration between MR and CT in the planning phase of the treatment. Treatment planning directly on MR images reduce the spatial uncertainty for prostate treatments

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties.

Science.gov (United States)

Shao, Xingling; Wang, Honglun

2015-01-01

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Statistical uncertainty of extreme wind storms over Europe derived from a probabilistic clustering technique

Science.gov (United States)

Walz, Michael; Leckebusch, Gregor C.

2016-04-01

Extratropical wind storms pose one of the most dangerous and loss intensive natural hazards for Europe. However, due to only 50 years of high quality observational data, it is difficult to assess the statistical uncertainty of these sparse events just based on observations. Over the last decade seasonal ensemble forecasts have become indispensable in quantifying the uncertainty of weather prediction on seasonal timescales. In this study seasonal forecasts are used in a climatological context: By making use of the up to 51 ensemble members, a broad and physically consistent statistical base can be created. This base can then be used to assess the statistical uncertainty of extreme wind storm occurrence more accurately. In order to determine the statistical uncertainty of storms with different paths of progression, a probabilistic clustering approach using regression mixture models is used to objectively assign storm tracks (either based on core pressure or on extreme wind speeds) to different clusters. The advantage of this technique is that the entire lifetime of a storm is considered for the clustering algorithm. Quadratic curves are found to describe the storm tracks most accurately. Three main clusters (diagonal, horizontal or vertical progression of the storm track) can be identified, each of which have their own particulate features. Basic storm features like average velocity and duration are calculated and compared for each cluster. The main benefit of this clustering technique, however, is to evaluate if the clusters show different degrees of uncertainty, e.g. more (less) spread for tracks approaching Europe horizontally (diagonally). This statistical uncertainty is compared for different seasonal forecast products.

Static Analysis for Event-Based XML Processing

DEFF Research Database (Denmark)

Møller, Anders

2008-01-01

Event-based processing of XML data - as exemplified by the popular SAX framework - is a powerful alternative to using W3C's DOM or similar tree-based APIs. The event-based approach is a streaming fashion with minimal memory consumption. This paper discusses challenges for creating program analyses...... for SAX applications. In particular, we consider the problem of statically guaranteeing the a given SAX program always produces only well-formed and valid XML output. We propose an analysis technique based on ecisting anglyses of Servlets, string operations, and XML graphs....

The uncertainty budget in pharmaceutical industry

DEFF Research Database (Denmark)

Heydorn, Kaj

of their uncertainty, exactly as described in GUM [2]. Pharmaceutical industry has therefore over the last 5 years shown increasing interest in accreditation according to ISO 17025 [3], and today uncertainty budgets are being developed for all so-called critical measurements. The uncertainty of results obtained...... that the uncertainty of a particular result is independent of the method used for its estimation. Several examples of uncertainty budgets for critical parameters based on the bottom-up procedure will be discussed, and it will be shown how the top-down method is used as a means of verifying uncertainty budgets, based...

Effect of uncertainties on probabilistic-based design capacity of hydrosystems

Science.gov (United States)

Tung, Yeou-Koung

2018-02-01

Hydrosystems engineering designs involve analysis of hydrometric data (e.g., rainfall, floods) and use of hydrologic/hydraulic models, all of which contribute various degrees of uncertainty to the design process. Uncertainties in hydrosystem designs can be generally categorized into aleatory and epistemic types. The former arises from the natural randomness of hydrologic processes whereas the latter are due to knowledge deficiency in model formulation and model parameter specification. This study shows that the presence of epistemic uncertainties induces uncertainty in determining the design capacity. Hence, the designer needs to quantify the uncertainty features of design capacity to determine the capacity with a stipulated performance reliability under the design condition. Using detention basin design as an example, the study illustrates a methodological framework by considering aleatory uncertainty from rainfall and epistemic uncertainties from the runoff coefficient, curve number, and sampling error in design rainfall magnitude. The effects of including different items of uncertainty and performance reliability on the design detention capacity are examined. A numerical example shows that the mean value of the design capacity of the detention basin increases with the design return period and this relation is found to be practically the same regardless of the uncertainty types considered. The standard deviation associated with the design capacity, when subject to epistemic uncertainty, increases with both design frequency and items of epistemic uncertainty involved. It is found that the epistemic uncertainty due to sampling error in rainfall quantiles should not be ignored. Even with a sample size of 80 (relatively large for a hydrologic application) the inclusion of sampling error in rainfall quantiles resulted in a standard deviation about 2.5 times higher than that considering only the uncertainty of the runoff coefficient and curve number. Furthermore, the

Electrophysiological correlates of strategic monitoring in event-based and time-based prospective memory.

Directory of Open Access Journals (Sweden)

Giorgia Cona

Full Text Available Prospective memory (PM is the ability to remember to accomplish an action when a particular event occurs (i.e., event-based PM, or at a specific time (i.e., time-based PM while performing an ongoing activity. Strategic Monitoring is one of the basic cognitive functions supporting PM tasks, and involves two mechanisms: a retrieval mode, which consists of maintaining active the intention in memory; and target checking, engaged for verifying the presence of the PM cue in the environment. The present study is aimed at providing the first evidence of event-related potentials (ERPs associated with time-based PM, and at examining differences and commonalities in the ERPs related to Strategic Monitoring mechanisms between event- and time-based PM tasks.The addition of an event-based or a time-based PM task to an ongoing activity led to a similar sustained positive modulation of the ERPs in the ongoing trials, mainly expressed over prefrontal and frontal regions. This modulation might index the retrieval mode mechanism, similarly engaged in the two PM tasks. On the other hand, two further ERP modulations were shown specifically in an event-based PM task. An increased positivity was shown at 400-600 ms post-stimulus over occipital and parietal regions, and might be related to target checking. Moreover, an early modulation at 130-180 ms post-stimulus seems to reflect the recruitment of attentional resources for being ready to respond to the event-based PM cue. This latter modulation suggests the existence of a third mechanism specific for the event-based PM; that is, the "readiness mode".

A formal treatment of uncertainty sources in a level 2 PSA

International Nuclear Information System (INIS)

Ahn, Kwang Il; Yang, Joon Eon

2003-01-01

The methodological framework of the level 2 PSA appears to be currently standardized in a formalized fashion, but there have been different opinions on the way the sources of uncertainty are characterized and treated. This is primarily because the level 2 PSA deals with complex phenomenological processes that are deterministic in nature rather than random processes, and there are no probabilistic models characterizing them clearly. As a result, the probabilistic quantification of the level 2 PSA is often subjected to two sources of uncertainty: (a) incomplete modeling of accident pathways or different predictions for the behavior of phenomenological events and (b) expert-to-expert variation in estimating the occurrence probability of phenomenological events. While a clear definition of the two sources of uncertainty involved in the level 2 PSA makes it possible to treat an uncertainty in a consistent manner, careless application of these different sources of uncertainty may produce different conclusions in the decision-making process. The primary purpose of this paper is to characterize typical sources of uncertainty that would often be addressed in the level 2 PSA and their impacts on the PSA level 2 risk results. An additional purpose of this paper is to give a formal approach on how to combine random uncertainties addressed in the level 1 PSA with subjectivistic uncertainties addressed in the level 2 PSA

An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology

Directory of Open Access Journals (Sweden)

Maryam Hourali

2011-01-01

Full Text Available In spite of the voluminous studies in the field of intelligent retrieval systems, effective retrieving of information has been remained an important unsolved problem. Implementations of different conceptual knowledge in the information retrieval process such as ontology have been considered as a solution to enhance the quality of results. Furthermore, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information due to the lack of clear-cut boundaries between concepts of the domains. To tackle this type of problems, one possible solution is to insert fuzzy logic into ontology construction process. In this article, a novel approach for fuzzy ontology generation with two uncertainty degrees is proposed. Hence, by implementing linguistic variables, uncertainty level in domain's concepts (Software Maintenance Engineering (SME domain has been modeled, and ontology relations have been modeled by fuzzy theory consequently. Then, we combined these uncertain models and proposed a new ontology with two degrees of uncertainty both in concept expression and relation expression. The generated fuzzy ontology was implemented for expansion of initial user's queries in SME domain. Experimental results showed that the proposed model has better overall retrieval performance comparing to keyword-based or crisp ontology-based retrieval systems.

A new uncertainty importance measure

International Nuclear Information System (INIS)

Borgonovo, E.

2007-01-01

Uncertainty in parameters is present in many risk assessment problems and leads to uncertainty in model predictions. In this work, we introduce a global sensitivity indicator which looks at the influence of input uncertainty on the entire output distribution without reference to a specific moment of the output (moment independence) and which can be defined also in the presence of correlations among the parameters. We discuss its mathematical properties and highlight the differences between the present indicator, variance-based uncertainty importance measures and a moment independent sensitivity indicator previously introduced in the literature. Numerical results are discussed with application to the probabilistic risk assessment model on which Iman [A matrix-based approach to uncertainty and sensitivity analysis for fault trees. Risk Anal 1987;7(1):22-33] first introduced uncertainty importance measures

Calculation uncertainty of distribution-like parameters in NPP of PAKS

International Nuclear Information System (INIS)

Szecsenyi, Zsolt; Korpas, Layos

2000-01-01

In the reactor-physical point of view there were two important events in the Nuclear Power Plant of PAKS in this year. The Russian type profiled assemblies were loaded into the PAKS Unit 3, and new limitation system was introduced on the same Unit. It was required to solve a lot of problems because of these both events. One of these problems was the determination of uncertainty of quantities of the new limitation considering the fabrication uncertainties for the profiled assembly. The importance of determination of uncertainty is to guarantee on 99.9% level the avoidance of fuel failure. In this paper the principles of determination of calculation accuracy, applied methods and obtained results are presented in case of distribution-like parameters. A few elements of the method have been presented on earlier symposiums, so in this paper the whole method is just outlined. For example the GPT method was presented in the following paper: Uncertainty analysis of pin wise power distribution of WWER-440 assembly considering fabrication uncertainties. Finally in the summary of this paper additional intrinsic opportunities in the method are presented. (Authors)

New Monte Carlo-based method to evaluate fission fraction uncertainties for the reactor antineutrino experiment

Energy Technology Data Exchange (ETDEWEB)

Ma, X.B., E-mail: maxb@ncepu.edu.cn; Qiu, R.M.; Chen, Y.X.

2017-02-15

Uncertainties regarding fission fractions are essential in understanding antineutrino flux predictions in reactor antineutrino experiments. A new Monte Carlo-based method to evaluate the covariance coefficients between isotopes is proposed. The covariance coefficients are found to vary with reactor burnup and may change from positive to negative because of balance effects in fissioning. For example, between {sup 235}U and {sup 239}Pu, the covariance coefficient changes from 0.15 to −0.13. Using the equation relating fission fraction and atomic density, consistent uncertainties in the fission fraction and covariance matrix were obtained. The antineutrino flux uncertainty is 0.55%, which does not vary with reactor burnup. The new value is about 8.3% smaller. - Highlights: • The covariance coefficients between isotopes vs reactor burnup may change its sign because of two opposite effects. • The relation between fission fraction uncertainty and atomic density are first studied. • A new MC-based method of evaluating the covariance coefficients between isotopes was proposed.

Multidimensional entropic uncertainty relation based on a commutator matrix in position and momentum spaces

Science.gov (United States)

Hertz, Anaelle; Vanbever, Luc; Cerf, Nicolas J.

2018-01-01

The uncertainty relation for continuous variables due to Byałinicki-Birula and Mycielski [I. Białynicki-Birula and J. Mycielski, Commun. Math. Phys. 44, 129 (1975), 10.1007/BF01608825] expresses the complementarity between two n -tuples of canonically conjugate variables (x1,x2,...,xn) and (p1,p2,...,pn) in terms of Shannon differential entropy. Here we consider the generalization to variables that are not canonically conjugate and derive an entropic uncertainty relation expressing the balance between any two n -variable Gaussian projective measurements. The bound on entropies is expressed in terms of the determinant of a matrix of commutators between the measured variables. This uncertainty relation also captures the complementarity between any two incompatible linear canonical transforms, the bound being written in terms of the corresponding symplectic matrices in phase space. Finally, we extend this uncertainty relation to Rényi entropies and also prove a covariance-based uncertainty relation which generalizes the Robertson relation.

THE UNCERTAINTIES ON THE GIS BASED LAND SUITABILITY ASSESSMENT FOR URBAN AND RURAL PLANNING

Directory of Open Access Journals (Sweden)

H. Liu

2017-09-01

Full Text Available The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the “Nature Breaks” method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

Impacts of reactivity feedback uncertainties on inherent shutdown in innovative designs

International Nuclear Information System (INIS)

Mueller, C.J.

1986-01-01

The concept of inherent shutdown is emphasized in the approach to the design of innovative, small pool-type liquid-metal reactors (LMRs). This paper reports an evaluation of reactivity feedback uncertainties used in the analyses of anticipated transients without scram for innovative LMRs, and the associated impacts on safety margins and inherent shutdown success probabilities on unprotected loss-of-flow (LOF) events. It then assesses the ultimate importance of these uncertainties on LOF and transient overpower events in evolving metal and oxide innovative designs

Impacts of reactivity feedback uncertainties on inherent shutdown in innovative designs

International Nuclear Information System (INIS)

Mueller, C.J.

1986-01-01

The concept of ''inherent shutdown'' is emphasized in the approach to the design of innovative, small pool-type liquid metal reactors (LMRs). This paper reports an evaluation of reactivity feedback uncertainties used in the analyses of anticipated transients without scram (ATWS) for innovative LMRs, and the associated impacts on safety margins and inherent shutdown success probabilities on unprotected loss-of-flow (LOF) events. It then assesses the ultimate importance of these uncertainties on LOF and transient overpower (TOP) events in evolving metal and oxide innovative designs

Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies (Final Report)

Science.gov (United States)

EPA announced the availability of the final report, Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies. This report summarizes some of the recent progress in characterizing uncertainty and variability in physi...

Probabilistic Dynamics for Integrated Analysis of Accident Sequences considering Uncertain Events

Directory of Open Access Journals (Sweden)

Robertas Alzbutas

2015-01-01

Full Text Available The analytical/deterministic modelling and simulation/probabilistic methods are used separately as a rule in order to analyse the physical processes and random or uncertain events. However, in the currently used probabilistic safety assessment this is an issue. The lack of treatment of dynamic interactions between the physical processes on one hand and random events on the other hand causes the limited assessment. In general, there are a lot of mathematical modelling theories, which can be used separately or integrated in order to extend possibilities of modelling and analysis. The Theory of Probabilistic Dynamics (TPD and its augmented version based on the concept of stimulus and delay are introduced for the dynamic reliability modelling and the simulation of accidents in hybrid (continuous-discrete systems considering uncertain events. An approach of non-Markovian simulation and uncertainty analysis is discussed in order to adapt the Stimulus-Driven TPD for practical applications. The developed approach and related methods are used as a basis for a test case simulation in view of various methods applications for severe accident scenario simulation and uncertainty analysis. For this and for wider analysis of accident sequences the initial test case specification is then extended and discussed. Finally, it is concluded that enhancing the modelling of stimulated dynamics with uncertainty and sensitivity analysis allows the detailed simulation of complex system characteristics and representation of their uncertainty. The developed approach of accident modelling and analysis can be efficiently used to estimate the reliability of hybrid systems and at the same time to analyze and possibly decrease the uncertainty of this estimate.

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

Science.gov (United States)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

Multivariate analysis methods to tag b quark events at LEP/SLC

International Nuclear Information System (INIS)

Brandl, B.; Falvard, A.; Guicheney, C.; Henrard, P.; Jousset, J.; Proriol, J.

1992-01-01

Multivariate analyses are applied to tag Z → bb-bar events at LEP/SLC. They are based on the specific b-event shape caused by the large b-quark mass. Discriminant analyses, classification trees and neural networks are presented and their performances are compared. It is shown that the neural network approach, due to its non-linearity, copes best with the complexity of the problem. As an example for an application of the developed methods the measurement of Γ(Z → bb-bar) is discussed. The usefulness of methods based on the global event shape is limited by the uncertainties introduced by the necessity of event simulation. As solution a double tag method is presented which can be applied to many tasks of LEP/SLC heavy flavour physics. (author) 29 refs.; 6 figs.; 1 tab

A task specific uncertainty analysis method for least-squares-based form characterization of ultra-precision freeform surfaces

International Nuclear Information System (INIS)

Ren, M J; Cheung, C F; Kong, L B

2012-01-01

In the measurement of ultra-precision freeform surfaces, least-squares-based form characterization methods are widely used to evaluate the form error of the measured surfaces. Although many methodologies have been proposed in recent years to improve the efficiency of the characterization process, relatively little research has been conducted on the analysis of associated uncertainty in the characterization results which may result from those characterization methods being used. As a result, this paper presents a task specific uncertainty analysis method with application in the least-squares-based form characterization of ultra-precision freeform surfaces. That is, the associated uncertainty in the form characterization results is estimated when the measured data are extracted from a specific surface with specific sampling strategy. Three factors are considered in this study which include measurement error, surface form error and sample size. The task specific uncertainty analysis method has been evaluated through a series of experiments. The results show that the task specific uncertainty analysis method can effectively estimate the uncertainty of the form characterization results for a specific freeform surface measurement

Application of a Novel Dose-Uncertainty Model for Dose-Uncertainty Analysis in Prostate Intensity-Modulated Radiotherapy

International Nuclear Information System (INIS)

Jin Hosang; Palta, Jatinder R.; Kim, You-Hyun; Kim, Siyong

2010-01-01

Purpose: To analyze dose uncertainty using a previously published dose-uncertainty model, and to assess potential dosimetric risks existing in prostate intensity-modulated radiotherapy (IMRT). Methods and Materials: The dose-uncertainty model provides a three-dimensional (3D) dose-uncertainty distribution in a given confidence level. For 8 retrospectively selected patients, dose-uncertainty maps were constructed using the dose-uncertainty model at the 95% CL. In addition to uncertainties inherent to the radiation treatment planning system, four scenarios of spatial errors were considered: machine only (S1), S1 + intrafraction, S1 + interfraction, and S1 + both intrafraction and interfraction errors. To evaluate the potential risks of the IMRT plans, three dose-uncertainty-based plan evaluation tools were introduced: confidence-weighted dose-volume histogram, confidence-weighted dose distribution, and dose-uncertainty-volume histogram. Results: Dose uncertainty caused by interfraction setup error was more significant than that of intrafraction motion error. The maximum dose uncertainty (95% confidence) of the clinical target volume (CTV) was smaller than 5% of the prescribed dose in all but two cases (13.9% and 10.2%). The dose uncertainty for 95% of the CTV volume ranged from 1.3% to 2.9% of the prescribed dose. Conclusions: The dose uncertainty in prostate IMRT could be evaluated using the dose-uncertainty model. Prostate IMRT plans satisfying the same plan objectives could generate a significantly different dose uncertainty because a complex interplay of many uncertainty sources. The uncertainty-based plan evaluation contributes to generating reliable and error-resistant treatment plans.

Predictability of prototype flash flood events in the Western Mediterranean under uncertainties of the precursor upper-level disturbance: the HYDROPTIMET case studies

Directory of Open Access Journals (Sweden)

R. Romero

2005-01-01

uncertainty in the representation of the upper-level disturbance and the necessity to cope with it within the operational context when attempting to issue short to mid-range numerical weather predictions of these high impact weather events, a systematic exploration of the predictability of the three selected case studies subject to uncertainties in the representation of the upper-level precursor disturbance is carried out in this paper. The study is based on an ensemble of mesoscale numerical simulations of each event with the MM5 non-hydrostatic model after perturbing in a systematic way the upper-level disturbance, in the sense of displacing slightly this disturbance upstream/downstream along the zonal direction and intensifying/weakening its amplitude. These perturbations are guided by a previous application of the MM5-adjoint model, which consistently shows high sensitivities of the dynamical control of the heavy rain to the flow configuration about the upper-level disturbance on the day before, thus confirming the precursor characteristics of this agent. The perturbations are introduced to the initial conditions by applying a potential vorticity (PV inversion procedure to the positive PV anomaly associated with the upper-level disturbance, and then using the inverted fields (wind, temperature and geopotential to modify under a physically consistent balance the model initial fields. The results generally show that the events dominated by mesoscale low-level disturbances (Catalogne and last stage of the Piémont episode are very sensitive to the initial uncertainties, such that the heavy rain location and magnitude are in some of the experiments strongly changed in response to the 'forecast errors' of the cyclone trajectory, intensity, shape and translational speed. In contrast, the other situations (Cévennes and initial stage of the Piémont episode, dominated by a larger scale system wich basically acts to guarantee the establishment and persistence of the southerly LLJ

Context, Experience, Expectation, and Action—Towards an Empirically Grounded, General Model for Analyzing Biographical Uncertainty

Directory of Open Access Journals (Sweden)

Herwig Reiter

2010-01-01

Full Text Available The article proposes a general, empirically grounded model for analyzing biographical uncertainty. The model is based on findings from a qualitative-explorative study of transforming meanings of unemployment among young people in post-Soviet Lithuania. In a first step, the particular features of the uncertainty puzzle in post-communist youth transitions are briefly discussed. A historical event like the collapse of state socialism in Europe, similar to the recent financial and economic crisis, is a generator of uncertainty par excellence: it undermines the foundations of societies and the taken-for-grantedness of related expectations. Against this background, the case of a young woman and how she responds to the novel threat of unemployment in the transition to the world of work is introduced. Her uncertainty management in the specific time perspective of certainty production is then conceptually rephrased by distinguishing three types or levels of biographical uncertainty: knowledge, outcome, and recognition uncertainty. Biographical uncertainty, it is argued, is empirically observable through the analysis of acting and projecting at the biographical level. The final part synthesizes the empirical findings and the conceptual discussion into a stratification model of biographical uncertainty as a general tool for the biographical analysis of uncertainty phenomena. URN: urn:nbn:de:0114-fqs100120

Introduction to risk and uncertainty in hydrosystem engineering

CERN Document Server

Goodarzi, Ehsan; Teang Shui, Lee

2013-01-01

Water engineers require knowledge of stochastic, frequency concepts, uncertainty analysis, risk assessment, and the processes that predict unexpected events. This book presents the basics of stochastic, risk and uncertainty analysis, and random sampling techniques in conjunction with straightforward examples which are solved step by step. In addition, appropriate Excel functions are included as an alternative to solve the examples, and two real case studies is presented in the last chapters of book.

Uncertainty and stress: Why it causes diseases and how it is mastered by the brain.

Science.gov (United States)

Peters, Achim; McEwen, Bruce S; Friston, Karl

2017-09-01

The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

OBEST: The Object-Based Event Scenario Tree Methodology

International Nuclear Information System (INIS)

WYSS, GREGORY D.; DURAN, FELICIA A.

2001-01-01

Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies

Assessment of Uncertainty-Based Screening Volumes for NASA Robotic LEO and GEO Conjunction Risk Assessment

Science.gov (United States)

Narvet, Steven W.; Frigm, Ryan C.; Hejduk, Matthew D.

2011-01-01

Conjunction Assessment operations require screening assets against the space object catalog by placing a pre-determined spatial volume around each asset and predicting when another object will violate that volume. The selection of the screening volume used for each spacecraft is a trade-off between observing all conjunction events that may pose a potential risk to the primary spacecraft and the ability to analyze those predicted events. If the screening volumes are larger, then more conjunctions can be observed and therefore the probability of a missed detection of a high risk conjunction event is small; however, the amount of data which needs to be analyzed increases. This paper characterizes the sensitivity of screening volume size to capturing typical orbit uncertainties and the expected number of conjunction events observed. These sensitivities are quantified in the form of a trade space that allows for selection of appropriate screen-ing volumes to fit the desired concept of operations, system limitations, and tolerable analyst workloads. This analysis will specifically highlight the screening volume determination and selection process for use in the NASA Conjunction Assessment Risk Analysis process but will also provide a general framework for other Owner / Operators faced with similar decisions.

Uncertainty analysis for results of thermal hydraulic codes of best-estimate-type; Analisis de incertidumbre para resultados de codigos termohidraulicos de mejor estimacion

Energy Technology Data Exchange (ETDEWEB)

Alva N, J.

2010-07-01

In this thesis, some fundamental knowledge is presented about uncertainty analysis and about diverse methodologies applied in the study of nuclear power plant transient event analysis, particularly related to thermal hydraulics phenomena. These concepts and methodologies mentioned in this work come from a wide bibliographical research in the nuclear power subject. Methodologies for uncertainty analysis have been developed by quite diverse institutions, and they have been widely used worldwide for application to results from best-estimate-type computer codes in nuclear reactor thermal hydraulics and safety analysis. Also, the main uncertainty sources, types of uncertainties, and aspects related to best estimate modeling and methods are introduced. Once the main bases of uncertainty analysis have been set, and some of the known methodologies have been introduced, it is presented in detail the CSAU methodology, which will be applied in the analyses. The main objective of this thesis is to compare the results of an uncertainty and sensibility analysis by using the Response Surface Technique to the application of W ilks formula, apply through a loss coolant experiment and an event of rise in a BWR. Both techniques are options in the part of uncertainty and sensibility analysis of the CSAU methodology, which was developed for the analysis of transients and accidents at nuclear power plants, and it is the base of most of the methodologies used in licensing of nuclear power plants practically everywhere. Finally, the results of applying both techniques are compared and discussed. (Author)

CMS DAQ Event Builder Based on Gigabit Ethernet

CERN Document Server

Bauer, G; Branson, J; Brett, A; Cano, E; Carboni, A; Ciganek, M; Cittolin, S; Erhan, S; Gigi, D; Glege, F; Gómez-Reino, Robert; Gulmini, M; Gutiérrez-Mlot, E; Gutleber, J; Jacobs, C; Kim, J C; Klute, M; Lipeles, E; Lopez-Perez, Juan Antonio; Maron, G; Meijers, F; Meschi, E; Moser, R; Murray, S; Oh, A; Orsini, L; Paus, C; Petrucci, A; Pieri, M; Pollet, L; Rácz, A; Sakulin, H; Sani, M; Schieferdecker, P; Schwick, C; Sumorok, K; Suzuki, I; Tsirigkas, D; Varela, J

2007-01-01

The CMS Data Acquisition System is designed to build and filter events originating from 476 detector data sources at a maximum trigger rate of 100 KHz. Different architectures and switch technologies have been evaluated to accomplish this purpose. Events will be built in two stages: the first stage will be a set of event builders called FED Builders. These will be based on Myrinet technology and will pre-assemble groups of about 8 data sources. The second stage will be a set of event builders called Readout Builders. These will perform the building of full events. A single Readout Builder will build events from 72 sources of 16 KB fragments at a rate of 12.5 KHz. In this paper we present the design of a Readout Builder based on TCP/IP over Gigabit Ethernet and the optimization that was required to achieve the design throughput. This optimization includes architecture of the Readout Builder, the setup of TCP/IP, and hardware selection.

Uncertainty Measures of Regional Flood Frequency Estimators

DEFF Research Database (Denmark)

Rosbjerg, Dan; Madsen, Henrik

1995-01-01

Regional flood frequency models have different assumptions regarding homogeneity and inter-site independence. Thus, uncertainty measures of T-year event estimators are not directly comparable. However, having chosen a particular method, the reliability of the estimate should always be stated, e...

Model uncertainty in safety assessment

International Nuclear Information System (INIS)

Pulkkinen, U.; Huovinen, T.

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.)

Model uncertainty in safety assessment

Energy Technology Data Exchange (ETDEWEB)

Pulkkinen, U; Huovinen, T [VTT Automation, Espoo (Finland). Industrial Automation

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.).

A Variation on Uncertainty Principle and Logarithmic Uncertainty Principle for Continuous Quaternion Wavelet Transforms

Directory of Open Access Journals (Sweden)

Mawardi Bahri

2017-01-01

Full Text Available The continuous quaternion wavelet transform (CQWT is a generalization of the classical continuous wavelet transform within the context of quaternion algebra. First of all, we show that the directional quaternion Fourier transform (QFT uncertainty principle can be obtained using the component-wise QFT uncertainty principle. Based on this method, the directional QFT uncertainty principle using representation of polar coordinate form is easily derived. We derive a variation on uncertainty principle related to the QFT. We state that the CQWT of a quaternion function can be written in terms of the QFT and obtain a variation on uncertainty principle related to the CQWT. Finally, we apply the extended uncertainty principles and properties of the CQWT to establish logarithmic uncertainty principles related to generalized transform.

Uncertainty in artificial intelligence

CERN Document Server

Kanal, LN

1986-01-01

How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

Trends and characteristics observed in nuclear events based on international nuclear event scale reports

International Nuclear Information System (INIS)

Watanabe, Norio

2001-01-01

The International Nuclear Event Scale (INES) is jointly operated by the IAEA and the OECD-NEA as a means designed for providing prompt, clear and consistent information related to nuclear events, that occurred at nuclear facilities, and facilitating communication between the nuclear community, the media and the public. Nuclear events are reported to the INES with the Scale', a consistent safety significance indicator, which runs from level 0, for events with no safety significance, to level 7 for a major accident with widespread health and environmental effects. Since the operation of INES was initiated in 1990, approximately 500 events have been reported and disseminated. The present paper discusses the trends observed in nuclear events, such as overall trends of the reported events and characteristics of safety significant events with level 2 or higher, based on the INES reports. (author)

An Oracle-based event index for ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00083337; The ATLAS collaboration; Dimitrov, Gancho

2017-01-01

The ATLAS Eventlndex System has amassed a set of key quantities for a large number of ATLAS events into a Hadoop based infrastructure for the purpose of providing the experiment with a number of event-wise services. Collecting this data in one place provides the opportunity to investigate various storage formats and technologies and assess which best serve the various use cases as well as consider what other benefits alternative storage systems provide. In this presentation we describe how the data are imported into an Oracle RDBMS (relational database management system), the services we have built based on this architecture, and our experience with it. We’ve indexed about 26 billion real data events thus far and have designed the system to accommodate future data which has expected rates of 5 and 20 billion events per year. We have found this system offers outstanding performance for some fundamental use cases. In addition, profiting from the co-location of this data with other complementary metadata in AT...

Uncertainty about probability: a decision analysis perspective

International Nuclear Information System (INIS)

Howard, R.A.

1988-01-01

The issue of how to think about uncertainty about probability is framed and analyzed from the viewpoint of a decision analyst. The failure of nuclear power plants is used as an example. The key idea is to think of probability as describing a state of information on an uncertain event, and to pose the issue of uncertainty in this quantity as uncertainty about a number that would be definitive: it has the property that you would assign it as the probability if you knew it. Logical consistency requires that the probability to assign to a single occurrence in the absence of further information be the mean of the distribution of this definitive number, not the medium as is sometimes suggested. Any decision that must be made without the benefit of further information must also be made using the mean of the definitive number's distribution. With this formulation, they find further that the probability of r occurrences in n exchangeable trials will depend on the first n moments of the definitive number's distribution. In making decisions, the expected value of clairvoyance on the occurrence of the event must be at least as great as that on the definitive number. If one of the events in question occurs, then the increase in probability of another such event is readily computed. This means, in terms of coin tossing, that unless one is absolutely sure of the fairness of a coin, seeing a head must increase the probability of heads, in distinction to usual thought. A numerical example for nuclear power shows that the failure of one plant of a group with a low probability of failure can significantly increase the probability that must be assigned to failure of a second plant in the group

Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models.

Science.gov (United States)

Hazarika, Subhashis; Biswas, Ayan; Shen, Han-Wei

2018-01-01

Distributions are often used to model uncertainty in many scientific datasets. To preserve the correlation among the spatially sampled grid locations in the dataset, various standard multivariate distribution models have been proposed in visualization literature. These models treat each grid location as a univariate random variable which models the uncertainty at that location. Standard multivariate distributions (both parametric and nonparametric) assume that all the univariate marginals are of the same type/family of distribution. But in reality, different grid locations show different statistical behavior which may not be modeled best by the same type of distribution. In this paper, we propose a new multivariate uncertainty modeling strategy to address the needs of uncertainty modeling in scientific datasets. Our proposed method is based on a statistically sound multivariate technique called Copula, which makes it possible to separate the process of estimating the univariate marginals and the process of modeling dependency, unlike the standard multivariate distributions. The modeling flexibility offered by our proposed method makes it possible to design distribution fields which can have different types of distribution (Gaussian, Histogram, KDE etc.) at the grid locations, while maintaining the correlation structure at the same time. Depending on the results of various standard statistical tests, we can choose an optimal distribution representation at each location, resulting in a more cost efficient modeling without significantly sacrificing on the analysis quality. To demonstrate the efficacy of our proposed modeling strategy, we extract and visualize uncertain features like isocontours and vortices in various real world datasets. We also study various modeling criterion to help users in the task of univariate model selection.

Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties

International Nuclear Information System (INIS)

Osmani, Atif; Zhang, Jun

2013-01-01

An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that “mean” value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. - Highlights: • Two-stage stochastic MILP model for maximizing profit of a multi-feedstock lignocellulosic-based bioethanol supply chain. • Multiple uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand, and bioethanol sale price. • Proposed stochastic model outperforms the traditional deterministic model under uncertainties. • Stochastic parameters significantly affect marginal land allocation for switchgrass cultivation and bioethanol production. • Location of biorefineries is found to be insensitive to the stochastic environment

Reliability- and performance-based robust design optimization of MEMS structures considering technological uncertainties

Science.gov (United States)

Martowicz, Adam; Uhl, Tadeusz

2012-10-01

The paper discusses the applicability of a reliability- and performance-based multi-criteria robust design optimization technique for micro-electromechanical systems, considering their technological uncertainties. Nowadays, micro-devices are commonly applied systems, especially in the automotive industry, taking advantage of utilizing both the mechanical structure and electronic control circuit on one board. Their frequent use motivates the elaboration of virtual prototyping tools that can be applied in design optimization with the introduction of technological uncertainties and reliability. The authors present a procedure for the optimization of micro-devices, which is based on the theory of reliability-based robust design optimization. This takes into consideration the performance of a micro-device and its reliability assessed by means of uncertainty analysis. The procedure assumes that, for each checked design configuration, the assessment of uncertainty propagation is performed with the meta-modeling technique. The described procedure is illustrated with an example of the optimization carried out for a finite element model of a micro-mirror. The multi-physics approach allowed the introduction of several physical phenomena to correctly model the electrostatic actuation and the squeezing effect present between electrodes. The optimization was preceded by sensitivity analysis to establish the design and uncertain domains. The genetic algorithms fulfilled the defined optimization task effectively. The best discovered individuals are characterized by a minimized value of the multi-criteria objective function, simultaneously satisfying the constraint on material strength. The restriction of the maximum equivalent stresses was introduced with the conditionally formulated objective function with a penalty component. The yielded results were successfully verified with a global uniform search through the input design domain.

An Oracle-based event index for ATLAS

Science.gov (United States)

Gallas, E. J.; Dimitrov, G.; Vasileva, P.; Baranowski, Z.; Canali, L.; Dumitru, A.; Formica, A.; ATLAS Collaboration

2017-10-01

The ATLAS Eventlndex System has amassed a set of key quantities for a large number of ATLAS events into a Hadoop based infrastructure for the purpose of providing the experiment with a number of event-wise services. Collecting this data in one place provides the opportunity to investigate various storage formats and technologies and assess which best serve the various use cases as well as consider what other benefits alternative storage systems provide. In this presentation we describe how the data are imported into an Oracle RDBMS (relational database management system), the services we have built based on this architecture, and our experience with it. We’ve indexed about 26 billion real data events thus far and have designed the system to accommodate future data which has expected rates of 5 and 20 billion events per year. We have found this system offers outstanding performance for some fundamental use cases. In addition, profiting from the co-location of this data with other complementary metadata in ATLAS, the system has been easily extended to perform essential assessments of data integrity and completeness and to identify event duplication, including at what step in processing the duplication occurred.

Conditional uncertainty principle

Science.gov (United States)

Gour, Gilad; Grudka, Andrzej; Horodecki, Michał; Kłobus, Waldemar; Łodyga, Justyna; Narasimhachar, Varun

2018-04-01

We develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. Our formalism is built upon a mathematical relation which we call conditional majorization. We define conditional majorization and, for the case of classical memory, we provide its thorough characterization in terms of monotones, i.e., functions that preserve the partial order under conditional majorization. We demonstrate the application of this framework by deriving two types of memory-assisted uncertainty relations, (1) a monotone-based conditional uncertainty relation and (2) a universal measure-independent conditional uncertainty relation, both of which set a lower bound on the minimal uncertainty that Bob has about Alice's pair of incompatible measurements, conditioned on arbitrary measurement that Bob makes on his own system. We next compare the obtained relations with their existing entropic counterparts and find that they are at least independent.

Decision-Making under Criteria Uncertainty

Science.gov (United States)

Kureychik, V. M.; Safronenkova, I. B.

2018-05-01

Uncertainty is an essential part of a decision-making procedure. The paper deals with the problem of decision-making under criteria uncertainty. In this context, decision-making under uncertainty, types and conditions of uncertainty were examined. The decision-making problem under uncertainty was formalized. A modification of the mathematical decision support method under uncertainty via ontologies was proposed. A critical distinction of the developed method is ontology usage as its base elements. The goal of this work is a development of a decision-making method under criteria uncertainty with the use of ontologies in the area of multilayer board designing. This method is oriented to improvement of technical-economic values of the examined domain.

Event-by-event jet quenching and higher Fourier moments of hard probes

Energy Technology Data Exchange (ETDEWEB)

Fries, Rainer J. [Cyclotron Institute and Department of Physics and Astronomy, Texas A and M University, College Station TX 77845 (United States); RIKEN/BNL Research Center, Brookhaven National Laboratory, Upton NY 11973 (United States); Rodriguez, Ricardo [Department of Mathematics and Physics, Ave Maria University, Ave Maria FL 34142 (United States); Cyclotron Institute, Texas A and M University, College Station TX 77845 (United States)

2011-04-01

We investigate the effect of event-by-event fluctuations of the fireball created in high energy nuclear collisions on hard probe observables. We show that spatial inhomogeneities lead to changes in the nuclear suppression factor of high momentum hadrons which can be absorbed in the quenching strength q-hat. This can increase the theoretical uncertainty on extracted values of q-hat by up to 50%. We also investigate effects on azimuthal asymmetries v{sub 2} and dihadron correlation functions. The latter show a promising residual signal of event-by-event quenching that might allow us to estimate the size of spatial inhomogeneities in the fireball from experimental data.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

Science.gov (United States)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Reproducing an extreme flood with uncertain post-event information

Directory of Open Access Journals (Sweden)

D. Fuentes-Andino

2017-07-01

Full Text Available Studies for the prevention and mitigation of floods require information on discharge and extent of inundation, commonly unavailable or uncertain, especially during extreme events. This study was initiated by the devastating flood in Tegucigalpa, the capital of Honduras, when Hurricane Mitch struck the city. In this study we hypothesized that it is possible to estimate, in a trustworthy way considering large data uncertainties, this extreme 1998 flood discharge and the extent of the inundations that followed from a combination of models and post-event measured data. Post-event data collected in 2000 and 2001 were used to estimate discharge peaks, times of peak, and high-water marks. These data were used in combination with rain data from two gauges to drive and constrain a combination of well-known modelling tools: TOPMODEL, Muskingum–Cunge–Todini routing, and the LISFLOOD-FP hydraulic model. Simulations were performed within the generalized likelihood uncertainty estimation (GLUE uncertainty-analysis framework. The model combination predicted peak discharge, times of peaks, and more than 90 % of the observed high-water marks within the uncertainty bounds of the evaluation data. This allowed an inundation likelihood map to be produced. Observed high-water marks could not be reproduced at a few locations on the floodplain. Identifications of these locations are useful to improve model set-up, model structure, or post-event data-estimation methods. Rainfall data were of central importance in simulating the times of peak and results would be improved by a better spatial assessment of rainfall, e.g. from radar data or a denser rain-gauge network. Our study demonstrated that it was possible, considering the uncertainty in the post-event data, to reasonably reproduce the extreme Mitch flood in Tegucigalpa in spite of no hydrometric gauging during the event. The method proposed here can be part of a Bayesian framework in which more events

Risk and sensitivity analysis in relation to external events

International Nuclear Information System (INIS)

Alzbutas, R.; Urbonas, R.; Augutis, J.

2001-01-01

This paper presents risk and sensitivity analysis of external events impacts on the safe operation in general and in particular the Ignalina Nuclear Power Plant safety systems. Analysis is based on the deterministic and probabilistic assumptions and assessment of the external hazards. The real statistic data are used as well as initial external event simulation. The preliminary screening criteria are applied. The analysis of external event impact on the NPP safe operation, assessment of the event occurrence, sensitivity analysis, and recommendations for safety improvements are performed for investigated external hazards. Such events as aircraft crash, extreme rains and winds, forest fire and flying parts of the turbine are analysed. The models are developed and probabilities are calculated. As an example for sensitivity analysis the model of aircraft impact is presented. The sensitivity analysis takes into account the uncertainty features raised by external event and its model. Even in case when the external events analysis show rather limited danger, the sensitivity analysis can determine the highest influence causes. These possible variations in future can be significant for safety level and risk based decisions. Calculations show that external events cannot significantly influence the safety level of the Ignalina NPP operation, however the events occurrence and propagation can be sufficiently uncertain.(author)

A Bayesian approach to model uncertainty

International Nuclear Information System (INIS)

Buslik, A.

1994-01-01

A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given

Uncertainty evaluation of a regional real-time system for rain-induced landslides

Science.gov (United States)

Kirschbaum, Dalia; Stanley, Thomas; Yatheendradas, Soni

2015-04-01

A new prototype regional model and evaluation framework has been developed over Central America and the Caribbean region using satellite-based information including precipitation estimates, modeled soil moisture, topography, soils, as well as regionally available datasets such as road networks and distance to fault zones. The algorithm framework incorporates three static variables: a susceptibility map; a 24-hr rainfall triggering threshold; and an antecedent soil moisture variable threshold, which have been calibrated using historic landslide events. The thresholds are regionally heterogeneous and are based on the percentile distribution of the rainfall or antecedent moisture time series. A simple decision tree algorithm framework integrates all three variables with the rainfall and soil moisture time series and generates a landslide nowcast in real-time based on the previous 24 hours over this region. This system has been evaluated using several available landslide inventories over the Central America and Caribbean region. Spatiotemporal uncertainty and evaluation metrics of the model are presented here based on available landslides reports. This work also presents a probabilistic representation of potential landslide activity over the region which can be used to further refine and improve the real-time landslide hazard assessment system as well as better identify and characterize the uncertainties inherent in this type of regional approach. The landslide algorithm provides a flexible framework to improve hazard estimation and reduce uncertainty at any spatial and temporal scale.

A Quantitative Measure For Evaluating Project Uncertainty Under Variation And Risk Effects

Directory of Open Access Journals (Sweden)

A. Chenarani

2017-10-01

Full Text Available The effects of uncertainty on a project and the risk event as the consequence of uncertainty are analyzed. The uncertainty index is proposed as a quantitative measure for evaluating the uncertainty of a project. This is done by employing entropy as the indicator of system disorder and lack of information. By employing this index, the uncertainty of each activity and its increase due to risk effects as well as project uncertainty changes as a function of time can be assessed. The results are implemented and analyzed for a small turbojet engine development project as the case study. The results of this study can be useful for project managers and other stakeholders for selecting the most effective risk management and uncertainty controlling method.

The French biofuels mandates under cost uncertainty - an assessment based on robust optimization

International Nuclear Information System (INIS)

Lorne, Daphne; Tchung-Ming, Stephane

2012-01-01

This paper investigates the impact of primary energy and technology cost uncertainty on the achievement of renewable and especially biofuel policies - mandates and norms - in France by 2030. A robust optimization technique that allows to deal with uncertainty sets of high dimensionality is implemented in a TIMES-based long-term planning model of the French energy transport and electricity sectors. The energy system costs and potential benefits (GHG emissions abatements, diversification) of the French renewable mandates are assessed within this framework. The results of this systemic analysis highlight how setting norms and mandates allows to reduce the variability of CO 2 emissions reductions and supply mix diversification when the costs of technological progress and prices are uncertain. Beyond that, we discuss the usefulness of robust optimization in complement of other techniques to integrate uncertainty in large-scale energy models. (authors)

Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty

Directory of Open Access Journals (Sweden)

Kyu-Hyung Jo

2018-03-01

Full Text Available With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM is presented and applied to the proposed stochastic UC problem. In this process, the initial input data of load and wind power are modeled as different levels using the mean absolute percentage error (MAPE. The load and wind scenarios are generated using Monte Carlo simulation (MCS that considers forecasting errors. These multiple scenarios are applied in the MSTM for solving the stochastic UC problem, including not only the load and wind power uncertainties, but also sudden outages of the thermal unit. When the UC problem has been formulated, the simulation is conducted for 24-h period by using the short-term UC model, and the operating costs and additional reserve requirements are thus obtained. The effectiveness of the proposed solution approach is demonstrated through a case study based on a modified IEEE-118 bus test system.

Uncertainty and sensitivity analysis of electro-mechanical impedance based SHM system

International Nuclear Information System (INIS)

Rosiek, M; Martowicz, A; Uhl, T

2010-01-01

The paper deals with the application of uncertainty and sensitivity analysis performed for FE simulations for electro-mechanical impedance based SHM system. The measurement of electro-mechanical impedance allows to follow changes of mechanical properties of monitored construction. Therefore it can be effectively applied to conclude about presence of damage. Coupled FE simulations have been carried out for simultaneous consideration of both structural dynamics and piezoelectric properties of a simple beam with bonded transducer. Several indexes have been used to assess the damage growth. In the paper the results obtained with both deterministic and stochastic simulations are shown and discussed. First, the relationship between size of introduced damage and its indexes has been studied. Second, ranges of variation of selected model properties have been assumed to find relationships between them and damage indexes. The most influential parameters have been found. Finally, the overall propagation of considered uncertainty has been assessed and related histograms plotted to discuss effectiveness and robustness of tested damage indexes based on the measurement of electro-mechanical impedance.

The effect of uncertainties in distance-based ranking methods for multi-criteria decision making

Science.gov (United States)

Jaini, Nor I.; Utyuzhnikov, Sergei V.

2017-08-01

Data in the multi-criteria decision making are often imprecise and changeable. Therefore, it is important to carry out sensitivity analysis test for the multi-criteria decision making problem. The paper aims to present a sensitivity analysis for some ranking techniques based on the distance measures in multi-criteria decision making. Two types of uncertainties are considered for the sensitivity analysis test. The first uncertainty is related to the input data, while the second uncertainty is towards the Decision Maker preferences (weights). The ranking techniques considered in this study are TOPSIS, the relative distance and trade-off ranking methods. TOPSIS and the relative distance method measure a distance from an alternative to the ideal and antiideal solutions. In turn, the trade-off ranking calculates a distance of an alternative to the extreme solutions and other alternatives. Several test cases are considered to study the performance of each ranking technique in both types of uncertainties.

Monte-Carlo-based uncertainty propagation with hierarchical models—a case study in dynamic torque

Science.gov (United States)

Klaus, Leonard; Eichstädt, Sascha

2018-04-01

For a dynamic calibration, a torque transducer is described by a mechanical model, and the corresponding model parameters are to be identified from measurement data. A measuring device for the primary calibration of dynamic torque, and a corresponding model-based calibration approach, have recently been developed at PTB. The complete mechanical model of the calibration set-up is very complex, and involves several calibration steps—making a straightforward implementation of a Monte Carlo uncertainty evaluation tedious. With this in mind, we here propose to separate the complete model into sub-models, with each sub-model being treated with individual experiments and analysis. The uncertainty evaluation for the overall model then has to combine the information from the sub-models in line with Supplement 2 of the Guide to the Expression of Uncertainty in Measurement. In this contribution, we demonstrate how to carry this out using the Monte Carlo method. The uncertainty evaluation involves various input quantities of different origin and the solution of a numerical optimisation problem.

Estimating the impact of extreme events on crude oil price. An EMD-based event analysis method

Energy Technology Data Exchange (ETDEWEB)

Zhang, Xun; Wang, Shouyang [Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); School of Mathematical Sciences, Graduate University of Chinese Academy of Sciences, Beijing 100190 (China); Yu, Lean [Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung [Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon (China)

2009-09-15

The impact of extreme events on crude oil markets is of great importance in crude oil price analysis due to the fact that those events generally exert strong impact on crude oil markets. For better estimation of the impact of events on crude oil price volatility, this study attempts to use an EMD-based event analysis approach for this task. In the proposed method, the time series to be analyzed is first decomposed into several intrinsic modes with different time scales from fine-to-coarse and an average trend. The decomposed modes respectively capture the fluctuations caused by the extreme event or other factors during the analyzed period. It is found that the total impact of an extreme event is included in only one or several dominant modes, but the secondary modes provide valuable information on subsequent factors. For overlapping events with influences lasting for different periods, their impacts are separated and located in different modes. For illustration and verification purposes, two extreme events, the Persian Gulf War in 1991 and the Iraq War in 2003, are analyzed step by step. The empirical results reveal that the EMD-based event analysis method provides a feasible solution to estimating the impact of extreme events on crude oil prices variation. (author)

Estimating the impact of extreme events on crude oil price. An EMD-based event analysis method

International Nuclear Information System (INIS)

Zhang, Xun; Wang, Shouyang; Yu, Lean; Lai, Kin Keung

2009-01-01

The impact of extreme events on crude oil markets is of great importance in crude oil price analysis due to the fact that those events generally exert strong impact on crude oil markets. For better estimation of the impact of events on crude oil price volatility, this study attempts to use an EMD-based event analysis approach for this task. In the proposed method, the time series to be analyzed is first decomposed into several intrinsic modes with different time scales from fine-to-coarse and an average trend. The decomposed modes respectively capture the fluctuations caused by the extreme event or other factors during the analyzed period. It is found that the total impact of an extreme event is included in only one or several dominant modes, but the secondary modes provide valuable information on subsequent factors. For overlapping events with influences lasting for different periods, their impacts are separated and located in different modes. For illustration and verification purposes, two extreme events, the Persian Gulf War in 1991 and the Iraq War in 2003, are analyzed step by step. The empirical results reveal that the EMD-based event analysis method provides a feasible solution to estimating the impact of extreme events on crude oil prices variation. (author)

Uncertainty during breast diagnostic evaluation: state of the science.

Science.gov (United States)

Montgomery, Mariann

2010-01-01

To present the state of the science on uncertainty in relationship to the experiences of women undergoing diagnostic evaluation for suspected breast cancer. Published articles from Medline, CINAHL, PubMED, and PsycINFO from 1983-2008 using the following key words: breast biopsy, mammography, uncertainty, reframing, inner strength, and disruption. Fifty research studies were examined with all reporting the presence of anxiety persisting throughout the diagnostic evaluation until certitude is achieved through the establishment of a definitive diagnosis. Indirect determinants of uncertainty for women undergoing breast diagnostic evaluation include measures of anxiety, depression, social support, emotional responses, defense mechanisms, and the psychological impact of events. Understanding and influencing the uncertainty experience have been suggested to be key in relieving psychosocial distress and positively influencing future screening behaviors. Several studies examine correlational relationships among anxiety, selection of coping methods, and demographic factors that influence uncertainty. A gap exists in the literature with regard to the relationship of inner strength and uncertainty. Nurses can be invaluable in assisting women in coping with the uncertainty experience by providing positive communication and support. Nursing interventions should be designed and tested for their effects on uncertainty experienced by women undergoing a breast diagnostic evaluation.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

Science.gov (United States)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

Science.gov (United States)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty

Sensitivity, uncertainty, and importance analysis of a risk assessment

International Nuclear Information System (INIS)

Andsten, R.S.; Vaurio, J.K.

1992-01-01

In this paper a number of supplementary studies and applications associated with probabilistic safety assessment (PSA) are described, including sensitivity and importance evaluations of failures, errors, systems, and groups of components. The main purpose is to illustrate the usefulness of a PSA for making decisions about safety improvements, training, allowed outage times, and test intervals. A useful measure of uncertainty importance is presented, and it points out areas needing development, such as reactor vessel aging phenomena, for reducing overall uncertainty. A time-dependent core damage frequency is also presented, illustrating the impact of testing scenarios and intervals. Tea methods and applications presented are based on the Level 1 PSA carried out for the internal initiating event of the Loviisa 1 nuclear power station. Steam generator leakages and associated operator actions are major contributors to the current core-damage frequency estimate of 2 x10 -4 /yr. The results are used to improve the plant and procedures and to guide future improvements

Reducing uncertainties in volumetric image based deformable organ registration

International Nuclear Information System (INIS)

Liang, J.; Yan, D.

2003-01-01

Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable--thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation

Decommissioning and equipment replacement of nuclear power plants under uncertainty

International Nuclear Information System (INIS)

Takashima, Ryuta; Naito, Yuta; Kimura, Hiroshi; Madarame, Haruki

2007-01-01

This study examines the optimal timing for the decommissioning and equipment replacement of nuclear power plants. We consider that the firm has two options of decommissioning and equipment replacement, and determines to exercise these options under electricity price uncertainty. This problem is formulated as two optimal stopping problems. The solution of this model provides the value of the nuclear power plant and the threshold values for decommissioning and replacement. The dependence of decommissioning and replacement strategies on uncertainty and each cost is shown. In order to investigate the probability of events for decommissioning and replacement, Monte Carlo calculations are performed. We also show the probability distribution and the conditional expected time for each event. (author)

Addressing uncertainty in adaptation planning for agriculture.

Science.gov (United States)

Vermeulen, Sonja J; Challinor, Andrew J; Thornton, Philip K; Campbell, Bruce M; Eriyagama, Nishadi; Vervoort, Joost M; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J; Hawkins, Ed; Smith, Daniel R

2013-05-21

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty

Science.gov (United States)

Denys Yemshanov; Frank H. Koch; D. Barry Lyons; Mark Ducey; Klaus Koehler

2012-01-01

Aim Uncertainty has been widely recognized as one of the most critical issues in predicting the expansion of ecological invasions. The uncertainty associated with the introduction and spread of invasive organisms influences how pest management decision makers respond to expanding incursions. We present a model-based approach to map risk of ecological invasions that...

Fast radio burst event rate counts - I. Interpreting the observations

Science.gov (United States)

Macquart, J.-P.; Ekers, R. D.

2018-02-01

The fluence distribution of the fast radio burst (FRB) population (the `source count' distribution, N (>F) ∝Fα), is a crucial diagnostic of its distance distribution, and hence the progenitor evolutionary history. We critically reanalyse current estimates of the FRB source count distribution. We demonstrate that the Lorimer burst (FRB 010724) is subject to discovery bias, and should be excluded from all statistical studies of the population. We re-examine the evidence for flat, α > -1, source count estimates based on the ratio of single-beam to multiple-beam detections with the Parkes multibeam receiver, and show that current data imply only a very weak constraint of α ≲ -1.3. A maximum-likelihood analysis applied to the portion of the Parkes FRB population detected above the observational completeness fluence of 2 Jy ms yields α = -2.6_{-1.3}^{+0.7 }. Uncertainties in the location of each FRB within the Parkes beam render estimates of the Parkes event rate uncertain in both normalizing survey area and the estimated post-beam-corrected completeness fluence; this uncertainty needs to be accounted for when comparing the event rate against event rates measured at other telescopes.

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

Science.gov (United States)

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-01-01

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC

Uncertainty estimation with bias-correction for flow series based on rating curve

Science.gov (United States)

Shao, Quanxi; Lerat, Julien; Podger, Geoff; Dutta, Dushmanta

2014-03-01

Streamflow discharge constitutes one of the fundamental data required to perform water balance studies and develop hydrological models. A rating curve, designed based on a series of concurrent stage and discharge measurements at a gauging location, provides a way to generate complete discharge time series with a reasonable quality if sufficient measurement points are available. However, the associated uncertainty is frequently not available even though it has a significant impact on hydrological modelling. In this paper, we identify the discrepancy of the hydrographers' rating curves used to derive the historical discharge data series and proposed a modification by bias correction which is also in the form of power function as the traditional rating curve. In order to obtain the uncertainty estimation, we propose a further both-side Box-Cox transformation to stabilize the regression residuals as close to the normal distribution as possible, so that a proper uncertainty can be attached for the whole discharge series in the ensemble generation. We demonstrate the proposed method by applying it to the gauging stations in the Flinders and Gilbert rivers in north-west Queensland, Australia.

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

Science.gov (United States)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

Directory of Open Access Journals (Sweden)

Jong Seok Lee

2018-03-01

Full Text Available The purpose of this paper is to compare the degree of uncertainty of the water scarcity footprint using the Monte Carlo statistical method and block bootstrap method. Using the hydrological data of a water drainage basin in Korea, characterization factors based on the available water remaining (AWARE model were obtained. The uncertainties of the water scarcity footprint considering temporal variations in paddy rice production in Korea were estimated. The block bootstrap method gave five-times smaller percentage uncertainty values of the model output compared to that of the two different Monte Carlo statistical method scenarios. Incorrect estimation of the probability distribution of the AWARE characterization factor model is what causes the higher uncertainty in the water scarcity footprint value calculated by the Monte Carlo statistical method in this study. This is because AWARE characterization factor values partly follows discrete distribution with extreme value on one side. Therefore, this study suggests that the block bootstrap method is a better choice in analyzing uncertainty compared to the Monte Carlo statistical method when using the AWARE model to quantify the water scarcity footprint.

Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms.

Science.gov (United States)

Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

2016-12-01

A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

Science.gov (United States)

Van Steenbergen, N.; Willems, P.

2012-04-01

Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the

A methodology for uncertainty quantification in quantitative technology valuation based on expert elicitation

Science.gov (United States)

Akram, Muhammad Farooq Bin

uncertainty propagation. Non-linear behavior in technology interactions is captured through expert elicitation based technology synergy matrices (TSM). Proposed TSMs increase the fidelity of current technology forecasting methods by including higher order technology interactions. A test case for quantification of epistemic uncertainty on a large scale problem of combined cycle power generation system was selected. A detailed multidisciplinary modeling and simulation environment was adopted for this problem. Results have shown that evidence theory based technique provides more insight on the uncertainties arising from incomplete information or lack of knowledge as compared to deterministic or probability theory methods. Margin analysis was also carried out for both the techniques. A detailed description of TSMs and their usage in conjunction with technology impact matrices and technology compatibility matrices is discussed. Various combination methods are also proposed for higher order interactions, which can be applied according to the expert opinion or historical data. The introduction of technology synergy matrix enabled capturing the higher order technology interactions, and improvement in predicted system performance.

Uncertainty governance: an integrated framework for managing and communicating uncertainties

International Nuclear Information System (INIS)

Umeki, H.; Naito, M.; Takase, H.

2004-01-01

Treatment of uncertainty, or in other words, reasoning with imperfect information is widely recognised as being of great importance within performance assessment (PA) of the geological disposal mainly because of the time scale of interest and spatial heterogeneity that geological environment exhibits. A wide range of formal methods have been proposed for the optimal processing of incomplete information. Many of these methods rely on the use of numerical information, the frequency based concept of probability in particular, to handle the imperfections. However, taking quantitative information as a base for models that solve the problem of handling imperfect information merely creates another problem, i.e., how to provide the quantitative information. In many situations this second problem proves more resistant to solution, and in recent years several authors have looked at a particularly ingenious way in accordance with the rules of well-founded methods such as Bayesian probability theory, possibility theory, and the Dempster-Shafer theory of evidence. Those methods, while drawing inspiration from quantitative methods, do not require the kind of complete numerical information required by quantitative methods. Instead they provide information that, though less precise than that provided by quantitative techniques, is often, if not sufficient, the best that could be achieved. Rather than searching for the best method for handling all imperfect information, our strategy for uncertainty management, that is recognition and evaluation of uncertainties associated with PA followed by planning and implementation of measures to reduce them, is to use whichever method best fits the problem at hand. Such an eclectic position leads naturally to integration of the different formalisms. While uncertainty management based on the combination of semi-quantitative methods forms an important part of our framework for uncertainty governance, it only solves half of the problem

Intolerance of uncertainty, cognitive complaints, and cancer-related distress in prostate cancer survivors.

Science.gov (United States)

Eisenberg, Stacy A; Kurita, Keiko; Taylor-Ford, Megan; Agus, David B; Gross, Mitchell E; Meyerowitz, Beth E

2015-02-01

Prostate cancer survivors have reported cognitive complaints following treatment, and these difficulties may be associated with survivors' ongoing cancer-related distress. Intolerance of uncertainty may exacerbate this hypothesized relationship by predisposing individuals to approach uncertain situations such as cancer survivorship in an inflexible and negative manner. We investigated whether greater cognitive complaints and higher intolerance of uncertainty would interact in their relation to more cancer-related distress symptoms. This cross-sectional, questionnaire-based study included 67 prostate cancer survivors who were 3 to 5 years post treatment. Hierarchical multiple regression analyses tested the extent to which intolerance of uncertainty, cognitive complaints, and their interaction were associated with cancer-related distress (measured with the Impact of Event Scale-Revised; IES-R) after adjusting for age, education, physical symptoms, and fear of cancer recurrence. Intolerance of uncertainty was positively associated with the IES-R avoidance and hyperarousal subscales. More cognitive complaints were associated with higher scores on the IES-R hyperarousal subscale. The interaction of intolerance of uncertainty and cognitive complaints was significantly associated with IES-R intrusion, such that greater cognitive complaints were associated with greater intrusive thoughts in survivors high in intolerance of uncertainty but not those low in it. Prostate cancer survivors who report cognitive difficulties or who find uncertainty uncomfortable and unacceptable may be at greater risk for cancer-related distress, even 3 to 5 years after completing treatment. It may be beneficial to address both cognitive complaints and intolerance of uncertainty in psychosocial interventions. Copyright © 2014 John Wiley & Sons, Ltd.

Reducing the top quark mass uncertainty with jet grooming

Science.gov (United States)

Andreassen, Anders; Schwartz, Matthew D.

2017-10-01

The measurement of the top quark mass has large systematic uncertainties coming from the Monte Carlo simulations that are used to match theory and experiment. We explore how much that uncertainty can be reduced by using jet grooming procedures. Using the ATLAS A14 tunes of pythia, we estimate the uncertainty from the choice of tuning parameters in what is meant by the Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty can be reduced by 60% to 200 MeV by calibrating to the W mass and by 70% to 140 MeV by additionally applying soft-drop jet grooming (or to 170 MeV using trimming). At e + e - colliders, the associated uncertainty is around 110 MeV, reducing to 50 MeV after calibrating to the W mass. By analyzing the tuning parameters, we conclude that the importance of jet grooming after calibrating to the W -mass is to reduce sensitivity to the underlying event.

Quantile arithmetic methodology for uncertainty propagation in fault trees

International Nuclear Information System (INIS)

Abdelhai, M.; Ragheb, M.

1986-01-01

A methodology based on quantile arithmetic, the probabilistic analog to interval analysis, is proposed for the computation of uncertainties propagation in fault tree analysis. The basic events' continuous probability density functions (pdf's) are represented by equivalent discrete distributions by dividing them into a number of quantiles N. Quantile arithmetic is then used to performthe binary arithmetical operations corresponding to the logical gates in the Boolean expression of the top event expression of a given fault tree. The computational advantage of the present methodology as compared with the widely used Monte Carlo method was demonstrated for the cases of summation of M normal variables through the efficiency ratio defined as the product of the labor and error ratios. The efficiency ratio values obtained by the suggested methodology for M = 2 were 2279 for N = 5, 445 for N = 25, and 66 for N = 45 when compared with the results for 19,200 Monte Carlo samples at the 40th percentile point. Another advantage of the approach is that the exact analytical value of the median is always obtained for the top event

A coupled hydrological-hydraulic flood inundation model calibrated using post-event measurements and integrated uncertainty analysis in a poorly gauged Mediterranean basin

Science.gov (United States)

Hdeib, Rouya; Abdallah, Chadi; Moussa, Roger; Colin, Francois

2017-04-01

Developing flood inundation maps of defined exceedance probabilities is required to provide information on the flood hazard and the associated risk. A methodology has been developed to model flood inundation in poorly gauged basins, where reliable information on the hydrological characteristics of floods are uncertain and partially captured by the traditional rain-gauge networks. Flood inundation is performed through coupling a hydrological rainfall-runoff (RR) model (HEC-HMS) with a hydraulic model (HEC-RAS). The RR model is calibrated against the January 2013 flood event in the Awali River basin, Lebanon (300 km2), whose flood peak discharge was estimated by post-event measurements. The resulting flows of the RR model are defined as boundary conditions of the hydraulic model, which is run to generate the corresponding water surface profiles and calibrated against 20 post-event surveyed cross sections after the January-2013 flood event. An uncertainty analysis is performed to assess the results of the models. Consequently, the coupled flood inundation model is simulated with design storms and flood inundation maps are generated of defined exceedance probabilities. The peak discharges estimated by the simulated RR model were in close agreement with the results from different empirical and statistical methods. This methodology can be extended to other poorly gauged basins facing common stage-gauge failure or characterized by floods with a stage exceeding the gauge measurement level, or higher than that defined by the rating curve.

SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

DEFF Research Database (Denmark)

Cuttone, Andrea; Bækgaard, Per; Sekara, Vedran

2017-01-01

We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400...... to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient....

Fundamental uncertainty limit of optical flow velocimetry according to Heisenberg's uncertainty principle.

Science.gov (United States)

Fischer, Andreas

2016-11-01

Optical flow velocity measurements are important for understanding the complex behavior of flows. Although a huge variety of methods exist, they are either based on a Doppler or a time-of-flight measurement principle. Doppler velocimetry evaluates the velocity-dependent frequency shift of light scattered at a moving particle, whereas time-of-flight velocimetry evaluates the traveled distance of a scattering particle per time interval. Regarding the aim of achieving a minimal measurement uncertainty, it is unclear if one principle allows to achieve lower uncertainties or if both principles can achieve equal uncertainties. For this reason, the natural, fundamental uncertainty limit according to Heisenberg's uncertainty principle is derived for Doppler and time-of-flight measurement principles, respectively. The obtained limits of the velocity uncertainty are qualitatively identical showing, e.g., a direct proportionality for the absolute value of the velocity to the power of 32 and an indirect proportionality to the square root of the scattered light power. Hence, both measurement principles have identical potentials regarding the fundamental uncertainty limit due to the quantum mechanical behavior of photons. This fundamental limit can be attained (at least asymptotically) in reality either with Doppler or time-of-flight methods, because the respective Cramér-Rao bounds for dominating photon shot noise, which is modeled as white Poissonian noise, are identical with the conclusions from Heisenberg's uncertainty principle.

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Directory of Open Access Journals (Sweden)

Lei Chen

2018-01-01

Full Text Available Conflict management in Dempster-Shafer theory (D-S theory is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

Uncertainty estimation with a small number of measurements, part II: a redefinition of uncertainty and an estimator method

Science.gov (United States)

Huang, Hening

2018-01-01

This paper is the second (Part II) in a series of two papers (Part I and Part II). Part I has quantitatively discussed the fundamental limitations of the t-interval method for uncertainty estimation with a small number of measurements. This paper (Part II) reveals that the t-interval is an ‘exact’ answer to a wrong question; it is actually misused in uncertainty estimation. This paper proposes a redefinition of uncertainty, based on the classical theory of errors and the theory of point estimation, and a modification of the conventional approach to estimating measurement uncertainty. It also presents an asymptotic procedure for estimating the z-interval. The proposed modification is to replace the t-based uncertainty with an uncertainty estimator (mean- or median-unbiased). The uncertainty estimator method is an approximate answer to the right question to uncertainty estimation. The modified approach provides realistic estimates of uncertainty, regardless of whether the population standard deviation is known or unknown, or if the sample size is small or large. As an application example of the modified approach, this paper presents a resolution to the Du-Yang paradox (i.e. Paradox 2), one of the three paradoxes caused by the misuse of the t-interval in uncertainty estimation.

Operational Implementation of a Pc Uncertainty Construct for Conjunction Assessment Risk Analysis

Science.gov (United States)

Newman, Lauri K.; Hejduk, Matthew D.; Johnson, Lauren C.

2016-01-01

Earlier this year the NASA Conjunction Assessment and Risk Analysis (CARA) project presented the theoretical and algorithmic aspects of a method to include the uncertainties in the calculation inputs when computing the probability of collision (Pc) between two space objects, principally uncertainties in the covariances and the hard-body radius. The output of this calculation approach is to produce rather than a single Pc value an entire probability density function that will represent the range of possible Pc values given the uncertainties in the inputs and bring CA risk analysis methodologies more in line with modern risk management theory. The present study provides results from the exercise of this method against an extended dataset of satellite conjunctions in order to determine the effect of its use on the evaluation of conjunction assessment (CA) event risk posture. The effects are found to be considerable: a good number of events are downgraded from or upgraded to a serious risk designation on the basis of consideration of the Pc uncertainty. The findings counsel the integration of the developed methods into NASA CA operations.

Transfer of Nuclear Data Uncertainties to the Uncertainties of Fuel Characteristic by Interval Calculation

International Nuclear Information System (INIS)

Ukraintsev, V.F.; Kolesov, V.V.

2006-01-01

Usually for evaluation of reactor functionals uncertainties, the perturbation theory and sensitivity analysis techniques are used. Of cause linearization approach of perturbation theory is used. This approach has several disadvantages and that is why a new method, based on application of a special interval calculations technique has been created. Basically, the problem of dependency of fuel cycle characteristic uncertainties from source group neutron cross-sections and decay parameters uncertainties can be solved (to some extent) as well by use of sensitivity analysis. However such procedure is rather labor consuming and does not give guaranteed estimations for received parameters since it works, strictly speaking, only for small deviations because it is initially based on linearization of the mathematical problems. The technique of fuel cycle characteristics uncertainties estimation is based on so-called interval analysis (or interval calculations). The basic advantage of this technique is the opportunity of deriving correct estimations. This technique consists in introducing a new special type of data such as Interval data in codes and the definition for them of all arithmetic operations. A technique of problem decision for system of linear equations (isotope kinetics) with use of interval arithmetic for the fuel burning up problem, has been realized. Thus there is an opportunity to compute a neutron flux, fission and capture cross-section uncertainties impact on nuclide concentration uncertainties and on fuel cycle characteristics (such as K eff , breeding ratio, decay heat power etc). By this time the code for interval calculation of burn-up computing has been developed and verified

The uncertainties in estimating measurement uncertainties

International Nuclear Information System (INIS)

Clark, J.P.; Shull, A.H.

1994-01-01

All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties

Embracing Uncertainty: The Interface of Bayesian Statistics and Cognitive Psychology

Directory of Open Access Journals (Sweden)

Judith L. Anderson

1998-06-01

Full Text Available Ecologists working in conservation and resource management are discovering the importance of using Bayesian analytic methods to deal explicitly with uncertainty in data analyses and decision making. However, Bayesian procedures require, as inputs and outputs, an idea that is problematic for the human brain: the probability of a hypothesis ("single-event probability". I describe several cognitive concepts closely related to single-event probabilities, and discuss how their interchangeability in the human mind results in "cognitive illusions," apparent deficits in reasoning about uncertainty. Each cognitive illusion implies specific possible pitfalls for the use of single-event probabilities in ecology and resource management. I then discuss recent research in cognitive psychology showing that simple tactics of communication, suggested by an evolutionary perspective on human cognition, help people to process uncertain information more effectively as they read and talk about probabilities. In addition, I suggest that carefully considered standards for methodology and conventions for presentation may also make Bayesian analyses easier to understand.

Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error

Science.gov (United States)

Joslyn, Susan L.; LeClerc, Jared E.

2012-01-01

Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather…

Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation.

Science.gov (United States)

Freni, G; La Loggia, G; Notaro, V

2010-01-01

Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly

A Short Review of FDTD-Based Methods for Uncertainty Quantification in Computational Electromagnetics

Directory of Open Access Journals (Sweden)

Theodoros T. Zygiridis

2017-01-01

Full Text Available We provide a review of selected computational methodologies that are based on the deterministic finite-difference time-domain algorithm and are suitable for the investigation of electromagnetic problems involving uncertainties. As it will become apparent, several alternatives capable of performing uncertainty quantification in a variety of cases exist, each one exhibiting different qualities and ranges of applicability, which we intend to point out here. Given the numerous available approaches, the purpose of this paper is to clarify the main strengths and weaknesses of the described methodologies and help the potential readers to safely select the most suitable approach for their problem under consideration.

Event-based Simulation Model for Quantum Optics Experiments

NARCIS (Netherlands)

De Raedt, H.; Michielsen, K.; Jaeger, G; Khrennikov, A; Schlosshauer, M; Weihs, G

2011-01-01

We present a corpuscular simulation model of optical phenomena that does not require the knowledge of the solution of a wave equation of the whole system and reproduces the results of Maxwell's theory by generating detection events one-by-one. The event-based corpuscular model gives a unified

Toward Sensor-Based Context Aware Systems

Directory of Open Access Journals (Sweden)

Kouhei Takada

2012-01-01

Full Text Available This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.

Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

Science.gov (United States)

Mortuza, M. R.; Demissie, Y.; Li, H. Y.

2014-12-01

Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.

Approach to uncertainty in risk analysis

Energy Technology Data Exchange (ETDEWEB)

Rish, W.R.

1988-08-01

In the Fall of 1985 EPA's Office of Radiation Programs (ORP) initiated a project to develop a formal approach to dealing with uncertainties encountered when estimating and evaluating risks to human health and the environment. Based on a literature review of modeling uncertainty, interviews with ORP technical and management staff, and input from experts on uncertainty analysis, a comprehensive approach was developed. This approach recognizes by design the constraints on budget, time, manpower, expertise, and availability of information often encountered in ''real world'' modeling. It is based on the observation that in practice risk modeling is usually done to support a decision process. As such, the approach focuses on how to frame a given risk modeling problem, how to use that framing to select an appropriate mixture of uncertainty analyses techniques, and how to integrate the techniques into an uncertainty assessment that effectively communicates important information and insight to decision-makers. The approach is presented in this report. Practical guidance on characterizing and analyzing uncertainties about model form and quantities and on effectively communicating uncertainty analysis results is included. Examples from actual applications are presented.

Approach to uncertainty in risk analysis

International Nuclear Information System (INIS)

Rish, W.R.

1988-08-01

In the Fall of 1985 EPA's Office of Radiation Programs (ORP) initiated a project to develop a formal approach to dealing with uncertainties encountered when estimating and evaluating risks to human health and the environment. Based on a literature review of modeling uncertainty, interviews with ORP technical and management staff, and input from experts on uncertainty analysis, a comprehensive approach was developed. This approach recognizes by design the constraints on budget, time, manpower, expertise, and availability of information often encountered in ''real world'' modeling. It is based on the observation that in practice risk modeling is usually done to support a decision process. As such, the approach focuses on how to frame a given risk modeling problem, how to use that framing to select an appropriate mixture of uncertainty analyses techniques, and how to integrate the techniques into an uncertainty assessment that effectively communicates important information and insight to decision-makers. The approach is presented in this report. Practical guidance on characterizing and analyzing uncertainties about model form and quantities and on effectively communicating uncertainty analysis results is included. Examples from actual applications are presented

Two-stage robust UC including a novel scenario-based uncertainty model for wind power applications

International Nuclear Information System (INIS)

Álvarez-Miranda, Eduardo; Campos-Valdés, Camilo; Rahmann, Claudia

2015-01-01

Highlights: • Methodological framework for obtaining Robust Unit Commitment (UC) policies. • Wind-power forecast using a revisited bootstrap predictive inference approach. • Novel scenario-based model for wind-power uncertainty. • Efficient modeling framework for obtaining nearly optimal UC policies in reasonable time. • Effective incorporation of wind-power uncertainty in the UC modeling. - Abstract: The complex processes involved in the determination of the availability of power from renewable energy sources, such as wind power, impose great challenges in the forecasting processes carried out by transmission system operators (TSOs). Nowadays, many of these TSOs use operation planning tools that take into account the uncertainty of the wind-power. However, most of these methods typically require strict assumptions about the probabilistic behavior of the forecast error, and usually ignore the dynamic nature of the forecasting process. In this paper a methodological framework to obtain Robust Unit Commitment (UC) policies is presented; such methodology considers a novel scenario-based uncertainty model for wind power applications. The proposed method is composed by three main phases. The first two phases generate a sound wind-power forecast using a bootstrap predictive inference approach. The third phase corresponds to modeling and solving a one-day ahead Robust UC considering the output of the first phase. The performance of proposed approach is evaluated using as case study a new wind farm to be incorporated into the Northern Interconnected System (NIS) of Chile. A projection of wind-based power installation, as well as different characteristic of the uncertain data, are considered in this study

An EPGPT-based approach for uncertainty quantification

International Nuclear Information System (INIS)

Wang, C.; Abdel-Khalik, H. S.

2012-01-01

Generalized Perturbation Theory (GPT) has been widely used by many scientific disciplines to perform sensitivity analysis and uncertainty quantification. This manuscript employs recent developments in GPT theory, collectively referred to as Exact-to-Precision Generalized Perturbation Theory (EPGPT), to enable uncertainty quantification for computationally challenging models, e.g. nonlinear models associated with many input parameters and many output responses and with general non-Gaussian parameters distributions. The core difference between EPGPT and existing GPT is in the way the problem is formulated. GPT formulates an adjoint problem that is dependent on the response of interest. It tries to capture via the adjoint solution the relationship between the response of interest and the constraints on the state variations. EPGPT recasts the problem in terms of a smaller set of what is referred to as the 'active' responses which are solely dependent on the physics model and the boundary and initial conditions rather than on the responses of interest. The objective of this work is to apply an EPGPT methodology to propagate cross-sections variations in typical reactor design calculations. The goal is to illustrate its use and the associated impact for situations where the typical Gaussian assumption for parameters uncertainties is not valid and when nonlinear behavior must be considered. To allow this demonstration, exaggerated variations will be employed to stimulate nonlinear behavior in simple prototypical neutronics models. (authors)

Event-Based Corpuscular Model for Quantum Optics Experiments

NARCIS (Netherlands)

Michielsen, K.; Jin, F.; Raedt, H. De

A corpuscular simulation model of optical phenomena that does not require the knowledge of the solution of a wave equation of the whole system and reproduces the results of Maxwell's theory by generating detection events one-by-one is presented. The event-based corpuscular model is shown to give a

Summary of existing uncertainty methods

International Nuclear Information System (INIS)

Glaeser, Horst

2013-01-01

A summary of existing and most used uncertainty methods is presented, and the main features are compared. One of these methods is the order statistics method based on Wilks' formula. It is applied in safety research as well as in licensing. This method has been first proposed by GRS for use in deterministic safety analysis, and is now used by many organisations world-wide. Its advantage is that the number of potential uncertain input and output parameters is not limited to a small number. Such a limitation was necessary for the first demonstration of the Code Scaling Applicability Uncertainty Method (CSAU) by the United States Regulatory Commission (USNRC). They did not apply Wilks' formula in their statistical method propagating input uncertainties to obtain the uncertainty of a single output variable, like peak cladding temperature. A Phenomena Identification and Ranking Table (PIRT) was set up in order to limit the number of uncertain input parameters, and consequently, the number of calculations to be performed. Another purpose of such a PIRT process is to identify the most important physical phenomena which a computer code should be suitable to calculate. The validation of the code should be focused on the identified phenomena. Response surfaces are used in some applications replacing the computer code for performing a high number of calculations. The second well known uncertainty method is the Uncertainty Methodology Based on Accuracy Extrapolation (UMAE) and the follow-up method 'Code with the Capability of Internal Assessment of Uncertainty (CIAU)' developed by the University Pisa. Unlike the statistical approaches, the CIAU does compare experimental data with calculation results. It does not consider uncertain input parameters. Therefore, the CIAU is highly dependent on the experimental database. The accuracy gained from the comparison between experimental data and calculated results are extrapolated to obtain the uncertainty of the system code predictions

Influence of uncertainty on framed decision-making with moral dilemma.

Science.gov (United States)

Merlhiot, Gaëtan; Mermillod, Martial; Le Pennec, Jean-Luc; Dutheil, Frédéric; Mondillon, Laurie

2018-01-01

In cases of impending natural disasters, most events are uncertain and emotionally relevant, both critical factors for decision-making. Moreover, for exposed individuals, the sensitivity to the framing of the consequences (gain or loss) and the moral judgments they have to perform (e.g., evacuate or help an injured person) constitute two central effects that have never been examined in the same context of decision-making. In a framed decision-making task with moral dilemma, we investigated whether uncertainty (i.e., unpredictably of events) and a threatening context would influence the framing effect (actions framed in loss are avoided in comparison to the ones framed in gain) and the personal intention effect (unintentional actions are more morally acceptable in comparison to intentional actions) on the perceived moral acceptability of taking action. Considering the impact of uncertainty and fear on the processes underlying these effects, we assumed that these emotions would lead to the negation of the two effects. Our results indicate that the exposure to uncertain events leads to the negation of the framing effect, but does not influence the moral acceptability and the effect of personal intention. We discuss our results in the light of dual-process models (i.e. systematic vs. heuristic), appraisal theories, and neurocognitive aspects. These elements highlight the importance of providing solutions to cope with uncertainty, both for scientists and local populations exposed to natural hazards.

Judgment under Uncertainty: Heuristics and Biases.

Science.gov (United States)

Tversky, A; Kahneman, D

1974-09-27

This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.

Ntuples for NLO Events at Hadron Colliders

CERN Document Server

Bern, Z.; Febres Cordero, F.; Höche, S.; Ita, H.; Kosower, D.A.; Maitre, D.

2014-01-01

We present an event-file format for the dissemination of next-to-leading-order (NLO) predictions for QCD processes at hadron colliders. The files contain all information required to compute generic jet-based infrared-safe observables at fixed order (without showering or hadronization), and to recompute observables with different factorization and renormalization scales. The files also make it possible to evaluate cross sections and distributions with different parton distribution functions. This in turn makes it possible to estimate uncertainties in NLO predictions of a wide variety of observables without recomputing the short-distance matrix elements. The event files allow a user to choose among a wide range of commonly-used jet algorithms and jet-size parameters. We provide event files for a $W$ or $Z$ boson accompanied by up to four jets, and for pure-jet events with up to four jets. The files are for the Large Hadron Collider with a center of mass energy of 7 or 8 TeV. A C++ library along with a Python in...

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models

Directory of Open Access Journals (Sweden)

Koen Degeling

2017-12-01

Full Text Available Abstract Background Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Methods Two approaches, 1 using non-parametric bootstrapping and 2 using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Results Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500, the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25, yielding infeasible modeling outcomes. Conclusions Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

Science.gov (United States)

Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik

2017-12-15

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

DEFF Research Database (Denmark)

Schaarup-Jensen, Kjeld; Rasmussen, Michael R.; Thorndahl, Søren

2008-01-01

In urban drainage modeling long term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties concerning long term prediction of maximum water levels and combined sewer...... overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO...... gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity...

To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

DEFF Research Database (Denmark)

Schaarup-Jensen, Kjeld; Rasmussen, Michael R.; Thorndahl, Søren

2009-01-01

In urban drainage modelling long term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties concerning long term prediction of maximum water levels and combined sewer...... overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO...... gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity...

Addressing land use change and uncertainty in the life-cycle assessment of wheat-based bioethanol

International Nuclear Information System (INIS)

Malça, João; Freire, Fausto

2012-01-01

Despite the significant growth in the number of published life-cycle assessments of biofuels, important aspects have not captured sufficient attention, namely soil carbon emissions from land use change (LUC) and uncertainty analysis. The main goal of this article is to evaluate the implications of different LUC scenarios and uncertainty in the life-cycle energy renewability efficiency and GHG (greenhouse gases) intensity of wheat-based bioethanol replacing gasoline. A comprehensive assessment of different LUC scenarios (grassland or cropland converted to wheat cultivation) and agricultural practices is conducted, which results in different carbon stock change values. The types of uncertainty addressed include parameter uncertainty (propagated into LC (life-cycle) results using Monte-Carlo simulation) and uncertainty concerning how bioethanol co-product credits are accounted for. Results show that GHG emissions have considerably higher uncertainty than energy efficiency values, mainly due to soil carbon emissions from direct LUC and N 2 O release from cultivated soil. Moreover, LUC dominates the GHG intensity of bioethanol. Very different GHG emissions are calculated depending on the LUC scenario considered. Conversion of full- or low-tillage croplands to wheat cultivation results in bioethanol GHG emissions lower than gasoline emissions, whereas conversion of grassland does not contribute to bioethanol GHG savings over gasoline in the short- to mid-term. -- Highlights: ► We address different LUC scenarios and uncertainty in the LCA of wheat bioethanol. ► GHG emissions have considerably higher uncertainty than energy efficiency values. ► Bioethanol contributes to primary energy savings over gasoline. ► Very different life-cycle GHG emissions are calculated depending on the LUC scenario. ► GHG savings over gasoline are only achieved if cropland is the reference land use.

Underlying Event and B-Hadron Decays in $t\\overline{t}$ Events

CERN Document Server

INSPIRE-00175000

2014-01-01

We present exploratory studies of the underlying event activity and of fragmentation and hadronization of b quarks using $t\\overline{t}$ candidate events in proton-proton collision data acquired by the CMS experiment. We reconstruct charm mesons in fully charged decay channels from the reconstructed tracks associated with the hadronization of b quarks from the top decay, and study their kinematics relative to the mother jet. A good agreement is found using MadGraph plus the Pythia 6 Tune Z2* simulation. The effects predicted by alternative settings and generators for the characterization of the underlying event are also explored. These results are expected to contribute in the future to more precise measurements in the top quark sector in particular of the top quark mass by either constraining systematic uncertainties related to the modeling of the underlying event in $t\\overline{t}$ events or by paving the way for alternative mass measurement methods.

Nuclear data uncertainties: I, Basic concepts of probability

Energy Technology Data Exchange (ETDEWEB)

Smith, D.L.

1988-12-01

Some basic concepts of probability theory are presented from a nuclear-data perspective, in order to provide a foundation for thorough understanding of the role of uncertainties in nuclear data research. Topics included in this report are: events, event spaces, calculus of events, randomness, random variables, random-variable distributions, intuitive and axiomatic probability, calculus of probability, conditional probability and independence, probability distributions, binomial and multinomial probability, Poisson and interval probability, normal probability, the relationships existing between these probability laws, and Bayes' theorem. This treatment emphasizes the practical application of basic mathematical concepts to nuclear data research, and it includes numerous simple examples. 34 refs.

Nuclear data uncertainties: I, Basic concepts of probability

International Nuclear Information System (INIS)

Smith, D.L.

1988-12-01

Some basic concepts of probability theory are presented from a nuclear-data perspective, in order to provide a foundation for thorough understanding of the role of uncertainties in nuclear data research. Topics included in this report are: events, event spaces, calculus of events, randomness, random variables, random-variable distributions, intuitive and axiomatic probability, calculus of probability, conditional probability and independence, probability distributions, binomial and multinomial probability, Poisson and interval probability, normal probability, the relationships existing between these probability laws, and Bayes' theorem. This treatment emphasizes the practical application of basic mathematical concepts to nuclear data research, and it includes numerous simple examples. 34 refs

Severe Accident Research Network (SARNET). Level 2 PSA work package: comparison of partners methods for uncertainties assessment

International Nuclear Information System (INIS)

Chaumont, B.; Haesendonck, M.; Vidal, S.; Eyink, J.; Loeffler, H.; Radu, G.; Kopustinskas, V.; Ming, A.; Guntay, S.; Gustavsson, V.; Ivanov, I.; Dienstbier, J.; Bareith, A.; Hollo, E.; Lajtha, G.

2007-01-01

The PSA2 work package (PSA2 WP) is a part of the Joined Programme Activity of the European Severe Accident Network (SARNET) related to level 2 PSA methodologies. The general objectives of this work package is to provide a comparison of the different methodologies used or under development for level 2 PSA application by the partners involved in the work package and to promote their harmonization. The PSA2 WP is organized into three main topics: methodologies in general, methodologies for uncertainties assessment, and dynamic reliability methods. The different tasks initially defined for these three topics are shortly described and the partners involved identified. Attention is then paid on the methodologies used so far by the different partners to assess the uncertainties in their level 2 PSA. A review of partners approaches to assess - as far as possible - the different sources of possible uncertainties is done for the different following topics: - uncertainties propagated from the level 1 PSA, - uncertainties (in sense of approximation) due to the binning of the level 1 sequences in Plant Damage, - uncertainties related to the structure of the Accident Progression Event Tree, - uncertainties related to the probabilities of stochastic events (system failure or recovery, human actions, some physical phenomena such as ignition of hydrogen combustion or triggering of steam explosion), - uncertainties elated to the modelling of the different physical phenomena, - uncertainties related to the cut-off frequency used in the probabilistic quantification of the Accident Progression Event Tree; - uncertainties related to the binning of level 2 sequences in Release Categories (variables not considered, values of eventual continuous variables). First conclusions of the comparison are given in terms of improvement needs and then of perspectives of the work for the following period of work. (authors)

Human based roots of failures in nuclear events investigations

Energy Technology Data Exchange (ETDEWEB)

Ziedelis, Stanislovas; Noel, Marc; Strucic, Miodrag [Commission of the European Communities, Petten (Netherlands). European Clearinghouse on Operational Experience Feedback for Nuclear Power Plants

2012-10-15

This paper aims for improvement of quality of the event investigations in the nuclear industry through analysis of the existing practices, identifying and removing the existing Human and Organizational Factors (HOF) and management related barriers. It presents the essential results of several studies performed by the European Clearinghouse on Operational Experience. Outcomes of studies are based on survey of currently existing event investigation practices typical for nuclear industry of 12 European countries, as well as on insights from analysis of numerous event investigation reports. System of operational experience feedback from information based on event investigation results is not enough effective to prevent and even to decrease frequency of recurring events due to existing methodological, HOF-related and/or knowledge management related constraints. Besides that, several latent root causes of unsuccessful event investigation are related to weaknesses in safety culture of personnel and managers. These weaknesses include focus on costs or schedule, political manipulation, arrogance, ignorance, entitlement and/or autocracy. Upgrades in safety culture of organization's personnel and its senior management especially seem to be an effective way to improvement. Increasing of competencies, capabilities and level of independency of event investigation teams, elaboration of comprehensive software, ensuring of positive approach, adequate support and impartiality of management could also facilitate for improvement of quality of the event investigations. (orig.)

Human based roots of failures in nuclear events investigations

International Nuclear Information System (INIS)

Ziedelis, Stanislovas; Noel, Marc; Strucic, Miodrag

2012-01-01

This paper aims for improvement of quality of the event investigations in the nuclear industry through analysis of the existing practices, identifying and removing the existing Human and Organizational Factors (HOF) and management related barriers. It presents the essential results of several studies performed by the European Clearinghouse on Operational Experience. Outcomes of studies are based on survey of currently existing event investigation practices typical for nuclear industry of 12 European countries, as well as on insights from analysis of numerous event investigation reports. System of operational experience feedback from information based on event investigation results is not enough effective to prevent and even to decrease frequency of recurring events due to existing methodological, HOF-related and/or knowledge management related constraints. Besides that, several latent root causes of unsuccessful event investigation are related to weaknesses in safety culture of personnel and managers. These weaknesses include focus on costs or schedule, political manipulation, arrogance, ignorance, entitlement and/or autocracy. Upgrades in safety culture of organization's personnel and its senior management especially seem to be an effective way to improvement. Increasing of competencies, capabilities and level of independency of event investigation teams, elaboration of comprehensive software, ensuring of positive approach, adequate support and impartiality of management could also facilitate for improvement of quality of the event investigations. (orig.)

Deterministic uncertainty analysis

International Nuclear Information System (INIS)

Worley, B.A.

1987-12-01

This paper presents a deterministic uncertainty analysis (DUA) method for calculating uncertainties that has the potential to significantly reduce the number of computer runs compared to conventional statistical analysis. The method is based upon the availability of derivative and sensitivity data such as that calculated using the well known direct or adjoint sensitivity analysis techniques. Formation of response surfaces using derivative data and the propagation of input probability distributions are discussed relative to their role in the DUA method. A sample problem that models the flow of water through a borehole is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. Propogation of uncertainties by the DUA method is compared for ten cases in which the number of reference model runs was varied from one to ten. The DUA method gives a more accurate representation of the true cumulative distribution of the flow rate based upon as few as two model executions compared to fifty model executions using a statistical approach. 16 refs., 4 figs., 5 tabs

A formal guidance for handling different uncertainty sources employed in the level 2 PSA

International Nuclear Information System (INIS)

Ahn, Kwang Il; Yang, Joon Eon; Ha, Jae Joo

2004-01-01

The methodological framework of the level 2 PSA appears to be currently standardized in a formalized fashion, but there have been different opinions on the way the sources of uncertainty are characterized and treated. This is primarily because the level 2 PSA deals with complex phenomenological processes that are deterministic in nature rather than random processes, and there are no probabilistic models characterizing them clearly. As a result, the probabilistic quantification of the level 2 PSA CET/APET is often subjected to two sources of uncertainty: (a) incomplete modeling of accident pathways or different predictions for the behavior of phenomenological events and (b) expert-to-expert variation in estimating the occurrence probability of phenomenological events. While a clear definition of the two sources of uncertainty involved in the level 2 PSA makes it possible to treat an uncertainty in a consistent manner, careless application of these different sources of uncertainty may produce different conclusions in the decision-making process. The primary purpose of this paper is to characterize typical sources of uncertainty that would often be addressed in the level 2 PSA and to provide a formal guidance for quantifying their impacts on the PSA level 2 risk results. An additional purpose of this paper is to give a formal approach on how to combine random uncertainties addressed in the level 1 PSA with subjectivistic uncertainties addressed in the level 2 PSA

Event Recognition Based on Deep Learning in Chinese Texts.

Directory of Open Access Journals (Sweden)

Yajun Zhang

Full Text Available Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM. Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN, then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Event Recognition Based on Deep Learning in Chinese Texts.

Science.gov (United States)

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Uncertainty Instability Risk Analysis of High Concrete Arch Dam Abutments

Directory of Open Access Journals (Sweden)

Xin Cao

2017-01-01

Full Text Available The uncertainties associated with concrete arch dams rise with the increased height of dams. Given the uncertainties associated with influencing factors, the stability of high arch dam abutments as a fuzzy random event was studied. In addition, given the randomness and fuzziness of calculation parameters as well as the failure criterion, hazard point and hazard surface uncertainty instability risk ratio models were proposed for high arch dam abutments on the basis of credibility theory. The uncertainty instability failure criterion was derived through the analysis of the progressive instability failure process on the basis of Shannon’s entropy theory. The uncertainties associated with influencing factors were quantized by probability or possibility distribution assignments. Gaussian random theory was used to generate random realizations for influence factors with spatial variability. The uncertainty stability analysis method was proposed by combining the finite element analysis and the limit equilibrium method. The instability risk ratio was calculated using the Monte Carlo simulation method and fuzzy random postprocessing. Results corroborate that the modeling approach is sound and that the calculation method is feasible.

Volcano!: An Event-Based Science Module. Student Edition. Geology Module.

Science.gov (United States)

Wright, Russell G.

This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

Impact of magnitude uncertainties on seismic catalogue properties

Science.gov (United States)

Leptokaropoulos, K. M.; Adamaki, A. K.; Roberts, R. G.; Gkarlaouni, C. G.; Paradisopoulou, P. M.

2018-05-01

Catalogue-based studies are of central importance in seismological research, to investigate the temporal, spatial and size distribution of earthquakes in specified study areas. Methods for estimating the fundamental catalogue parameters like the Gutenberg-Richter (G-R) b-value and the completeness magnitude (Mc) are well established and routinely applied. However, the magnitudes reported in seismicity catalogues contain measurement uncertainties which may significantly distort the estimation of the derived parameters. In this study, we use numerical simulations of synthetic data sets to assess the reliability of different methods for determining b-value and Mc, assuming the G-R law validity. After contaminating the synthetic catalogues with Gaussian noise (with selected standard deviations), the analysis is performed for numerous data sets of different sample size (N). The noise introduced to the data generally leads to a systematic overestimation of magnitudes close to and above Mc. This fact causes an increase of the average number of events above Mc, which in turn leads to an apparent decrease of the b-value. This may result to a significant overestimation of seismicity rate even well above the actual completeness level. The b-value can in general be reliably estimated even for relatively small data sets (N < 1000) when only magnitudes higher than the actual completeness level are used. Nevertheless, a correction of the total number of events belonging in each magnitude class (i.e. 0.1 unit) should be considered, to deal with the magnitude uncertainty effect. Because magnitude uncertainties (here with the form of Gaussian noise) are inevitable in all instrumental catalogues, this finding is fundamental for seismicity rate and seismic hazard assessment analyses. Also important is that for some data analyses significant bias cannot necessarily be avoided by choosing a high Mc value for analysis. In such cases, there may be a risk of severe miscalculation of

Uncertainty analysis of an integrated energy system based on information theory

International Nuclear Information System (INIS)

Fu, Xueqian; Sun, Hongbin; Guo, Qinglai; Pan, Zhaoguang; Xiong, Wen; Wang, Li

2017-01-01

Currently, a custom-designed configuration of different renewable technologies named the integrated energy system (IES) has become popular due to its high efficiency, benefiting from complementary multi-energy technologies. This paper proposes an information entropy approach to quantify uncertainty in an integrated energy system based on a stochastic model that drives a power system model derived from an actual network on Barry Island. Due to the complexity of co-behaviours between generators, a copula-based approach is utilized to articulate the dependency structure of the generator outputs with regard to such factors as weather conditions. Correlation coefficients and mutual information, which are effective for assessing the dependence relationships, are applied to judge whether the stochastic IES model is correct. The calculated information values can be used to analyse the impacts of the coupling of power and heat on power flows and heat flows, and this approach will be helpful for improving the operation of IES. - Highlights: • The paper explores uncertainty of an integrated energy system. • The dependent weather model is verified from the perspective of correlativity. • The IES model considers the dependence between power and heat. • The information theory helps analyse the complexity of IES operation. • The application of the model is studied using an operational system on Barry Island.

Incorporating Forecast Uncertainty in Utility Control Center

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2014-07-09

Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

Uncertainty study of the PWR pressure vessel fluence. Adjustment of the nuclear data base

International Nuclear Information System (INIS)

Kodeli, I.A.

1994-01-01

The code system devoted to the calculation of the sensitivity and uncertainty of of the neutron flux and reaction rates calculated by the transport codes, has been developed. Adjustment of the basic data to experimental results can be performed as well. Various sources of uncertainties can be taken into account, such as those due to the uncertainties in the cross-sections, response functions, fission spectrum and space distribution of neutron source, geometry and material composition uncertainties... One -As well as two- dimensional analysis can be performed. Linear perturbation theory is applied. The code system is sufficiently general to be used for various analysis in the fields of fission and fusion. The principal objective of our studies concerns the capsule dosimetry study realized in the framework of the 900 MWe PWR pressure vessel surveillance program. The analysis indicates that the present calculations, performed by the code TRIPOLI-2, using the ENDF/B-IV based, non-perturbed neutron cross-section library in 315 energy groups, allows to estimate the neutron flux and the reaction rates in the surveillance capsules and in the most calculated and measured reaction rates permits to reduce these uncertainties. The results obtained with the adjusted iron cross-sections, response functions and fission spectrum show that the agreement between the calculation and the experiment was improved to become within 10% approximately. The neutron flux deduced from the experiment is then extrapolated from the capsule to the most exposed pressure vessel location using the calculated lead factor. The uncertainty in this factor was estimated to be about 7%. (author). 39 refs., 52 figs., 30 tabs

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Directory of Open Access Journals (Sweden)

Hao Zhu

2017-04-01

Full Text Available Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO, probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Institute of Scientific and Technical Information of China (English)

Zhu Hao; Tian Hui; Cai Guobiao

2017-01-01

Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO) method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncer-tainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO), probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM) used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.

Illness uncertainty and treatment motivation in type 2 diabetes patients.

Science.gov (United States)

Apóstolo, João Luís Alves; Viveiros, Catarina Sofia Castro; Nunes, Helena Isabel Ribeiro; Domingues, Helena Raquel Faustino

2007-01-01

To characterize the uncertainty in illness and the motivation for treatment and to evaluate the existing relation between these variables in individuals with type 2 diabetes. Descriptive, correlational study, using a sample of 62 individuals in diabetes consultation sessions. The Uncertainty Stress Scale and the Treatment Self-Regulation Questionnaire were used. The individuals with type 2 diabetes present low levels of uncertainty in illness and a high motivation for treatment, with a stronger intrinsic than extrinsic motivation. A negative correlation was verified between the uncertainty in the face of the prognosis and treatment and the intrinsic motivation. These individuals are already adapted, acting according to the meanings they attribute to illness. Uncertainty can function as a threat, intervening negatively in the attribution of meaning to the events related to illness and in the process of adaptation and motivation to adhere to treatment. Intrinsic motivation seems to be essential to adhere to treatment.

An evaluation of the uncertainty of extreme events statistics at the WMO/CIMO Lead Centre on precipitation intensity

Science.gov (United States)

Colli, M.; Lanza, L. G.; La Barbera, P.

2012-12-01

Improving the quality of point-scale rainfall measurements is a crucial issue fostered in recent years by the WMO Commission for Instruments and Methods of Observation (CIMO) by providing recommendations on the standardization of equipment and exposure, instrument calibration and data correction as a consequence of various comparative campaigns involving manufacturers and national meteorological services from the participating countries. The WMO/CIMO Lead Centre on Precipitation Intensity (LC) was recently constituted, in a joint effort between the Dep. of Civil, Chemical and Environmental Engineering of the University of Genova and the Italian Air Force Met Service, gathering the considerable asset of data and information achieved by the past infield and laboratory campaigns with the aim of researching novel methodologies for improving the accuracy of rainfall intensity (RI) measurement techniques. Among the ongoing experimental activities carried out by the LC laboratory particular attention is paid to the reliability evaluation of extreme rainfall events statistics , a common tool in the engineering practice for urban and non urban drainage system design, based on real world observations obtained from weighing gauges. Extreme events statistics were proven already to be highly affected by the traditional tipping-bucket rain gauge RI measurement inaccuracy (La Barbera et al., 2002) and the time resolution of the available RI series certainly constitutes another key-factor in the reliability of the derived hyetographs. The present work reports the LC laboratory efforts in assembling a rainfall simulation system to reproduce the inner temporal structure of the rainfall process by means of dedicated calibration and validation tests. This allowed testing of catching type rain gauges under non-steady flow conditions and quantifying, in a first instance, the dynamic behaviour of the investigated instruments. Considerations about the influence of the dynamic response on

Uncertainty during pain anticipation: the adaptive value of preparatory processes.

Science.gov (United States)

Seidel, Eva-Maria; Pfabigan, Daniela M; Hahn, Andreas; Sladky, Ronald; Grahl, Arvina; Paul, Katharina; Kraus, Christoph; Küblböck, Martin; Kranz, Georg S; Hummer, Allan; Lanzenberger, Rupert; Windischberger, Christian; Lamm, Claus

2015-02-01

Anticipatory processes prepare the organism for upcoming experiences. The aim of this study was to investigate neural responses related to anticipation and processing of painful stimuli occurring with different levels of uncertainty. Twenty-five participants (13 females) took part in an electroencephalography and functional magnetic resonance imaging (fMRI) experiment at separate times. A visual cue announced the occurrence of an electrical painful or nonpainful stimulus, delivered with certainty or uncertainty (50% chance), at some point during the following 15 s. During the first 2 s of the anticipation phase, a strong effect of uncertainty was reflected in a pronounced frontal stimulus-preceding negativity (SPN) and increased fMRI activation in higher visual processing areas. In the last 2 s before stimulus delivery, we observed stimulus-specific preparatory processes indicated by a centroparietal SPN and posterior insula activation that was most pronounced for the certain pain condition. Uncertain anticipation was associated with attentional control processes. During stimulation, the results revealed that unexpected painful stimuli produced the strongest activation in the affective pain processing network and a more pronounced offset-P2. Our results reflect that during early anticipation uncertainty is strongly associated with affective mechanisms and seems to be a more salient event compared to certain anticipation. During the last 2 s before stimulation, attentional control mechanisms are initiated related to the increased salience of uncertainty. Furthermore, stimulus-specific preparatory mechanisms during certain anticipation also shaped the response to stimulation, underlining the adaptive value of stimulus-targeted preparatory activity which is less likely when facing an uncertain event. © 2014 Wiley Periodicals, Inc.

Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study

Directory of Open Access Journals (Sweden)

B. Dittes

2018-05-01

Full Text Available Planning authorities are faced with a range of questions when planning flood protection measures: is the existing protection adequate for current and future demands or should it be extended? How will flood patterns change in the future? How should the uncertainty pertaining to this influence the planning decision, e.g., for delaying planning or including a safety margin? Is it sufficient to follow a protection criterion (e.g., to protect from the 100-year flood or should the planning be conducted in a risk-based way? How important is it for flood protection planning to accurately estimate flood frequency (changes, costs and damage? These are questions that we address for a medium-sized pre-alpine catchment in southern Germany, using a sequential Bayesian decision making framework that quantitatively addresses the full spectrum of uncertainty. We evaluate different flood protection systems considered by local agencies in a test study catchment. Despite large uncertainties in damage, cost and climate, the recommendation is robust for the most conservative approach. This demonstrates the feasibility of making robust decisions under large uncertainty. Furthermore, by comparison to a previous study, it highlights the benefits of risk-based planning over the planning of flood protection to a prescribed return period.

Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study

Science.gov (United States)

Dittes, Beatrice; Kaiser, Maria; Špačková, Olga; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2018-05-01

Planning authorities are faced with a range of questions when planning flood protection measures: is the existing protection adequate for current and future demands or should it be extended? How will flood patterns change in the future? How should the uncertainty pertaining to this influence the planning decision, e.g., for delaying planning or including a safety margin? Is it sufficient to follow a protection criterion (e.g., to protect from the 100-year flood) or should the planning be conducted in a risk-based way? How important is it for flood protection planning to accurately estimate flood frequency (changes), costs and damage? These are questions that we address for a medium-sized pre-alpine catchment in southern Germany, using a sequential Bayesian decision making framework that quantitatively addresses the full spectrum of uncertainty. We evaluate different flood protection systems considered by local agencies in a test study catchment. Despite large uncertainties in damage, cost and climate, the recommendation is robust for the most conservative approach. This demonstrates the feasibility of making robust decisions under large uncertainty. Furthermore, by comparison to a previous study, it highlights the benefits of risk-based planning over the planning of flood protection to a prescribed return period.

Volcano!: An Event-Based Science Module. Teacher's Guide. Geology Module.

Science.gov (United States)

Wright, Russell G.

This book is designed for middle school earth science teachers to help their students learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research,…

Propagating Mixed Uncertainties in Cyber Attacker Payoffs: Exploration of Two-Phase Monte Carlo Sampling and Probability Bounds Analysis

Energy Technology Data Exchange (ETDEWEB)

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.; Halappanavar, Mahantesh

2016-09-16

Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker and system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.

The cerebellum and decision making under uncertainty.

Science.gov (United States)

Blackwood, Nigel; Ffytche, Dominic; Simmons, Andrew; Bentall, Richard; Murray, Robin; Howard, Robert

2004-06-01

This study aimed to identify the neural basis of probabilistic reasoning, a type of inductive inference that aids decision making under conditions of uncertainty. Eight normal subjects performed two separate two-alternative-choice tasks (the balls in a bottle and personality survey tasks) while undergoing functional magnetic resonance imaging (fMRI). The experimental conditions within each task were chosen so that they differed only in their requirement to make a decision under conditions of uncertainty (probabilistic reasoning and frequency determination required) or under conditions of certainty (frequency determination required). The same visual stimuli and motor responses were used in the experimental conditions. We provide evidence that the neo-cerebellum, in conjunction with the premotor cortex, inferior parietal lobule and medial occipital cortex, mediates the probabilistic inferences that guide decision making under uncertainty. We hypothesise that the neo-cerebellum constructs internal working models of uncertain events in the external world, and that such probabilistic models subserve the predictive capacity central to induction. Copyright 2004 Elsevier B.V.

A survey of resilience, burnout, and tolerance of uncertainty in Australian general practice registrars

Directory of Open Access Journals (Sweden)

Cooke Georga PE

2013-01-01

Full Text Available Abstract Background Burnout and intolerance of uncertainty have been linked to low job satisfaction and lower quality patient care. While resilience is related to these concepts, no study has examined these three concepts in a cohort of doctors. The objective of this study was to measure resilience, burnout, compassion satisfaction, personal meaning in patient care and intolerance of uncertainty in Australian general practice (GP registrars. Methods We conducted a paper-based cross-sectional survey of GP registrars in Australia from June to July 2010, recruited from a newsletter item or registrar education events. Survey measures included the Resilience Scale-14, a single-item scale for burnout, Professional Quality of Life (ProQOL scale, Personal Meaning in Patient Care scale, Intolerance of Uncertainty-12 scale, and Physician Response to Uncertainty scale. Results 128 GP registrars responded (response rate 90%. Fourteen percent of registrars were found to be at risk of burnout using the single-item scale for burnout, but none met the criteria for burnout using the ProQOL scale. Secondary traumatic stress, general intolerance of uncertainty, anxiety due to clinical uncertainty and reluctance to disclose uncertainty to patients were associated with being at higher risk of burnout, but sex, age, practice location, training duration, years since graduation, and reluctance to disclose uncertainty to physicians were not. Only ten percent of registrars had high resilience scores. Resilience was positively associated with compassion satisfaction and personal meaning in patient care. Resilience was negatively associated with burnout, secondary traumatic stress, inhibitory anxiety, general intolerance to uncertainty, concern about bad outcomes and reluctance to disclose uncertainty to patients. Conclusions GP registrars in this survey showed a lower level of burnout than in other recent surveys of the broader junior doctor population in both Australia

A statistical kinematic source inversion approach based on the QUESO library for uncertainty quantification and prediction

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Zielke, Olaf; McDougall, Damon; Mai, Martin; Babuska, Ivo

2014-05-01

Seismic, often augmented with geodetic data, are frequently used to invert for the spatio-temporal evolution of slip along a rupture plane. The resulting images of the slip evolution for a single event, inferred by different research teams, often vary distinctly, depending on the adopted inversion approach and rupture model parameterization. This observation raises the question, which of the provided kinematic source inversion solutions is most reliable and most robust, and — more generally — how accurate are fault parameterization and solution predictions? These issues are not included in "standard" source inversion approaches. Here, we present a statistical inversion approach to constrain kinematic rupture parameters from teleseismic body waves. The approach is based a) on a forward-modeling scheme that computes synthetic (body-)waves for a given kinematic rupture model, and b) on the QUESO (Quantification of Uncertainty for Estimation, Simulation, and Optimization) library that uses MCMC algorithms and Bayes theorem for sample selection. We present Bayesian inversions for rupture parameters in synthetic earthquakes (i.e. for which the exact rupture history is known) in an attempt to identify the cross-over at which further model discretization (spatial and temporal resolution of the parameter space) is no longer attributed to a decreasing misfit. Identification of this cross-over is of importance as it reveals the resolution power of the studied data set (i.e. teleseismic body waves), enabling one to constrain kinematic earthquake rupture histories of real earthquakes at a resolution that is supported by data. In addition, the Bayesian approach allows for mapping complete posterior probability density functions of the desired kinematic source parameters, thus enabling us to rigorously assess the uncertainties in earthquake source inversions.

Uncertainty, Pluralism, and the Knowledge-Based Theory of the Firm

DEFF Research Database (Denmark)

Reihlen, Markus; Ringberg, Torsten

2013-01-01

-cultural conventions and other social processes. Although comprehensive in scope, we argue that a knowledge-based theory of the firm needs to integrate a cognitivist approach that includes the synergetic production of tacit and explicit knowledge, the role of reflective thinking in resolving strategic uncertainties......, and the interaction between the individual and the social. This socio-cognitive theory of the firm posits that sustained competitive advantage of a firm is founded on the ability to align knowledge internally within the firm as well as externally with its stakeholders through the individual sense-making of feedback...

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

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Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

2010-01-01

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

State-independent uncertainty relations and entanglement detection

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Qian, Chen; Li, Jun-Li; Qiao, Cong-Feng

2018-04-01

The uncertainty relation is one of the key ingredients of quantum theory. Despite the great efforts devoted to this subject, most of the variance-based uncertainty relations are state-dependent and suffering from the triviality problem of zero lower bounds. Here we develop a method to get uncertainty relations with state-independent lower bounds. The method works by exploring the eigenvalues of a Hermitian matrix composed by Bloch vectors of incompatible observables and is applicable for both pure and mixed states and for arbitrary number of N-dimensional observables. The uncertainty relation for the incompatible observables can be explained by geometric relations related to the parallel postulate and the inequalities in Horn's conjecture on Hermitian matrix sum. Practical entanglement criteria are also presented based on the derived uncertainty relations.

A Discussion on Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

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National Aeronautics and Space Administration — This article presented a discussion on uncertainty representation and management for model-based prog- nostics methodologies based on the Bayesian tracking framework...

Sensitivity and uncertainty analyses in aging risk-based prioritizations

International Nuclear Information System (INIS)

Hassan, M.; Uryas'ev, S.; Vesely, W.E.

1993-01-01

Aging risk evaluations of nuclear power plants using Probabilistic Risk Analyses (PRAs) involve assessments of the impact of aging structures, systems, and components (SSCs) on plant core damage frequency (CDF). These assessments can be used to prioritize the contributors to aging risk reflecting the relative risk potential of the SSCs. Aging prioritizations are important for identifying the SSCs contributing most to plant risk and can provide a systematic basis on which aging risk control and management strategies for a plant can be developed. However, these prioritizations are subject to variabilities arising from uncertainties in data, and/or from various modeling assumptions. The objective of this paper is to present an evaluation of the sensitivity of aging prioritizations of active components to uncertainties in aging risk quantifications. Approaches for robust prioritization of SSCs also are presented which are less susceptible to the uncertainties

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

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Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design

It's the parameters, stupid! Moving beyond multi-model and multi-physics approaches to characterize and reduce predictive uncertainty in process-based hydrological models

Science.gov (United States)

Clark, Martyn; Samaniego, Luis; Freer, Jim

2014-05-01

Multi-model and multi-physics approaches are a popular tool in environmental modelling, with many studies focusing on optimally combining output from multiple model simulations to reduce predictive errors and better characterize predictive uncertainty. However, a careful and systematic analysis of different hydrological models reveals that individual models are simply small permutations of a master modeling template, and inter-model differences are overwhelmed by uncertainty in the choice of the parameter values in the model equations. Furthermore, inter-model differences do not explicitly represent the uncertainty in modeling a given process, leading to many situations where different models provide the wrong results for the same reasons. In other cases, the available morphological data does not support the very fine spatial discretization of the landscape that typifies many modern applications of process-based models. To make the uncertainty characterization problem worse, the uncertain parameter values in process-based models are often fixed (hard-coded), and the models lack the agility necessary to represent the tremendous heterogeneity in natural systems. This presentation summarizes results from a systematic analysis of uncertainty in process-based hydrological models, where we explicitly analyze the myriad of subjective decisions made throughout both the model development and parameter estimation process. Results show that much of the uncertainty is aleatory in nature - given a "complete" representation of dominant hydrologic processes, uncertainty in process parameterizations can be represented using an ensemble of model parameters. Epistemic uncertainty associated with process interactions and scaling behavior is still important, and these uncertainties can be represented using an ensemble of different spatial configurations. Finally, uncertainty in forcing data can be represented using ensemble methods for spatial meteorological analysis. Our systematic

Quantification of Uncertainty in the Flood Frequency Analysis

Science.gov (United States)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.

Numerical solution of dynamic equilibrium models under Poisson uncertainty

DEFF Research Database (Denmark)

Posch, Olaf; Trimborn, Timo

2013-01-01

We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retar...... solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households....

Statistical uncertainties and unrecognized relationships

International Nuclear Information System (INIS)

Rankin, J.P.

1985-01-01

Hidden relationships in specific designs directly contribute to inaccuracies in reliability assessments. Uncertainty factors at the system level may sometimes be applied in attempts to compensate for the impact of such unrecognized relationships. Often uncertainty bands are used to relegate unknowns to a miscellaneous category of low-probability occurrences. However, experience and modern analytical methods indicate that perhaps the dominant, most probable and significant events are sometimes overlooked in statistical reliability assurances. The author discusses the utility of two unique methods of identifying the otherwise often unforeseeable system interdependencies for statistical evaluations. These methods are sneak circuit analysis and a checklist form of common cause failure analysis. Unless these techniques (or a suitable equivalent) are also employed along with the more widely-known assurance tools, high reliability of complex systems may not be adequately assured. This concern is indicated by specific illustrations. 8 references, 5 figures

Treatment of measurement uncertainties at the power burst facility

International Nuclear Information System (INIS)

Meyer, L.C.

1980-01-01

The treatment of measurement uncertainty at the Power Burst Facility provides a means of improving data integrity as well as meeting standard practice reporting requirements. This is accomplished by performing the uncertainty analysis in two parts, test independent uncertainty analysis and test dependent uncertainty analysis. The test independent uncertainty analysis is performed on instrumentation used repeatedly from one test to the next, and does not have to be repeated for each test except for improved or new types of instruments. A test dependent uncertainty analysis is performed on each test based on the test independent uncertainties modified as required by test specifications, experiment fixture design, and historical performance of instruments on similar tests. The methodology for performing uncertainty analysis based on the National Bureau of Standards method is reviewed with examples applied to nuclear instrumentation

Event-based user classification in Weibo media.

Science.gov (United States)

Guo, Liang; Wang, Wendong; Cheng, Shiduan; Que, Xirong

2014-01-01

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

Latent class analysis of indicators of intolerance of uncertainty

NARCIS (Netherlands)

Boelen, P.A.|info:eu-repo/dai/nl/174011954; Lenferink, L.I.M.|info:eu-repo/dai/nl/411295896

Intolerance of Uncertainty (IU) is a transdiagnostic vulnerability factor involved in depression and anxiety symptoms and disorders. IU encompasses Prospective IU (“Unforeseen events upset me greatly”) and Inhibitory IU (“The smallest doubt can stop me from acting”). Research has yet to explore

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system.

Science.gov (United States)

Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Botsis, Taxiarchis; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard

2018-01-01

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

Influence of uncertainty on framed decision-making with moral dilemma

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Mermillod, Martial; Le Pennec, Jean-Luc; Dutheil, Frédéric; Mondillon, Laurie

2018-01-01

In cases of impending natural disasters, most events are uncertain and emotionally relevant, both critical factors for decision-making. Moreover, for exposed individuals, the sensitivity to the framing of the consequences (gain or loss) and the moral judgments they have to perform (e.g., evacuate or help an injured person) constitute two central effects that have never been examined in the same context of decision-making. In a framed decision-making task with moral dilemma, we investigated whether uncertainty (i.e., unpredictably of events) and a threatening context would influence the framing effect (actions framed in loss are avoided in comparison to the ones framed in gain) and the personal intention effect (unintentional actions are more morally acceptable in comparison to intentional actions) on the perceived moral acceptability of taking action. Considering the impact of uncertainty and fear on the processes underlying these effects, we assumed that these emotions would lead to the negation of the two effects. Our results indicate that the exposure to uncertain events leads to the negation of the framing effect, but does not influence the moral acceptability and the effect of personal intention. We discuss our results in the light of dual-process models (i.e. systematic vs. heuristic), appraisal theories, and neurocognitive aspects. These elements highlight the importance of providing solutions to cope with uncertainty, both for scientists and local populations exposed to natural hazards. PMID:29847589

Risk classification and uncertainty propagation for virtual water distribution systems

International Nuclear Information System (INIS)

Torres, Jacob M.; Brumbelow, Kelly; Guikema, Seth D.

2009-01-01

While the secrecy of real water distribution system data is crucial, it poses difficulty for research as results cannot be publicized. This data includes topological layouts of pipe networks, pump operation schedules, and water demands. Therefore, a library of virtual water distribution systems can be an important research tool for comparative development of analytical methods. A virtual city, 'Micropolis', has been developed, including a comprehensive water distribution system, as a first entry into such a library. This virtual city of 5000 residents is fully described in both geographic information systems (GIS) and EPANet hydraulic model frameworks. A risk classification scheme and Monte Carlo analysis are employed for an attempted water supply contamination attack. Model inputs to be considered include uncertainties in: daily water demand, seasonal demand, initial storage tank levels, the time of day a contamination event is initiated, duration of contamination event, and contaminant quantity. Findings show that reasonable uncertainties in model inputs produce high variability in exposure levels. It is also shown that exposure level distributions experience noticeable sensitivities to population clusters within the contaminant spread area. High uncertainties in exposure patterns lead to greater resources needed for more effective mitigation strategies.

Event- and interval-based measurement of stuttering: a review.

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Valente, Ana Rita S; Jesus, Luis M T; Hall, Andreia; Leahy, Margaret

2015-01-01

Event- and interval-based measurements are two different ways of computing frequency of stuttering. Interval-based methodology emerged as an alternative measure to overcome problems associated with reproducibility in the event-based methodology. No review has been made to study the effect of methodological factors in interval-based absolute reliability data or to compute the agreement between the two methodologies in terms of inter-judge, intra-judge and accuracy (i.e., correspondence between raters' scores and an established criterion). To provide a review related to reproducibility of event-based and time-interval measurement, and to verify the effect of methodological factors (training, experience, interval duration, sample presentation order and judgment conditions) on agreement of time-interval measurement; in addition, to determine if it is possible to quantify the agreement between the two methodologies The first two authors searched for articles on ERIC, MEDLINE, PubMed, B-on, CENTRAL and Dissertation Abstracts during January-February 2013 and retrieved 495 articles. Forty-eight articles were selected for review. Content tables were constructed with the main findings. Articles related to event-based measurements revealed values of inter- and intra-judge greater than 0.70 and agreement percentages beyond 80%. The articles related to time-interval measures revealed that, in general, judges with more experience with stuttering presented significantly higher levels of intra- and inter-judge agreement. Inter- and intra-judge values were beyond the references for high reproducibility values for both methodologies. Accuracy (regarding the closeness of raters' judgements with an established criterion), intra- and inter-judge agreement were higher for trained groups when compared with non-trained groups. Sample presentation order and audio/video conditions did not result in differences in inter- or intra-judge results. A duration of 5 s for an interval appears to be

Evaluation of cutting force uncertainty components in turning

DEFF Research Database (Denmark)

Axinte, Dragos Aurelian; Belluco, Walter; De Chiffre, Leonardo

2000-01-01

A procedure is proposed for the evaluation of those uncertainty components of a single cutting force measurement in turning that are related to the contributions of the dynamometer calibration and the cutting process itself. Based on an empirical model including errors form both sources......, the uncertainty for a single measurement of cutting force is presented, and expressions for the expected uncertainty vs. cutting parameters are proposed. This approach gives the possibility of evaluating cutting force uncertainty components in turning, for a defined range of cutting parameters, based on few...

The uncertainty processing theory of motivation.

Science.gov (United States)

Anselme, Patrick

2010-04-02

Most theories describe motivation using basic terminology (drive, 'wanting', goal, pleasure, etc.) that fails to inform well about the psychological mechanisms controlling its expression. This leads to a conception of motivation as a mere psychological state 'emerging' from neurophysiological substrates. However, the involvement of motivation in a large number of behavioural parameters (triggering, intensity, duration, and directedness) and cognitive abilities (learning, memory, decision, etc.) suggest that it should be viewed as an information processing system. The uncertainty processing theory (UPT) presented here suggests that motivation is the set of cognitive processes allowing organisms to extract information from the environment by reducing uncertainty about the occurrence of psychologically significant events. This processing of information is shown to naturally result in the highlighting of specific stimuli. The UPT attempts to solve three major problems: (i) how motivations can affect behaviour and cognition so widely, (ii) how motivational specificity for objects and events can result from nonspecific neuropharmacological causal factors (such as mesolimbic dopamine), and (iii) how motivational interactions can be conceived in psychological terms, irrespective of their biological correlates. The UPT is in keeping with the conceptual tradition of the incentive salience hypothesis while trying to overcome the shortcomings inherent to this view. Copyright 2009 Elsevier B.V. All rights reserved.

Event-based historical value-at-risk

NARCIS (Netherlands)

Hogenboom, F.P.; Winter, Michael; Hogenboom, A.C.; Jansen, Milan; Frasincar, F.; Kaymak, U.

2012-01-01

Value-at-Risk (VaR) is an important tool to assess portfolio risk. When calculating VaR based on historical stock return data, we hypothesize that this historical data is sensitive to outliers caused by news events in the sampled period. In this paper, we research whether the VaR accuracy can be

Propagation of uncertainties for an evaluation of the Azores-Gibraltar Fracture Zone tsunamigenic potential

Science.gov (United States)

Antoshchenkova, Ekaterina; Imbert, David; Richet, Yann; Bardet, Lise; Duluc, Claire-Marie; Rebour, Vincent; Gailler, Audrey; Hébert, Hélène

2016-04-01

The aim of this study is to assess evaluation the tsunamigenic potential of the Azores-Gibraltar Fracture Zone (AGFZ). This work is part of the French project TANDEM (Tsunamis in the Atlantic and English ChaNnel: Definition of the Effects through numerical Modeling; www-tandem.cea.fr), special attention is paid to French Atlantic coasts. Structurally, the AGFZ region is complex and not well understood. However, a lot of its faults produce earthquakes with significant vertical slip, of a type that can result in tsunami. We use the major tsunami event of the AGFZ on purpose to have a regional estimation of the tsunamigenic potential of this zone. The major reported event for this zone is the 1755 Lisbon event. There are large uncertainties concerning source location and focal mechanism of this earthquake. Hence, simple deterministic approach is not sufficient to cover on the one side the whole AGFZ with its geological complexity and on the other side the lack of information concerning the 1755 Lisbon tsunami. A parametric modeling environment Promethée (promethee.irsn.org/doku.php) was coupled to tsunami simulation software based on shallow water equations with the aim of propagation of uncertainties. Such a statistic point of view allows us to work with multiple hypotheses simultaneously. In our work we introduce the seismic source parameters in a form of distributions, thus giving a data base of thousands of tsunami scenarios and tsunami wave height distributions. Exploring our tsunami scenarios data base we present preliminary results for France. Tsunami wave heights (within one standard deviation of the mean) can be about 0.5 m - 1 m for the Atlantic coast and approaching 0.3 m for the English Channel.

Causal uncertainty, claimed and behavioural self-handicapping.

Science.gov (United States)

Thompson, Ted; Hepburn, Jonathan

2003-06-01

Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

Directory of Open Access Journals (Sweden)

M. E. Gorbunov

2018-01-01

Full Text Available A new reference occultation processing system (rOPS will include a Global Navigation Satellite System (GNSS radio occultation (RO retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA retrieval in the lower troposphere and introduce (1 an empirically estimated boundary layer bias (BLB model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2 the estimation of (residual systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors, where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

Science.gov (United States)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and

To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

Science.gov (United States)

Schaarup-Jensen, K; Rasmussen, M R; Thorndahl, S

2009-01-01

In urban drainage modelling long-term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties with regards to long-term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally, long-term rainfall series, from a local rain gauge, are unavailable. In the present case study, however, long and local rain series are available. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled, e.g. by introducing an "averaging procedure" based on the variability within the set of statistics. All simulations are performed by means of the MOUSE LTS model.

Power quality events recognition using a SVM-based method

Energy Technology Data Exchange (ETDEWEB)

Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

2008-09-15

In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

Science.gov (United States)

Höllermann, Britta; Evers, Mariele

2017-04-01

Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working

How to deal with climate change uncertainty in the planning of engineering systems

Science.gov (United States)

Spackova, Olga; Dittes, Beatrice; Straub, Daniel

2016-04-01

The effect of extreme events such as floods on the infrastructure and built environment is associated with significant uncertainties: These include the uncertain effect of climate change, uncertainty on extreme event frequency estimation due to limited historic data and imperfect models, and, not least, uncertainty on future socio-economic developments, which determine the damage potential. One option for dealing with these uncertainties is the use of adaptable (flexible) infrastructure that can easily be adjusted in the future without excessive costs. The challenge is in quantifying the value of adaptability and in finding the optimal sequence of decision. Is it worth to build a (potentially more expensive) adaptable system that can be adjusted in the future depending on the future conditions? Or is it more cost-effective to make a conservative design without counting with the possible future changes to the system? What is the optimal timing of the decision to build/adjust the system? We develop a quantitative decision-support framework for evaluation of alternative infrastructure designs under uncertainties, which: • probabilistically models the uncertain future (trough a Bayesian approach) • includes the adaptability of the systems (the costs of future changes) • takes into account the fact that future decisions will be made under uncertainty as well (using pre-posterior decision analysis) • allows to identify the optimal capacity and optimal timing to build/adjust the infrastructure. Application of the decision framework will be demonstrated on an example of flood mitigation planning in Bavaria.

Do Orthopaedic Surgeons Acknowledge Uncertainty?

Science.gov (United States)

Teunis, Teun; Janssen, Stein; Guitton, Thierry G; Ring, David; Parisien, Robert

2016-06-01

Much of the decision-making in orthopaedics rests on uncertain evidence. Uncertainty is therefore part of our normal daily practice, and yet physician uncertainty regarding treatment could diminish patients' health. It is not known if physician uncertainty is a function of the evidence alone or if other factors are involved. With added experience, uncertainty could be expected to diminish, but perhaps more influential are things like physician confidence, belief in the veracity of what is published, and even one's religious beliefs. In addition, it is plausible that the kind of practice a physician works in can affect the experience of uncertainty. Practicing physicians may not be immediately aware of these effects on how uncertainty is experienced in their clinical decision-making. We asked: (1) Does uncertainty and overconfidence bias decrease with years of practice? (2) What sociodemographic factors are independently associated with less recognition of uncertainty, in particular belief in God or other deity or deities, and how is atheism associated with recognition of uncertainty? (3) Do confidence bias (confidence that one's skill is greater than it actually is), degree of trust in the orthopaedic evidence, and degree of statistical sophistication correlate independently with recognition of uncertainty? We created a survey to establish an overall recognition of uncertainty score (four questions), trust in the orthopaedic evidence base (four questions), confidence bias (three questions), and statistical understanding (six questions). Seven hundred six members of the Science of Variation Group, a collaboration that aims to study variation in the definition and treatment of human illness, were approached to complete our survey. This group represents mainly orthopaedic surgeons specializing in trauma or hand and wrist surgery, practicing in Europe and North America, of whom the majority is involved in teaching. Approximately half of the group has more than 10 years

Address-event-based platform for bioinspired spiking systems

Science.gov (United States)

Jiménez-Fernández, A.; Luján, C. D.; Linares-Barranco, A.; Gómez-Rodríguez, F.; Rivas, M.; Jiménez, G.; Civit, A.

2007-05-01

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows a real-time virtual massive connectivity between huge number neurons, located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate "events" according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems, it is absolutely necessary to have a computer interface that allows (a) reading AER interchip traffic into the computer and visualizing it on the screen, and (b) converting conventional frame-based video stream in the computer into AER and injecting it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. In the other hand, the use of a commercial personal computer implies to depend on software tools and operating systems that can make the system slower and un-robust. This paper addresses the problem of communicating several AER based chips to compose a powerful processing system. The problem was discussed in the Neuromorphic Engineering Workshop of 2006. The platform is based basically on an embedded computer, a powerful FPGA and serial links, to make the system faster and be stand alone (independent from a PC). A new platform is presented that allow to connect up to eight AER based chips to a Spartan 3 4000 FPGA. The FPGA is responsible of the network communication based in Address-Event and, at the same time, to map and transform the address space of the traffic to implement a pre-processing. A MMU microprocessor (Intel XScale 400MHz Gumstix Connex computer) is also connected to the FPGA

Uncertainty estimates of purity measurements based on current information: toward a "live validation" of purity methods.

Science.gov (United States)

Apostol, Izydor; Kelner, Drew; Jiang, Xinzhao Grace; Huang, Gang; Wypych, Jette; Zhang, Xin; Gastwirt, Jessica; Chen, Kenneth; Fodor, Szilan; Hapuarachchi, Suminda; Meriage, Dave; Ye, Frank; Poppe, Leszek; Szpankowski, Wojciech

2012-12-01

To predict precision and other performance characteristics of chromatographic purity methods, which represent the most widely used form of analysis in the biopharmaceutical industry. We have conducted a comprehensive survey of purity methods, and show that all performance characteristics fall within narrow measurement ranges. This observation was used to develop a model called Uncertainty Based on Current Information (UBCI), which expresses these performance characteristics as a function of the signal and noise levels, hardware specifications, and software settings. We applied the UCBI model to assess the uncertainty of purity measurements, and compared the results to those from conventional qualification. We demonstrated that the UBCI model is suitable to dynamically assess method performance characteristics, based on information extracted from individual chromatograms. The model provides an opportunity for streamlining qualification and validation studies by implementing a "live validation" of test results utilizing UBCI as a concurrent assessment of measurement uncertainty. Therefore, UBCI can potentially mitigate the challenges associated with laborious conventional method validation and facilitates the introduction of more advanced analytical technologies during the method lifecycle.

Uncertainty Analysis with Considering Resonance Self-shielding Effect

Energy Technology Data Exchange (ETDEWEB)

Han, Tae Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

If infinitely diluted multi-group cross sections were used for the sensitivity, the covariance data from the evaluated nuclear data library (ENDL) was directly applied. However, in case of using a self-shielded multi-group cross section, the covariance data should be corrected considering self-shielding effect. Usually, implicit uncertainty can be defined as the uncertainty change by the resonance self-shielding effect as described above. MUSAD ( Modules of Uncertainty and Sensitivity Analysis for DeCART ) has been developed for a multiplication factor and cross section uncertainty based on the generalized perturbation theory and it, however, can only quantify the explicit uncertainty by the self-shielded multi-group cross sections without considering the implicit effect. Thus, this paper addresses the implementation of the implicit uncertainty analysis module into the code and the numerical results for the verification are provided. The implicit uncertainty analysis module has been implemented into MUSAD based on infinitely-diluted cross section-based consistent method. The verification calculation was performed on MHTGR 350 Ex.I-1a and the differences with McCARD result decrease from 40% to 1% in CZP case and 3% in HFP case. From this study, it is expected that MUSAD code can reasonably produce the complete uncertainty on VHTR or LWR where the resonance self-shielding effect should be significantly considered.

Uncertainty Analysis with Considering Resonance Self-shielding Effect

International Nuclear Information System (INIS)

Han, Tae Young

2016-01-01

If infinitely diluted multi-group cross sections were used for the sensitivity, the covariance data from the evaluated nuclear data library (ENDL) was directly applied. However, in case of using a self-shielded multi-group cross section, the covariance data should be corrected considering self-shielding effect. Usually, implicit uncertainty can be defined as the uncertainty change by the resonance self-shielding effect as described above. MUSAD ( Modules of Uncertainty and Sensitivity Analysis for DeCART ) has been developed for a multiplication factor and cross section uncertainty based on the generalized perturbation theory and it, however, can only quantify the explicit uncertainty by the self-shielded multi-group cross sections without considering the implicit effect. Thus, this paper addresses the implementation of the implicit uncertainty analysis module into the code and the numerical results for the verification are provided. The implicit uncertainty analysis module has been implemented into MUSAD based on infinitely-diluted cross section-based consistent method. The verification calculation was performed on MHTGR 350 Ex.I-1a and the differences with McCARD result decrease from 40% to 1% in CZP case and 3% in HFP case. From this study, it is expected that MUSAD code can reasonably produce the complete uncertainty on VHTR or LWR where the resonance self-shielding effect should be significantly considered

Orientation and uncertainties

International Nuclear Information System (INIS)

Peters, H.P.; Hennen, L.

1990-01-01

The authors report on the results of three representative surveys that made a closer inquiry into perceptions and valuations of information and information sources concering Chernobyl. If turns out that the information sources are generally considered little trustworthy. This was generally attributable to the interpretation of the events being tied to attitudes in the atmonic energy issue. The greatest credit was given to television broadcasting. The authors summarize their discourse as follows: There is good reason to interpret the widespread uncertainty after Chernobyl as proof of the fact that large parts of the population are prepared and willing to assume a critical stance towards information and prefer to draw their information from various sources representing different positions. (orig.) [de

[Method for optimal sensor placement in water distribution systems with nodal demand uncertainties].

Science.gov (United States)

Liu, Shu-Ming; Wu, Xue; Ouyang, Le-Yan

2013-08-01

The notion of identification fitness was proposed for optimizing sensor placement in water distribution systems. Nondominated Sorting Genetic Algorithm II was used to find the Pareto front between minimum overlap of possible detection times of two events and the best probability of detection, taking nodal demand uncertainties into account. This methodology was applied to an example network. The solutions show that the probability of detection and the number of possible locations are not remarkably affected by nodal demand uncertainties, but the sources identification accuracy declines with nodal demand uncertainties.

Event-Based User Classification in Weibo Media

Directory of Open Access Journals (Sweden)

Liang Guo

2014-01-01

Full Text Available Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

Potential applicability of fuzzy set theory to analyses of containment response and uncertainty for postulated severe accidents

International Nuclear Information System (INIS)

Chun, M.H.; Ahn, K.I.

1991-01-01

An important issue faced by contemporary risk analysts of nuclear power plants is how to deal with uncertainties that arise in each phase of probabilistic risk assessments. The major uncertainty addressed in this paper is the one that arises in the accident-progression event trees (APETs), which treat the physical processes affecting the core after an initiating event occurs. Recent advances in the theory of fuzzy sets make it possible to analyze the uncertainty related to complex physical phenomena that may occur during a severe accident of nuclear power plants by means of fuzzy set or possibility concept. The main purpose of this paper is to prevent the results of assessment of the potential applicability of the fuzzy set theory to the uncertainty analysis of APETs as a possible alternative procedure to that used in the most recent risk assessment

A ROOT based event display software for JUNO

Science.gov (United States)

You, Z.; Li, K.; Zhang, Y.; Zhu, J.; Lin, T.; Li, W.

2018-02-01

An event display software SERENA has been designed for the Jiangmen Underground Neutrino Observatory (JUNO). The software has been developed in the JUNO offline software system and is based on the ROOT display package EVE. It provides an essential tool to display detector and event data for better understanding of the processes in the detectors. The software has been widely used in JUNO detector optimization, simulation, reconstruction and physics study.

Daily Setup Uncertainties and Organ Motion Based on the Tomoimages in Prostatic Radiotherapy

International Nuclear Information System (INIS)

Cho, Jeong Hee; Lee, Sang Kyu; Kim, Sei Joon; Na, Soo Kyung

2007-01-01

The patient's position and anatomy during the treatment course little bit varies to some extend due to setup uncertainties and organ motions. These factors could affected to not only the dose coverage of the gross tumor but over dosage of normal tissue. Setup uncertainties and organ motions can be minimized by precise patient positioning and rigid immobilization device but some anatomical site such as prostate, the internal organ motion due to physiological processes are challenge. In planning procedure, the clinical target volume is a little bit enlarged to create a planning target volume that accounts for setup uncertainties and organ motion as well. These uncertainties lead to differences between the calculated dose by treatment planning system and the actually delivered dose. The purpose of this study was to evaluate the differences of interfractional displacement of organ and GTV based on the tomoimages. Over the course of 3 months, 3 patients, those who has applied rectal balloon, treated for prostatic cancer patient's tomoimage were studied. During the treatment sessions 26 tomoimages per patient, Total 76 tomoimages were collected. Tomoimage had been taken everyday after initial setup with lead marker attached on the patient's skin center to comparing with C-T simulation images. Tomoimage was taken after rectal balloon inflated with 60 cc of air for prostate gland immobilization for daily treatment just before treatment and it was used routinely in each case. The intrarectal balloon was inserted to a depth of 6 cm from the anal verge. MVCT image was taken with 5 mm slice thickness after the intrarectal balloon in place and inflated. For this study, lead balls are used to guide the registration between the MVCT and CT simulation images. There are three image fusion methods in the tomotherapy, bone technique, bone/tissue technique, and full image technique. We used all this 3 methods to analysis the setup errors. Initially, image fusions were based on the

Daily Setup Uncertainties and Organ Motion Based on the Tomoimages in Prostatic Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Cho, Jeong Hee; Lee, Sang Kyu [Dept. of Radiation Oncology, Yensei Univesity Health System, Seoul (Korea, Republic of); Kim, Sei Joon [Dept. of Radiation Oncology,Yongdong Severance Hospital , Seoul (Korea, Republic of); Na, Soo Kyung [Dept. of Radiological Science, Gimcheon College, Gimcheon (Korea, Republic of)

2007-09-15

The patient's position and anatomy during the treatment course little bit varies to some extend due to setup uncertainties and organ motions. These factors could affected to not only the dose coverage of the gross tumor but over dosage of normal tissue. Setup uncertainties and organ motions can be minimized by precise patient positioning and rigid immobilization device but some anatomical site such as prostate, the internal organ motion due to physiological processes are challenge. In planning procedure, the clinical target volume is a little bit enlarged to create a planning target volume that accounts for setup uncertainties and organ motion as well. These uncertainties lead to differences between the calculated dose by treatment planning system and the actually delivered dose. The purpose of this study was to evaluate the differences of interfractional displacement of organ and GTV based on the tomoimages. Over the course of 3 months, 3 patients, those who has applied rectal balloon, treated for prostatic cancer patient's tomoimage were studied. During the treatment sessions 26 tomoimages per patient, Total 76 tomoimages were collected. Tomoimage had been taken everyday after initial setup with lead marker attached on the patient's skin center to comparing with C-T simulation images. Tomoimage was taken after rectal balloon inflated with 60 cc of air for prostate gland immobilization for daily treatment just before treatment and it was used routinely in each case. The intrarectal balloon was inserted to a depth of 6 cm from the anal verge. MVCT image was taken with 5 mm slice thickness after the intrarectal balloon in place and inflated. For this study, lead balls are used to guide the registration between the MVCT and CT simulation images. There are three image fusion methods in the tomotherapy, bone technique, bone/tissue technique, and full image technique. We used all this 3 methods to analysis the setup errors. Initially, image fusions were

Improved Event Location Uncertainty Estimates

Science.gov (United States)

2008-06-30

and large ones at another site . Therefore we analyze only data for Degelen Mountains of the Semipalatinsk testing grounds which includes explosions...for other test sites 31 4.1.1.3. Transportability of the NTS mb-based measurement error model 33 4.1.2. SNR-dependent bias and variance 37...China test site . b) Trajectory of median mislocation using subnetworks starting with 6-station networks and gradually increasing to 400 stations (solid

Uncertainty and its propagation in dynamics models

International Nuclear Information System (INIS)

Devooght, J.

1994-01-01

The purpose of this paper is to bring together some characteristics due to uncertainty when we deal with dynamic models and therefore to propagation of uncertainty. The respective role of uncertainty and inaccuracy is examined. A mathematical formalism based on Chapman-Kolmogorov equation allows to define a open-quotes subdynamicsclose quotes where the evolution equation takes the uncertainty into account. The problem of choosing or combining models is examined through a loss function associated to a decision

Identifying and assessing uncertainty in hydrological pathways: a novel approach to end member mixing in a Scottish agricultural catchment

Science.gov (United States)

Soulsby, C.; Petry, J.; Brewer, M. J.; Dunn, S. M.; Ott, B.; Malcolm, I. A.

2003-04-01

A hydrograph separation based upon end member mixing was carried out to assess the relative importance of the hydrological pathways providing the main sources of runoff during five storm events in a 14.5 km 2 agricultural catchment in north east Scotland. The method utilised event specific end member chemistries to differentiate three catchment-scale hydrological pathways on the basis of observed Si and NO 3-N concentrations in sampled source waters. These were overland flow (OF) (low Si and intermediate NO 3-N); subsurface storm flow (high Si and high NO 3-N) and groundwater flow (high Si and intermediate NO 3-N). The hydrograph separation explicitly accounted for uncertainty in the spatial and temporal variation in end member chemistry using Bayesian statistical methods which assumed that each end member arose from a bivariate normal distribution whose mean vectors and co-variance matrices could be estimated. Markov Chain-Monte Carlo methods were used to model the average and 95 percentile maximum and minimum contributions that each end member made to stream water samples during storm events. Although there is large uncertainty over the contributions of each end member to specific events, the analysis produced hydrograph separations that were broadly believable on the basis of hydrometric observations in the catchment. Moreover, by using event specific end member compositions, the method appeared sensitive to the unique combination of event characteristics, antecedent conditions and seasonality in terms of producing feasible separations for very different events. It is concluded that OF generally dominates the storm peak and provides the main flow path by which P is transferred to stream channels during storm events, whilst subsurface storm flows usually dominate the storm hydrograph volumetrically and route NO 3-rich soil water into streams. Consequently, nutrient enrichment in such streams is largely mediated by event-based hydrological flow paths, a finding

Wastewater treatment modelling: dealing with uncertainties

DEFF Research Database (Denmark)

Belia, E.; Amerlinck, Y.; Benedetti, L.

2009-01-01

This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation...

Measurement-based climatology of aerosol direct radiative effect, its sensitivities, and uncertainties from a background southeast US site

Science.gov (United States)

Sherman, James P.; McComiskey, Allison

2018-03-01

Aerosol optical properties measured at Appalachian State University's co-located NASA AERONET and NOAA ESRL aerosol network monitoring sites over a nearly four-year period (June 2012-Feb 2016) are used, along with satellite-based surface reflectance measurements, to study the seasonal variability of diurnally averaged clear sky aerosol direct radiative effect (DRE) and radiative efficiency (RE) at the top-of-atmosphere (TOA) and at the surface. Aerosol chemistry and loading at the Appalachian State site are likely representative of the background southeast US (SE US), home to high summertime aerosol loading and one of only a few regions not to have warmed during the 20th century. This study is the first multi-year ground truth DRE study in the SE US, using aerosol network data products that are often used to validate satellite-based aerosol retrievals. The study is also the first in the SE US to quantify DRE uncertainties and sensitivities to aerosol optical properties and surface reflectance, including their seasonal dependence.Median DRE for the study period is -2.9 W m-2 at the TOA and -6.1 W m-2 at the surface. Monthly median and monthly mean DRE at the TOA (surface) are -1 to -2 W m-2 (-2 to -3 W m-2) during winter months and -5 to -6 W m-2 (-10 W m-2) during summer months. The DRE cycles follow the annual cycle of aerosol optical depth (AOD), which is 9 to 10 times larger in summer than in winter. Aerosol RE is anti-correlated with DRE, with winter values 1.5 to 2 times more negative than summer values. Due to the large seasonal dependence of aerosol DRE and RE, we quantify the sensitivity of DRE to aerosol optical properties and surface reflectance, using a calendar day representative of each season (21 December for winter; 21 March for spring, 21 June for summer, and 21 September for fall). We use these sensitivities along with measurement uncertainties of aerosol optical properties and surface reflectance to calculate DRE uncertainties. We also estimate

Uncertainty analysis of the radiological characteristics of radioactive waste using a method based on log-normal distributions

International Nuclear Information System (INIS)

Gigase, Yves

2007-01-01

Available in abstract form only. Full text of publication follows: The uncertainty on characteristics of radioactive LILW waste packages is difficult to determine and often very large. This results from a lack of knowledge of the constitution of the waste package and of the composition of the radioactive sources inside. To calculate a quantitative estimate of the uncertainty on a characteristic of a waste package one has to combine these various uncertainties. This paper discusses an approach to this problem, based on the use of the log-normal distribution, which is both elegant and easy to use. It can provide as example quantitative estimates of uncertainty intervals that 'make sense'. The purpose is to develop a pragmatic approach that can be integrated into existing characterization methods. In this paper we show how our method can be applied to the scaling factor method. We also explain how it can be used when estimating other more complex characteristics such as the total uncertainty of a collection of waste packages. This method could have applications in radioactive waste management, more in particular in those decision processes where the uncertainty on the amount of activity is considered to be important such as in probability risk assessment or the definition of criteria for acceptance or categorization. (author)

SU-G-BRB-14: Uncertainty of Radiochromic Film Based Relative Dose Measurements

Energy Technology Data Exchange (ETDEWEB)

Devic, S; Tomic, N; DeBlois, F; Seuntjens, J [McGill University, Montreal, QC (Canada); Lewis, D [RCF Consulting, LLC, Monroe, CT (United States); Aldelaijan, S [King Faisal Specialist Hospital & Research Center, Riyadh (Saudi Arabia)

2016-06-15

Purpose: Due to inherently non-linear dose response, measurement of relative dose distribution with radiochromic film requires measurement of absolute dose using a calibration curve following previously established reference dosimetry protocol. On the other hand, a functional form that converts the inherently non-linear dose response curve of the radiochromic film dosimetry system into linear one has been proposed recently [Devic et al, Med. Phys. 39 4850–4857 (2012)]. However, there is a question what would be the uncertainty of such measured relative dose. Methods: If the relative dose distribution is determined going through the reference dosimetry system (conversion of the response by using calibration curve into absolute dose) the total uncertainty of such determined relative dose will be calculated by summing in quadrature total uncertainties of doses measured at a given and at the reference point. On the other hand, if the relative dose is determined using linearization method, the new response variable is calculated as ζ=a(netOD)n/ln(netOD). In this case, the total uncertainty in relative dose will be calculated by summing in quadrature uncertainties for a new response function (σζ) for a given and the reference point. Results: Except at very low doses, where the measurement uncertainty dominates, the total relative dose uncertainty is less than 1% for the linear response method as compared to almost 2% uncertainty level for the reference dosimetry method. The result is not surprising having in mind that the total uncertainty of the reference dose method is dominated by the fitting uncertainty, which is mitigated in the case of linearization method. Conclusion: Linearization of the radiochromic film dose response provides a convenient and a more precise method for relative dose measurements as it does not require reference dosimetry and creation of calibration curve. However, the linearity of the newly introduced function must be verified. Dave Lewis

The analysis of the initiating events in thorium-based molten salt reactor

International Nuclear Information System (INIS)

Zuo Jiaxu; Song Wei; Jing Jianping; Zhang Chunming

2014-01-01

The initiation events analysis and evaluation were the beginning of nuclear safety analysis and probabilistic safety analysis, and it was the key points of the nuclear safety analysis. Currently, the initiation events analysis method and experiences both focused on water reactor, but no methods and theories for thorium-based molten salt reactor (TMSR). With TMSR's research and development in China, the initiation events analysis and evaluation was increasingly important. The research could be developed from the PWR analysis theories and methods. Based on the TMSR's design, the theories and methods of its initiation events analysis could be researched and developed. The initiation events lists and analysis methods of the two or three generation PWR, high-temperature gascooled reactor and sodium-cooled fast reactor were summarized. Based on the TMSR's design, its initiation events would be discussed and developed by the logical analysis. The analysis of TMSR's initiation events was preliminary studied and described. The research was important to clarify the events analysis rules, and useful to TMSR's designs and nuclear safety analysis. (authors)

Rational consensus under uncertainty: Expert judgment in the EC-USNRC uncertainty study

International Nuclear Information System (INIS)

Cooke, R.; Kraan, B.; Goossens, L.

1999-01-01

Governmental bodies are confronted with the problem of achieving rational consensus in the face of substantial uncertainties. The area of accident consequence management for nuclear power plants affords a good example. Decisions with regard to evacuation, decontamination, and food bans must be taken on the basis of predictions of environmental transport of radioactive material, contamination through the food chain, cancer induction, and the like. These predictions use mathematical models containing scores of uncertain parameters. Decision makers want to take, and want to be perceived to take, these decisions in a rational manner. The question is, how can this be accomplished in the face of large uncertainties? Indeed, the very presence of uncertainty poses a threat to rational consensus. Decision makers will necessarily base their actions on the judgments of experts. The experts, however, will not agree among themselves, as otherwise we would not speak of large uncertainties. Any given expert's viewpoint will be favorable to the interests of some stakeholders, and hostile to the interests of others. If a decision maker bases his/her actions on the views of one single expert, then (s)he is invariably open to charges of partiality toward the interests favored by this viewpoint. An appeal to 'impartial' or 'disinterested' experts will fail for two reasons. First, experts have interests; they have jobs, mortgages and professional reputations. Second, even if expert interests could somehow be quarantined, even then the experts would disagree. Expert disagreement is not explained by diverging interests, and consensus cannot be reached by shielding the decision process from expert interests. If rational consensus requires expert agreement, then rational consensus is simply not possible in the face of uncertainty. If rational consensus under uncertainty is to be achieved, then evidently the views of a diverse set of experts must be taken into account. The question is how

Performance Assessment Uncertainty Analysis for Japan's HLW Program Feasibility Study (H12)

International Nuclear Information System (INIS)

BABA, T.; ISHIGURO, K.; ISHIHARA, Y.; SAWADA, A.; UMEKI, H.; WAKASUGI, K.; WEBB, ERIK K.

1999-01-01

Most HLW programs in the world recognize that any estimate of long-term radiological performance must be couched in terms of the uncertainties derived from natural variation, changes through time and lack of knowledge about the essential processes. The Japan Nuclear Cycle Development Institute followed a relatively standard procedure to address two major categories of uncertainty. First, a FEatures, Events and Processes (FEPs) listing, screening and grouping activity was pursued in order to define the range of uncertainty in system processes as well as possible variations in engineering design. A reference and many alternative cases representing various groups of FEPs were defined and individual numerical simulations performed for each to quantify the range of conceptual uncertainty. Second, parameter distributions were developed for the reference case to represent the uncertainty in the strength of these processes, the sequencing of activities and geometric variations. Both point estimates using high and low values for individual parameters as well as a probabilistic analysis were performed to estimate parameter uncertainty. A brief description of the conceptual model uncertainty analysis is presented. This paper focuses on presenting the details of the probabilistic parameter uncertainty assessment

Event-Based control of depth of hypnosis in anesthesia.

Science.gov (United States)

Merigo, Luca; Beschi, Manuel; Padula, Fabrizio; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

2017-08-01

In this paper, we propose the use of an event-based control strategy for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. A new event generator with high noise-filtering properties is employed in addition to a PIDPlus controller. The tuning of the parameters is performed off-line by using genetic algorithms by considering a given data set of patients. The effectiveness and robustness of the method is verified in simulation by implementing a Monte Carlo method to address the intra-patient and inter-patient variability. A comparison with a standard PID control structure shows that the event-based control system achieves a reduction of the total variation of the manipulated variable of 93% in the induction phase and of 95% in the maintenance phase. The use of event based automatic control in anesthesia yields a fast induction phase with bounded overshoot and an acceptable disturbance rejection. A comparison with a standard PID control structure shows that the technique effectively mimics the behavior of the anesthesiologist by providing a significant decrement of the total variation of the manipulated variable. Copyright © 2017 Elsevier B.V. All rights reserved.

Abstracting event-based control models for high autonomy systems

Science.gov (United States)

Luh, Cheng-Jye; Zeigler, Bernard P.

1993-01-01

A high autonomy system needs many models on which to base control, management, design, and other interventions. These models differ in level of abstraction and in formalism. Concepts and tools are needed to organize the models into a coherent whole. The paper deals with the abstraction processes for systematic derivation of related models for use in event-based control. The multifaceted modeling methodology is briefly reviewed. The morphism concepts needed for application to model abstraction are described. A theory for supporting the construction of DEVS models needed for event-based control is then presented. An implemented morphism on the basis of this theory is also described.

Role of information theoretic uncertainty relations in quantum theory

International Nuclear Information System (INIS)

Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo

2015-01-01

Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed

Role of information theoretic uncertainty relations in quantum theory

Energy Technology Data Exchange (ETDEWEB)

Jizba, Petr, E-mail: p.jizba@fjfi.cvut.cz [FNSPE, Czech Technical University in Prague, Břehová 7, 115 19 Praha 1 (Czech Republic); ITP, Freie Universität Berlin, Arnimallee 14, D-14195 Berlin (Germany); Dunningham, Jacob A., E-mail: J.Dunningham@sussex.ac.uk [Department of Physics and Astronomy, University of Sussex, Falmer, Brighton, BN1 9QH (United Kingdom); Joo, Jaewoo, E-mail: j.joo@surrey.ac.uk [Advanced Technology Institute and Department of Physics, University of Surrey, Guildford, GU2 7XH (United Kingdom)

2015-04-15

Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

Directory of Open Access Journals (Sweden)

Shujuan Wang

2015-01-01

Full Text Available This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

IBES: A Tool for Creating Instructions Based on Event Segmentation

Directory of Open Access Journals (Sweden)

Katharina eMura

2013-12-01

Full Text Available Receiving informative, well-structured, and well-designed instructions supports performance and memory in assembly tasks. We describe IBES, a tool with which users can quickly and easily create multimedia, step-by-step instructions by segmenting a video of a task into segments. In a validation study we demonstrate that the step-by-step structure of the visual instructions created by the tool corresponds to the natural event boundaries, which are assessed by event segmentation and are known to play an important role in memory processes. In one part of the study, twenty participants created instructions based on videos of two different scenarios by using the proposed tool. In the other part of the study, ten and twelve participants respectively segmented videos of the same scenarios yielding event boundaries for coarse and fine events. We found that the visual steps chosen by the participants for creating the instruction manual had corresponding events in the event segmentation. The number of instructional steps was a compromise between the number of fine and coarse events. Our interpretation of results is that the tool picks up on natural human event perception processes of segmenting an ongoing activity into events and enables the convenient transfer into meaningful multimedia instructions for assembly tasks. We discuss the practical application of IBES, for example, creating manuals for differing expertise levels, and give suggestions for research on user-oriented instructional design based on this tool.

IBES: a tool for creating instructions based on event segmentation.

Science.gov (United States)

Mura, Katharina; Petersen, Nils; Huff, Markus; Ghose, Tandra

2013-12-26

Receiving informative, well-structured, and well-designed instructions supports performance and memory in assembly tasks. We describe IBES, a tool with which users can quickly and easily create multimedia, step-by-step instructions by segmenting a video of a task into segments. In a validation study we demonstrate that the step-by-step structure of the visual instructions created by the tool corresponds to the natural event boundaries, which are assessed by event segmentation and are known to play an important role in memory processes. In one part of the study, 20 participants created instructions based on videos of two different scenarios by using the proposed tool. In the other part of the study, 10 and 12 participants respectively segmented videos of the same scenarios yielding event boundaries for coarse and fine events. We found that the visual steps chosen by the participants for creating the instruction manual had corresponding events in the event segmentation. The number of instructional steps was a compromise between the number of fine and coarse events. Our interpretation of results is that the tool picks up on natural human event perception processes of segmenting an ongoing activity into events and enables the convenient transfer into meaningful multimedia instructions for assembly tasks. We discuss the practical application of IBES, for example, creating manuals for differing expertise levels, and give suggestions for research on user-oriented instructional design based on this tool.

Measurement uncertainty: Friend or foe?

Science.gov (United States)

Infusino, Ilenia; Panteghini, Mauro

2018-02-02

The definition and enforcement of a reference measurement system, based on the implementation of metrological traceability of patients' results to higher order reference methods and materials, together with a clinically acceptable level of measurement uncertainty, are fundamental requirements to produce accurate and equivalent laboratory results. The uncertainty associated with each step of the traceability chain should be governed to obtain a final combined uncertainty on clinical samples fulfilling the requested performance specifications. It is important that end-users (i.e., clinical laboratory) may know and verify how in vitro diagnostics (IVD) manufacturers have implemented the traceability of their calibrators and estimated the corresponding uncertainty. However, full information about traceability and combined uncertainty of calibrators is currently very difficult to obtain. Laboratory professionals should investigate the need to reduce the uncertainty of the higher order metrological references and/or to increase the precision of commercial measuring systems. Accordingly, the measurement uncertainty should not be considered a parameter to be calculated by clinical laboratories just to fulfil the accreditation standards, but it must become a key quality indicator to describe both the performance of an IVD measuring system and the laboratory itself. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment

Science.gov (United States)

Ahmadalipour, Ali; Moradkhani, Hamid

2017-12-01

Hydrologic modeling is one of the primary tools utilized for drought monitoring and drought early warning systems. Several sources of uncertainty in hydrologic modeling have been addressed in the literature. However, few studies have assessed the uncertainty of gridded observation datasets from a drought monitoring perspective. This study provides a hydrologic modeling oriented analysis of the gridded observation data uncertainties over the Pacific Northwest (PNW) and its implications on drought assessment. We utilized a recently developed 100-member ensemble-based observed forcing data to simulate hydrologic fluxes at 1/8° spatial resolution using Variable Infiltration Capacity (VIC) model, and compared the results with a deterministic observation. Meteorological and hydrological droughts are studied at multiple timescales over the basin, and seasonal long-term trends and variations of drought extent is investigated for each case. Results reveal large uncertainty of observed datasets at monthly timescale, with systematic differences for temperature records, mainly due to different lapse rates. The uncertainty eventuates in large disparities of drought characteristics. In general, an increasing trend is found for winter drought extent across the PNW. Furthermore, a ∼3% decrease per decade is detected for snow water equivalent (SWE) over the PNW, with the region being more susceptible to SWE variations of the northern Rockies than the western Cascades. The agricultural areas of southern Idaho demonstrate decreasing trend of natural soil moisture as a result of precipitation decline, which implies higher appeal for anthropogenic water storage and irrigation systems.

Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor

International Nuclear Information System (INIS)

Wilson, G.E.

1992-01-01

The Analytic Hierarchy Process (AHP) has been used to help determine the importance of components and phenomena in thermal-hydraulic safety analyses of nuclear reactors. The AHP results are based, in part on expert opinion. Therefore, it is prudent to evaluate the uncertainty of the AHP ranks of importance. Prior applications have addressed uncertainty with experimental data comparisons and bounding sensitivity calculations. These methods work well when a sufficient experimental data base exists to justify the comparisons. However, in the case of limited or no experimental data the size of the uncertainty is normally made conservatively large. Accordingly, the author has taken another approach, that of performing a statistically based uncertainty analysis. The new work is based on prior evaluations of the importance of components and phenomena in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor (ANSR), a new facility now in the design phase. The uncertainty during large break loss of coolant, and decay heat removal scenarios is estimated by assigning a probability distribution function (pdf) to the potential error in the initial expert estimates of pair-wise importance between the components. Using a Monte Carlo sampling technique, the error pdfs are propagated through the AHP software solutions to determine a pdf of uncertainty in the system wide importance of each component. To enhance the generality of the results, study of one other problem having different number of elements is reported, as are the effects of a larger assumed pdf error in the expert ranks. Validation of the Monte Carlo sample size and repeatability are also documented

Quantification of dose uncertainties for the bladder in prostate cancer radiotherapy based on dominant eigenmodes

Science.gov (United States)

Rios, Richard; Acosta, Oscar; Lafond, Caroline; Espinosa, Jairo; de Crevoisier, Renaud

2017-11-01

In radiotherapy for prostate cancer the dose at the treatment planning for the bladder may be a bad surrogate of the actual delivered dose as the bladder presents the largest inter-fraction shape variations during treatment. This paper presents PCA models as a virtual tool to estimate dosimetric uncertainties for the bladder produced by motion and deformation between fractions. Our goal is to propose a methodology to determine the minimum number of modes required to quantify dose uncertainties of the bladder for motion/deformation models based on PCA. We trained individual PCA models using the bladder contours available from three patients with a planning computed tomography (CT) and on-treatment cone-beam CTs (CBCTs). Based on the above models and via deformable image registration (DIR), we estimated two accumulated doses: firstly, an accumulated dose obtained by integrating the planning dose over the Gaussian probability distribution of the PCA model; and secondly, an accumulated dose obtained by simulating treatment courses via a Monte Carlo approach. We also computed a reference accumulated dose for each patient using his available images via DIR. Finally, we compared the planning dose with the three accumulated doses, and we calculated local dose variability and dose-volume histogram uncertainties.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

Science.gov (United States)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Applications of Decisions under Uncertainty in the Case of Omniasig-Life Insurance S.A.

Directory of Open Access Journals (Sweden)

Stelian STANCU

2006-01-01

Full Text Available Uncertainty is given because we don’t know the nature state event. The company can only estimate the demand of policies in order to estimate the received premiums. If the insurance company doesn’t choose correctly the alternative and the number and the damages will be greater then what it was estimated, then it will come to the point of not being able to pay all the damages. Because of the adverse selection, the insurer meets uncertainty in every day life. It is well known the fact that persons who have a higher risk of producing the insured event, they also have a higher inclination towards contracting insurance.

Systematic uncertainties on Monte Carlo simulation of lead based ADS

International Nuclear Information System (INIS)

Embid, M.; Fernandez, R.; Garcia-Sanz, J.M.; Gonzalez, E.

1999-01-01

Computer simulations of the neutronic behaviour of ADS systems foreseen for actinide and fission product transmutation are affected by many sources of systematic uncertainties, both from the nuclear data and by the methodology selected when applying the codes. Several actual ADS Monte Carlo simulations are presented, comparing different options both for the data and for the methodology, evaluating the relevance of the different uncertainties. (author)

DYNAMIC AUTHORIZATION BASED ON THE HISTORY OF EVENTS

Directory of Open Access Journals (Sweden)

Maxim V. Baklanovsky

2016-11-01

Full Text Available The new paradigm in the field of access control systems with fuzzy authorization is proposed. Let there is a set of objects in a single data transmissionnetwork. The goal is to develop dynamic authorization protocol based on correctness of presentation of events (news occurred earlier in the network. We propose mathematical method that keeps compactly the history of events, neglects more distant and least-significant events, composes and verifies authorization data. The history of events is represented as vectors of numbers. Each vector is multiplied by several stochastic vectors. The result is known that if vectors of events are sparse, then by solving the problem of -optimization they can be restored with high accuracy. Results of experiments for vectors restoring have shown that the greater the number of stochastic vectors is, the better accuracy of restored vectors is observed. It has been established that the largest absolute components are restored earlier. Access control system with the proposed dynamic authorization method enables to compute fuzzy confidence coefficients in networks with frequently changing set of participants, mesh-networks, multi-agent systems.

Event-based state estimation a stochastic perspective

CERN Document Server

Shi, Dawei; Chen, Tongwen

2016-01-01

This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs ...

Climate Change Extreme Events: Meeting the Information Needs of Water Resource Managers

Science.gov (United States)

Quay, R.; Garfin, G. M.; Dominguez, F.; Hirschboeck, K. K.; Woodhouse, C. A.; Guido, Z.; White, D. D.

2013-12-01

Information about climate has long been used by water managers to develop short term and long term plans and strategies for regional and local water resources. Inherent within longer term forecasts is an element of uncertainty, which is particularly evident in Global Climate model results for precipitation. For example in the southwest estimates in the flow of the Colorado River based on GCM results indicate changes from 120% or current flow to 60%. Many water resource managers are now using global climate model down scaled estimates results as indications of potential climate change as part of that planning. They are addressing the uncertainty within these estimates by using an anticipatory planning approach looking at a range of possible futures. One aspect of climate that is important for such planning are estimates of future extreme storm (short term) and drought (long term) events. However, the climate science of future possible changes in extreme events is less mature than general climate change science. At a recent workshop among climate scientists and water managers in the southwest, it was concluded the science of climate change extreme events is at least a decade away from being robust enough to be useful for water managers in their water resource management activities. However, it was proposed that there are existing estimates and records of past flooding and drought events that could be combined with general climate change science to create possible future events. These derived events could be of sufficient detail to be used by water resource managers until such time that the science of extreme events is able to provide more detailed estimates. Based on the results of this workshop and other work being done by the Decision Center for a Desert City at Arizona State University and the Climate Assessment for the Southwest center at University of Arizona., this article will 1) review what are the extreme event data needs of Water Resource Managers in the

Behavior coordination of mobile robotics using supervisory control of fuzzy discrete event systems.

Science.gov (United States)

Jayasiri, Awantha; Mann, George K I; Gosine, Raymond G

2011-10-01

In order to incorporate the uncertainty and impreciseness present in real-world event-driven asynchronous systems, fuzzy discrete event systems (DESs) (FDESs) have been proposed as an extension to crisp DESs. In this paper, first, we propose an extension to the supervisory control theory of FDES by redefining fuzzy controllable and uncontrollable events. The proposed supervisor is capable of enabling feasible uncontrollable and controllable events with different possibilities. Then, the extended supervisory control framework of FDES is employed to model and control several navigational tasks of a mobile robot using the behavior-based approach. The robot has limited sensory capabilities, and the navigations have been performed in several unmodeled environments. The reactive and deliberative behaviors of the mobile robotic system are weighted through fuzzy uncontrollable and controllable events, respectively. By employing the proposed supervisory controller, a command-fusion-type behavior coordination is achieved. The observability of fuzzy events is incorporated to represent the sensory imprecision. As a systematic analysis of the system, a fuzzy-state-based controllability measure is introduced. The approach is implemented in both simulation and real time. A performance evaluation is performed to quantitatively estimate the validity of the proposed approach over its counterparts.