International Nuclear Information System (INIS)
Barker, Kash; Haimes, Yacov Y.
2009-01-01
Risk-based decision making often relies upon expert probability assessments, particularly in the consequences of disruptive events and when such events are extreme or catastrophic in nature. Naturally, such expert-elicited probability distributions can be fraught with errors, as they describe events which occur very infrequently and for which only sparse data exist. This paper presents a quantitative framework, the extreme event uncertainty sensitivity impact method (EE-USIM), for measuring the sensitivity of extreme event consequences to uncertainties in the parameters of the underlying probability distribution. The EE-USIM is demonstrated with the Inoperability input-output model (IIM), a model with which to evaluate the propagation of inoperability throughout an interdependent set of economic and infrastructure sectors. The EE-USIM also makes use of a two-sided power distribution function generated by expert elicitation of extreme event consequences
Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology
DEFF Research Database (Denmark)
Thorndahl, Søren; Beven, K.J.; Jensen, Jacob Birk
2008-01-01
of combined sewer overflow. The GLUE methodology is used to test different conceptual setups in order to determine if one model setup gives a better goodness of fit conditional on the observations than the other. Moreover, different methodological investigations of GLUE are conducted in order to test......In the present paper an uncertainty analysis on an application of the commercial urban drainage model MOUSE is conducted. Applying the Generalized Likelihood Uncertainty Estimation (GLUE) methodology the model is conditioned on observation time series from two flow gauges as well as the occurrence...... if the uncertainty analysis is unambiguous. It is shown that the GLUE methodology is very applicable in uncertainty analysis of this application of an urban drainage model, although it was shown to be quite difficult of get good fits of the whole time series....
International Nuclear Information System (INIS)
Khalaquzzaman, M.; Lee, Seung Jun; Cho, Jaehyun; Jung, Wondea
2016-01-01
Recently, the input-profile-based testing for safety critical software has been proposed for determining the number of test cases and quantifying the failure probability of the software. Input-profile of a reactor protection system (RPS) software is the input which causes activation of the system for emergency shutdown of a reactor. This paper presents a method to determine the input-profile of a RPS software which considers concurrent events/transients. A deviation of a process parameter value begins through an event and increases owing to the concurrent multi-events depending on the correlation of process parameters and severity of incidents. A case of reactor trip caused by feedwater loss and main steam line break is simulated and analyzed to determine the RPS software input-profile and estimate the number of test cases. The different sizes of the main steam line breaks (e.g., small, medium, large break) with total loss of feedwater supply are considered in constructing the input-profile. The uncertainties of the simulation related to the input-profile-based software testing are also included. Our study is expected to provide an option to determine test cases and quantification of RPS software failure probability. (author)
Improved Event Location Uncertainty Estimates
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
Event-by-event simulation of single-neutron experiments to test uncertainty relations
International Nuclear Information System (INIS)
Raedt, H De; Michielsen, K
2014-01-01
Results from a discrete-event simulation of a recent single-neutron experiment that tests Ozawa's generalization of Heisenberg's uncertainty relation are presented. The event-based simulation algorithm reproduces the results of the quantum theoretical description of the experiment but does not require the knowledge of the solution of a wave equation, nor does it rely on detailed concepts of quantum theory. In particular, the data from these non-quantum simulations satisfy uncertainty relations derived in the context of quantum theory. (paper)
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.
Soleimani, Hossein; Hensman, James; Saria, Suchi
2017-08-21
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.
On epistemic uncertainties in event tree success criteria
International Nuclear Information System (INIS)
Jordan Cizelj, R.; Parzer, I.
2003-01-01
Uncertainty analysis of parameters, which are used as success criteria in PSA event trees, is presented in the paper. The influence of parameters on PSA model is indirect, and they are rather subject to epistemic uncertainties. Consequently, point estimates of these parameters cannot be automatically exchanged with probability distributions. For each PSA parameter, the analysis of several influencing factors is performed. As a result, recommended parameters' values for sensitivity analysis of the influence of these parameters on PSA results are given. In particular, the parameters related to exposure times were investigated. The values of the exposure times are assessed using different methodologies. The analysis of three parameters is presented in the paper, based on the comparison between the results of MAAP 3.0B and RELAP5/MOD2 codes. (author)
Tinti, Stefano; Armigliato, Alberto; Pagnoni, Gianluca; Zaniboni, Filippo
2013-04-01
Geoscientists deal often with hazardous processes like earthquakes, volcanic eruptions, tsunamis, hurricanes, etc., and their research is aimed not only to a better understanding of the physical processes, but also to provide assessment of the space and temporal evolution of a given individual event (i.e. to provide short-term prediction) and of the expected evolution of a group of events (i.e. to provide statistical estimates referred to a given return period, and a given geographical area). One of the main issues of any scientific method is how to cope with measurement errors, a topic which in case of forecast of ongoing or of future events translates into how to deal with forecast uncertainties. In general, the more data are available and processed to make a prediction, the more accurate the prediction is expected to be if the scientific approach is sound, and the smaller the associated uncertainties are. However, there are several important cases where assessment is to be made with insufficient data or insufficient time for processing, which leads to large uncertainties. Two examples can be given taken from tsunami science, since tsunamis are rare events that may have destructive power and very large impact. One example is the case of warning for a tsunami generated by a near-coast earthquake, which is an issue at the focus of the European funded project NearToWarn. Warning has to be launched before tsunami hits the coast, that is in a few minutes after its generation. This may imply that data collected in such a short time are not yet enough for an accurate evaluation, also because the implemented monitoring system (if any) could be inadequate (f.i. one reason of inadequacy could be that implementing a dense instrumental network could be judged too expensive for rare events) The second case is the long term prevention from tsunami strikes. Tsunami infrequency may imply that the historical record for a given piece of coast is too short to capture a statistical
Climate change impacts on extreme events in the United States: an uncertainty analysis
Extreme weather and climate events, such as heat waves, droughts and severe precipitation events, have substantial impacts on ecosystems and the economy. However, future climate simulations display large uncertainty in mean changes. As a result, the uncertainty in future changes ...
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.
Lin, Huiyan; Liang, Jiafeng; Jin, Hua; Zhao, Dongmei
2018-07-01
Previous studies have investigated whether uncertainty influences neural responses to emotional events. The findings of such studies, particularly with respect to event-related potentials (ERPs), have been controversial due to several factors, such as the stimuli that serve as cues and the emotional content of the events. However, it is still unknown whether the effects of uncertainty on ERP responses to emotional events are influenced by anticipation patterns (e.g., explicit or implicit anticipation). To address this issue, participants in the present study were presented with anticipatory cues and then emotional (negative and neutral) pictures. The cues either did or did not signify the emotional content of the upcoming picture. In the inter-stimulus intervals between cues and pictures, participants were asked to estimate the expected probability of the occurrence of a specific emotional category of the subsequent picture based on a scale in the explicit anticipation condition, while in the implicit condition, participants were asked to indicate, using a number on a scale, which color was different from the others. The results revealed that in the explicit condition, uncertainty increased late positive potential (LPP) responses, particularly for negative pictures, whereas LPP responses were larger for certain negative pictures than for uncertain negative pictures in the implicit condition. The findings in the present study suggest that the anticipation pattern influences the effects of uncertainty when evaluation of negative events. Copyright © 2018 Elsevier B.V. All rights reserved.
Disruptive event uncertainties in a perturbation approach to nuclear waste repository risk analysis
Energy Technology Data Exchange (ETDEWEB)
Harvey, T.F.
1980-09-01
A methodology is developed for incorporating a full range of the principal forecasting uncertainties into a risk analysis of a nuclear waste repository. The result of this methodology is a set of risk curves similar to those used by Rasmussen in WASH-1400. The set of curves is partially derived from a perturbation approach to analyze potential disruptive event sequences. Such a scheme could be useful in truncating the number of disruptive event scenarios and providing guidance to those establishing data-base development priorities.
Slinskey, E. A.; Loikith, P. C.; Waliser, D. E.; Goodman, A.
2017-12-01
Extreme precipitation events are associated with numerous societal and environmental impacts. Furthermore, anthropogenic climate change is projected to alter precipitation intensity across portions of the Continental United States (CONUS). Therefore, a spatial understanding and intuitive means of monitoring extreme precipitation over time is critical. Towards this end, we apply an event-based indicator, developed as a part of NASA's support of the ongoing efforts of the US National Climate Assessment, which assigns categories to extreme precipitation events based on 3-day storm totals as a basis for dataset intercomparison. To assess observational uncertainty across a wide range of historical precipitation measurement approaches, we intercompare in situ station data from the Global Historical Climatology Network (GHCN), satellite-derived precipitation data from NASA's Tropical Rainfall Measuring Mission (TRMM), gridded in situ station data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), global reanalysis from NASA's Modern Era Retrospective-Analysis version 2 (MERRA 2), and regional reanalysis with gauge data assimilation from NCEP's North American Regional Reanalysis (NARR). Results suggest considerable variability across the five-dataset suite in the frequency, spatial extent, and magnitude of extreme precipitation events. Consistent with expectations, higher resolution datasets were found to resemble station data best and capture a greater frequency of high-end extreme events relative to lower spatial resolution datasets. The degree of dataset agreement varies regionally, however all datasets successfully capture the seasonal cycle of precipitation extremes across the CONUS. These intercomparison results provide additional insight about observational uncertainty and the ability of a range of precipitation measurement and analysis products to capture extreme precipitation event climatology. While the event category threshold is fixed
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...
Uncertainties Related to Extreme Event Statistics of Sewer System Surcharge and Overflow
DEFF Research Database (Denmark)
Schaarup-Jensen, Kjeld; Johansen, C.; Thorndahl, Søren Liedtke
2005-01-01
Today it is common practice - in the major part of Europe - to base design of sewer systems in urban areas on recommended minimum values of flooding frequencies related to either pipe top level, basement level in buildings or level of road surfaces. Thus storm water runoff in sewer systems is only...... proceeding in an acceptable manner, if flooding of these levels is having an average return period bigger than a predefined value. This practice is also often used in functional analysis of existing sewer systems. If a sewer system can fulfil recommended flooding frequencies or not, can only be verified...... by performing long term simulations - using a sewer flow simulation model - and draw up extreme event statistics from the model simulations. In this context it is important to realize that uncertainties related to the input parameters of rainfall runoff models will give rise to uncertainties related...
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...
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....
Alvizuri, Celso R.
We present a catalog of full seismic moment tensors for 63 events from Uturuncu volcano in Bolivia. The events were recorded during 2011-2012 in the PLUTONS seismic array of 24 broadband stations. Most events had magnitudes between 0.5 and 2.0 and did not generate discernible surface waves; the largest event was Mw 2.8. For each event we computed the misfit between observed and synthetic waveforms, and we used first-motion polarity measurements to reduce the number of possible solutions. Each moment tensor solution was obtained using a grid search over the six-dimensional space of moment tensors. For each event we show the misfit function in eigenvalue space, represented by a lune. We identify three subsets of the catalog: (1) 6 isotropic events, (2) 5 tensional crack events, and (3) a swarm of 14 events southeast of the volcanic center that appear to be double couples. The occurrence of positively isotropic events is consistent with other published results from volcanic and geothermal regions. Several of these previous results, as well as our results, cannot be interpreted within the context of either an oblique opening crack or a crack-plus-double-couple model. Proper characterization of uncertainties for full moment tensors is critical for distinguishing among physical models of source processes. A seismic moment tensor is a 3x3 symmetric matrix that provides a compact representation of a seismic source. We develop an algorithm to estimate moment tensors and their uncertainties from observed seismic data. For a given event, the algorithm performs a grid search over the six-dimensional space of moment tensors by generating synthetic waveforms for each moment tensor and then evaluating a misfit function between the observed and synthetic waveforms. 'The' moment tensor M0 for the event is then the moment tensor with minimum misfit. To describe the uncertainty associated with M0, we first convert the misfit function to a probability function. The uncertainty, or
Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)
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.
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
A simplified analysis of uncertainty propagation in inherently controlled ATWS events
International Nuclear Information System (INIS)
Wade, D.C.
1987-01-01
The quasi static approach can be used to provide useful insight concerning the propagation of uncertainties in the inherent response to ATWS events. At issue is how uncertainties in the reactivity coefficients and in the thermal-hydraulics and materials properties propagate to yield uncertainties in the asymptotic temperatures attained upon inherent shutdown. The basic notion to be quantified is that many of the same physical phenomena contribute to both the reactivity increase of power reduction and the reactivity decrease of core temperature rise. Since these reactivities cancel by definition, a good deal of uncertainty cancellation must also occur of necessity. For example, if the Doppler coefficient is overpredicted, too large a positive reactivity insertion is predicted upon power reduction and collapse of the ΔT across the fuel pin. However, too large a negative reactivity is also predicted upon the compensating increase in the isothermal core average temperature - which includes the fuel Doppler effect
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)
Uncertainty analysis of one Main Circulation Pump trip event at the Ignalina NPP
International Nuclear Information System (INIS)
Vileiniskis, V.; Kaliatka, A.; Uspuras, E.
2004-01-01
One Main Circulation Pump (MCP) trip event is an anticipated transient with expected frequency of approximately one event per year. There were a few events when one MCP was inadvertently tripped. The throughput of the rest running pumps in the affected Main Circulation Circuit loop increased, however, the total coolant flow through the affected loop decreased. The main question arises whether this coolant flow rate is sufficient for adequate core cooling. This paper presents an investigation of one MCP trip event at the Ignalina NPP. According to international practice, the transient analysis should consist of deterministic analysis by employing best-estimate codes and uncertainty analysis. For that purpose, the plant's RELAP5 model and the GRS (Germany) System for Uncertainty and Sensitivity Analysis package (SUSA) were employed. Uncertainty analysis of flow energy loss in different parts of the Main Circulation Circuit, initial conditions and code-selected models was performed. Such analysis allows to estimate the influence of separate parameters on calculation results and to find the modelling parameters that have the largest impact on the event studied. On the basis of this analysis, recommendations for the further improvement of the model have been developed. (author)
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.
Quantum uncertainty relation based on the mean deviation
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...
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.
Measuring the Higgs boson mass using event-by-event uncertainties
Castelli, A.
2015-01-01
The thesis presents a measurement of the properties of the Higgs particle, performed by using the data collected by the ATLAS experiment in 2011 and 2012. The measurement is performed by using a three-dimensional model based on analytic functions to describe the signal produced by the Higgs boson
Indian Academy of Sciences (India)
To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...
International Nuclear Information System (INIS)
Silva, T.A. da
1988-01-01
The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt
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.
Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty
International Nuclear Information System (INIS)
Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro
2015-01-01
Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis
Development of a Prototype Model-Form Uncertainty Knowledge Base
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.
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).
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.
Uncertainty visualization in HARDI based on ensembles of ODFs
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.
Uncertainty visualization in HARDI based on ensembles of ODFs
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.
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
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
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....
Scenario-based approach for flexible resource loading under uncertainty
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
Can agent based models effectively reduce fisheries management implementation uncertainty?
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.
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
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)
Interval-based reconstruction for uncertainty quantification in PET
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.
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
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
National Aeronautics and Space Administration — We propose to develop a new system for quantitative assessment of uncertainties in LEO satellite position caused by storm time changes in space environmental...
Reducing uncertainty in Climate Response Time Scale by Bayesian Analysis of the 8.2 ka event
Lorenz, A.; Held, H.; Bauer, E.; Schneider von Deimling, T.
2009-04-01
We analyze the possibility of uncertainty reduction in Climate Response Time Scale by utilizing Greenland ice-core data that contain the 8.2 ka event within a Bayesian model-data intercomparison with the Earth system model of intermediate complexity, CLIMBER-2.3. Within a stochastic version of the model it has been possible to mimic the 8.2 ka event within a plausible experimental setting and with relatively good accuracy considering the timing of the event in comparison to other modeling exercises [1]. The simulation of the centennial cold event is effectively determined by the oceanic cooling rate which depends largely on the ocean diffusivity described by diffusion coefficients of relatively wide uncertainty ranges. The idea now is to discriminate between the different values of diffusivities according to their likelihood to rightly represent the duration of the 8.2 ka event and thus to exploit the paleo data to constrain uncertainty in model parameters in analogue to [2]. Implementing this inverse Bayesian Analysis with this model the technical difficulty arises to establish the related likelihood numerically in addition to the uncertain model parameters: While mainstream uncertainty analyses can assume a quasi-Gaussian shape of likelihood, with weather fluctuating around a long term mean, the 8.2 ka event as a highly nonlinear effect precludes such an a priori assumption. As a result of this study [3] the Bayesian Analysis showed a reduction of uncertainty in vertical ocean diffusivity parameters of factor 2 compared to prior knowledge. This learning effect on the model parameters is propagated to other model outputs of interest; e.g. the inverse ocean heat capacity, which is important for the dominant time scale of climate response to anthropogenic forcing which, in combination with climate sensitivity, strongly influences the climate systems reaction for the near- and medium-term future. 1 References [1] E. Bauer, A. Ganopolski, M. Montoya: Simulation of the
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
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
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.
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
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.
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
Managing wildfire events: risk-based decision making among a group of federal fire managers
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...
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.
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
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
Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs
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
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
Facing uncertainty in ecosystem services-based resource management.
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.
Edouard, Simon; Vincendon, Béatrice; Ducrocq, Véronique
2018-05-01
Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.
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
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
Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling
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,...
Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation
Directory of Open Access Journals (Sweden)
Xinchi Chen
2016-01-01
Full Text Available Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.
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
Qi, D.; Majda, A.
2017-12-01
A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with
An Efficient Deterministic Approach to Model-based Prediction Uncertainty
National Aeronautics and Space Administration — Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the...
Parameter uncertainty effects on variance-based sensitivity analysis
International Nuclear Information System (INIS)
Yu, W.; Harris, T.J.
2009-01-01
In the past several years there has been considerable commercial and academic interest in methods for variance-based sensitivity analysis. The industrial focus is motivated by the importance of attributing variance contributions to input factors. A more complete understanding of these relationships enables companies to achieve goals related to quality, safety and asset utilization. In a number of applications, it is possible to distinguish between two types of input variables-regressive variables and model parameters. Regressive variables are those that can be influenced by process design or by a control strategy. With model parameters, there are typically no opportunities to directly influence their variability. In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters. A sequential analysis is proposed, where first an sensitivity analysis is performed with respect to the regressive variables. In the second step, the uncertainty effects arising from the model parameters are included. This strategy can be quite useful in understanding process variability and in developing strategies to reduce overall variability. When this method is used for nonlinear models which are linear in the parameters, analytical solutions can be utilized. In the more general case of models that are nonlinear in both the regressive variables and the parameters, either first order approximations can be used, or numerically intensive methods must be used
Uncertainty in temperature-based determination of time of death
Weiser, Martin; Erdmann, Bodo; Schenkl, Sebastian; Muggenthaler, Holger; Hubig, Michael; Mall, Gita; Zachow, Stefan
2018-03-01
Temperature-based estimation of time of death (ToD) can be performed either with the help of simple phenomenological models of corpse cooling or with detailed mechanistic (thermodynamic) heat transfer models. The latter are much more complex, but allow a higher accuracy of ToD estimation as in principle all relevant cooling mechanisms can be taken into account. The potentially higher accuracy depends on the accuracy of tissue and environmental parameters as well as on the geometric resolution. We investigate the impact of parameter variations and geometry representation on the estimated ToD. For this, numerical simulation of analytic heat transport models is performed on a highly detailed 3D corpse model, that has been segmented and geometrically reconstructed from a computed tomography (CT) data set, differentiating various organs and tissue types. From that and prior information available on thermal parameters and their variability, we identify the most crucial parameters to measure or estimate, and obtain an a priori uncertainty quantification for the ToD.
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)
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
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
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.
DD4Hep based event reconstruction
AUTHOR|(SzGeCERN)683529; Frank, Markus; Gaede, Frank-Dieter; Hynds, Daniel; Lu, Shaojun; Nikiforou, Nikiforos; Petric, Marko; Simoniello, Rosa; Voutsinas, Georgios Gerasimos
The DD4HEP detector description toolkit offers a flexible and easy-to-use solution for the consistent and complete description of particle physics detectors in a single system. The sub-component DDREC provides a dedicated interface to the detector geometry as needed for event reconstruction. With DDREC there is no need to define an additional, separate reconstruction geometry as is often done in HEP, but one can transparently extend the existing detailed simulation model to be also used for the reconstruction. Based on the extension mechanism of DD4HEP, DDREC allows one to attach user defined data structures to detector elements at all levels of the geometry hierarchy. These data structures define a high level view onto the detectors describing their physical properties, such as measurement layers, point resolutions, and cell sizes. For the purpose of charged particle track reconstruction, dedicated surface objects can be attached to every volume in the detector geometry. These surfaces provide the measuremen...
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.
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.
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
Reducing uncertainty based on model fitness: Application to a ...
African Journals Online (AJOL)
A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially ... The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model.
A posteriori uncertainty quantification of PIV-based pressure data
Azijli, I.; Sciacchitano, A.; Ragni, D.; Palha Da Silva Clérigo, A.; Dwight, R.P.
2016-01-01
A methodology for a posteriori uncertainty quantification of pressure data retrieved from particle image velocimetry (PIV) is proposed. It relies upon the Bayesian framework, where the posterior distribution (probability distribution of the true velocity, given the PIV measurements) is obtained from
Centralizing Data Management with Considerations of Uncertainty and Information-Based Flexibility
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...
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)
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....
Rule-Based Event Processing and Reaction Rules
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.
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)
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)
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
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)
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.
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…
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
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
An event-based model for contracts
Directory of Open Access Journals (Sweden)
Tiziana Cimoli
2013-02-01
Full Text Available We introduce a basic model for contracts. Our model extends event structures with a new relation, which faithfully captures the circular dependencies among contract clauses. We establish whether an agreement exists which respects all the contracts at hand (i.e. all the dependencies can be resolved, and we detect the obligations of each participant. The main technical contribution is a correspondence between our model and a fragment of the contract logic PCL. More precisely, we show that the reachable events are exactly those which correspond to provable atoms in the logic. Despite of this strong correspondence, our model improves previous work on PCL by exhibiting a finer-grained notion of culpability, which takes into account the legitimate orderings of events.
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
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
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...
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
Inflated Uncertainty in Multimodel-Based Regional Climate Projections
Madsen, Marianne Sloth; Langen, Peter L.; Boberg, Fredrik; Christensen, Jens Hesselbjerg
2017-11-01
Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.
Plasticity models of material variability based on uncertainty quantification techniques
Energy Technology Data Exchange (ETDEWEB)
Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2017-11-01
The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
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.)
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.
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...
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)
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)
Statistical-uncertainty-based adaptive filtering of lidar signals
International Nuclear Information System (INIS)
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-01-01
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H 2 O and the N 2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America
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.
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.
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
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
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
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
Crashworthiness uncertainty analysis of typical civil aircraft based on Box–Behnken method
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...
An event-based account of conformity.
Kim, Diana; Hommel, Bernhard
2015-04-01
People often change their behavior and beliefs when confronted with deviating behavior and beliefs of others, but the mechanisms underlying such phenomena of conformity are not well understood. Here we suggest that people cognitively represent their own actions and others' actions in comparable ways (theory of event coding), so that they may fail to distinguish these two categories of actions. If so, other people's actions that have no social meaning should induce conformity effects, especially if those actions are similar to one's own actions. We found that female participants adjusted their manual judgments of the beauty of female faces in the direction consistent with distracting information without any social meaning (numbers falling within the range of the judgment scale) and that this effect was enhanced when the distracting information was presented in movies showing the actual manual decision-making acts. These results confirm that similarity between an observed action and one's own action matters. We also found that the magnitude of the standard conformity effect was statistically equivalent to the movie-induced effect. © The Author(s) 2015.
A scenario based approach for flexible resource loading under uncertainty
Wullink, Gerhard; Gademann, Noud; Hans, Elias W.; van Harten, Aart
2003-01-01
Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach
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)
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.
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...
The effect of uncertainties in distance-based ranking methods for multi-criteria decision making
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.
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.
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.
Model-based uncertainty in species range prediction
DEFF Research Database (Denmark)
Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel
2006-01-01
Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...
Moment based model predictive control for systems with additive uncertainty
Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.
2017-01-01
In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We
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.
Managing Knowledge-Based Resource Capabilities Under Uncertainty
Janice E. Carrillo; Cheryl Gaimon
2004-01-01
A firm's ability to manage its knowledge-based resource capabilities has become increasingly important as a result of performance threats triggered by technology change and intense competition. At the manufacturing plant level, we focus on three repositories of knowledge that drive performance. First, the physical production or information systems represent knowledge embedded in the plant's technical systems. Second, the plant's workforce has knowledge, including diverse scientific informatio...
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 in hydrological signatures
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
Event-based Simulation Model for Quantum Optics Experiments
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
Event-Based Corpuscular Model for Quantum Optics Experiments
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
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.
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.
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.
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
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...
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)
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...
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
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.)
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.
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.
Lognormal Approximations of Fault Tree Uncertainty Distributions.
El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P
2018-01-26
Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.
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....
Ontology-based prediction of surgical events in laparoscopic surgery
Katić, Darko; Wekerle, Anna-Laura; Gärtner, Fabian; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie
2013-03-01
Context-aware technologies have great potential to help surgeons during laparoscopic interventions. Their underlying idea is to create systems which can adapt their assistance functions automatically to the situation in the OR, thus relieving surgeons from the burden of managing computer assisted surgery devices manually. To this purpose, a certain kind of understanding of the current situation in the OR is essential. Beyond that, anticipatory knowledge of incoming events is beneficial, e.g. for early warnings of imminent risk situations. To achieve the goal of predicting surgical events based on previously observed ones, we developed a language to describe surgeries and surgical events using Description Logics and integrated it with methods from computational linguistics. Using n-Grams to compute probabilities of followup events, we are able to make sensible predictions of upcoming events in real-time. The system was evaluated on professionally recorded and labeled surgeries and showed an average prediction rate of 80%.
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.
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)
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.
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...
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.
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.
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.
Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy
Directory of Open Access Journals (Sweden)
Joseph Y.J. Chow
2016-10-01
Full Text Available Given the rapid advances in unmanned aerial vehicles, or drones, and increasing need to monitor at a city level, one of the current research gaps is how to systematically deploy drones over multiple periods. We propose a real-time data-driven approach: we formulate the first deterministic arc-inventory routing problem and derive its stochastic dynamic policy. The policy is expected to be of greatest value in scenarios where uncertainty is highest and costliest, such as city monitoring during major events. The Bellman equation for an approximation of the proposed inventory routing policy is formulated as a selective vehicle routing problem. We propose an approximate dynamic programming algorithm based on Least Squares Monte Carlo simulation to find that policy. The algorithm has been modified so that the least squares dependent variable is defined to be the “expected stock out cost upon the next replenishment”. The new algorithm is tested on 30 simulated instances of real time trajectories over 5 time periods of the selective vehicle routing problem to evaluate the proposed policy and algorithm. Computational results on the selected instances show that the algorithm on average outperforms the myopic policy by 23–28%, depending on the parametric design. Further tests are conducted on classic benchmark arc routing problem instances. The 11-link instance gdb19 (Golden et al., 1983 is expanded into a sequential 15-period stochastic dynamic example and used to demonstrate why a naïve static multi-period deployment plan would not be effective in real networks.
International Nuclear Information System (INIS)
Sharma, Vivek; Sharma, B.B.; Nath, R.
2017-01-01
In the present manuscript, observer based synchronization and message recovery scheme is discussed for a system with uncertainties. LMI conditions are analytically derived solution of which gives the observer design matrices. Earlier approaches have used adaptive laws to address the uncertainties, however in present work, decoupling approach is used to make observer robust against uncertainties. The methodology requires upper bounds on nonlinearity and the message signal and estimates for these bounds are generated adaptively. Thus no information about the nature of nonlinearity and associated Lipschitz constant is needed in proposed approach. Message signal is recovered using equivalent output injection which is a low pass filtered equivalent of the discontinuous effort required to maintain the sliding motion. Finally, the efficacy of proposed Nonlinear Unknown Input Sliding Mode Observer (NUISMO) for chaotic communication is verified by conducting simulation studies on two chaotic systems i.e. third order Chua circuit and Rossler system.
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)
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.
Multi Agent System Based Wide Area Protection against Cascading Events
DEFF Research Database (Denmark)
Liu, Zhou; Chen, Zhe; Liu, Leo
2012-01-01
In this paper, a multi-agent system based wide area protection scheme is proposed in order to prevent long term voltage instability induced cascading events. The distributed relays and controllers work as a device agent which not only executes the normal function automatically but also can...... the effectiveness of proposed protection strategy. The simulation results indicate that the proposed multi agent control system can effectively coordinate the distributed relays and controllers to prevent the long term voltage instability induced cascading events....
Preventing Medication Error Based on Knowledge Management Against Adverse Event
Hastuti, Apriyani Puji; Nursalam, Nursalam; Triharini, Mira
2017-01-01
Introductions: Medication error is one of many types of errors that could decrease the quality and safety of healthcare. Increasing number of adverse events (AE) reflects the number of medication errors. This study aimed to develop a model of medication error prevention based on knowledge management. This model is expected to improve knowledge and skill of nurses to prevent medication error which is characterized by the decrease of adverse events (AE). Methods: This study consisted of two sta...
A ROOT based event display software for JUNO
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.
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.
Abstracting event-based control models for high autonomy systems
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.
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.
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.
Event-based Sensing for Space Situational Awareness
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
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
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.
An Oracle-based Event Index for ATLAS
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 ...
CMS DAQ Event Builder Based on Gigabit Ethernet
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.
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
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
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.
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.
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.
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
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.
Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering
Directory of Open Access Journals (Sweden)
Matthew Parkan
2018-02-01
Full Text Available Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys, using a probabilistic approach. First, a coarse segmentation (marker controlled watershed is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes. By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered segmentation.
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.
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.
Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty
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
Monte-Carlo-based uncertainty propagation with hierarchical models—a case study in dynamic torque
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.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2015-09-01
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
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.
An Oracle-based event index for ATLAS
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.
Rocchio-based relevance feedback in video event retrieval
Pingen, G.L.J.; de Boer, M.H.T.; Aly, Robin; Amsaleg, Laurent; Guðmundsson, Gylfi Þór; Gurrin, Cathal; Jónsson, Björn Þór; Satoh, Shin’ichi
This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a
Simulation of quantum computation : A deterministic event-based approach
Michielsen, K; De Raedt, K; De Raedt, H
We demonstrate that locally connected networks of machines that have primitive learning capabilities can be used to perform a deterministic, event-based simulation of quantum computation. We present simulation results for basic quantum operations such as the Hadamard and the controlled-NOT gate, and
Simulation of Quantum Computation : A Deterministic Event-Based Approach
Michielsen, K.; Raedt, K. De; Raedt, H. De
2005-01-01
We demonstrate that locally connected networks of machines that have primitive learning capabilities can be used to perform a deterministic, event-based simulation of quantum computation. We present simulation results for basic quantum operations such as the Hadamard and the controlled-NOT gate, and
An XML-Based Protocol for Distributed Event Services
Smith, Warren; Gunter, Dan; Quesnel, Darcy; Biegel, Bryan (Technical Monitor)
2001-01-01
This viewgraph presentation provides information on the application of an XML (extensible mark-up language)-based protocol to the developing field of distributed processing by way of a computational grid which resembles an electric power grid. XML tags would be used to transmit events between the participants of a transaction, namely, the consumer and the producer of the grid scheme.
Event-based historical value-at-risk
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
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.
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...
Directory of Open Access Journals (Sweden)
Igor V. Karyakin
2016-02-01
Full Text Available The 9th ARRCN Symposium 2015 was held during 21st–25th October 2015 at the Novotel Hotel, Chumphon, Thailand, one of the most favored travel destinations in Asia. The 10th ARRCN Symposium 2017 will be held during October 2017 in the Davao, Philippines. International Symposium on the Montagu's Harrier (Circus pygargus «The Montagu's Harrier in Europe. Status. Threats. Protection», organized by the environmental organization «Landesbund für Vogelschutz in Bayern e.V.» (LBV was held on November 20-22, 2015 in Germany. The location of this event was the city of Wurzburg in Bavaria.
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.
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%.
Directory of Open Access Journals (Sweden)
Vulević Branislav D.
2011-01-01
Full Text Available This paper is a summary of broadband measurement values of radiofrequency radiation around GSM base stations in the vicinity of residential areas in Belgrade and 12 other cities in Serbia. It will be useful for determining non-ionizing radiation exposure levels of the general public in the future. The purpose of this paper is also an appropriate representation of basic information on the evaluation of measurement uncertainty.
Event-Based Stabilization over Networks with Transmission Delays
Directory of Open Access Journals (Sweden)
Xiangyu Meng
2012-01-01
Full Text Available This paper investigates asymptotic stabilization for linear systems over networks based on event-driven communication. A new communication logic is proposed to reduce the feedback effort, which has some advantages over traditional ones with continuous feedback. Considering the effect of time-varying transmission delays, the criteria for the design of both the feedback gain and the event-triggering mechanism are derived to guarantee the stability and performance requirements. Finally, the proposed techniques are illustrated by an inverted pendulum system and a numerical example.
Event-Based control of depth of hypnosis in anesthesia.
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.
Event- and interval-based measurement of stuttering: a review.
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
Event-based state estimation a stochastic perspective
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 ...
Event-based cluster synchronization of coupled genetic regulatory networks
Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang
2017-09-01
In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.
International Nuclear Information System (INIS)
Luo, Pengcheng; Hu, Yang
2013-01-01
During system operation, the environmental, operational and usage conditions are time-varying, which causes the fluctuations of the system state variables (SSVs). These fluctuations change the accidents’ probabilities and then result in the system risk evolution (SRE). This inherent relation makes it feasible to realize risk control by monitoring the SSVs in real time, herein, the quantitative analysis of SRE is essential. Besides, some events in the process of SRE are critical to system risk, because they act like the “demarcative points” of safety and accident, and this characteristic makes each of them a key point of risk control. Therefore, analysis of SRE and identification of risk critical events (RCEs) are remarkably meaningful to ensure the system to operate safely. In this context, an event sequence diagram (ESD) based method of SRE analysis and the related Monte Carlo solution are presented; RCE and risk sensitive variable (RSV) are defined, and the corresponding identification methods are also proposed. Finally, the proposed approaches are exemplified with an accident scenario of an aircraft getting into the icing region
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.
Event-based user classification in Weibo media.
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.
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.
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.
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.
Modeling and query the uncertainty of network constrained moving objects based on RFID data
Han, Liang; Xie, Kunqing; Ma, Xiujun; Song, Guojie
2007-06-01
The management of network constrained moving objects is more and more practical, especially in intelligent transportation system. In the past, the location information of moving objects on network is collected by GPS, which cost high and has the problem of frequent update and privacy. The RFID (Radio Frequency IDentification) devices are used more and more widely to collect the location information. They are cheaper and have less update. And they interfere in the privacy less. They detect the id of the object and the time when moving object passed by the node of the network. They don't detect the objects' exact movement in side the edge, which lead to a problem of uncertainty. How to modeling and query the uncertainty of the network constrained moving objects based on RFID data becomes a research issue. In this paper, a model is proposed to describe the uncertainty of network constrained moving objects. A two level index is presented to provide efficient access to the network and the data of movement. The processing of imprecise time-slice query and spatio-temporal range query are studied in this paper. The processing includes four steps: spatial filter, spatial refinement, temporal filter and probability calculation. Finally, some experiments are done based on the simulated data. In the experiments the performance of the index is studied. The precision and recall of the result set are defined. And how the query arguments affect the precision and recall of the result set is also discussed.
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.
An Oracle-based event index for ATLAS
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...
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.
Sutton, Jonathan E.; Guo, Wei; Katsoulakis, Markos A.; Vlachos, Dionisios G.
2016-04-01
Kinetic models based on first principles are becoming common place in heterogeneous catalysis because of their ability to interpret experimental data, identify the rate-controlling step, guide experiments and predict novel materials. To overcome the tremendous computational cost of estimating parameters of complex networks on metal catalysts, approximate quantum mechanical calculations are employed that render models potentially inaccurate. Here, by introducing correlative global sensitivity analysis and uncertainty quantification, we show that neglecting correlations in the energies of species and reactions can lead to an incorrect identification of influential parameters and key reaction intermediates and reactions. We rationalize why models often underpredict reaction rates and show that, despite the uncertainty being large, the method can, in conjunction with experimental data, identify influential missing reaction pathways and provide insights into the catalyst active site and the kinetic reliability of a model. The method is demonstrated in ethanol steam reforming for hydrogen production for fuel cells.
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...
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
Poisson-event-based analysis of cell proliferation.
Summers, Huw D; Wills, John W; Brown, M Rowan; Rees, Paul
2015-05-01
A protocol for the assessment of cell proliferation dynamics is presented. This is based on the measurement of cell division events and their subsequent analysis using Poisson probability statistics. Detailed analysis of proliferation dynamics in heterogeneous populations requires single cell resolution within a time series analysis and so is technically demanding to implement. Here, we show that by focusing on the events during which cells undergo division rather than directly on the cells themselves a simplified image acquisition and analysis protocol can be followed, which maintains single cell resolution and reports on the key metrics of cell proliferation. The technique is demonstrated using a microscope with 1.3 μm spatial resolution to track mitotic events within A549 and BEAS-2B cell lines, over a period of up to 48 h. Automated image processing of the bright field images using standard algorithms within the ImageJ software toolkit yielded 87% accurate recording of the manually identified, temporal, and spatial positions of the mitotic event series. Analysis of the statistics of the interevent times (i.e., times between observed mitoses in a field of view) showed that cell division conformed to a nonhomogeneous Poisson process in which the rate of occurrence of mitotic events, λ exponentially increased over time and provided values of the mean inter mitotic time of 21.1 ± 1.2 hours for the A549 cells and 25.0 ± 1.1 h for the BEAS-2B cells. Comparison of the mitotic event series for the BEAS-2B cell line to that predicted by random Poisson statistics indicated that temporal synchronisation of the cell division process was occurring within 70% of the population and that this could be increased to 85% through serum starvation of the cell culture. © 2015 International Society for Advancement of Cytometry.
Directory of Open Access Journals (Sweden)
Arun Kaintura
2018-02-01
Full Text Available Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standard Monte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems.
DEFF Research Database (Denmark)
Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan
) weighted-least-square regression. 3) Initialization of estimation by use of linear algebra providing a first guess. 4) Sequential parameter and simultaneous GC parameter by using of 4 different minimization algorithms. 5) Thorough uncertainty analysis: a) based on asymptotic approximation of parameter...... covariance matrix b) based on boot strap method. Providing 95%-confidence intervals of parameters and predicted property. 6) Performance statistics analysis and model application. The application of the methodology is shown for a new GC model built to predict lower flammability limit (LFL) for refrigerants...... their credibility and robustness in wider industrial and scientific applications....
Intelligent Transportation Control based on Proactive Complex Event Processing
Wang Yongheng; Geng Shaofeng; Li Qian
2016-01-01
Complex Event Processing (CEP) has become the key part of Internet of Things (IoT). Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to intelligent transportation control. In this paper, we propose a proactive CEP architecture and method for intelligent transportation control. Based on basic CEP technology and predictive analytic technology, a networked distributed Markov decision processes model with predicting states is p...
Deep learning based beat event detection in action movie franchises
Ejaz, N.; Khan, U. A.; Martínez-del-Amor, M. A.; Sparenberg, H.
2018-04-01
Automatic understanding and interpretation of movies can be used in a variety of ways to semantically manage the massive volumes of movies data. "Action Movie Franchises" dataset is a collection of twenty Hollywood action movies from five famous franchises with ground truth annotations at shot and beat level of each movie. In this dataset, the annotations are provided for eleven semantic beat categories. In this work, we propose a deep learning based method to classify shots and beat-events on this dataset. The training dataset for each of the eleven beat categories is developed and then a Convolution Neural Network is trained. After finding the shot boundaries, key frames are extracted for each shot and then three classification labels are assigned to each key frame. The classification labels for each of the key frames in a particular shot are then used to assign a unique label to each shot. A simple sliding window based method is then used to group adjacent shots having the same label in order to find a particular beat event. The results of beat event classification are presented based on criteria of precision, recall, and F-measure. The results are compared with the existing technique and significant improvements are recorded.
Track-based event recognition in a realistic crowded environment
van Huis, Jasper R.; Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; den Hollander, Richard J. M.; Dijk, Judith; van Rest, Jeroen H.
2014-10-01
Automatic detection of abnormal behavior in CCTV cameras is important to improve the security in crowded environments, such as shopping malls, airports and railway stations. This behavior can be characterized at different time scales, e.g., by small-scale subtle and obvious actions or by large-scale walking patterns and interactions between people. For example, pickpocketing can be recognized by the actual snatch (small scale), when he follows the victim, or when he interacts with an accomplice before and after the incident (longer time scale). This paper focusses on event recognition by detecting large-scale track-based patterns. Our event recognition method consists of several steps: pedestrian detection, object tracking, track-based feature computation and rule-based event classification. In the experiment, we focused on single track actions (walk, run, loiter, stop, turn) and track interactions (pass, meet, merge, split). The experiment includes a controlled setup, where 10 actors perform these actions. The method is also applied to all tracks that are generated in a crowded shopping mall in a selected time frame. The results show that most of the actions can be detected reliably (on average 90%) at a low false positive rate (1.1%), and that the interactions obtain lower detection rates (70% at 0.3% FP). This method may become one of the components that assists operators to find threatening behavior and enrich the selection of videos that are to be observed.
FIREDATA, Nuclear Power Plant Fire Event Data Base
International Nuclear Information System (INIS)
Wheelis, W.T.
2001-01-01
1 - Description of program or function: FIREDATA contains raw fire event data from 1965 through June 1985. These data were obtained from a number of reference sources including the American Nuclear Insurers, Licensee Event Reports, Nuclear Power Experience, Electric Power Research Institute Fire Loss Data and then collated into one database developed in the personal computer database management system, dBASE III. FIREDATA is menu-driven and asks interactive questions of the user that allow searching of the database for various aspects of a fire such as: location, mode of plant operation at the time of the fire, means of detection and suppression, dollar loss, etc. Other features include the capability of searching for single or multiple criteria (using Boolean 'and' or 'or' logical operations), user-defined keyword searches of fire event descriptions, summary displays of fire event data by plant name of calendar date, and options for calculating the years of operating experience for all commercial nuclear power plants from any user-specified date and the ability to display general plant information. 2 - Method of solution: The six database files used to store nuclear power plant fire event information, FIRE, DESC, SUM, OPEXPER, OPEXBWR, and EXPERPWR, are accessed by software to display information meeting user-specified criteria or to perform numerical calculations (e.g., to determine the operating experience of a nuclear plant). FIRE contains specific searchable data relating to each of 354 fire events. A keyword concept is used to search each of the 31 separate entries or fields. DESC contains written descriptions of each of the fire events. SUM holds basic plant information for all plants proposed, under construction, in operation, or decommissioned. This includes the initial criticality and commercial operation dates, the physical location of the plant, and its operating capacity. OPEXPER contains date information and data on how various plant locations are
A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.
Zheng, Chaojie; Wang, Xiuying; Feng, Dagan
2015-01-01
PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.
Uncertainty estimation with bias-correction for flow series based on rating curve
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.
Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification
International Nuclear Information System (INIS)
Li, Qi; Hu, Guiping
2014-01-01
An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers' annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefinery, while the second stage determines the biomass and biofuels flows. A case study based on Iowa in the U.S. illustrates that it is economically feasible to meet desired demand using corn stover as the biomass feedstock. The results show that the locations of fast pyrolysis plants are sensitive to uncertainties while the capacity levels are insensitive. The stochastic model outperforms the deterministic model in the stochastic environment, especially when there is insufficient biomass. Also, farmers' participation can have a significant impact on the profitability and robustness of this supply chain. - Highlights: • Decentralized supply chain design for advanced biofuel production is considered. • A two-stage stochastic programming is formulated to consider uncertainties. • Farmers' participation has a significant impact on the biofuel supply chain design
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.
Energy Technology Data Exchange (ETDEWEB)
Lv, Y., E-mail: lvyying@hotmail.com [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Huang, G.H., E-mail: huang@iseis.org [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Guo, L., E-mail: guoli8658@hotmail.com [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Li, Y.P., E-mail: yongping.li@iseis.org [MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206 (China); Dai, C., E-mail: daichao321@gmail.com [College of Environmental Sciences and Engineering, Peking University, Beijing 100871 (China); Wang, X.W., E-mail: wangxingwei0812@gamil.com [State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875 (China); Sun, W., E-mail: sunwei@iseis.org [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada)
2013-02-15
Highlights: ► An interval-parameter joint-probabilistic integer programming method is developed. ► It is useful for nuclear emergency management practices under uncertainties. ► It can schedule optimal routes with maximizing evacuees during a finite time. ► Scenario-based analysis enhances robustness in controlling system risk. ► The method will help to improve the capability of disaster responses. -- Abstract: Nuclear emergency evacuation is important to prevent radioactive harms by hazardous materials and to limit the accidents’ consequences; however, uncertainties are involved in the components and processes of such a management system. In the study, an interval-parameter joint-probabilistic integer programming (IJIP) method is developed for emergency evacuation management under uncertainties. Optimization techniques of interval-parameter programming (IPP) and joint-probabilistic constrained (JPC) programming are incorporated into an integer linear programming framework, so that the approach can deal with uncertainties expressed as joint probability and interval values. The IJIP method can schedule the optimal routes to guarantee the maximum population evacuated away from the effected zone during a finite time. Furthermore, it can also facilitate post optimization analysis to enhance robustness in controlling system violation risk imposed on the joint-probabilistic constraints. The developed method has been applied to a case study of nuclear emergency management; meanwhile, a number of scenarios under different system conditions have been analyzed. It is indicated that the solutions are useful for evacuation management practices. The result of the IJIP method can not only help to raise the capability of disaster responses in a systematic manner, but also provide an insight into complex relationships among evacuation planning, resources utilizations, policy requirements and system risks.
Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models.
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.
Address-event-based platform for bioinspired spiking systems
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
Short-Period Surface Wave Based Seismic Event Relocation
White-Gaynor, A.; Cleveland, M.; Nyblade, A.; Kintner, J. A.; Homman, K.; Ammon, C. J.
2017-12-01
Accurate and precise seismic event locations are essential for a broad range of geophysical investigations. Superior location accuracy generally requires calibration with ground truth information, but superb relative location precision is often achievable independently. In explosion seismology, low-yield explosion monitoring relies on near-source observations, which results in a limited number of observations that challenges our ability to estimate any locations. Incorporating more distant observations means relying on data with lower signal-to-noise ratios. For small, shallow events, the short-period (roughly 1/2 to 8 s period) fundamental-mode and higher-mode Rayleigh waves (including Rg) are often the most stable and visible portion of the waveform at local distances. Cleveland and Ammon [2013] have shown that teleseismic surface waves are valuable observations for constructing precise, relative event relocations. We extend the teleseismic surface wave relocation method, and apply them to near-source distances using Rg observations from the Bighorn Arche Seismic Experiment (BASE) and the Earth Scope USArray Transportable Array (TA) seismic stations. Specifically, we present relocation results using short-period fundamental- and higher-mode Rayleigh waves (Rg) in a double-difference relative event relocation for 45 delay-fired mine blasts and 21 borehole chemical explosions. Our preliminary efforts are to explore the sensitivity of the short-period surface waves to local geologic structure, source depth, explosion magnitude (yield), and explosion characteristics (single-shot vs. distributed source, etc.). Our results show that Rg and the first few higher-mode Rayleigh wave observations can be used to constrain the relative locations of shallow low-yield events.
Zielke, O.; McDougall, D.; Mai, P. M.; Babuska, I.
2014-12-01
One fundamental aspect of seismic hazard mitigation is gaining a better understanding of the rupture process. Because direct observation of the relevant parameters and properties is not possible, other means such as kinematic source inversions are used instead. By constraining the spatial and temporal evolution of fault slip during an earthquake, those inversion approaches may enable valuable insights in the physics of the rupture process. However, due to the underdetermined nature of this inversion problem (i.e., inverting a kinematic source model for an extended fault based on seismic data), the provided solutions are generally non-unique. Here we present a statistical (Bayesian) inversion approach based on an open-source library for uncertainty quantification (UQ) called QUESO that was developed at ICES (UT Austin). The approach has advantages with respect to deterministic inversion approaches as it provides not only a single (non-unique) solution but also provides uncertainty bounds with it. Those uncertainty bounds help to qualitatively and quantitatively judge how well constrained an inversion solution is and how much rupture complexity the data reliably resolve. The presented inversion scheme uses only tele-seismically recorded body waves but future developments may lead us towards joint inversion schemes. After giving an insight in the inversion scheme ifself (based on delayed rejection adaptive metropolis, DRAM) we explore the method's resolution potential. For that, we synthetically generate tele-seismic data, add for example different levels of noise and/or change fault plane parameterization and then apply our inversion scheme in the attempt to extract the (known) kinematic rupture model. We conclude with exemplary inverting real tele-seismic data of a recent large earthquake and compare those results with deterministically derived kinematic source models provided by other research groups.
A Study on Data Base for the Pyroprocessing Material Flow and MUF Uncertainty Simulation
International Nuclear Information System (INIS)
Sitompul, Yos Panagaman; Shin, Heesung; Han, Boyoung; Kim, Hodong
2011-01-01
The data base for the pyroprocessing material flow and MUF uncertainty simulation has been implemented well. There is no error in the data base processing and it is relatively fast by using OLEDB and MySQL. The important issue is the data base size. In OLEDB the data base size is limited to 2 Gb. To reduce the data base size, we give an option for users to filter the input nuclides based on their masses and activities. A simulation program called PYMUS has been developed to study the pyroprocessing material flow and MUF. In the program, there is a data base system that controls the data processing in the simulation. The data base system consists of input data base, data processing, and output data base. The data base system has been designed in such a way to be efficient. One example is using the OLEDB and MySQL. The data base system is explained in detail in this paper. The result shows that the data base system works well in the simulation
Temporal and Location Based RFID Event Data Management and Processing
Wang, Fusheng; Liu, Peiya
Advance of sensor and RFID technology provides significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management. RFID data are temporal and history oriented, multi-dimensional, and carrying implicit semantics. Moreover, RFID applications are heterogeneous. RFID data management or data warehouse systems need to support generic and expressive data modeling for tracking and monitoring physical objects, and provide automated data interpretation and processing. We develop a powerful temporal and location oriented data model for modeling and queryingRFID data, and a declarative event and rule based framework for automated complex RFID event processing. The approach is general and can be easily adapted for different RFID-enabled applications, thus significantly reduces the cost of RFID data integration.
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
DEFF Research Database (Denmark)
Frutiger, Jerome; Marcarie, Camille; Abildskov, Jens
2016-01-01
regression and outlier treatment have been applied to achieve high accuracy. Furthermore, linear error propagation based on covariance matrix of estimated parameters was performed. Therefore, every estimated property value of the flammability-related properties is reported together with its corresponding 95......%-confidence interval of the prediction. Compared to existing models the developed ones have a higher accuracy, are simple to apply and provide uncertainty information on the calculated prediction. The average relative error and correlation coefficient are 11.5% and 0.99 for LFL, 15.9% and 0.91 for UFL, 2...
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...
DEFF Research Database (Denmark)
Morales Rodriguez, Ricardo; Meyer, Anne S.; Gernaey, Krist
2011-01-01
This study presents the development and application of a systematic model-based framework for bioprocess optimization, evaluated on a cellulosic ethanol production case study. The implementation of the framework involves the use of dynamic simulations, sophisticated uncertainty analysis (Monte...
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
International Nuclear Information System (INIS)
Raimbault, P.
2004-01-01
The development of a safety case for disposal of high level and medium level long-lived waste in a geological formation has to handle two main difficulties: - uncertainties associated to natural systems; - uncertainties associated to the consideration of long time scales. Licensing of the different steps leading to geological disposal implies thus that a sufficient level of confidence in the safety case will be obtained, at each step, among the different stakeholders. The confidence in the safety case relies on the whole set of arguments of different natures which complement each other and build up the file. This means that, to be defensible, the safety case should be organised in such a way that it can be reviewed and scrutinized in a structured manner. This also means that individual elements of the safety case will have to be considered separately even if all elements should fit well in the integrated safety case. This segregation implies some inherent decoupling of parts of the system, of its evolution over time and of the events that may impact on it. This decoupling will thus introduce inherent uncertainties that risk or non-risk based approaches have to deal with since both approaches have to introduce transparency in the analysis. In the non-risk based or deterministic approach this segregation is pushed further in order to put into perspective the different elements of appreciation that allow to judge the safety case as a whole. The French regulation on deep disposal presented in the basic safety rule RFS III.3.f, issued in 1991, takes these points into consideration to set the basis for the safety case in the framework of a deterministic approach. This basic safety rule is currently being revised in order to clarify some concepts and to take account evolution of ideas at the national and international level. However the basic rationale behind the safety assessment methodology will remain the same. The approach presented in RFS III.2.f implies that at
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
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
Directory of Open Access Journals (Sweden)
Weihua Jin
2013-01-01
Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
Optimal design and planning of glycerol-based biorefinery supply chains under uncertainty
DEFF Research Database (Denmark)
Loureiro da Costa Lira Gargalo, Carina; Carvalho, Ana; Gernaey, Krist V.
2017-01-01
-echelon mixed integer linear programming problem is proposed based upon a previous model, GlyThink. In the new formulation, market uncertainties are taken into account at the strategic planning level. The robustness of the supply chain structures is analyzed based on statistical data provided...... by the implementation of the Monte Carlo method, where a deterministic optimization problem is solved for each scenario. Furthermore, the solution of the stochastic multi-objective optimization model, points to the Pareto set of trade-off solutions obtained when maximizing the NPV and minimizing environmental......The optimal design and planning of glycerol-based biorefinery supply chains is critical for the development and implementation of this concept in a sustainable manner. To achieve this, a decision-making framework is proposed in this work, to holistically optimize the design and planning...
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)
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)
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.
A Bayesian Model for Event-based Trust
DEFF Research Database (Denmark)
Nielsen, Mogens; Krukow, Karl; Sassone, Vladimiro
2007-01-01
The application scenarios envisioned for ‘global ubiquitous computing’ have unique requirements that are often incompatible with traditional security paradigms. One alternative currently being investigated is to support security decision-making by explicit representation of principals' trusting...... of the systems from the computational trust literature; the comparison is derived formally, rather than obtained via experimental simulation as traditionally done. With this foundation in place, we formalise a general notion of information about past behaviour, based on event structures. This yields a flexible...
MAS Based Event-Triggered Hybrid Control for Smart Microgrids
DEFF Research Database (Denmark)
Dou, Chunxia; Liu, Bin; Guerrero, Josep M.
2013-01-01
This paper is focused on an advanced control for autonomous microgrids. In order to improve the performance regarding security and stability, a hierarchical decentralized coordinated control scheme is proposed based on multi-agents structure. Moreover, corresponding to the multi-mode and the hybrid...... haracteristics of microgrids, an event-triggered hybrid control, including three kinds of switching controls, is designed to intelligently reconstruct operation mode when the security stability assessment indexes or the constraint conditions are violated. The validity of proposed control scheme is demonstrated...
Intelligent Transportation Control based on Proactive Complex Event Processing
Directory of Open Access Journals (Sweden)
Wang Yongheng
2016-01-01
Full Text Available Complex Event Processing (CEP has become the key part of Internet of Things (IoT. Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to intelligent transportation control. In this paper, we propose a proactive CEP architecture and method for intelligent transportation control. Based on basic CEP technology and predictive analytic technology, a networked distributed Markov decision processes model with predicting states is proposed as sequential decision model. A Q-learning method is proposed for this model. The experimental evaluations show that this method works well when used to control congestion in in intelligent transportation systems.
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
A Recourse-Based Type-2 Fuzzy Programming Method for Water Pollution Control under Uncertainty
Directory of Open Access Journals (Sweden)
Jing Liu
2017-11-01
Full Text Available In this study, a recourse-based type-2 fuzzy programming (RTFP method is developed for supporting water pollution control of basin systems under uncertainty. The RTFP method incorporates type-2 fuzzy programming (TFP within a two-stage stochastic programming with recourse (TSP framework to handle uncertainties expressed as type-2 fuzzy sets (i.e., a fuzzy set in which the membership function is also fuzzy and probability distributions, as well as to reflect the trade-offs between conflicting economic benefits and penalties due to violated policies. The RTFP method is then applied to a real case of water pollution control in the Heshui River Basin (a rural area of China, where chemical oxygen demand (COD, total nitrogen (TN, total phosphorus (TP, and soil loss are selected as major indicators to identify the water pollution control strategies. Solutions of optimal production plans of economic activities under each probabilistic pollutant discharge allowance level and membership grades are obtained. The results are helpful for the authorities in exploring the trade-off between economic objective and pollutant discharge decision-making based on river water pollution control.
Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation
Energy Technology Data Exchange (ETDEWEB)
Alam, Azimil Gani; Rahman, Haolia; Kim, Jung-Kyung; Han, Hwataik [Kookmin University, Seoul (Korea, Republic of)
2017-05-15
Demand control ventilation is employed to save energy by adjusting airflow rate according to the ventilation load of a building. This paper investigates a method for occupancy estimation by using a dynamic neural network model based on carbon dioxide concentration in an occupied zone. The method can be applied to most commercial and residential buildings where human effluents to be ventilated. An indoor simulation program CONTAMW is used to generate indoor CO{sub 2} data corresponding to various occupancy schedules and airflow patterns to train neural network models. Coefficients of variation are obtained depending on the complexities of the physical parameters as well as the system parameters of neural networks, such as the numbers of hidden neurons and tapped delay lines. We intend to identify the uncertainties caused by the model parameters themselves, by excluding uncertainties in input data inherent in measurement. Our results show estimation accuracy is highly influenced by the frequency of occupancy variation but not significantly influenced by fluctuation in the airflow rate. Furthermore, we discuss the applicability and validity of the present method based on passive environmental conditions for estimating occupancy in a room from the viewpoint of demand control ventilation applications.
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 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.
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.
Reduction and Uncertainty Analysis of Chemical Mechanisms Based on Local and Global Sensitivities
Esposito, Gaetano
Numerical simulations of critical reacting flow phenomena in hypersonic propulsion devices require accurate representation of finite-rate chemical kinetics. The chemical kinetic models available for hydrocarbon fuel combustion are rather large, involving hundreds of species and thousands of reactions. As a consequence, they cannot be used in multi-dimensional computational fluid dynamic calculations in the foreseeable future due to the prohibitive computational cost. In addition to the computational difficulties, it is also known that some fundamental chemical kinetic parameters of detailed models have significant level of uncertainty due to limited experimental data available and to poor understanding of interactions among kinetic parameters. In the present investigation, local and global sensitivity analysis techniques are employed to develop a systematic approach of reducing and analyzing detailed chemical kinetic models. Unlike previous studies in which skeletal model reduction was based on the separate analysis of simple cases, in this work a novel strategy based on Principal Component Analysis of local sensitivity values is presented. This new approach is capable of simultaneously taking into account all the relevant canonical combustion configurations over different composition, temperature and pressure conditions. Moreover, the procedure developed in this work represents the first documented inclusion of non-premixed extinction phenomena, which is of great relevance in hypersonic combustors, in an automated reduction algorithm. The application of the skeletal reduction to a detailed kinetic model consisting of 111 species in 784 reactions is demonstrated. The resulting reduced skeletal model of 37--38 species showed that the global ignition/propagation/extinction phenomena of ethylene-air mixtures can be predicted within an accuracy of 2% of the full detailed model. The problems of both understanding non-linear interactions between kinetic parameters and
Wang, Xiaolan L.; Feng, Yang; Swail, Val R.
2015-05-01
This study uses the analysis of variance approaches to quantify the climate change signal and uncertainty in multimodel ensembles of statistical simulations of significant wave height (Hs), which are based on the CMIP5 historical, RCP4.5 and RCP8.5 forcing scenario simulations of sea level pressure. Here the signal of climate change refers to the temporal variations caused by the prescribed forcing. "Significant" means "significantly different from zero at 5% level." In a four-model ensemble of Hs simulations, the common signal—the signal that is simulated in all the four models—is found to strengthen over time. For the historical followed by RCP8.5 scenario, the common signal in annual mean Hs is found to be significant in 16.6% and 82.2% of the area by year 2005 and 2099, respectively. The global average of the variance proportion of the common signal increases from 0.75% in year 2005 to 12.0% by year 2099. The signal is strongest in the eastern tropical Pacific (ETP), featuring significant increases in both the annual mean and maximum of Hs in this region. The climate model uncertainty (i.e., intermodel variability) is significant nearly globally; its magnitude is comparable to or greater than that of the common signal in most areas, except in the ETP where the signal is much larger. In a 20-model ensemble of Hs simulations for the period 2006-2099, the model uncertainty is found to be significant globally; it is about 10 times as large as the variability between the RCP4.5 and RCP8.5 scenarios. The copyright line for this article was changed on 10 JUNE 2015 after original online publication.
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
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
Event-based soil loss models for construction sites
Trenouth, William R.; Gharabaghi, Bahram
2015-05-01
The elevated rates of soil erosion stemming from land clearing and grading activities during urban development, can result in excessive amounts of eroded sediments entering waterways and causing harm to the biota living therein. However, construction site event-based soil loss simulations - required for reliable design of erosion and sediment controls - are one of the most uncertain types of hydrologic models. This study presents models with improved degree of accuracy to advance the design of erosion and sediment controls for construction sites. The new models are developed using multiple linear regression (MLR) on event-based permutations of the Universal Soil Loss Equation (USLE) and artificial neural networks (ANN). These models were developed using surface runoff monitoring datasets obtained from three sites - Greensborough, Cookstown, and Alcona - in Ontario and datasets mined from the literature for three additional sites - Treynor, Iowa, Coshocton, Ohio and Cordoba, Spain. The predictive MLR and ANN models can serve as both diagnostic and design tools for the effective sizing of erosion and sediment controls on active construction sites, and can be used for dynamic scenario forecasting when considering rapidly changing land use conditions during various phases of construction.
Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer
2018-02-01
Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and
Single event upset threshold estimation based on local laser irradiation
International Nuclear Information System (INIS)
Chumakov, A.I.; Egorov, A.N.; Mavritsky, O.B.; Yanenko, A.V.
1999-01-01
An approach for estimation of ion-induced SEU threshold based on local laser irradiation is presented. Comparative experiment and software simulation research were performed at various pulse duration and spot size. Correlation of single event threshold LET to upset threshold laser energy under local irradiation was found. The computer analysis of local laser irradiation of IC structures was developed for SEU threshold LET estimation. The correlation of local laser threshold energy with SEU threshold LET was shown. Two estimation techniques were suggested. The first one is based on the determination of local laser threshold dose taking into account the relation of sensitive area to local irradiated area. The second technique uses the photocurrent peak value instead of this relation. The agreement between the predicted and experimental results demonstrates the applicability of this approach. (authors)
Uncertainties related to the fault tree reliability data
International Nuclear Information System (INIS)
Apostol, Minodora; Nitoi, Mirela; Farcasiu, M.
2003-01-01
Uncertainty analyses related to the fault trees evaluate the system variability which appears from the uncertainties of the basic events probabilities. Having a logical model which describes a system, to obtain outcomes means to evaluate it, using estimations for each basic event of the model. If the model has basic events that incorporate uncertainties, then the results of the model should incorporate the uncertainties of the events. Uncertainties estimation in the final result of the fault tree means first the uncertainties evaluation for the basic event probabilities and then combination of these uncertainties, to calculate the top event uncertainty. To calculate the propagating uncertainty, a knowledge of the probability density function as well as the range of possible values of the basic event probabilities is required. The following data are defined, using suitable probability density function: the components failure rates; the human error probabilities; the initiating event frequencies. It was supposed that the possible value distribution of the basic event probabilities is given by the lognormal probability density function. To know the range of possible value of the basic event probabilities, the error factor or the uncertainty factor is required. The aim of this paper is to estimate the error factor for the failure rates and for the human errors probabilities from the reliability data base used in Cernavoda Probabilistic Safety Evaluation. The top event chosen as an example is FEED3, from the Pressure and Inventory Control System. The quantitative evaluation of this top event was made by using EDFT code, developed in Institute for Nuclear Research Pitesti (INR). It was supposed that the error factors for the component failures are the same as for the failure rates. Uncertainty analysis was made with INCERT application, which uses the moment method and Monte Carlo method. The reliability data base used at INR Pitesti does not contain the error factors (ef
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.
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
Tian, Zhen; Yuan, Jingqi; Xu, Liang; Zhang, Xiang; Wang, Jingcheng
2018-05-25
As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction. Copyright © 2018. Published by Elsevier Ltd.
Decision-making model of generation technology under uncertainty based on real option theory
International Nuclear Information System (INIS)
Ming, Zeng; Ping, Zhang; Shunkun, Yu; Ge, Zhang
2016-01-01
Highlights: • A decision-making model of generation technology investment is proposed. • The irreversible investment concept and real option theory is introduced. • Practical data was used to prove the validity of the model. • Impact of electricity and fuel price fluctuation on investment was analyzed. - Abstract: The introduction of market competition and the increased uncertainty factors makes the generators have to decide not only on whether to invest generation capacity or not but also on what kind of generation technology to choose. In this paper, a decision-making model of generation technology investment is proposed. The irreversible investment concept and real option theory is introduced as the fundamental of the model. In order to explain the decision-making process of generator’s investment, the decision-making optimization model was built considering two generation technologies, i.e., the heat-only system and the combined heat and power generation. Also, we discussed the theory deducing process, which explained how to eliminate the overrated economic potential caused by risk hazard, based on economic evaluation of both generation technologies. Finally, practical data from electricity market of Inner Mongolia was used to prove the validity of the model and the impact of uncertainties of electricity and fuel price fluctuation on investment was analyzed according to the simulated results.
International Nuclear Information System (INIS)
Fast, Ivan; Bosbach, Dirk; Aksyutina, Yuliya; Tietze-Jaensch, Holger
2015-01-01
A requisite for the official approval of the safe final disposal of SNF is a comprehensive specification and declaration of the nuclear inventory in SNF by the waste supplier. In the verification process both the values of the radionuclide (RN) activities and their uncertainties are required. Burn-up (BU) calculations based on typical and generic reactor operational parameters do not encompass any possible uncertainties observed in real reactor operations. At the same time, the details of the irradiation history are often not well known, which complicates the assessment of declared RN inventories. Here, we have compiled a set of burnup calculations accounting for the operational history of 339 published or anonymized real PWR fuel assemblies (FA). These histories were used as a basis for a 'SRP analysis', to provide information about the range of the values of the associated secondary reactor parameters (SRP's). Hence, we can calculate the realistic variation or spectrum of RN inventories. SCALE 6.1 has been employed for the burn-up calculations. The results have been validated using experimental data from the online database - SFCOMPO-1 and -2. (authors)
Energy Technology Data Exchange (ETDEWEB)
Fast, Ivan; Bosbach, Dirk [Institute of Energy- and Climate Research, Nuclear Waste Management and Reactor Safety Research, IEK-6, Forschungszentrum, Julich GmbH, (Germany); Aksyutina, Yuliya; Tietze-Jaensch, Holger [German Product Control Office for Radioactive Waste (PKS), Institute of Energy- and Climate Research, Nuclear Waste Management and Reactor Safety Research, IEK-6, Forschungszentrum Julich GmbH, (Germany)
2015-07-01
A requisite for the official approval of the safe final disposal of SNF is a comprehensive specification and declaration of the nuclear inventory in SNF by the waste supplier. In the verification process both the values of the radionuclide (RN) activities and their uncertainties are required. Burn-up (BU) calculations based on typical and generic reactor operational parameters do not encompass any possible uncertainties observed in real reactor operations. At the same time, the details of the irradiation history are often not well known, which complicates the assessment of declared RN inventories. Here, we have compiled a set of burnup calculations accounting for the operational history of 339 published or anonymized real PWR fuel assemblies (FA). These histories were used as a basis for a 'SRP analysis', to provide information about the range of the values of the associated secondary reactor parameters (SRP's). Hence, we can calculate the realistic variation or spectrum of RN inventories. SCALE 6.1 has been employed for the burn-up calculations. The results have been validated using experimental data from the online database - SFCOMPO-1 and -2. (authors)
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
Effect of uncertainties on probabilistic-based design capacity of hydrosystems
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
Demissie, Y. K.; Mortuza, M. R.; Li, H. Y.
2015-12-01
The observed and anticipated increasing trends in extreme storm magnitude and frequency, as well as the associated flooding risk in the Pacific Northwest highlighted the need for revising and updating the local intensity-duration-frequency (IDF) curves, which are commonly used for designing critical water infrastructure. In Washington State, much of the drainage system installed in the last several decades uses IDF curves that are outdated by as much as half a century, making the system inadequate and vulnerable for flooding as seen more frequently in recent years. In this study, we have developed new and forward looking rainfall and runoff IDF curves for each county in Washington State using recently observed and projected precipitation data. Regional frequency analysis coupled with Bayesian uncertainty quantification and model averaging methods were used to developed and update the rainfall IDF curves, which were then used in watershed and snow models to develop the runoff IDF curves that explicitly account for effects of snow and drainage characteristic into the IDF curves and related designs. The resulted rainfall and runoff IDF curves provide more reliable, forward looking, and spatially resolved characteristics of storm events that can assist local decision makers and engineers to thoroughly review and/or update the current design standards for urban and rural storm water management infrastructure in order to reduce the potential ramifications of increasing severe storms and resulting floods on existing and planned storm drainage and flood management systems in the state.
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...
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".
Entanglement criterion for tripartite systems based on local sum uncertainty relations
Akbari-Kourbolagh, Y.; Azhdargalam, M.
2018-04-01
We propose a sufficient criterion for the entanglement of tripartite systems based on local sum uncertainty relations for arbitrarily chosen observables of subsystems. This criterion generalizes the tighter criterion for bipartite systems introduced by Zhang et al. [C.-J. Zhang, H. Nha, Y.-S. Zhang, and G.-C. Guo, Phys. Rev. A 81, 012324 (2010), 10.1103/PhysRevA.81.012324] and can be used for both discrete- and continuous-variable systems. It enables us to detect the entanglement of quantum states without having a complete knowledge of them. Its utility is illustrated by some examples of three-qubit, qutrit-qutrit-qubit, and three-mode Gaussian states. It is found that, in comparison with other criteria, this criterion is able to detect some three-qubit bound entangled states more efficiently.
Directory of Open Access Journals (Sweden)
D. Marcus Appleby
2014-03-01
Full Text Available Recently there has been much effort in the quantum information community to prove (or disprove the existence of symmetric informationally complete (SIC sets of quantum states in arbitrary finite dimension. This paper strengthens the urgency of this question by showing that if SIC-sets exist: (1 by a natural measure of orthonormality, they are as close to being an orthonormal basis for the space of density operators as possible; and (2 in prime dimensions, the standard construction for complete sets of mutually unbiased bases and Weyl-Heisenberg covariant SIC-sets are intimately related: The latter represent minimum uncertainty states for the former in the sense of Wootters and Sussman. Finally, we contribute to the question of existence by conjecturing a quadratic redundancy in the equations for Weyl-Heisenberg SIC-sets.
The nexus between geopolitical uncertainty and crude oil markets: An entropy-based wavelet analysis
Uddin, Gazi Salah; Bekiros, Stelios; Ahmed, Ali
2018-04-01
The global financial crisis and the subsequent geopolitical turbulence in energy markets have brought increased attention to the proper statistical modeling especially of the crude oil markets. In particular, we utilize a time-frequency decomposition approach based on wavelet analysis to explore the inherent dynamics and the casual interrelationships between various types of geopolitical, economic and financial uncertainty indices and oil markets. Via the introduction of a mixed discrete-continuous multiresolution analysis, we employ the entropic criterion for the selection of the optimal decomposition level of a MODWT as well as the continuous-time coherency and phase measures for the detection of business cycle (a)synchronization. Overall, a strong heterogeneity in the revealed interrelationships is detected over time and across scales.
Mean-variance model for portfolio optimization with background risk based on uncertainty theory
Zhai, Jia; Bai, Manying
2018-04-01
The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.
Directory of Open Access Journals (Sweden)
Chuanfeng Li
2017-01-01
Full Text Available Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.
VLSI-based video event triggering for image data compression
Williams, Glenn L.
1994-02-01
Long-duration, on-orbit microgravity experiments require a combination of high resolution and high frame rate video data acquisition. The digitized high-rate video stream presents a difficult data storage problem. Data produced at rates of several hundred million bytes per second may require a total mission video data storage requirement exceeding one terabyte. A NASA-designed, VLSI-based, highly parallel digital state machine generates a digital trigger signal at the onset of a video event. High capacity random access memory storage coupled with newly available fuzzy logic devices permits the monitoring of a video image stream for long term (DC-like) or short term (AC-like) changes caused by spatial translation, dilation, appearance, disappearance, or color change in a video object. Pre-trigger and post-trigger storage techniques are then adaptable to archiving only the significant video images.
Energy Technology Data Exchange (ETDEWEB)
Serghiuta, D.; Tholammakkil, J.; Shen, W., E-mail: Dumitru.Serghiuta@cnsc-ccsn.gc.ca [Canadian Nuclear Safety Commission, Ottawa, Ontario (Canada)
2014-07-01
A stochastic-deterministic approach based on representation of uncertainties by subjective probabilities is proposed for evaluation of bounding values of functional failure probability and assessment of probabilistic safety margins. The approach is designed for screening and limited independent review verification. Its application is illustrated for a postulated generic CANDU LBLOCA and evaluation of the possibility distribution function of maximum bundle enthalpy considering the reactor physics part of LBLOCA power pulse simulation only. The computer codes HELIOS and NESTLE-CANDU were used in a stochastic procedure driven by the computer code DAKOTA to simulate the LBLOCA power pulse using combinations of core neutronic characteristics randomly generated from postulated subjective probability distributions with deterministic constraints and fixed transient bundle-wise thermal hydraulic conditions. With this information, a bounding estimate of functional failure probability using the limit for the maximum fuel bundle enthalpy can be derived for use in evaluation of core damage frequency. (author)
Event-based proactive interference in rhesus monkeys.
Devkar, Deepna T; Wright, Anthony A
2016-10-01
Three rhesus monkeys (Macaca mulatta) were tested in a same/different memory task for proactive interference (PI) from prior trials. PI occurs when a previous sample stimulus appears as a test stimulus on a later trial, does not match the current sample stimulus, and the wrong response "same" is made. Trial-unique pictures (scenes, objects, animals, etc.) were used on most trials, except on trials where the test stimulus matched potentially interfering sample stimulus from a prior trial (1, 2, 4, 8, or 16 trials prior). Greater interference occurred when fewer trials separated interference and test. PI functions showed a continuum of interference. Delays between sample and test stimuli and intertrial intervals were manipulated to test how PI might vary as a function of elapsed time. Contrary to a similar study with pigeons, these time manipulations had no discernable effect on the monkey's PI, as shown by compete overlap of PI functions with no statistical differences or interactions. These results suggested that interference was strictly based upon the number of intervening events (trials with other pictures) without regard to elapsed time. The monkeys' apparent event-based interference was further supported by retesting with a novel set of 1,024 pictures. PI from novel pictures 1 or 2 trials prior was greater than from familiar pictures, a familiar set of 1,024 pictures. Moreover, when potentially interfering novel stimuli were 16 trials prior, performance accuracy was actually greater than accuracy on baseline trials (no interference), suggesting that remembering stimuli from 16 trials prior was a cue that this stimulus was not the sample stimulus on the current trial-a somewhat surprising conclusion particularly given monkeys.
Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter
2017-02-01
It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.
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
Ontology-Based Vaccine Adverse Event Representation and Analysis.
Xie, Jiangan; He, Yongqun
2017-01-01
Vaccine is the one of the greatest inventions of modern medicine that has contributed most to the relief of human misery and the exciting increase in life expectancy. In 1796, an English country physician, Edward Jenner, discovered that inoculating mankind with cowpox can protect them from smallpox (Riedel S, Edward Jenner and the history of smallpox and vaccination. Proceedings (Baylor University. Medical Center) 18(1):21, 2005). Based on the vaccination worldwide, we finally succeeded in the eradication of smallpox in 1977 (Henderson, Vaccine 29:D7-D9, 2011). Other disabling and lethal diseases, like poliomyelitis and measles, are targeted for eradication (Bonanni, Vaccine 17:S120-S125, 1999).Although vaccine development and administration are tremendously successful and cost-effective practices to human health, no vaccine is 100% safe for everyone because each person reacts to vaccinations differently given different genetic background and health conditions. Although all licensed vaccines are generally safe for the majority of people, vaccinees may still suffer adverse events (AEs) in reaction to various vaccines, some of which can be serious or even fatal (Haber et al., Drug Saf 32(4):309-323, 2009). Hence, the double-edged sword of vaccination remains a concern.To support integrative AE data collection and analysis, it is critical to adopt an AE normalization strategy. In the past decades, different controlled terminologies, including the Medical Dictionary for Regulatory Activities (MedDRA) (Brown EG, Wood L, Wood S, et al., Drug Saf 20(2):109-117, 1999), the Common Terminology Criteria for Adverse Events (CTCAE) (NCI, The Common Terminology Criteria for Adverse Events (CTCAE). Available from: http://evs.nci.nih.gov/ftp1/CTCAE/About.html . Access on 7 Oct 2015), and the World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) (WHO, The WHO Adverse Reaction Terminology - WHO-ART. Available from: https://www.umc-products.com/graphics/28010.pdf
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
On the role of model-based monitoring for adaptive planning under uncertainty
Raso, Luciano; Kwakkel, Jan; Timmermans, Jos; Haasnoot, Mariolijn
2016-04-01
, triggered by the challenge of uncertainty in operational control, may offer solutions from which monitoring for adaptive planning can benefit. Specifically: (i) in control, observations are incorporated into the model through data assimilation, updating the present state, boundary conditions, and parameters based on new observations, diminishing the shadow of the past; (ii) adaptive control is a way to modify the characteristics of the internal model, incorporating new knowledge on the system, countervailing the inhibition of learning; and (iii) in closed-loop control, a continuous system update equips the controller with "inherent robustness", i.e. to capacity to adapts to new conditions even when these were not initially considered. We aim to explore how inherent robustness addresses the challenge of surprise. Innovations in model-based control might help to improve and adapt the models used to support adaptive delta management to new information (reducing uncertainty). Moreover, this would offer a starting point for using these models not only in the design of adaptive plans, but also as part of the monitoring. The proposed research requires multidisciplinary cooperation between control theory, the policy sciences, and integrated assessment modeling.
Model-based verification method for solving the parameter uncertainty in the train control system
International Nuclear Information System (INIS)
Cheng, Ruijun; Zhou, Jin; Chen, Dewang; Song, Yongduan
2016-01-01
This paper presents a parameter analysis method to solve the parameter uncertainty problem for hybrid system and explore the correlation of key parameters for distributed control system. For improving the reusability of control model, the proposed approach provides the support for obtaining the constraint sets of all uncertain parameters in the abstract linear hybrid automata (LHA) model when satisfying the safety requirements of the train control system. Then, in order to solve the state space explosion problem, the online verification method is proposed to monitor the operating status of high-speed trains online because of the real-time property of the train control system. Furthermore, we construct the LHA formal models of train tracking model and movement authority (MA) generation process as cases to illustrate the effectiveness and efficiency of the proposed method. In the first case, we obtain the constraint sets of uncertain parameters to avoid collision between trains. In the second case, the correlation of position report cycle and MA generation cycle is analyzed under both the normal and the abnormal condition influenced by packet-loss factor. Finally, considering stochastic characterization of time distributions and real-time feature of moving block control system, the transient probabilities of wireless communication process are obtained by stochastic time petri nets. - Highlights: • We solve the parameters uncertainty problem by using model-based method. • We acquire the parameter constraint sets by verifying linear hybrid automata models. • Online verification algorithms are designed to monitor the high-speed trains. • We analyze the correlation of key parameters and uncritical parameters. • The transient probabilities are obtained by using reliability analysis.
Directory of Open Access Journals (Sweden)
Rehan Balqis M.
2016-01-01
Full Text Available Current practice in flood frequency analysis assumes that the stochastic properties of extreme floods follow that of stationary conditions. As human intervention and anthropogenic climate change influences in hydrometeorological variables are becoming evident in some places, there have been suggestions that nonstationary statistics would be better to represent the stochastic properties of the extreme floods. The probabilistic estimation of non-stationary models, however, is surrounded with uncertainty related to scarcity of observations and modelling complexities hence the difficulty to project the future condition. In the face of uncertain future and the subjectivity of model choices, this study attempts to demonstrate the practical implications of applying a nonstationary model and compares it with a stationary model in flood risk assessment. A fully integrated framework to simulate decision makers’ behaviour in flood frequency analysis is thereby developed. The framework is applied to hypothetical flood risk management decisions and the outcomes are compared with those of known underlying future conditions. Uncertainty of the economic performance of the risk-based decisions is assessed through Monte Carlo simulations. Sensitivity of the results is also tested by varying the possible magnitude of future changes. The application provides quantitative and qualitative comparative results that satisfy a preliminary analysis of whether the nonstationary model complexity should be applied to improve the economic performance of decisions. Results obtained from the case study shows that the relative differences of competing models for all considered possible future changes are small, suggesting that stationary assumptions are preferred to a shift to nonstationary statistics for practical application of flood risk management. Nevertheless, nonstationary assumption should also be considered during a planning stage in addition to stationary assumption
Uncertainty in sap flow-based transpiration due to xylem properties
Looker, N. T.; Hu, J.; Martin, J. T.; Jencso, K. G.
2014-12-01
Transpiration, the evaporative loss of water from plants through their stomata, is a key component of the terrestrial water balance, influencing streamflow as well as regional convective systems. From a plant physiological perspective, transpiration is both a means of avoiding destructive leaf temperatures through evaporative cooling and a consequence of water loss through stomatal uptake of carbon dioxide. Despite its hydrologic and ecological significance, transpiration remains a notoriously challenging process to measure in heterogeneous landscapes. Sap flow methods, which estimate transpiration by tracking the velocity of a heat pulse emitted into the tree sap stream, have proven effective for relating transpiration dynamics to climatic variables. To scale sap flow-based transpiration from the measured domain (often area) to the whole-tree level, researchers generally assume constancy of scale factors (e.g., wood thermal diffusivity (k), radial and azimuthal distributions of sap velocity, and conducting sapwood area (As)) through time, across space, and within species. For the widely used heat-ratio sap flow method (HRM), we assessed the sensitivity of transpiration estimates to uncertainty in k (a function of wood moisture content and density) and As. A sensitivity analysis informed by distributions of wood moisture content, wood density and As sampled across a gradient of water availability indicates that uncertainty in these variables can impart substantial error when scaling sap flow measurements to the whole tree. For species with variable wood properties, the application of the HRM assuming a spatially constant k or As may systematically over- or underestimate whole-tree transpiration rates, resulting in compounded error in ecosystem-scale estimates of transpiration.
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
Janardhanan, S.; Datta, B.
2011-12-01
Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of
International Nuclear Information System (INIS)
Koch, J.
2014-01-01
Since it is commonly accepted that exposure to ionizing radiation, even at the low levels encountered in the workplace, can cause malignant diseases, radiation workers are at some risk, although much is done to optimize radiation protection and reduce occupational exposure to levels a s low as reasonably achievable . However, the causal relationship between exposure to radiation and malignant diseases is difficult to establish, since cancer is such a frequent disease and many other factors may contribute to its development. Ideally, those workers who developed cancer as a result of occupational exposure to radiation should be compensated. Guidance on procedures and methodology to assess attributability of cancer to occupational exposure to radiation and to assist decision-making in compensating workers is provided in a recent joint IAEA/ILO/WHO publication.This guide also reviews compensation schemes in place in several countries, with an emphasis on those based on the probability of causation (POC), also known as assigned share (AS) methodology. The POC method provides a scientifically based framework to assess cancer attributability to occupational exposure and was extensively reviewed by Wakeford et al. This paper presents a comparison of two well-known compensation schemes based on the POC approach with regard to their treatment of uncertainty
Pu239 Cross-Section Variations Based on Experimental Uncertainties and Covariances
Energy Technology Data Exchange (ETDEWEB)
Sigeti, David Edward [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Williams, Brian J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Parsons, D. Kent [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-10-18
Algorithms and software have been developed for producing variations in plutonium-239 neutron cross sections based on experimental uncertainties and covariances. The varied cross-section sets may be produced as random samples from the multi-variate normal distribution defined by an experimental mean vector and covariance matrix, or they may be produced as Latin-Hypercube/Orthogonal-Array samples (based on the same means and covariances) for use in parametrized studies. The variations obey two classes of constraints that are obligatory for cross-section sets and which put related constraints on the mean vector and covariance matrix that detemine the sampling. Because the experimental means and covariances do not obey some of these constraints to sufficient precision, imposing the constraints requires modifying the experimental mean vector and covariance matrix. Modification is done with an algorithm based on linear algebra that minimizes changes to the means and covariances while insuring that the operations that impose the different constraints do not conflict with each other.
Directory of Open Access Journals (Sweden)
Qiang Feng
2014-01-01
Full Text Available An optimization method for condition based maintenance (CBM of aircraft fleet considering prognostics uncertainty is proposed. The CBM and dispatch process of aircraft fleet is analyzed first, and the alternative strategy sets for single aircraft are given. Then, the optimization problem of fleet CBM with lower maintenance cost and dispatch risk is translated to the combinatorial optimization problem of single aircraft strategy. Remain useful life (RUL distribution of the key line replaceable Module (LRM has been transformed into the failure probability of the aircraft and the fleet health status matrix is established. And the calculation method of the costs and risks for mission based on health status matrix and maintenance matrix is given. Further, an optimization method for fleet dispatch and CBM under acceptable risk is proposed based on an improved genetic algorithm. Finally, a fleet of 10 aircrafts is studied to verify the proposed method. The results shows that it could realize optimization and control of the aircraft fleet oriented to mission success.
Event Completion: Event Based Inferences Distort Memory in a Matter of Seconds
Strickland, Brent; Keil, Frank
2011-01-01
We present novel evidence that implicit causal inferences distort memory for events only seconds after viewing. Adults watched videos of someone launching (or throwing) an object. However, the videos omitted the moment of contact (or release). Subjects falsely reported seeing the moment of contact when it was implied by subsequent footage but did…
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)
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.
Rijpkema, W.A.; Hendrix, E.M.T.; Rossi, R.; Vorst, van der J.G.A.J.
2016-01-01
To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality.
Uncertainty budget for final assay of a pharmaceutical product based on RP-HPLC
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas; Byrialsen, Kirsten
2003-01-01
). The reported example illustrates the estimation of uncertainty for the final determination of a protein concentration by HPLC using UV detection, using the approach described by EURACHEM/CITAC. The combined standard uncertainty for a protein concentration of 2400 mumol/L was estimated to be 14 mumol/L. All...
Evaluating uncertainty in 7Be-based soil erosion estimates: an experimental plot approach
Blake, Will; Taylor, Alex; Abdelli, Wahid; Gaspar, Leticia; Barri, Bashar Al; Ryken, Nick; Mabit, Lionel
2014-05-01
Soil erosion remains a major concern for the international community and there is a growing need to improve the sustainability of agriculture to support future food security. High resolution soil erosion data are a fundamental requirement for underpinning soil conservation and management strategies but representative data on soil erosion rates are difficult to achieve by conventional means without interfering with farming practice and hence compromising the representativeness of results. Fallout radionuclide (FRN) tracer technology offers a solution since FRN tracers are delivered to the soil surface by natural processes and, where irreversible binding can be demonstrated, redistributed in association with soil particles. While much work has demonstrated the potential of short-lived 7Be (half-life 53 days), particularly in quantification of short-term inter-rill erosion, less attention has focussed on sources of uncertainty in derived erosion measurements and sampling strategies to minimise these. This poster outlines and discusses potential sources of uncertainty in 7Be-based soil erosion estimates and the experimental design considerations taken to quantify these in the context of a plot-scale validation experiment. Traditionally, gamma counting statistics have been the main element of uncertainty propagated and reported but recent work has shown that other factors may be more important such as: (i) spatial variability in the relaxation mass depth that describes the shape of the 7Be depth distribution for an uneroded point; (ii) spatial variability in fallout (linked to rainfall patterns and shadowing) over both reference site and plot; (iii) particle size sorting effects; (iv) preferential mobility of fallout over active runoff contributing areas. To explore these aspects in more detail, a plot of 4 x 35 m was ploughed and tilled to create a bare, sloped soil surface at the beginning of winter 2013/2014 in southwest UK. The lower edge of the plot was bounded by
Derin, Y.; Anagnostou, E. N.; Anagnostou, M.; Kalogiros, J. A.; Casella, D.; Marra, A. C.; Panegrossi, G.; Sanò, P.
2017-12-01
Difficulties in representation of high rainfall variability over mountainous areas using ground based sensors make satellite remote sensing techniques attractive for hydrologic studies over these regions. Even though satellite-based rainfall measurements are quasi global and available at high spatial resolution, these products have uncertainties that necessitate use of error characterization and correction procedures based upon more accurate in situ rainfall measurements. Such measurements can be obtained from field campaigns facilitated by research quality sensors such as locally deployed weather radar and in situ weather stations. This study uses such high quality and resolution rainfall estimates derived from dual-polarization X-band radar (XPOL) observations from three field experiments in Mid-Atlantic US East Coast (NASA IPHEX experiment), the Olympic Peninsula of Washington State (NASA OLYMPEX experiment), and the Mediterranean to characterize the error characteristics of multiple passive microwave (PMW) sensor retrievals. The study first conducts an independent error analysis of the XPOL radar reference rainfall fields against in situ rain gauges and disdrometer observations available by the field experiments. Then the study evaluates different PMW precipitation products using the XPOL datasets (GR) over the three aforementioned complex terrain study areas. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW field-of-view sampling volumes, and scaled GR parameters to the satellite products' nominal spatial resolution. The following PMW precipitation retrieval algorithms are evaluated: the NASA Goddard PROFiling algorithm (GPROF), standard and climatology-based products (V 3, 4 and 5) from four PMW sensors (SSMIS, MHS, GMI, and AMSR2), and the precipitation products based on the algorithms Cloud Dynamics and Radiation Database (CDRD) for SSMIS and Passive microwave Neural network
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
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
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
Hyun, J. Y.; Yang, Y. C. E.; Tidwell, V. C.; Macknick, J.
2017-12-01
Modeling human behaviors and decisions in water resources management is a challenging issue due to its complexity and uncertain characteristics that affected by both internal (such as stakeholder's beliefs on any external information) and external factors (such as future policies and weather/climate forecast). Stakeholders' decision regarding how much water they need is usually not entirely rational in the real-world cases, so it is not quite suitable to model their decisions with a centralized (top-down) approach that assume everyone in a watershed follow the same order or pursue the same objective. Agent-based modeling (ABM) uses a decentralized approach (bottom-up) that allow each stakeholder to make his/her own decision based on his/her own objective and the belief of information acquired. In this study, we develop an ABM which incorporates the psychological human decision process by the theory of risk perception. The theory of risk perception quantifies human behaviors and decisions uncertainties using two sequential methodologies: the Bayesian Inference and the Cost-Loss Problem. The developed ABM is coupled with a regulation-based water system model: Riverware (RW) to evaluate different human decision uncertainties in water resources management. The San Juan River Basin in New Mexico (Figure 1) is chosen as a case study area, while we define 19 major irrigation districts as water use agents and their primary decision is to decide the irrigated area on an annual basis. This decision will be affected by three external factors: 1) upstream precipitation forecast (potential amount of water availability), 2) violation of the downstream minimum flow (required to support ecosystems), and 3) enforcement of a shortage sharing plan (a policy that is currently undertaken in the region for drought years). Three beliefs (as internal factors) that correspond to these three external factors will also be considered in the modeling framework. The objective of this study is
Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts.
Krause, Andreas; Pugh, Thomas A M; Bayer, Anita D; Li, Wei; Leung, Felix; Bondeau, Alberte; Doelman, Jonathan C; Humpenöder, Florian; Anthoni, Peter; Bodirsky, Benjamin L; Ciais, Philippe; Müller, Christoph; Murray-Tortarolo, Guillermo; Olin, Stefan; Popp, Alexander; Sitch, Stephen; Stehfest, Elke; Arneth, Almut
2018-07-01
Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy. © 2018 John
Search for gamma-ray events in the BATSE data base
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.
A process-oriented event-based programming language
DEFF Research Database (Denmark)
Hildebrandt, Thomas; Zanitti, Francesco
2012-01-01
Vi præsenterer den første version af PEPL, et deklarativt Proces-orienteret, Event-baseret Programmeringssprog baseret på den fornyligt introducerede Dynamic Condition Response (DCR) Graphs model. DCR Graphs tillader specifikation, distribuerede udførsel og verifikation af pervasive event...
SPEED : a semantics-based pipeline for economic event detection
Hogenboom, F.P.; Hogenboom, A.C.; Frasincar, F.; Kaymak, U.; Meer, van der O.; Schouten, K.; Vandic, D.; Parsons, J.; Motoshi, S.; Shoval, P.; Woo, C.; Wand, Y.
2010-01-01
Nowadays, emerging news on economic events such as acquisitions has a substantial impact on the financial markets. Therefore, it is important to be able to automatically and accurately identify events in news items in a timely manner. For this, one has to be able to process a large amount of
Semantics-based information extraction for detecting economic events
A.C. Hogenboom (Alexander); F. Frasincar (Flavius); K. Schouten (Kim); O. van der Meer
2013-01-01
textabstractAs today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for
Logical Discrete Event Systems in a trace theory based setting
Smedinga, R.
1993-01-01
Discrete event systems can be modelled using a triple consisting of some alphabet (representing the events that might occur), and two trace sets (sets of possible strings) denoting the possible behaviour and the completed tasks of the system. Using this definition we are able to formulate and solve
Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology
Ratto, M.; Young, P. C.; Romanowicz, R.; Pappenberger, F.; Saltelli, A.; Pagano, A.
2007-05-01
In this paper, we discuss a joint approach to calibration and uncertainty estimation for hydrologic systems that combines a top-down, data-based mechanistic (DBM) modelling methodology; and a bottom-up, reductionist modelling methodology. The combined approach is applied to the modelling of the River Hodder catchment in North-West England. The top-down DBM model provides a well identified, statistically sound yet physically meaningful description of the rainfall-flow data, revealing important characteristics of the catchment-scale response, such as the nature of the effective rainfall nonlinearity and the partitioning of the effective rainfall into different flow pathways. These characteristics are defined inductively from the data without prior assumptions about the model structure, other than it is within the generic class of nonlinear differential-delay equations. The bottom-up modelling is developed using the TOPMODEL, whose structure is assumed a priori and is evaluated by global sensitivity analysis (GSA) in order to specify the most sensitive and important parameters. The subsequent exercises in calibration and validation, performed with Generalized Likelihood Uncertainty Estimation (GLUE), are carried out in the light of the GSA and DBM analyses. This allows for the pre-calibration of the the priors used for GLUE, in order to eliminate dynamical features of the TOPMODEL that have little effect on the model output and would be rejected at the structure identification phase of the DBM modelling analysis. In this way, the elements of meaningful subjectivity in the GLUE approach, which allow the modeler to interact in the modelling process by constraining the model to have a specific form prior to calibration, are combined with other more objective, data-based benchmarks for the final uncertainty estimation. GSA plays a major role in building a bridge between the hypothetico-deductive (bottom-up) and inductive (top-down) approaches and helps to improve the
Enhancing emotion-based learning in decision-making under uncertainty.
Alarcón, David; Amián, Josué G; Sánchez-Medina, José A
2015-01-01
The Iowa Gambling Task (IGT) is widely used to study decision-making differences between several clinical and healthy populations. Unlike the healthy participants, clinical participants have difficulty choosing between advantageous options, which yield long-term benefits, and disadvantageous options, which give high immediate rewards but lead to negative profits. However, recent studies have found that healthy participants avoid the options with a higher frequency of losses regardless of whether or not they are profitable in the long run. The aim of this study was to control for the confounding effect of the frequency of losses between options to improve the performance of healthy participants on the IGT. Eighty healthy participants were randomly assigned to the original IGT or a modified version of the IGT that diminished the gap in the frequency of losses between options. The participants who used the modified IGT version learned to make better decisions based on long-term profit, as indicated by an earlier ability to discriminate good from bad options, and took less time to make their choices. This research represents an advance in the study of decision making under uncertainty by showing that emotion-based learning is improved by controlling for the loss-frequency bias effect.
Statistical analysis tolerance using jacobian torsor model based on uncertainty propagation method
Directory of Open Access Journals (Sweden)
W Ghie
2016-04-01
Full Text Available One risk inherent in the use of assembly components is that the behaviourof these components is discovered only at the moment an assembly isbeing carried out. The objective of our work is to enable designers to useknown component tolerances as parameters in models that can be usedto predict properties at the assembly level. In this paper we present astatistical approach to assemblability evaluation, based on tolerance andclearance propagations. This new statistical analysis method for toleranceis based on the Jacobian-Torsor model and the uncertainty measurementapproach. We show how this can be accomplished by modeling thedistribution of manufactured dimensions through applying a probabilitydensity function. By presenting an example we show how statisticaltolerance analysis should be used in the Jacobian-Torsor model. This workis supported by previous efforts aimed at developing a new generation ofcomputational tools for tolerance analysis and synthesis, using theJacobian-Torsor approach. This approach is illustrated on a simple threepartassembly, demonstrating the method’s capability in handling threedimensionalgeometry.
Directory of Open Access Journals (Sweden)
Shaopeng Zhong
2013-01-01
Full Text Available Focusing on the first-best marginal cost pricing (MCP in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1 the effect of stochastic perception error (SPE on the perceived travel time distribution and the components of road toll; (2 the effect of road toll on the actual travel time distribution and its reliability measures; (3 the effect of road toll on the total network travel time distribution and its statistics; and (4 the effect of travel demand level and the value of reliability (VoR level on the components of road toll.
A model-based approach to operational event groups ranking
Energy Technology Data Exchange (ETDEWEB)
Simic, Zdenko [European Commission Joint Research Centre, Petten (Netherlands). Inst. for Energy and Transport; Maqua, Michael [Gesellschaft fuer Anlagen- und Reaktorsicherheit mbH (GRS), Koeln (Germany); Wattrelos, Didier [Institut de Radioprotection et de Surete Nucleaire (IRSN), Fontenay-aux-Roses (France)
2014-04-15
The operational experience (OE) feedback provides improvements in all industrial activities. Identification of the most important and valuable groups of events within accumulated experience is important in order to focus on a detailed investigation of events. The paper describes the new ranking method and compares it with three others. Methods have been described and applied to OE events utilised by nuclear power plants in France and Germany for twenty years. The results show that different ranking methods only roughly agree on which of the event groups are the most important ones. In the new ranking method the analytical hierarchy process is applied in order to assure consistent and comprehensive weighting determination for ranking indexes. The proposed method allows a transparent and flexible event groups ranking and identification of the most important OE for further more detailed investigation in order to complete the feedback. (orig.)
Prediction problem for target events based on the inter-event waiting time
Shapoval, A.
2010-11-01
In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082-1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749-755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.
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.
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.
Gao, Xueping; Liu, Yinzhu; Sun, Bowen
2018-06-05
The risk of water shortage caused by uncertainties, such as frequent drought, varied precipitation, multiple water resources, and different water demands, brings new challenges to the water transfer projects. Uncertainties exist for transferring water and local surface water; therefore, the relationship between them should be thoroughly studied to prevent water shortage. For more effective water management, an uncertainty-based water shortage risk assessment model (UWSRAM) is developed to study the combined effect of multiple water resources and analyze the shortage degree under uncertainty. The UWSRAM combines copula-based Monte Carlo stochastic simulation and the chance-constrained programming-stochastic multiobjective optimization model, using the Lunan water-receiving area in China as an example. Statistical copula functions are employed to estimate the joint probability of available transferring water and local surface water and sampling from the multivariate probability distribution, which are used as inputs for the optimization model. The approach reveals the distribution of water shortage and is able to emphasize the importance of improving and updating transferring water and local surface water management, and examine their combined influence on water shortage risk assessment. The possible available water and shortages can be calculated applying the UWSRAM, also with the corresponding allocation measures under different water availability levels and violating probabilities. The UWSRAM is valuable for mastering the overall multi-water resource and water shortage degree, adapting to the uncertainty surrounding water resources, establishing effective water resource planning policies for managers and achieving sustainable development.
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
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
Diet Activity Characteristic of Large-scale Sports Events Based on HACCP Management Model
Xiao-Feng Su; Li Guo; Li-Hua Gao; Chang-Zhuan Shao
2015-01-01
The study proposed major sports events dietary management based on "HACCP" management model. According to the characteristic of major sports events catering activities. Major sports events are not just showcase level of competitive sports activities which have become comprehensive special events including social, political, economic, cultural and other factors, complex. Sporting events conferred reach more diverse goals and objectives of economic, political, cultural, technological and other ...
Autocorrel I: A Neural Network Based Network Event Correlation Approach
National Research Council Canada - National Science Library
Japkowicz, Nathalie; Smith, Reuben
2005-01-01
.... We use the autoassociator to build prototype software to cluster network alerts generated by a Snort intrusion detection system, and discuss how the results are significant, and how they can be applied to other types of network events.
Balboa: A Framework for Event-Based Process Data Analysis
National Research Council Canada - National Science Library
Cook, Jonathan E; Wolf, Alexander L
1998-01-01
.... We have built Balboa as a bridge between the data collection and the analysis tools, facilitating the gathering and management of event data, and simplifying the construction of tools to analyze the data...
Directory of Open Access Journals (Sweden)
Xingfa Yang
2018-01-01
Full Text Available Nondeterministic parameters of certain distribution are employed to model structural uncertainties, which are usually assumed as stochastic factors. However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness, and unknown-but-bounded conditions. To this end, interval and fuzzy parameters are implemented and an efficient approach to structural reliability analysis with random-interval-fuzzy hybrid parameters is proposed in this study. Fuzzy parameters are first converted to equivalent random ones based on the equal entropy principle. 3σ criterion is then employed to transform the equivalent random and the original random parameters to interval variables. In doing this, the hybrid reliability problem is transformed into the one only with interval variables, in other words, nonprobabilistic reliability analysis problem. Nevertheless, the problem of interval extension existed in interval arithmetic, especially for the nonlinear systems. Therefore, universal grey mathematics, which can tackle the issue of interval extension, is employed to solve the nonprobabilistic reliability analysis problem. The results show that the proposed method can obtain more conservative results of the hybrid structural reliability.
Directory of Open Access Journals (Sweden)
Saeid Jahan
2017-11-01
Full Text Available Structural damage detection is based on that the dynamic response of structure will change because of damage. Hence, it is possible to estimate the location and severity of damage leads to changes in the dynamic response before and after the damage. In this study, the genetic fuzzy system has been used for bridge structural health monitoring. A key objective of using genetic algorithms is to automate the design of fuzzy systems. This method is used for damage detection of a single span railway bridge with steel girders and a concrete bridge. For studying damage detection, the numerical models of these two bridges are built with the measured dynamic characteristics. A three-dimensional finite element model and a single two-dimensional girders model of the bridge have been constructed to study usefulness of the genetic fuzzy system for damage detection and the effectiveness of modeling. After analysis to control the uncertainties, the measured frequencies are contaminated with some noise and the effect of that on the achievement of damage detection method is evaluated. The present study has shown that the natural frequency has appropriate sensitivity to different damage scenarios in the structure. In addition, the natural frequency in comparison with other modal parameters, is less affected by random noise. Increasing the number of measurement modes and using torsional modes, will lead to an accurate damage diagnosis even in symmetrical structures.
FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements
Agha-mohammadi, A.-a.; Chakravorty, S.; Amato, N. M.
2013-01-01
In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. © The Author(s) 2013.
FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements
Agha-mohammadi, A.-a.
2013-11-15
In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. © The Author(s) 2013.
A meta model-based methodology for an energy savings uncertainty assessment of building retrofitting
Directory of Open Access Journals (Sweden)
Caucheteux Antoine
2016-01-01
Full Text Available To reduce greenhouse gas emissions, energy retrofitting of building stock presents significant potential for energy savings. In the design stage, energy savings are usually assessed through Building Energy Simulation (BES. The main difficulty is to first assess the energy efficiency of the existing buildings, in other words, to calibrate the model. As calibration is an under determined problem, there is many solutions for building representation in simulation tools. In this paper, a method is proposed to assess not only energy savings but also their uncertainty. Meta models, using experimental designs, are used to identify many acceptable calibrations: sets of parameters that provide the most accurate representation of the building are retained to calculate energy savings. The method was applied on an existing office building modeled with the TRNsys BES. The meta model, using 13 parameters, is built with no more than 105 simulations. The evaluation of the meta model on thousands of new simulations gives a normalized mean bias error between the meta model and BES of <4%. Energy savings are assessed based on six energy savings concepts, which indicate savings of 2–45% with a standard deviation ranging between 1.3% and 2.5%.
Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle
Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun
2018-05-01
The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.
Vibration-based damage detection of structural joints in presence of uncertainty
Directory of Open Access Journals (Sweden)
Al-Bugharbee Hussein
2018-01-01
Full Text Available Early damage detection of structure’s joints is essential in order to ensure the integrity of structures. Vibration-based methods are the most popular way of diagnosing damage in machinery joints. Any technique that is used for such a purpose requires dealing with the variability inherent to the system due to manufacturing tolerances, environmental conditions or aging. The level of variability in vibrational response can be very high for mass-produced complex structures that possess a large number of components. In this study, a simple and efficient time frequency method is proposed for detection of damage in connecting joints. The method suggests using singular spectrum analysis for building a reference space from the signals measured on a healthy structure and then compares all other signals to that reference space in order to detect the presence of faults. A model of two plates connected by a series of mounts is used to examine the effectiveness of the method where the uncertainty in the mount properties is taken into account to model the variability in the built-up structure. The motivation behind the simplified model is to identify the faulty mounts in trim-structure joints of an automotive vehicle where a large number of simple plastic clips are used to connect the trims to the vehicle structure.
Gómez-Beas, R.; Moñino, A.; Polo, M. J.
2012-05-01
In compliance with the development of the Water Framework Directive, there is a need for an integrated management of water resources, which involves the elaboration of reservoir management models. These models should include the operational and technical aspects which allow us to forecast an optimal management in the short term, besides the factors that may affect the volume of water stored in the medium and long term. The climate fluctuations of the water cycle that affect the reservoir watershed should be considered, as well as the social and economic aspects of the area. This paper shows the development of a management model for Rules reservoir (southern Spain), through which the water supply is regulated based on set criteria, in a sustainable way with existing commitments downstream, with the supply capacity being well established depending on demand, and the probability of failure when the operating requirements are not fulfilled. The results obtained allowed us: to find out the reservoir response at different time scales, to introduce an uncertainty analysis and to demonstrate the potential of the methodology proposed here as a tool for decision making.
Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.
Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang
2015-01-01
Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.
Integrated analyzing method for the progress event based on subjects and predicates in events
International Nuclear Information System (INIS)
Minowa, Hirotsugu; Munesawa, Yoshiomi
2014-01-01
It is expected to make use of the knowledge that was extracted by analyzing the mistakes of the past to prevent recurrence of accidents. Currently main analytic style is an analytic style that experts decipher deeply the accident cases, but cross-analysis has come to an end with extracting the common factors in the accident cases. We propose an integrated analyzing method for progress events to analyze among accidents in this study. Our method realized the integration of many accident cases by the integration connecting the common keyword called as 'Subject' or 'Predicate' that are extracted from each progress event in accident cases or near-miss cases. Our method can analyze and visualize the partial risk identification and the frequency to cause accidents and the risk assessment from the data integrated accident cases. The result of applying our method to PEC-SAFER accident cases identified 8 hazardous factors which can be caused from tank again, and visualized the high frequent factors that the first factor was damage of tank 26% and the second factor was the corrosion 21%, and visualized the high risks that the first risk was the damage 3.3 x 10 -2 [risk rank / year] and the second risk was the destroy 2.5 x 10 -2 [risk rank / year]. (author)
Zou, Xiao-Duan; Li, Jian-Yang; Clark, Beth Ellen; Golish, Dathon
2018-01-01
The OSIRIS-REx spacecraft, launched in September, 2016, will study the asteroid Bennu and return a sample from its surface to Earth in 2023. Bennu is a near-Earth carbonaceous asteroid which will provide insight into the formation and evolution of the solar system. OSIRIS-REx will first approach Bennu in August 2018 and will study the asteroid for approximately two years before sampling. OSIRIS-REx will develop its photometric model (including Lommel-Seelinger, ROLO, McEwen, Minnaert and Akimov) of Bennu with OCAM and OVIRS during the Detailed Survey mission phase. The model developed during this phase will be used to photometrically correct the OCAM and OVIRS data.Here we present the analysis of the error for the photometric corrections. Based on our testing data sets, we find:1. The model uncertainties is only correct when we use the covariance matrix to calculate, because the parameters are highly correlated.2. No evidence of domination of any parameter in each model.3. And both model error and the data error contribute to the final correction error comparably.4. We tested the uncertainty module on fake and real data sets, and find that model performance depends on the data coverage and data quality. These tests gave us a better understanding of how different model behave in different case.5. L-S model is more reliable than others. Maybe because the simulated data are based on L-S model. However, the test on real data (SPDIF) does show slight advantage of L-S, too. ROLO is not reliable to use when calculating bond albedo. The uncertainty of McEwen model is big in most cases. Akimov performs unphysical on SOPIE 1 data.6. Better use L-S as our default choice, this conclusion is based mainly on our test on SOPIE data and IPDIF.
Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn
2013-04-01
SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.
DEFF Research Database (Denmark)
Carli, Lorenzo; Genta, G; Cantatore, Angela
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 ...
Energy Technology Data Exchange (ETDEWEB)
Le Pallec, J. C.; Crouzet, N.; Bergeaud, V.; Delavaud, C. [CEA/DEN/DM2S, CEA/Saclay, 91191 Gif sur Yvette Cedex (France)
2012-07-01
The control of uncertainties in the field of reactor physics and their propagation in best-estimate modeling are a major issue in safety analysis. In this framework, the CEA develops a methodology to perform multi-physics simulations including uncertainties analysis. The present paper aims to present and apply this methodology for the analysis of an accidental situation such as REA (Rod Ejection Accident). This accident is characterized by a strong interaction between the different areas of the reactor physics (neutronic, fuel thermal and thermal hydraulic). The modeling is performed with CRONOS2 code. The uncertainties analysis has been conducted with the URANIE platform developed by the CEA: For each identified response from the modeling (output) and considering a set of key parameters with their uncertainties (input), a surrogate model in the form of a neural network has been produced. The set of neural networks is then used to carry out a sensitivity analysis which consists on a global variance analysis with the determination of the Sobol indices for all responses. The sensitivity indices are obtained for the input parameters by an approach based on the use of polynomial chaos. The present exercise helped to develop a methodological flow scheme, to consolidate the use of URANIE tool in the framework of parallel calculations. Finally, the use of polynomial chaos allowed computing high order sensitivity indices and thus highlighting and classifying the influence of identified uncertainties on each response of the analysis (single and interaction effects). (authors)
Energy Technology Data Exchange (ETDEWEB)
Frey, H. Christopher [North Carolina State University, Raleigh, NC (United States); Rhodes, David S. [North Carolina State University, Raleigh, NC (United States)
1999-04-30
This is Volume 1 of a two-volume set of reports describing work conducted at North Carolina State University sponsored by Grant Number DE-FG05-95ER30250 by the U.S. Department of Energy. The title of the project is “Quantitative Analysis of Variability and Uncertainty in Acid Rain Assessments.” The work conducted under sponsorship of this grant pertains primarily to two main topics: (1) development of new methods for quantitative analysis of variability and uncertainty applicable to any type of model; and (2) analysis of variability and uncertainty in the performance, emissions, and cost of electric power plant combustion-based NOx control technologies. These two main topics are reported separately in Volumes 1 and 2.
Event-based text mining for biology and functional genomics
Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B.
2015-01-01
The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research. PMID:24907365
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
Automated cleaning and uncertainty attribution of archival bathymetry based on a priori knowledge
Ladner, Rodney Wade; Elmore, Paul; Perkins, A. Louise; Bourgeois, Brian; Avera, Will
2017-09-01
Hydrographic offices hold large valuable historic bathymetric data sets, many of which were collected using older generation survey systems that contain little or no metadata and/or uncertainty estimates. These bathymetric data sets generally contain large outlier (errant) data points to clean, yet standard practice does not include rigorous automated procedures for systematic cleaning of these historical data sets and their subsequent conversion into reusable data formats. In this paper, we propose an automated method for this task. We utilize statistically diverse threshold tests, including a robust least trimmed squared method, to clean the data. We use LOESS weighted regression residuals together with a Student-t distribution to attribute uncertainty for each retained sounding; the resulting uncertainty values compare favorably with native estimates of uncertainty from co-located data sets which we use to estimate a point-wise goodness-of-fit measure. Storing a cleansed validated data set augmented with uncertainty in a re-usable format provides the details of this analysis for subsequent users. Our test results indicate that the method significantly improves the quality of the data set while concurrently providing confidence interval estimates and point-wise goodness-of-fit estimates as referenced to current hydrographic practices.
International Nuclear Information System (INIS)
Li, Jing; He, Li; Lu, Hongwei; Fan, Xing
2014-01-01
Highlights: • We propose an integrated optimal groundwater remediation design approach. • The approach can address stochasticity in carcinogenic risks. • Goal programming is used to make the system approaching to ideal operation and remediation effects. • The uncertainty in slope factor is evaluated under different confidence levels. • Optimal strategies are obtained to support remediation design under uncertainty. - Abstract: An optimal design approach for groundwater remediation is developed through incorporating numerical simulation, health risk assessment, uncertainty analysis and nonlinear optimization within a general framework. Stochastic analysis and goal programming are introduced into the framework to handle uncertainties in real-world groundwater remediation systems. Carcinogenic risks associated with remediation actions are further evaluated at four confidence levels. The differences between ideal and predicted constraints are minimized by goal programming. The approach is then applied to a contaminated site in western Canada for creating a set of optimal remediation strategies. Results from the case study indicate that factors including environmental standards, health risks and technical requirements mutually affected and restricted themselves. Stochastic uncertainty existed in the entire process of remediation optimization, which should to be taken into consideration in groundwater remediation design
Risk Analysis of Reservoir Flood Routing Calculation Based on Inflow Forecast Uncertainty
Directory of Open Access Journals (Sweden)
Binquan Li
2016-10-01
Full Text Available Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accounting for inflow forecast uncertainty in a sub-basin of Huaihe River, China. The Xinanjiang model was used to provide deterministic flood forecasts, and was combined with the Hydrologic Uncertainty Processor (HUP to quantify reservoir inflow uncertainty in the probability density function (PDF form. Furthermore, the PDFs of reservoir water level (RWL and the risk rate of RWL exceeding a defined safety control level could be obtained. Results suggested that the median forecast (50th percentiles of HUP showed better agreement with observed inflows than the Xinanjiang model did in terms of the performance measures of flood process, peak, and volume. In addition, most observations (77.2% were bracketed by the uncertainty band of 90% confidence interval, with some small exceptions of high flows. Results proved that this framework of risk analysis could provide not only the deterministic forecasts of inflow and RWL, but also the fundamental uncertainty information (e.g., 90% confidence band for the reservoir flood routing calculation.
A phantom-based study for assessing the error and uncertainty of a neuronavigation system
Directory of Open Access Journals (Sweden)
Natalia Izquierdo-Cifuentes
2017-01-01
Full Text Available This document describes a calibration protocol with the intention to introduce a guide to standardize the metrological vocabulary among manufacturers of image-guided surgery systems. Two stages were developed to measure the errors and estimate the uncertainty of a neuronavigator in different situations, on the first one it was determined a mechanical error on a virtual model of an acrylic phantom, on the second it was determined a coordinate error on the computerized axial tomography scan of the same phantom. Ten standard coordinates of the phantom were compared with the coordinates generated by the NeuroCPS. After measurement model was established, there were identified the sources of uncertainty and the data was processed according the guide to the expression of uncertainty in measurement.
Characterising Event-Based DOM Inputs to an Urban Watershed
Croghan, D.; Bradley, C.; Hannah, D. M.; Van Loon, A.; Sadler, J. P.
2017-12-01
Dissolved Organic Matter (DOM) composition in urban streams is dominated by terrestrial inputs after rainfall events. Urban streams have particularly strong terrestrial-riverine connections due to direct input from terrestrial drainage systems. Event driven DOM inputs can have substantial adverse effects on water quality. Despite this, DOM from important catchment sources such as road drains and Combined Sewage Overflows (CSO's) remains poorly characterised within urban watersheds. We studied DOM sources within an urbanised, headwater watershed in Birmingham, UK. Samples from terrestrial sources (roads, roofs and a CSO), were collected manually after the onset of rainfall events of varying magnitude, and again within 24-hrs of the event ending. Terrestrial samples were analysed for fluorescence, absorbance and Dissolved Organic Carbon (DOC) concentration. Fluorescence and absorbance indices were calculated, and Parallel Factor Analysis (PARAFAC) was undertaken to aid sample characterization. Substantial differences in fluorescence, absorbance, and DOC were observed between source types. PARAFAC-derived components linked to organic pollutants were generally highest within road derived samples, whilst humic-like components tended to be highest within roof samples. Samples taken from the CSO generally contained low fluorescence, however this likely represents a dilution effect. Variation within source groups was particularly high, and local land use seemed to be the driving factor for road and roof drain DOM character and DOC quantity. Furthermore, high variation in fluorescence, absorbance and DOC was apparent between all sources depending on event type. Drier antecedent conditions in particular were linked to greater presence of terrestrially-derived components and higher DOC content. Our study indicates that high variations in DOM character occur between source types, and over small spatial scales. Road drains located on main roads appear to contain the poorest
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.
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.
International Nuclear Information System (INIS)
Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha
2013-01-01
Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water
DEFF Research Database (Denmark)
Loureiro da Costa Lira Gargalo, Carina; Gernaey, Krist; Sin, Gürkan
2016-01-01
to propagate the market price and technical uncertainties to the economic indicator calculations and to quantify the respective economic risk. The results clearly indicated that under the given market price uncertainties, the probability of obtaining a negative NPV is 0.95. This is a very high probability...
Smith, B. D.; White, J.; Kress, W. H.; Clark, B. R.; Barlow, J.
2016-12-01
reduced. The FOSM forecast uncertainty estimates were then recalculated and compared to the base forecast uncertainty estimates. The resulting reduction in forecast uncertainty is a measure of the effect on the model from the AEM survey. Iterations through this process, results in optimization of flight line location.
Event Management for Teacher-Coaches: Risk and Supervision Considerations for School-Based Sports
Paiement, Craig A.; Payment, Matthew P.
2011-01-01
A professional sports event requires considerable planning in which years are devoted to the success of that single activity. School-based sports events do not have that luxury, because high schools across the country host athletic events nearly every day. It is not uncommon during the fall sports season for a combination of boys' and girls'…
Lindley, Dennis V
2013-01-01
Praise for the First Edition ""...a reference for everyone who is interested in knowing and handling uncertainty.""-Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
Web-based online system for recording and examing of events in power plants
International Nuclear Information System (INIS)
Seyd Farshi, S.; Dehghani, M.
2004-01-01
Occurrence of events in power plants could results in serious drawbacks in generation of power. This suggests high degree of importance for online recording and examing of events. In this paper an online web-based system is introduced, which records and examines events in power plants. Throughout the paper, procedures for design and implementation of this system, its features and results gained are explained. this system provides predefined level of online access to all data of events for all its users in power plants, dispatching, regional utilities and top-level managers. By implementation of electric power industry intranet, an expandable modular system to be used in different sectors of industry is offered. Web-based online recording and examing system for events offers the following advantages: - Online recording of events in power plants. - Examing of events in regional utilities. - Access to event' data. - Preparing managerial reports
Model Based Verification of Cyber Range Event Environments
2015-11-13
that may include users, applications, operating systems, servers, hosts, routers, switches, control planes , and instrumentation planes , many of...which lack models for their configuration. Our main contributions in this paper are the following. First, we have developed a configuration ontology...configuration errors in environment designs for several cyber range events. The rest of the paper is organized as follows. Section 2 provides an overview of
DEFF Research Database (Denmark)
Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2011-01-01
Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...
Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts
2015-07-01
uncertainty analyses throughout the lifecycle of planning, designing, and operating of Civil Works flood risk management projects as described in...Education 140:3–14. Doherty, J. 2004. PEST : Model-independent parameter estimation, User Manual. 5th ed. Brisbane, Queensland, Australia: Watermark
Satellite-based emission constraint for nitrogen oxides: Capability and uncertainty
Lin, J.; McElroy, M. B.; Boersma, F.; Nielsen, C.; Zhao, Y.; Lei, Y.; Liu, Y.; Zhang, Q.; Liu, Z.; Liu, H.; Mao, J.; Zhuang, G.; Roozendael, M.; Martin, R.; Wang, P.; Spurr, R. J.; Sneep, M.; Stammes, P.; Clemer, K.; Irie, H.
2013-12-01
Vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) retrieved from satellite remote sensing have been employed widely to constrain emissions of nitrogen oxides (NOx). A major strength of satellite-based emission constraint is analysis of emission trends and variability, while a crucial limitation is errors both in satellite NO2 data and in model simulations relating NOx emissions to NO2 columns. Through a series of studies, we have explored these aspects over China. We separate anthropogenic from natural sources of NOx by exploiting their different seasonality. We infer trends of NOx emissions in recent years and effects of a variety of socioeconomic events at different spatiotemporal scales including the general economic growth, global financial crisis, Chinese New Year, and Beijing Olympics. We further investigate the impact of growing NOx emissions on particulate matter (PM) pollution in China. As part of recent developments, we identify and correct errors in both satellite NO2 retrieval and model simulation that ultimately affect NOx emission constraint. We improve the treatments of aerosol optical effects, clouds and surface reflectance in the NO2 retrieval process, using as reference ground-based MAX-DOAS measurements to evaluate the improved retrieval results. We analyze the sensitivity of simulated NO2 to errors in the model representation of major meteorological and chemical processes with a subsequent correction of model bias. Future studies will implement these improvements to re-constrain NOx emissions.
Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian
2018-06-01
Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.
Fault trees based on past accidents. Factorial analysis of events
International Nuclear Information System (INIS)
Vaillant, M.
1977-01-01
The method of the fault tree is already useful in the qualitative step before any reliability calculation. The construction of the tree becomes even simpler when we just want to describe how the events happened. Differently from screenplays that introduce several possibilities by means of the conjunction OR, you only have here the conjunction AND, which will not be written at all. This method is presented by INRS (1) for the study of industrial injuries; it may also be applied to material damages. (orig.) [de
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.
Directory of Open Access Journals (Sweden)
Enrique Campbell
2016-04-01
Full Text Available The core idea behind sectorization of Water Supply Networks (WSNs is to establish areas partially isolated from the rest of the network to improve operational control. Besides the benefits associated with sectorization, some drawbacks must be taken into consideration by water operators: the economic investment associated with both boundary valves and flowmeters and the reduction of both pressure and system resilience. The target of sectorization is to properly balance these negative and positive aspects. Sectorization methodologies addressing the economic aspects mainly consider costs of valves and flowmeters and of energy, and the benefits in terms of water saving linked to pressure reduction. However, sectorization entails other benefits, such as the reduction of domestic consumption, the reduction of burst frequency and the enhanced capacity to detect and intervene over future leakage events. We implement a development proposed by the International Water Association (IWA to estimate the aforementioned benefits. Such a development is integrated in a novel sectorization methodology based on a social network community detection algorithm, combined with a genetic algorithm optimization method and Monte Carlo simulation. The methodology is implemented over a fraction of the WSN of Managua city, capital of Nicaragua, generating a net benefit of 25,572 $/year.
Continuum topology optimization considering uncertainties in load locations based on the cloud model
Liu, Jie; Wen, Guilin
2018-06-01
Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.
A quantum uncertainty relation based on Fisher's information
Energy Technology Data Exchange (ETDEWEB)
Sanchez-Moreno, P; Plastino, A R; Dehesa, J S, E-mail: pablos@ugr.es, E-mail: arplastino@ugr.es, E-mail: dehesa@ugr.es [Departamento de Fisica Atomica, Molecular y Nuclear and Instituto Carlos I de Fisica Teorica y Computacional, University of Granada, Granada (Spain)
2011-02-11
We explore quantum uncertainty relations involving the Fisher information functionals I{sub x} and I{sub p} evaluated, respectively, on a wavefunction {Psi}(x) defined on a D-dimensional configuration space and the concomitant wavefunction {Psi}-tilde(p) on the conjugate momentum space. We prove that the associated Fisher functionals obey the uncertainty relation I{sub x}I{sub p} {>=} 4D{sup 2} when either {Psi}(x) or {Psi}-tilde(p) is real. On the other hand, there is no lower bound to the above product for arbitrary complex wavefunctions. We give explicit examples of complex wavefunctions not obeying the above bound. In particular, we provide a parametrized wavefunction for which the product I{sub x}I{sub p} can be made arbitrarily small.
Edwards, Jonathan; Lallier, Florent; Caumon, Guillaume; Carpentier, Cédric
2018-02-01
We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in basins and reservoirs. From an input set of wells, we evaluate the probability for two stratigraphic units to be associated using an analog stratigraphic model. In the presence of multiple wells, this method sequentially updates a stratigraphic column defining the stratigraphic layering for each possible set of realizations. The resulting correlations are then used to create stratigraphic grids in three dimensions. We apply this method on a set of synthetic wells sampling a forward stratigraphic model built with Dionisos. To perform cross-validation of the method, we introduce a distance comparing the relative geological time of two models for each geographic position, and we compare the models in terms of volumes. Results show the ability of the method to automatically generate stratigraphic correlation scenarios, and also highlight some challenges when sampling stratigraphic uncertainties from multiple wells.
Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun
2015-01-01
The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.
Sunway Medical Laboratory Quality Control Plans Based on Six Sigma, Risk Management and Uncertainty.
Jairaman, Jamuna; Sakiman, Zarinah; Li, Lee Suan
2017-03-01
Sunway Medical Centre (SunMed) implemented Six Sigma, measurement uncertainty, and risk management after the CLSI EP23 Individualized Quality Control Plan approach. Despite the differences in all three approaches, each implementation was beneficial to the laboratory, and none was in conflict with another approach. A synthesis of these approaches, built on a solid foundation of quality control planning, can help build a strong quality management system for the entire laboratory. Copyright © 2016 Elsevier Inc. All rights reserved.
Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples.
Dołęgowska, Sabina
2016-11-01
In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During the fieldwork, each primary sample composed of 8 to 10 increments (subsamples) was taken over an area of 10 m 2 whereas duplicate samples were collected in the same way at a distance of 1-2 m. Subsequently, all samples were triple rinsed with deionized water, dried, milled, and digested (8 mL HNO 3 (1:1) + 1 mL 30 % H 2 O 2 ) in a closed microwave system Multiwave 3000. The prepared solutions were analyzed twice for Cu, Fe, Mn, and Zn using FAAS and GFAAS techniques. All datasets were checked for normality and for normally distributed elements (Cu from Piaski, Zn from Posłowice, Fe, Zn from Wierna Rzeka). The sampling uncertainty was computed with (i) classical ANOVA, (ii) classical RANOVA, (iii) modified RANOVA, and (iv) range statistics. For the remaining elements, the sampling uncertainty was calculated with traditional and/or modified RANOVA (if the amount of outliers did not exceed 10 %) or classical ANOVA after Box-Cox transformation (if the amount of outliers exceeded 10 %). The highest concentrations of all elements were found in moss samples from Piaski, whereas the sampling uncertainty calculated with different statistical methods ranged from 4.1 to 22 %.
An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology
Maryam Hourali; Gholam Ali Montazer
2011-01-01
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 ...
A phantom-based study for assessing the error and uncertainty of a neuronavigation system
Natalia Izquierdo-Cifuentes; Genaro Daza-Santacoloma; Walter Serna-Serna
2017-01-01
This document describes a calibration protocol with the intention to introduce a guide to standardize the metrological vocabulary among manufacturers of image-guided surgery systems. Two stages were developed to measure the errors and estimate the uncertainty of a neuronavigator in different situations, on the first one it was determined a mechanical error on a virtual model of an acrylic phantom, on the second it was determined a coordinate error on the computerized axial tomography scan of ...
Uncertainty Analysis of Power Grid Investment Capacity Based on Monte Carlo
Qin, Junsong; Liu, Bingyi; Niu, Dongxiao
By analyzing the influence factors of the investment capacity of power grid, to depreciation cost, sales price and sales quantity, net profit, financing and GDP of the second industry as the dependent variable to build the investment capacity analysis model. After carrying out Kolmogorov-Smirnov test, get the probability distribution of each influence factor. Finally, obtained the grid investment capacity uncertainty of analysis results by Monte Carlo simulation.
Microseismic Event Grouping Based on PageRank Linkage at the Newberry Volcano Geothermal Site
Aguiar, A. C.; Myers, S. C.
2016-12-01
The Newberry Volcano DOE FORGE site in Central Oregon has been stimulated two times using high-pressure fluid injection to study the Enhanced Geothermal Systems (EGS) technology. Several hundred microseismic events were generated during the first stimulation in the fall of 2012. Initial locations of this microseismicity do not show well defined subsurface structure in part because event location uncertainties are large (Foulger and Julian, 2013). We focus on this stimulation to explore the spatial and temporal development of microseismicity, which is key to understanding how subsurface stimulation modifies stress, fractures rock, and increases permeability. We use PageRank, Google's initial search algorithm, to determine connectivity within the events (Aguiar and Beroza, 2014) and assess signal-correlation topology for the micro-earthquakes. We then use this information to create signal families and compare these to the spatial and temporal proximity of associated earthquakes. We relocate events within families (identified by PageRank linkage) using the Bayesloc approach (Myers et al., 2007). Preliminary relocations show tight spatial clustering of event families as well as evidence of events relocating to a different cluster than originally reported. We also find that signal similarity (linkage) at several stations, not just one or two, is needed in order to determine that events are in close proximity to one another. We show that indirect linkage of signals using PageRank is a reliable way to increase the number of events that are confidently determined to be similar to one another, which may lead to efficient and effective grouping of earthquakes with similar physical characteristics, such as focal mechanisms and stress drop. Our ultimate goal is to determine whether changes in the state of stress and/or changes in the generation of subsurface fracture networks can be detected using PageRank topology as well as aid in the event relocation to obtain more accurate
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
Li, Y P; Huang, G H
2010-09-15
Considerable public concerns have been raised in the past decades since a large amount of pollutant emissions from municipal solid waste (MSW) disposal of processes pose risks on surrounding environment and human health. Moreover, in MSW management, various uncertainties exist in the related costs, impact factors and objectives, which can affect the optimization processes and the decision schemes generated. In this study, an interval-based possibilistic programming (IBPP) method is developed for planning the MSW management with minimized system cost and environmental impact under uncertainty. The developed method can deal with uncertainties expressed as interval values and fuzzy sets in the left- and right-hand sides of constraints and objective function. An interactive algorithm is provided for solving the IBPP problem, which does not lead to more complicated intermediate submodels and has a relatively low computational requirement. The developed model is applied to a case study of planning a MSW management system, where mixed integer linear programming (MILP) technique is introduced into the IBPP framework to facilitate dynamic analysis for decisions of timing, sizing and siting in terms of capacity expansion for waste-management facilities. Three cases based on different waste-management policies are examined. The results obtained indicate that inclusion of environmental impacts in the optimization model can change the traditional waste-allocation pattern merely based on the economic-oriented planning approach. The results obtained can help identify desired alternatives for managing MSW, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty. Copyright 2010 Elsevier B.V. All rights reserved.
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
Koch, Michael
Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.
Fire!: An Event-Based Science Module. Teacher's Guide. Chemistry and Fire Ecology Module.
Wright, Russell G.
This book is designed for middle school earth science or physical 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,…
Volcano!: An Event-Based Science Module. Student Edition. Geology Module.
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…
Volcano!: An Event-Based Science Module. Teacher's Guide. Geology Module.
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,…
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
Avendaño-Valencia, Luis David; Fassois, Spilios D.
2017-12-01
The problem of vibration-based damage diagnosis in structures characterized by time-dependent dynamics under significant environmental and/or operational uncertainty is considered. A stochastic framework consisting of a Gaussian Mixture Random Coefficient model of the uncertain time-dependent dynamics under each structural health state, proper estimation methods, and Bayesian or minimum distance type decision making, is postulated. The Random Coefficient (RC) time-dependent stochastic model with coefficients following a multivariate Gaussian Mixture Model (GMM) allows for significant flexibility in uncertainty representation. Certain of the model parameters are estimated via a simple procedure which is founded on the related Multiple Model (MM) concept, while the GMM weights are explicitly estimated for optimizing damage diagnostic performance. The postulated framework is demonstrated via damage detection in a simple simulated model of a quarter-car active suspension with time-dependent dynamics and considerable uncertainty on the payload. Comparisons with a simpler Gaussian RC model based method are also presented, with the postulated framework shown to be capable of offering considerable improvement in diagnostic performance.
International Nuclear Information System (INIS)
Byung Ryul Jung; Ho Cheol Jang; Byung Jin Lee; Se Jin Baik; Woo Hyun Jang
2005-01-01
Most of Pressurized Water Reactors (PWRs) utilize the venturi meters (VMs) to measure the feedwater (FW) flow rate to the steam generator in the calorimetric measurement, which is used in the reactor thermal power (RTP) estimation. However, measurement drifts have been experienced due to some anomalies on the venturi meter (generally called the venturi meter fouling). The VM's fouling tends to increase the measured pressure drop across the meter, which results in indication of increased feedwater flow rate. Finally, the reactor thermal power is overestimated and the actual reactor power is to be reduced to remain within the regulatory limits. To overcome this VM's fouling problem, the Ultrasonic Flow Meter (UFM) has recently been gaining attention in the measurement of the feedwater flow rate. This paper presents the applicability of a UFM based feedwater flow rate in the estimation of reactor thermal power uncertainty. The FW and RTP uncertainties are compared in terms of sensitivities between the VM- and UFM-based feedwater flow rates. Data from typical Optimized Power Reactor 1000 (OPR1000) plants are used to estimate the uncertainty. (authors)
Uncertainty in artificial intelligence
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.
Event-building and PC farm based level-3 trigger at the CDF experiment
Anikeev, K; Furic, I K; Holmgren, D; Korn, A J; Kravchenko, I V; Mulhearn, M; Ngan, P; Paus, C; Rakitine, A; Rechenmacher, R; Shah, T; Sphicas, Paris; Sumorok, K; Tether, S; Tseng, J
2000-01-01
In the technical design report the event building process at Fermilab's CDF experiment is required to function at an event rate of 300 events/sec. The events are expected to have an average size of 150 kBytes (kB) and are assembled from fragments of 16 readout locations. The fragment size from the different locations varies between 12 kB and 16 kB. Once the events are assembled they are fed into the Level-3 trigger which is based on processors running programs to filter events using the full event information. Computing power on the order of a second on a Pentium II processor is required per event. The architecture design is driven by the cost and is therefore based on commodity components: VME processor modules running VxWorks for the readout, an ATM switch for the event building, and Pentium PCs running Linux as an operation system for the Level-3 event processing. Pentium PCs are also used to receive events from the ATM switch and further distribute them to the processing nodes over multiple 100 Mbps Ether...
A norm-based approach to the quantification of model uncertainty
International Nuclear Information System (INIS)
Zio, E.; Apostolakis, G.E.
1996-01-01
Various mathematical formulations have been proposed for the treatment of model uncertainty. These formulations can be categorized as model-focused or prediction focused, according to whether the attention is directed towards the plausibility of the model hypotheses or to the accuracy of its predictions. In this paper we embrace the model-focused approach and propose a new tool for the quantitative analysis of the alternate models hypotheses, and for the evaluation of the probabilities representing the degree of belief on the validity of these hypotheses
Directory of Open Access Journals (Sweden)
Bifeng Hu
2018-04-01
Full Text Available Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI and Nemerow integrated pollution index (NIPI were calculated for every surface sample (0–20 cm to assess the degree of heavy metal pollution. Ordinary kriging (OK was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK. The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.
Zhao, Ruiying; Chen, Songchao; Zhou, Yue; Jin, Bin; Li, Yan
2018-01-01
Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution. PMID:29642623
Hu, Bifeng; Zhao, Ruiying; Chen, Songchao; Zhou, Yue; Jin, Bin; Li, Yan; Shi, Zhou
2018-04-10
Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0-20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.
Applying an animal model to quantify the uncertainties of an image-based 4D-CT algorithm
International Nuclear Information System (INIS)
Pierce, Greg; Battista, Jerry; Wang, Kevin; Lee, Ting-Yim
2012-01-01
The purpose of this paper is to use an animal model to quantify the spatial displacement uncertainties and test the fundamental assumptions of an image-based 4D-CT algorithm in vivo. Six female Landrace cross pigs were ventilated and imaged using a 64-slice CT scanner (GE Healthcare) operating in axial cine mode. The breathing amplitude pattern of the pigs was varied by periodically crimping the ventilator gas return tube during the image acquisition. The image data were used to determine the displacement uncertainties that result from matching CT images at the same respiratory phase using normalized cross correlation (NCC) as the matching criteria. Additionally, the ability to match the respiratory phase of a 4.0 cm subvolume of the thorax to a reference subvolume using only a single overlapping 2D slice from the two subvolumes was tested by varying the location of the overlapping matching image within the subvolume and examining the effect this had on the displacement relative to the reference volume. The displacement uncertainty resulting from matching two respiratory images using NCC ranged from 0.54 ± 0.10 mm per match to 0.32 ± 0.16 mm per match in the lung of the animal. The uncertainty was found to propagate in quadrature, increasing with number of NCC matches performed. In comparison, the minimum displacement achievable if two respiratory images were matched perfectly in phase ranged from 0.77 ± 0.06 to 0.93 ± 0.06 mm in the lung. The assumption that subvolumes from separate cine scan could be matched by matching a single overlapping 2D image between to subvolumes was validated. An in vivo animal model was developed to test an image-based 4D-CT algorithm. The uncertainties associated with using NCC to match the respiratory phase of two images were quantified and the assumption that a 4.0 cm 3D subvolume can by matched in respiratory phase by matching a single 2D image from the 3D subvolume was validated. The work in this paper shows the image-based 4D
Phenomena-based Uncertainty Quantification in Predictive Coupled- Physics Reactor Simulations
Energy Technology Data Exchange (ETDEWEB)
Adams, Marvin [Texas A & M Univ., College Station, TX (United States)
2017-06-12
This project has sought to develop methodologies, tailored to phenomena that govern nuclearreactor behavior, to produce predictions (including uncertainties) for quantities of interest (QOIs) in the simulation of steady-state and transient reactor behavior. Examples of such predictions include, for each QOI, an expected value as well as a distribution around this value and an assessment of how much of the distribution stems from each major source of uncertainty. The project has sought to test its methodologies by comparing against measured experimental outcomes. The main experimental platform has been a 1-MW TRIGA reactor. This is a flexible platform for a wide range of experiments, including steady state with and without temperature feedback, slow transients with and without feedback, and rapid transients with strong feedback. The original plan was for the primary experimental data to come from in-core neutron detectors. We made considerable progress toward this goal but did not get as far along as we had planned. We have designed, developed, installed, and tested vertical guide tubes, each able to accept a detector or stack of detectors that can be moved axially inside the tube, and we have tested several new detector designs. One of these shows considerable promise.
Phenomena-based Uncertainty Quantification in Predictive Coupled- Physics Reactor Simulations
International Nuclear Information System (INIS)
Adams, Marvin
2017-01-01
This project has sought to develop methodologies, tailored to phenomena that govern nuclearreactor behavior, to produce predictions (including uncertainties) for quantities of interest (QOIs) in the simulation of steady-state and transient reactor behavior. Examples of such predictions include, for each QOI, an expected value as well as a distribution around this value and an assessment of how much of the distribution stems from each major source of uncertainty. The project has sought to test its methodologies by comparing against measured experimental outcomes. The main experimental platform has been a 1-MW TRIGA reactor. This is a flexible platform for a wide range of experiments, including steady state with and without temperature feedback, slow transients with and without feedback, and rapid transients with strong feedback. The original plan was for the primary experimental data to come from in-core neutron detectors. We made considerable progress toward this goal but did not get as far along as we had planned. We have designed, developed, installed, and tested vertical guide tubes, each able to accept a detector or stack of detectors that can be moved axially inside the tube, and we have tested several new detector designs. One of these shows considerable promise.
International Nuclear Information System (INIS)
Simakov, S.P.; Konobeyev, A.Yu.; Koning, A.
2016-01-01
The goal of this work is a calculation of the covariance matrices for the physical quantities used to characterize the neutron induced radiation damage in the materials. Such quantities usually encompass: the charged particles kinetic energy deposition KERMA (locally deposited nuclear heating), damage energy (to calculate then the number of displaced atoms) and gas production cross sections [(n,xα), (n,xt), (n,xp) … to calculate then transmuting of target nuclei to gases]. The uncertainties and energy-energy or reaction-reaction correlations for such quantities were not assessed so far, whereas the covariances for many underlying cross sections are often presented in the evaluated data libraries. Due to the dependence of damage quantities on many reactions channels, on both total and differential cross sections, and in particular on the energy distribution of reaction recoils, the evaluation of uncertainty is not straightforward. To reach a goal, we used the method based on idea of Total Monte Carlo application to the Nuclear Data. This report summarises the current results for evaluation, validation and representation in the ENDF-6 format of the radiation damage covariances for n + 56 Fe from thermal energy up to 20 MeV. This study was motivated by the IAEA Coordinated Research Project ''Primary Radiation Damage Cross Sections'' and by present dedicated Technical Meeting “Nuclear Reaction Data and Uncertainties for Radiation Damage”
Knowledge based query expansion in complex multimedia event detection
Boer, M. de; Schutte, K.; Kraaij, W.
2016-01-01
A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap
Knowledge based query expansion in complex multimedia event detection
Boer, M.H.T. de; Schutte, K.; Kraaij, W.
2015-01-01
A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap
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
Tag and Neighbor based Recommender systems for Medical events
DEFF Research Database (Denmark)
Bayyapu, Karunakar Reddy; Dolog, Peter
2010-01-01
This paper presents an extension of a multifactor recommendation approach based on user tagging with term neighbours. Neighbours of words in tag vectors and documents provide for hitting larger set of documents and not only those matching with direct tag vectors or content of the documents. Tag...... in the situations where the quality of tags is lower. We discuss the approach on the examples from the existing Medworm system to indicate the usefulness of the approach....
GPS-based PWV for precipitation forecasting and its application to a typhoon event
Zhao, Qingzhi; Yao, Yibin; Yao, Wanqiang
2018-01-01
The temporal variability of precipitable water vapour (PWV) derived from Global Navigation Satellite System (GNSS) observations can be used to forecast precipitation events. A number of case studies of precipitation events have been analysed in Zhejiang Province, and a forecasting method for precipitation events was proposed. The PWV time series retrieved from the Global Positioning System (GPS) observations was processed by using a least-squares fitting method, so as to obtain the line tendency of ascents and descents over PWV. The increment of PWV for a short time (two to six hours) and PWV slope for a longer time (a few hours to more than ten hours) during the PWV ascending period are considered as predictive factors with which to forecast the precipitation event. The numerical results show that about 80%-90% of precipitation events and more than 90% of heavy rain events can be forecasted two to six hours in advance of the precipitation event based on the proposed method. 5-minute PWV data derived from GPS observations based on real-time precise point positioning (RT-PPP) were used for the typhoon event that passed over Zhejiang Province between 10 and 12 July, 2015. A good result was acquired using the proposed method and about 74% of precipitation events were predicted at some ten to thirty minutes earlier than their onset with a false alarm rate of 18%. This study shows that the GPS-based PWV was promising for short-term and now-casting precipitation forecasting.
Measure of uncertainty in regional grade variability
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
Energy Technology Data Exchange (ETDEWEB)
Allen, J; Velsko, S
2009-11-16
This report explores the question of whether meaningful conclusions can be drawn regarding the transmission relationship between two microbial samples on the basis of differences observed between the two sample's respective genomes. Unlike similar forensic applications using human DNA, the rapid rate of microbial genome evolution combined with the dynamics of infectious disease require a shift in thinking on what it means for two samples to 'match' in support of a forensic hypothesis. Previous outbreaks for SARS-CoV, FMDV and HIV were examined to investigate the question of how microbial sequence data can be used to draw inferences that link two infected individuals by direct transmission. The results are counter intuitive with respect to human DNA forensic applications in that some genetic change rather than exact matching improve confidence in inferring direct transmission links, however, too much genetic change poses challenges, which can weaken confidence in inferred links. High rates of infection coupled with relatively weak selective pressure observed in the SARS-CoV and FMDV data lead to fairly low confidence for direct transmission links. Confidence values for forensic hypotheses increased when testing for the possibility that samples are separated by at most a few intermediate hosts. Moreover, the observed outbreak conditions support the potential to provide high confidence values for hypothesis that exclude direct transmission links. Transmission inferences are based on the total number of observed or inferred genetic changes separating two sequences rather than uniquely weighing the importance of any one genetic mismatch. Thus, inferences are surprisingly robust in the presence of sequencing errors provided the error rates are randomly distributed across all samples in the reference outbreak database and the novel sequence samples in question. When the number of observed nucleotide mutations are limited due to characteristics of the
Esquinas, Pedro L; Tanguay, Jesse; Gonzalez, Marjorie; Vuckovic, Milan; Rodríguez-Rodríguez, Cristina; Häfeli, Urs O; Celler, Anna
2016-12-01
In the nuclear medicine department, the activity of radiopharmaceuticals is measured using dose calibrators (DCs) prior to patient injection. The DC consists of an ionization chamber that measures current generated by ionizing radiation (emitted from the radiotracer). In order to obtain an activity reading, the current is converted into units of activity by applying an appropriate calibration factor (also referred to as DC dial setting). Accurate determination of DC dial settings is crucial to ensure that patients receive the appropriate dose in diagnostic scans or radionuclide therapies. The goals of this study were (1) to describe a practical method to experimentally determine dose calibrator settings using a thyroid-probe (TP) and (2) to investigate the accuracy, reproducibility, and uncertainties of the method. As an illustration, the TP method was applied to determine 188 Re dial settings for two dose calibrator models: Atomlab 100plus and Capintec CRC-55tR. Using the TP to determine dose calibrator settings involved three measurements. First, the energy-dependent efficiency of the TP was determined from energy spectra measurements of two calibration sources ( 152 Eu and 22 Na). Second, the gamma emissions from the investigated isotope ( 188 Re) were measured using the TP and its activity was determined using γ-ray spectroscopy methods. Ambient background, scatter, and source-geometry corrections were applied during the efficiency and activity determination steps. Third, the TP-based 188 Re activity was used to determine the dose calibrator settings following the calibration curve method [B. E. Zimmerman et al., J. Nucl. Med. 40, 1508-1516 (1999)]. The interobserver reproducibility of TP measurements was determined by the coefficient of variation (COV) and uncertainties associated to each step of the measuring process were estimated. The accuracy of activity measurements using the proposed method was evaluated by comparing the TP activity estimates of 99m Tc
Discrete Event System Based Pyroprocessing Modeling and Simulation: Oxide Reduction
International Nuclear Information System (INIS)
Lee, H. J.; Ko, W. I.; Choi, S. Y.; Kim, S. K.; Hur, J. M.; Choi, E. Y.; Im, H. S.; Park, K. I.; Kim, I. T.
2014-01-01
Dynamic changes according to the batch operation cannot be predicted in an equilibrium material flow. This study began to build a dynamic material balance model based on the previously developed pyroprocessing flowsheet. As a mid- and long-term research, an integrated pyroprocessing simulator is being developed at the Korea Atomic Energy Research Institute (KAERI) to cope with a review on the technical feasibility, safeguards assessment, conceptual design of facility, and economic feasibility evaluation. The most fundamental thing in such a simulator development is to establish the dynamic material flow framework. This study focused on the operation modeling of pyroprocessing to implement a dynamic material flow. As a case study, oxide reduction was investigated in terms of a dynamic material flow. DES based modeling was applied to build a pyroprocessing operation model. A dynamic material flow as the basic framework for an integrated pyroprocessing was successfully implemented through ExtendSim's internal database and item blocks. Complex operation logic behavior was verified, for example, an oxide reduction process in terms of dynamic material flow. Compared to the equilibrium material flow, a model-based dynamic material flow provides such detailed information that a careful analysis of every batch is necessary to confirm the dynamic material balance results. With the default scenario of oxide reduction, the batch mass balance was verified in comparison with a one-year equilibrium mass balance. This study is still under progress with a mid-and long-term goal, the development of a multi-purpose pyroprocessing simulator that is able to cope with safeguards assessment, economic feasibility, technical evaluation, conceptual design, and support of licensing for a future pyroprocessing facility
Khan, Azizuddin; Sharma, Narendra K; Dixit, Shikha
2008-09-01
Prospective memory is memory for the realization of delayed intention. Researchers distinguish 2 kinds of prospective memory: event- and time-based (G. O. Einstein & M. A. McDaniel, 1990). Taking that distinction into account, the present authors explored participants' comparative performance under event- and time-based tasks. In an experimental study of 80 participants, the authors investigated the roles of cognitive load and task condition in prospective memory. Cognitive load (low vs. high) and task condition (event- vs. time-based task) were the independent variables. Accuracy in prospective memory was the dependent variable. Results showed significant differential effects under event- and time-based tasks. However, the effect of cognitive load was more detrimental in time-based prospective memory. Results also revealed that time monitoring is critical in successful performance of time estimation and so in time-based prospective memory. Similarly, participants' better performance on the event-based prospective memory task showed that they acted on the basis of environment cues. Event-based prospective memory was environmentally cued; time-based prospective memory required self-initiation.
Crump, William J.; Janik, Daniel S.; Thomas, L. Dale
1990-01-01
U.S. space missions have to this point used water either made on board or carried from earth and discarded after use. For Space Station Freedom, long duration life support will include air and water recycling using a series of physical-chemical subsystems. The Environmental Control and Life Support System (ECLSS) designed for this application must be tested extensively at all stages of hardware maturity. Human test subjects are required to conduct some of these tests, and the risks associated with the use of development hardware must be addressed. Federal guidelines for protection of human subjects require careful consideration of risks and potential benefits by an Institutional Review Board (IRB) before and during testing. This paper reviews the ethical principles guiding this consideration, details the problems and uncertainties inherent in current hardware testing, and presents an incremental approach to risk assessment for ECLSS testing.
Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties
Directory of Open Access Journals (Sweden)
Qinghai Zhao
2015-01-01
Full Text Available A robust topology optimization (RTO approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach.
Module-based Hybrid Uncertainty Quantification for Multi-physics Applications: Theory and Software
Energy Technology Data Exchange (ETDEWEB)
Tong, Charles [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chen, Xiao [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Iaccarino, Gianluca [Stanford Univ., CA (United States); Mittal, Akshay [Stanford Univ., CA (United States)
2013-10-08
In this project we proposed to develop an innovative uncertainty quantification methodology that captures the best of the two competing approaches in UQ, namely, intrusive and non-intrusive approaches. The idea is to develop the mathematics and the associated computational framework and algorithms to facilitate the use of intrusive or non-intrusive UQ methods in different modules of a multi-physics multi-module simulation model in a way that physics code developers for different modules are shielded (as much as possible) from the chores of accounting for the uncertain ties introduced by the other modules. As the result of our research and development, we have produced a number of publications, conference presentations, and a software product.
Uncertainty and Sensitivity Analysis for an Ibuprofen Synthesis Model Based on Hoechst Path
DEFF Research Database (Denmark)
da Conceicao Do Carmo Montes, Frederico; Gernaey, Krist V.; Sin, Gürkan
2017-01-01
into consideration the effects of temperature, acidity, and the choice of the catalyst. Parameter estimation and uncertainty analysis were conducted on the kinetic model parameters using experimental data available in the literature. Finally, one factor at a time sensitivity analysis in the form of deviations......The pharmaceutical industry faces several challenges and barriers when implementing new or improving current pharmaceutical processes, such as competition from generic drug manufacturers and stricter regulations from the U.S. Food and Drug Administration and the European Medicine agency. The demand...... for efficient and reliable models to simulate and design/improve pharmaceutical processes is therefore increasing. For the case of ibuprofen, a well-known anti-inflammatory drug, the existing models do not include its complete synthesis path, usually referring only to one out of aset of different reactions...
Customization of UWB 3D-RTLS Based on the New Uncertainty Model of the AoA Ranging Technique
Directory of Open Access Journals (Sweden)
Bartosz Jachimczyk
2017-01-01
Full Text Available The increased potential and effectiveness of Real-time Locating Systems (RTLSs substantially influence their application spectrum. They are widely used, inter alia, in the industrial sector, healthcare, home care, and in logistic and security applications. The research aims to develop an analytical method to customize UWB-based RTLS, in order to improve their localization performance in terms of accuracy and precision. The analytical uncertainty model of Angle of Arrival (AoA localization in a 3D indoor space, which is the foundation of the customization concept, is established in a working environment. Additionally, a suitable angular-based 3D localization algorithm is introduced. The paper investigates the following issues: the influence of the proposed correction vector on the localization accuracy; the impact of the system’s configuration and LS’s relative deployment on the localization precision distribution map. The advantages of the method are verified by comparing them with a reference commercial RTLS localization engine. The results of simulations and physical experiments prove the value of the proposed customization method. The research confirms that the analytical uncertainty model is the valid representation of RTLS’ localization uncertainty in terms of accuracy and precision and can be useful for its performance improvement. The research shows, that the Angle of Arrival localization in a 3D indoor space applying the simple angular-based localization algorithm and correction vector improves of localization accuracy and precision in a way that the system challenges the reference hardware advanced localization engine. Moreover, the research guides the deployment of location sensors to enhance the localization precision.
Customization of UWB 3D-RTLS Based on the New Uncertainty Model of the AoA Ranging Technique.
Jachimczyk, Bartosz; Dziak, Damian; Kulesza, Wlodek J
2017-01-25
The increased potential and effectiveness of Real-time Locating Systems (RTLSs) substantially influence their application spectrum. They are widely used, inter alia, in the industrial sector, healthcare, home care, and in logistic and security applications. The research aims to develop an analytical method to customize UWB-based RTLS, in order to improve their localization performance in terms of accuracy and precision. The analytical uncertainty model of Angle of Arrival (AoA) localization in a 3D indoor space, which is the foundation of the customization concept, is established in a working environment. Additionally, a suitable angular-based 3D localization algorithm is introduced. The paper investigates the following issues: the influence of the proposed correction vector on the localization accuracy; the impact of the system's configuration and LS's relative deployment on the localization precision distribution map. The advantages of the method are verified by comparing them with a reference commercial RTLS localization engine. The results of simulations and physical experiments prove the value of the proposed customization method. The research confirms that the analytical uncertainty model is the valid representation of RTLS' localization uncertainty in terms of accuracy and precision and can be useful for its performance improvement. The research shows, that the Angle of Arrival localization in a 3D indoor space applying the simple angular-based localization algorithm and correction vector improves of localization accuracy and precision in a way that the system challenges the reference hardware advanced localization engine. Moreover, the research guides the deployment of location sensors to enhance the localization precision.
Parton Shower Uncertainties with Herwig 7: Benchmarks at Leading Order
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.
Design a Learning-Oriented Fall Event Reporting System Based on Kirkpatrick Model.
Zhou, Sicheng; Kang, Hong; Gong, Yang
2017-01-01
Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. We summarized these limitations through literature review, and developed an improved web-based fall event reporting system. The Kirkpatrick model, widely used in the business area for training program evaluation, has been integrated during the design of our system. Different from traditional event reporting systems that only collect and store the reports, our system automatically annotates and analyzes the reported events, and provides users with timely knowledge support specific to the reported event. The paper illustrates the design of our system and how its features are intended to reduce patient falls by learning from previous errors.
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)
A scheme for PET data normalization in event-based motion correction
International Nuclear Information System (INIS)
Zhou, Victor W; Kyme, Andre Z; Fulton, Roger; Meikle, Steven R
2009-01-01
Line of response (LOR) rebinning is an event-based motion-correction technique for positron emission tomography (PET) imaging that has been shown to compensate effectively for rigid motion. It involves the spatial transformation of LORs to compensate for motion during the scan, as measured by a motion tracking system. Each motion-corrected event is then recorded in the sinogram bin corresponding to the transformed LOR. It has been shown previously that the corrected event must be normalized using a normalization factor derived from the original LOR, that is, based on the pair of detectors involved in the original coincidence event. In general, due to data compression strategies (mashing), sinogram bins record events detected on multiple LORs. The number of LORs associated with a sinogram bin determines the relative contribution of each LOR. This paper provides a thorough treatment of event-based normalization during motion correction of PET data using LOR rebinning. We demonstrate theoretically and experimentally that normalization of the corrected event during LOR rebinning should account for the number of LORs contributing to the sinogram bin into which the motion-corrected event is binned. Failure to account for this factor may cause artifactual slice-to-slice count variations in the transverse slices and visible horizontal stripe artifacts in the coronal and sagittal slices of the reconstructed images. The theory and implementation of normalization in conjunction with the LOR rebinning technique is described in detail, and experimental verification of the proposed normalization method in phantom studies is presented.
Resilience-based performance metrics for water resources management under uncertainty
Roach, Tom; Kapelan, Zoran; Ledbetter, Ralph
2018-06-01
This paper aims to develop new, resilience type metrics for long-term water resources management under uncertain climate change and population growth. Resilience is defined here as the ability of a water resources management system to 'bounce back', i.e. absorb and then recover from a water deficit event, restoring the normal system operation. Ten alternative metrics are proposed and analysed addressing a range of different resilience aspects including duration, magnitude, frequency and volume of related water deficit events. The metrics were analysed on a real-world case study of the Bristol Water supply system in the UK and compared with current practice. The analyses included an examination of metrics' sensitivity and correlation, as well as a detailed examination into the behaviour of metrics during water deficit periods. The results obtained suggest that multiple metrics which cover different aspects of resilience should be used simultaneously when assessing the resilience of a water resources management system, leading to a more complete understanding of resilience compared with current practice approaches. It was also observed that calculating the total duration of a water deficit period provided a clearer and more consistent indication of system performance compared to splitting the deficit periods into the time to reach and time to recover from the worst deficit events.
Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J
2017-06-01
In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision
Jay, Sylvain; Guillaume, Mireille; Chami, Malik; Minghelli, Audrey; Deville, Yannick; Lafrance, Bruno; Serfaty, Véronique
2018-01-22
We present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently for any set of ocean color parameters and any sensor configuration, directly providing the minimum estimation variance that can be possibly attained by any unbiased estimator of any targeted parameter. Here, CRBs are explicitly developed using (1) two water reflectance models corresponding to deep and shallow waters, resp., and (2) four probabilistic models describing the environmental noises observed within four Sentinel-2 MSI, HICO, Sentinel-3 OLCI and MODIS images, resp. For both deep and shallow waters, CRBs are shown to be consistent with the experimental estimation variances obtained using two published remote-sensing methods, while not requiring one to perform any inversion. CRBs are also used to investigate to what extent perfect a priori knowledge on one or several geophysical parameters can improve the estimation of remaining unknown parameters. For example, using pre-existing knowledge of bathymetry (e.g., derived from LiDAR) within the inversion is shown to greatly improve the retrieval of bottom cover for shallow waters. Finally, CRBs are shown to provide valuable information on the best estimation performances that may be achieved with the MSI, HICO, OLCI and MODIS configurations for a variety of oceanic, coastal and inland waters. CRBs are thus demonstrated to be an informative and efficient tool to characterize minimum uncertainties in inverted ocean color geophysical parameters.
Conditional uncertainty principle
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.
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
Energy Technology Data Exchange (ETDEWEB)
Brown, C.S., E-mail: csbrown3@ncsu.edu [Department of Nuclear Engineering, North Carolina State University, 2500 Stinson Drive, Raleigh, NC 27695-7909 (United States); Zhang, H., E-mail: Hongbin.Zhang@inl.gov [Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3870 (United States); Kucukboyaci, V., E-mail: kucukbvn@westinghouse.com [Westinghouse Electric Company, 1000 Westinghouse Drive, Cranberry Township, PA 16066 (United States); Sung, Y., E-mail: sungy@westinghouse.com [Westinghouse Electric Company, 1000 Westinghouse Drive, Cranberry Township, PA 16066 (United States)
2016-12-01
Highlights: • Best estimate plus uncertainty (BEPU) analyses of PWR core responses under main steam line break (MSLB) accident. • CASL’s coupled neutron transport/subchannel code VERA-CS. • Wilks’ nonparametric statistical method. • MDNBR 95/95 tolerance limit. - Abstract: VERA-CS (Virtual Environment for Reactor Applications, Core Simulator) is a coupled neutron transport and thermal-hydraulics subchannel code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). VERA-CS was applied to simulate core behavior of a typical Westinghouse-designed 4-loop pressurized water reactor (PWR) with 17 × 17 fuel assemblies in response to two main steam line break (MSLB) accident scenarios initiated at hot zero power (HZP) at the end of the first fuel cycle with the most reactive rod cluster control assembly stuck out of the core. The reactor core boundary conditions at the most DNB limiting time step were determined by a system analysis code. The core inlet flow and temperature distributions were obtained from computational fluid dynamics (CFD) simulations. The two MSLB scenarios consisted of the high and low flow situations, where reactor coolant pumps either continue to operate with offsite power or do not continue to operate since offsite power is unavailable. The best estimate plus uncertainty (BEPU) analysis method was applied using Wilks’ nonparametric statistical approach. In this demonstration of BEPU application, 59 full core simulations were performed for each accident scenario to provide the minimum departure from nucleate boiling ratio (MDNBR) at the 95/95 (95% probability with 95% confidence level) tolerance limit. A parametric goodness-of-fit approach was also applied to the results to obtain the MDNBR value at the 95/95 tolerance limit. Initial sensitivity analysis was performed with the 59 cases per accident scenario by use of Pearson correlation coefficients. The results show that this typical PWR core
International Nuclear Information System (INIS)
Brown, C.S.; Zhang, H.; Kucukboyaci, V.; Sung, Y.
2016-01-01
Highlights: • Best estimate plus uncertainty (BEPU) analyses of PWR core responses under main steam line break (MSLB) accident. • CASL’s coupled neutron transport/subchannel code VERA-CS. • Wilks’ nonparametric statistical method. • MDNBR 95/95 tolerance limit. - Abstract: VERA-CS (Virtual Environment for Reactor Applications, Core Simulator) is a coupled neutron transport and thermal-hydraulics subchannel code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). VERA-CS was applied to simulate core behavior of a typical Westinghouse-designed 4-loop pressurized water reactor (PWR) with 17 × 17 fuel assemblies in response to two main steam line break (MSLB) accident scenarios initiated at hot zero power (HZP) at the end of the first fuel cycle with the most reactive rod cluster control assembly stuck out of the core. The reactor core boundary conditions at the most DNB limiting time step were determined by a system analysis code. The core inlet flow and temperature distributions were obtained from computational fluid dynamics (CFD) simulations. The two MSLB scenarios consisted of the high and low flow situations, where reactor coolant pumps either continue to operate with offsite power or do not continue to operate since offsite power is unavailable. The best estimate plus uncertainty (BEPU) analysis method was applied using Wilks’ nonparametric statistical approach. In this demonstration of BEPU application, 59 full core simulations were performed for each accident scenario to provide the minimum departure from nucleate boiling ratio (MDNBR) at the 95/95 (95% probability with 95% confidence level) tolerance limit. A parametric goodness-of-fit approach was also applied to the results to obtain the MDNBR value at the 95/95 tolerance limit. Initial sensitivity analysis was performed with the 59 cases per accident scenario by use of Pearson correlation coefficients. The results show that this typical PWR core
THE EFFECT OF DEVOTEE-BASED BRAND EQUITY ON RELIGIOUS EVENTS
Directory of Open Access Journals (Sweden)
MUHAMMAD JAWAD IQBAL
2016-04-01
Full Text Available The objective of this research is to apply DBBE model to discover the constructs to measure the religious event as a business brand on the bases of devotees’ perception. SEM technique was applied to measure the hypothesized model of which CFA put to analyze the model and a theoretical model was made to measure the model fit. Sample size was of 500. The base of brand loyalty was affected directly by image and quality. This information might be beneficial to event management and sponsors in making brand and operating visitors’ destinations. More importantly, the brand of these religious events in Pakistan can be built as a strong tourism product.
International Nuclear Information System (INIS)
Wu Weidong; Rao, S.S.
2007-01-01
Many uncertain factors influence the accuracy and repeatability of robots. These factors include manufacturing and assembly tolerances and deviations in actuators and controllers. The effects of these uncertain factors must be carefully analyzed to obtain a clear insight into the manipulator performance. In order to ensure the position and orientation accuracy of a robot end effector as well as to reduce the manufacturing cost of the robot, it is necessary to quantify the influence of the uncertain factors and optimally allocate the tolerances. This involves a study of the direct and inverse kinematics of robot end effectors in the presence of uncertain factors. This paper focuses on the optimal allocation of joint tolerances with consideration of the positional and directional errors of the robot end effector and the manufacturing cost. The interval analysis is used for predicting errors in the performance of robot manipulators. The Stanford manipulator is considered for illustration. The unknown joint variables are modeled as interval parameters due to the inherent uncertainty. The cost-tolerance model is assumed to be of an exponential form during optimization. The effects of the upper bounds on the minimum cost and relative deviations of the directional and positional errors of the end effector are also studied
International Nuclear Information System (INIS)
Machado, Marcio Dornellas
1998-09-01
One of the most important thermalhydraulics safety parameters is the DNBR (Departure from Nucleate Boiling Ratio). The current methodology in use at Eletronuclear to determine DNBR is extremely conservative and may result in penalties to the reactor power due to an increase plugging level of steam generator tubes. This work uses a new methodology to evaluate DNBR, named mini-RTDP. The standard methodology (STDP) currently in use establishes a limit design value which cannot be surpassed. This limit value is determined taking into account the uncertainties of the empirical correlation used in COBRA IIC/MIT code, modified to Angra 1 conditions. The correlation used is the Westinghouse's W-3 and the minimum DNBR (MDBR) value cannot be less than 1.3. The new methodology reduces the excessive level of conservatism associated with the parameters used in the DNBR calculation, which take most unfavorable values in the STDP methodology, by using their best estimate values. The final goal is to obtain a new DNBR design limit which will provide a margin gain due to more realistic parameters values used in the methodology. (author)
International Nuclear Information System (INIS)
Ang, M.L.; Grindon, E.; Dutton, L.M.C.; Garcia-Sedano, P.; Santamaria, C.S.; Centner, B.; Auglaire, M.; Routamo, T.; Outa, S.; Jokiniemi, J.; Gustavsson, V.; Wennerstrom, H.; Spanier, L.; Gren, M.; Boschiero, M-H; Droulas, J-L; Friederichs, H-G; Sonnenkalb, M.
2001-01-01
The purpose of this project is to address the key uncertainties associated with a number of fission product release and transport phenomena in a wider context and to assess their relevance to key severe accident sequences. This project is a wide-based analysis involving eight reactor designs that are representative of the reactors currently operating in the European Union (EU). In total, 20 accident sequences covering a wide range of conditions have been chosen to provide the basis for sensitivity studies. The appraisal is achieved through a systematic risk-based framework developed within this project. Specifically, this is a quantitative interpretation of the sensitivity calculations on the basis of 'significance indicators', applied above defined threshold values. These threshold values represent a good surrogate for 'large release', which is defined in a number of EU countries. In addition, the results are placed in the context of in-containment source term limits, for advanced light water reactor designs, as defined by international guidelines. Overall, despite the phenomenological uncertainties, the predicted source terms (both into the containment, and subsequently, into the environment) do not display a high degree of sensitivity to the individual fission product issues addressed in this project. This is due, mainly, to the substantial capacity for the attenuation of airborne fission products by the designed safety provisions and the natural fission product retention mechanisms within the containment
International Nuclear Information System (INIS)
Flari, Villie; Chaudhry, Qasim; Neslo, Rabin; Cooke, Roger
2011-01-01
Currently, risk assessment of nanotechnology-enabled food products is considered difficult due to the large number of uncertainties involved. We developed an approach which could address some of the main uncertainties through the use of expert judgment. Our approach employs a multi-criteria decision model, based on probabilistic inversion that enables capturing experts’ preferences in regard to safety of nanotechnology-enabled food products, and identifying their opinions in regard to the significance of key criteria that are important in determining the safety of such products. An advantage of these sample-based techniques is that they provide out-of-sample validation and therefore a robust scientific basis. This validation in turn adds predictive power to the model developed. We achieved out-of-sample validation in two ways: (1) a portion of the expert preference data was excluded from the model’s fitting and was then predicted by the model fitted on the remaining rankings and (2) a (partially) different set of experts generated new scenarios, using the same criteria employed in the model, and ranked them; their ranks were compared with ranks predicted by the model. The degree of validation in each method was less than perfect but reasonably substantial. The validated model we applied captured and modelled experts’ preferences regarding safety of hypothetical nanotechnology-enabled food products. It appears therefore that such an approach can provide a promising route to explore further for assessing the risk of nanotechnology-enabled food products.
WILBER and PyWEED: Event-based Seismic Data Request Tools
Falco, N.; Clark, A.; Trabant, C. M.
2017-12-01
WILBER and PyWEED are two user-friendly tools for requesting event-oriented seismic data. Both tools provide interactive maps and other controls for browsing and filtering event and station catalogs, and downloading data for selected event/station combinations, where the data window for each event/station pair may be defined relative to the arrival time of seismic waves from the event to that particular station. Both tools allow data to be previewed visually, and can download data in standard miniSEED, SAC, and other formats, complete with relevant metadata for performing instrument correction. WILBER is a web application requiring only a modern web browser. Once the user has selected an event, WILBER identifies all data available for that time period, and allows the user to select stations based on criteria such as the station's distance and orientation relative to the event. When the user has finalized their request, the data is collected and packaged on the IRIS server, and when it is ready the user is sent a link to download. PyWEED is a downloadable, cross-platform (Macintosh / Windows / Linux) application written in Python. PyWEED allows a user to select multiple events and stations, and will download data for each event/station combination selected. PyWEED is built around the ObsPy seismic toolkit, and allows direct interaction and control of the application through a Python interactive console.
A semi-supervised learning framework for biomedical event extraction based on hidden topics.
Zhou, Deyu; Zhong, Dayou
2015-05-01
Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely
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.
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
Zhang, Huifeng; Lei, Xiaohui; Wang, Chao; Yue, Dong; Xie, Xiangpeng
2017-01-01
Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications.
Event-based aquifer-to-atmosphere modeling over the European CORDEX domain
Keune, J.; Goergen, K.; Sulis, M.; Shrestha, P.; Springer, A.; Kusche, J.; Ohlwein, C.; Kollet, S. J.
2014-12-01
Despite the fact that recent studies focus on the impact of soil moisture on climate and especially land-energy feedbacks, groundwater dynamics are often neglected or conceptual groundwater flow models are used. In particular, in the context of climate change and the occurrence of droughts and floods, a better understanding and an improved simulation of the physical processes involving groundwater on continental scales is necessary. This requires the implementation of a physically consistent terrestrial modeling system, which explicitly incorporates groundwater dynamics and the connection with shallow soil moisture. Such a physics-based system enables simulations and monitoring of groundwater storage and enhanced representations of the terrestrial energy and hydrologic cycles over long time periods. On shorter timescales, the prediction of groundwater-related extremes, such as floods and droughts, are expected to improve, because of the improved simulation of components of the hydrological cycle. In this study, we present a fully coupled aquifer-to-atmosphere modeling system over the European CORDEX domain. The integrated Terrestrial Systems Modeling Platform, TerrSysMP, consisting of the three-dimensional subsurface model ParFlow, the Community Land Model CLM3.5 and the numerical weather prediction model COSMO of the German Weather Service, is used. The system is set up with a spatial resolution of 0.11° (12.5km) and closes the terrestrial water and energy cycles from aquifers into the atmosphere. Here, simulations of the fully coupled system are performed over events, such as the 2013 flood in Central Europe and the 2003 European heat wave, and over extended time periods on the order of 10 years. State and flux variables of the terrestrial hydrologic and energy cycle are analyzed and compared to both in situ (e.g. stream and water level gauge networks, FLUXNET) and remotely sensed observations (e.g. GRACE, ESA ICC ECV soil moisture and SMOS). Additionally, the
Katsu, Noriko; Yamada, Kazunori; Nakamichi, Masayuki
2017-01-01
We investigated the use of vocalizations called "grunts," "girneys," and "coos" accompanied by post-conflict affiliative interaction between former opponents (reconciliation) in Japanese macaques (Macaca fuscata). Although reconciliation functions to repair bonds, such interactions sometimes entail risks of receiving further aggression. Vocalizations can be used at a distance from the former opponent; thus, we predict that vocalizations are used particularly by victims of a conflict, and are frequently used in situations of uncertainty when it is difficult for them to estimate whether the former opponent will resume aggression. In addition, we predict that vocalizations are effective in preventing further aggression. To test these hypotheses, we conducted observations of post-conflict and matched-control situations in female Japanese macaques living in a free-ranging group. We found that former opponents tended to be attracted to each other within the first minute following a conflict, thus demonstrating reconciliation behavior. Vocalizations were more frequently used by the victims in post-conflict interactions than under control situations; however, this tendency was not found in aggressors. When affiliation with the former opponent occurred, victims were more likely to use vocalizations towards less familiar opponents. These findings suggest that Japanese macaques used vocalizations more often when interacting with less predictable former opponents. Victims were more likely to receive aggression from former aggressors when engaged in affiliations with them than under no such affiliations. No significant differences were found in the probability of the victims receiving aggression, regardless of whether they used vocalizations; thus, whether the victim benefits from using vocalizations in these contexts remains unclear. Japanese macaques form despotic societies and therefore, further aggression was inevitable, to some degree, after a conflict. The use of
Directory of Open Access Journals (Sweden)
Noriko Katsu
Full Text Available We investigated the use of vocalizations called "grunts," "girneys," and "coos" accompanied by post-conflict affiliative interaction between former opponents (reconciliation in Japanese macaques (Macaca fuscata. Although reconciliation functions to repair bonds, such interactions sometimes entail risks of receiving further aggression. Vocalizations can be used at a distance from the former opponent; thus, we predict that vocalizations are used particularly by victims of a conflict, and are frequently used in situations of uncertainty when it is difficult for them to estimate whether the former opponent will resume aggression. In addition, we predict that vocalizations are effective in preventing further aggression. To test these hypotheses, we conducted observations of post-conflict and matched-control situations in female Japanese macaques living in a free-ranging group. We found that former opponents tended to be attracted to each other within the first minute following a conflict, thus demonstrating reconciliation behavior. Vocalizations were more frequently used by the victims in post-conflict interactions than under control situations; however, this tendency was not found in aggressors. When affiliation with the former opponent occurred, victims were more likely to use vocalizations towards less familiar opponents. These findings suggest that Japanese macaques used vocalizations more often when interacting with less predictable former opponents. Victims were more likely to receive aggression from former aggressors when engaged in affiliations with them than under no such affiliations. No significant differences were found in the probability of the victims receiving aggression, regardless of whether they used vocalizations; thus, whether the victim benefits from using vocalizations in these contexts remains unclear. Japanese macaques form despotic societies and therefore, further aggression was inevitable, to some degree, after a conflict
Central FPGA-based Destination and Load Control in the LHCb MHz Event Readout
Jacobsson, Richard
2012-01-01
The readout strategy of the LHCb experiment [1] is based on complete event readout at 1 MHz [2]. Over 300 sub-detector readout boards transmit event fragments at 1 MHz over a commercial 70 Gigabyte/s switching network to a distributed event building and trigger processing farm with 1470 individual multi-core computer nodes [3]. In the original specifications, the readout was based on a pure push protocol. This paper describes the proposal, implementation, and experience of a powerful non-conventional mixture of a push and a pull protocol, akin to credit-based flow control. A high-speed FPGA-based central master module controls the event fragment packing in the readout boards, the assignment of the farm node destination for each event, and controls the farm load based on an asynchronous pull mechanism from each farm node. This dynamic readout scheme relies on generic event requests and the concept of node credit allowing load balancing and trigger rate regulation as a function of the global farm load. It also ...
Central FPGA-based destination and load control in the LHCb MHz event readout
Jacobsson, R.
2012-10-01
The readout strategy of the LHCb experiment is based on complete event readout at 1 MHz. A set of 320 sub-detector readout boards transmit event fragments at total rate of 24.6 MHz at a bandwidth usage of up to 70 GB/s over a commercial switching network based on Gigabit Ethernet to a distributed event building and high-level trigger processing farm with 1470 individual multi-core computer nodes. In the original specifications, the readout was based on a pure push protocol. This paper describes the proposal, implementation, and experience of a non-conventional mixture of a push and a pull protocol, akin to credit-based flow control. An FPGA-based central master module, partly operating at the LHC bunch clock frequency of 40.08 MHz and partly at a double clock speed, is in charge of the entire trigger and readout control from the front-end electronics up to the high-level trigger farm. One FPGA is dedicated to controlling the event fragment packing in the readout boards, the assignment of the farm node destination for each event, and controls the farm load based on an asynchronous pull mechanism from each farm node. This dynamic readout scheme relies on generic event requests and the concept of node credit allowing load control and trigger rate regulation as a function of the global farm load. It also allows the vital task of fast central monitoring and automatic recovery in-flight of failing nodes while maintaining dead-time and event loss at a minimum. This paper demonstrates the strength and suitability of implementing this real-time task for a very large distributed system in an FPGA where no random delays are introduced, and where extreme reliability and accurate event accounting are fundamental requirements. It was in use during the entire commissioning phase of LHCb and has been in faultless operation during the first two years of physics luminosity data taking.
Central FPGA-based destination and load control in the LHCb MHz event readout
International Nuclear Information System (INIS)
Jacobsson, R.
2012-01-01
The readout strategy of the LHCb experiment is based on complete event readout at 1 MHz. A set of 320 sub-detector readout boards transmit event fragments at total rate of 24.6 MHz at a bandwidth usage of up to 70 GB/s over a commercial switching network based on Gigabit Ethernet to a distributed event building and high-level trigger processing farm with 1470 individual multi-core computer nodes. In the original specifications, the readout was based on a pure push protocol. This paper describes the proposal, implementation, and experience of a non-conventional mixture of a push and a pull protocol, akin to credit-based flow control. An FPGA-based central master module, partly operating at the LHC bunch clock frequency of 40.08 MHz and partly at a double clock speed, is in charge of the entire trigger and readout control from the front-end electronics up to the high-level trigger farm. One FPGA is dedicated to controlling the event fragment packing in the readout boards, the assignment of the farm node destination for each event, and controls the farm load based on an asynchronous pull mechanism from each farm node. This dynamic readout scheme relies on generic event requests and the concept of node credit allowing load control and trigger rate regulation as a function of the global farm load. It also allows the vital task of fast central monitoring and automatic recovery in-flight of failing nodes while maintaining dead-time and event loss at a minimum. This paper demonstrates the strength and suitability of implementing this real-time task for a very large distributed system in an FPGA where no random delays are introduced, and where extreme reliability and accurate event accounting are fundamental requirements. It was in use during the entire commissioning phase of LHCb and has been in faultless operation during the first two years of physics luminosity data taking.
Directory of Open Access Journals (Sweden)
Ronald Jean Degen
2017-12-01
Full Text Available Wisdom, uncertainty, and ambiguity will always exist in management decisions. One danger for firms lies in managers making decisions based on faulty theories acquired through personal experiences or learned from the experiences of others. Often, these decisions don’t generate the expected outcome and may even put the future of the firm at risk. Managers, to avoid this risk, are required to become wiser, more discerning, and more appropriately skeptical toward personal theories or theories learned from management gurus that propose simplistic formulas and quick-fix remedies. In this paper, the author discusses the risk of decisions based on personal theories or theories learned from others, the business research methods used to substantiate these theories, the philosophical assumptions of business research, the strength and weaknesses of qualitative and quantitative research methods, the benefits of combining both methods, and the trustworthiness of research methods in general for substantiating the theories used by managers in their decision-making.
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.
Visualizing Uncertainty of Point Phenomena by Redesigned Error Ellipses
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.
An Event-Based Approach to Distributed Diagnosis of Continuous Systems
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.
Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs
Xu, Xianghua; Gao, Xueyong; Wan, Jian; Xiong, Naixue
2011-01-01
This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms. PMID:22163972
Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs
Directory of Open Access Journals (Sweden)
Jian Wan
2011-06-01
Full Text Available This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.
Tian, X.; Zhang, Y.
2018-03-01
Herglotz variational principle, in which the functional is defined by a differential equation, generalizes the classical ones defining the functional by an integral. The principle gives a variational principle description of nonconservative systems even when the Lagrangian is independent of time. This paper focuses on studying the Noether's theorem and its inverse of a Birkhoffian system in event space based on the Herglotz variational problem. Firstly, according to the Herglotz variational principle of a Birkhoffian system, the principle of a Birkhoffian system in event space is established. Secondly, its parametric equations and two basic formulae for the variation of Pfaff-Herglotz action of a Birkhoffian system in event space are obtained. Furthermore, the definition and criteria of Noether symmetry of the Birkhoffian system in event space based on the Herglotz variational problem are given. Then, according to the relationship between the Noether symmetry and conserved quantity, the Noether's theorem is derived. Under classical conditions, Noether's theorem of a Birkhoffian system in event space based on the Herglotz variational problem reduces to the classical ones. In addition, Noether's inverse theorem of the Birkhoffian system in event space based on the Herglotz variational problem is also obtained. In the end of the paper, an example is given to illustrate the application of the results.
Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data
Directory of Open Access Journals (Sweden)
Xiaolu Zhou
2017-03-01
Full Text Available Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation and a social event (Pope Francis’ visit to the US in the New York City—Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information, this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.
Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation
Directory of Open Access Journals (Sweden)
Mengdi Wang
2017-01-01
Full Text Available Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.
Kim, Jung-Jae; Rebholz-Schuhmann, Dietrich
2011-10-06
The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision) and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.
Evaluation of extreme temperature events in northern Spain based on process control charts
Villeta, M.; Valencia, J. L.; Saá, A.; Tarquis, A. M.
2018-02-01
Extreme climate events have recently attracted the attention of a growing number of researchers because these events impose a large cost on agriculture and associated insurance planning. This study focuses on extreme temperature events and proposes a new method for their evaluation based on statistical process control tools, which are unusual in climate studies. A series of minimum and maximum daily temperatures for 12 geographical areas of a Spanish region between 1931 and 2009 were evaluated by applying statistical process control charts to statistically test whether evidence existed for an increase or a decrease of extreme temperature events. Specification limits were determined for each geographical area and used to define four types of extreme anomalies: lower and upper extremes for the minimum and maximum anomalies. A new binomial Markov extended process that considers the autocorrelation between extreme temperature events was generated for each geographical area and extreme anomaly type to establish the attribute control charts for the annual fraction of extreme days and to monitor the occurrence of annual extreme days. This method was used to assess the significance of changes and trends of extreme temperature events in the analysed region. The results demonstrate the effectiveness of an attribute control chart for evaluating extreme temperature events. For example, the evaluation of extreme maximum temperature events using the proposed statistical process control charts was consistent with the evidence of an increase in maximum temperatures during the last decades of the last century.
Directory of Open Access Journals (Sweden)
Kim Jung-jae
2011-10-01
Full Text Available Abstract Background The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. Results We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Conclusions Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.
Fluence-based and microdosimetric event-based methods for radiation protection in space
International Nuclear Information System (INIS)
Curtis, S.B.
2002-01-01
The National Council on Radiation Protection and Measurements (NCRP) has recently published a report (Report no.137) that discusses various aspects of the concepts used in radiation protection and the difficulties in measuring the radiation environment in spacecraft for the estimation of radiation risk to space travelers. Two novel dosimetric methodologies, fluence-based and microdosimetric event-based methods, are discussed and evaluated, along with the more conventional quality factor/linear energy transfer (LET) method. It was concluded that for the present, any reason to switch to a new methodology is not compelling. It is suggested that because of certain drawbacks in the presently-used conventional method, these alternative methodologies should be kept in mind. As new data become available and dosimetric techniques become more refined, the question should be revisited and that in the future, significant improvement might be realized. In addition, such concepts as equivalent dose and organ dose equivalent are discussed and various problems regarding the measurement/estimation of these quantities are presented. (author)
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
Event-based motion correction for PET transmission measurements with a rotating point source
International Nuclear Information System (INIS)
Zhou, Victor W; Kyme, Andre Z; Meikle, Steven R; Fulton, Roger
2011-01-01
Accurate attenuation correction is important for quantitative positron emission tomography (PET) studies. When performing transmission measurements using an external rotating radioactive source, object motion during the transmission scan can distort the attenuation correction factors computed as the ratio of the blank to transmission counts, and cause errors and artefacts in reconstructed PET images. In this paper we report a compensation method for rigid body motion during PET transmission measurements, in which list mode transmission data are motion corrected event-by-event, based on known motion, to ensure that all events which traverse the same path through the object are recorded on a common line of response (LOR). As a result, the motion-corrected transmission LOR may record a combination of events originally detected on different LORs. To ensure that the corresponding blank LOR records events from the same combination of contributing LORs, the list mode blank data are spatially transformed event-by-event based on the same motion information. The number of counts recorded on the resulting blank LOR is then equivalent to the number of counts that would have been recorded on the corresponding motion-corrected transmission LOR in the absence of any attenuating object. The proposed method has been verified in phantom studies with both stepwise movements and continuous motion. We found that attenuation maps derived from motion-corrected transmission and blank data agree well with those of the stationary phantom and are significantly better than uncorrected attenuation data.
A robust neural network-based approach for microseismic event detection
Akram, Jubran
2017-08-17
We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.
Duerdoth, Ian
2009-01-01
The subject of uncertainties (sometimes called errors) is traditionally taught (to first-year science undergraduates) towards the end of a course on statistics that defines probability as the limit of many trials, and discusses probability distribution functions and the Gaussian distribution. We show how to introduce students to the concepts of…
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas
2002-01-01
Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration...
International Nuclear Information System (INIS)
Miller, R.; Wilpert, B.; Fahlbruch, B.
1999-01-01
This paper discusses a method for analysing safety-relevant events in NPP which is known as 'SOL', safety based on organisational learning. After discussion of the specific organisational and psychological problems examined in the event analysis, the analytic process using the SOL approach is explained as well as the required general setting. The SOL approach has been tested both with scientific experiments and from the practical perspective, by operators of NPPs and experts from other branches of industry. (orig./CB) [de
Rates for parallax-shifted microlensing events from ground-based observations of the galactic bulge
International Nuclear Information System (INIS)
Buchalter, A.; Kamionkowski, M.
1997-01-01
The parallax effect in ground-based microlensing (ML) observations consists of a distortion to the standard ML light curve arising from the Earth's orbital motion. This can be used to partially remove the degeneracy among the system parameters in the event timescale, t 0 . In most cases, the resolution in current ML surveys is not accurate enough to observe this effect, but parallax could conceivably be detected with frequent follow-up observations of ML events in progress, providing the photometric errors are small enough. We calculate the expected fraction of ML events where the shape distortions will be observable by such follow-up observations, adopting Galactic models for the lens and source distributions that are consistent with observed microlensing timescale distributions. We study the dependence of the rates for parallax-shifted events on the frequency of follow-up observations and on the precision of the photometry. For example, we find that for hourly observations with typical photometric errors of 0.01 mag, 6% of events where the lens is in the bulge, and 31% of events where the lens is in the disk (or ∼10% of events overall), will give rise to a measurable parallax shift at the 95% confidence level. These fractions may be increased by improved photometric accuracy and increased sampling frequency. While long-duration events are favored, the surveys would be effective in picking out such distortions in events with timescales as low as t 0 ∼20 days. We study the dependence of these fractions on the assumed disk mass function and find that a higher parallax incidence is favored by mass functions with higher mean masses. Parallax measurements yield the reduced transverse speed, v, which gives both the relative transverse speed and lens mass as a function of distance. We give examples of the accuracies with which v may be measured in typical parallax events. (Abstract Truncated)
Knowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty
Ud Din, Muhayy; Akbari, Aliakbar; Rosell Gratacòs, Jan
2017-01-01
Grasping an object in unstructured and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exits. High-level knowledge and reasoning processes, as well as the allowing of interaction between objects, can enhance the planning efficiency in such environments. In this direction, this study proposes a knowledge-oriented physics-based motion planning approach for a hand-arm system that uses a high-level knowledge-based reasoning to partition the wor...
Full-waveform detection of non-impulsive seismic events based on time-reversal methods
Solano, Ericka Alinne; Hjörleifsdóttir, Vala; Liu, Qinya
2017-12-01
We present a full-waveform detection method for non-impulsive seismic events, based on time-reversal principles. We use the strain Green's tensor as a matched filter, correlating it with continuous observed seismograms, to detect non-impulsive seismic events. We show that this is mathematically equivalent to an adjoint method for detecting earthquakes. We define the detection function, a scalar valued function, which depends on the stacked correlations for a group of stations. Event detections are given by the times at which the amplitude of the detection function exceeds a given value relative to the noise level. The method can make use of the whole seismic waveform or any combination of time-windows with different filters. It is expected to have an advantage compared to traditional detection methods for events that do not produce energetic and impulsive P waves, for example glacial events, landslides, volcanic events and transform-fault earthquakes for events which velocity structure along the path is relatively well known. Furthermore, the method has advantages over empirical Greens functions template matching methods, as it does not depend on records from previously detected events, and therefore is not limited to events occurring in similar regions and with similar focal mechanisms as these events. The method is not specific to any particular way of calculating the synthetic seismograms, and therefore complicated structural models can be used. This is particularly beneficial for intermediate size events that are registered on regional networks, for which the effect of lateral structure on the waveforms can be significant. To demonstrate the feasibility of the method, we apply it to two different areas located along the mid-oceanic ridge system west of Mexico where non-impulsive events have been reported. The first study area is between Clipperton and Siqueiros transform faults (9°N), during the time of two earthquake swarms, occurring in March 2012 and May
Supervision in the PC based prototype for the ATLAS event filter
Bee, C P; Etienne, F; Fede, E; Meessen, C; Nacasch, R; Qian, Z; Touchard, F
1999-01-01
A prototype of the ATLAS event filter based on commodity PCs linked by a Fast Ethernet switch has been developed in Marseille. The present contribution focus on the supervision aspects of the prototype based on Java and Java mobile agents technology. (5 refs).
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)
Robust optimization-based DC optimal power flow for managing wind generation uncertainty
Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn
2012-11-01
Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.
Zahmatkesh, Zahra; Karamouz, Mohammad
2017-10-17
The continued development efforts around the world, growing population, and the increased probability of occurrence of extreme hydrologic events have adversely affected natural and built environments. Flood damages and loss of lives from the devastating storms, such as Irene and Sandy on the East Coast of the USA, are examples of the vulnerability to flooding that even developed countries have to face. The odds of coastal flooding disasters have been increased due to accelerated sea level rise, climate change impacts, and communities' interest to live near the coastlines. Climate change, for instance, is becoming a major threat to sustainable development because of its adverse impacts on the hydrologic cycle. Effective management strategies are thus required for flood vulnerability reduction and disaster preparedness. This paper is an extension to the flood resilience studies in the New York City coastal watershed. Here, a framework is proposed to quantify coastal flood vulnerability while accounting for climate change impacts. To do so, a multi-criteria decision making (MCDM) approach that combines watershed characteristics (factors) and their weights is proposed to quantify flood vulnerability. Among the watershed characteristics, potential variation in the hydrologic factors under climate change impacts is modeled utilizing the general circulation models' (GCMs) outputs. The considered factors include rainfall, extreme water level, and sea level rise that exacerbate flood vulnerability through increasing exposure and susceptibility to flooding. Uncertainty in the weights as well as values of factors is incorporated in the analysis using the Monte Carlo (MC) sampling method by selecting the best-fitted distributions to the parameters with random nature. A number of low impact development (LID) measures are then proposed to improve watershed adaptive capacity to deal with coastal flooding. Potential range of current and future vulnerability to flooding is
Research on reverse logistics location under uncertainty environment based on grey prediction
Zhenqiang, Bao; Congwei, Zhu; Yuqin, Zhao; Quanke, Pan
This article constructs reverse logistic network based on uncertain environment, integrates the reverse logistics network and distribution network, and forms a closed network. An optimization model based on cost is established to help intermediate center, manufacturing center and remanufacturing center make location decision. A gray model GM (1, 1) is used to predict the product holdings of the collection points, and then prediction results are carried into the cost optimization model and a solution is got. Finally, an example is given to verify the effectiveness and feasibility of the model.
Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation
Minasny, B.; Vrugt, J.A.; McBratney, A.B.
2011-01-01
This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior
DEFF Research Database (Denmark)
Morales Rodriguez, Ricardo; Meyer, Anne S.; Gernaey, Krist
2012-01-01
of up to 0.13 USD/gal-ethanol. Further stochastic optimization demonstrated the options for further reduction of the production costs with different processing configurations, reaching a reduction of up to 28% in the production cost in the SHCF configuration compared to the base case operation. Further...
Turbidity-based sediment monitoring in northern Thailand: Hysteresis, variability, and uncertainty
Annual total suspended solid (TSS) loads in the Mae Sa Catchment in northern Thailand, determined with an automated, turbidity-based monitoring approach, were approximately 62,000, 33,000, and 14,000 Mg during the three years of observation. These loads were equivalent to basin y...
We present a multi-faceted sensitivity analysis of a spatially explicit, individual-based model (IBM) (HexSim) of a threatened species, the Northern Spotted Owl (Strix occidentalis caurina) on a national forest in Washington, USA. Few sensitivity analyses have been conducted on ...
Neural correlates of attentional and mnemonic processing in event-based prospective memory
Directory of Open Access Journals (Sweden)
Justin B Knight
2010-02-01
Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
Neural correlates of attentional and mnemonic processing in event-based prospective memory.
Knight, Justin B; Ethridge, Lauren E; Marsh, Richard L; Clementz, Brett A
2010-01-01
Prospective memory (PM), or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT), followed by a LDT with an embedded PM component. Event-based cues were constituted by color and lexicality (red words). Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP) revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
Directory of Open Access Journals (Sweden)
Sol Ha
2016-01-01
Full Text Available This paper suggests an event-based scenario manager capable of creating and editing a scenario for shipbuilding process simulation based on multibody dynamics. To configure various situation in shipyards and easily connect with multibody dynamics, the proposed method has two main concepts: an Actor and an Action List. The Actor represents the anatomic unit of action in the multibody dynamics and can be connected to a specific component of the dynamics kernel such as the body and joint. The user can make a scenario up by combining the actors. The Action List contains information for arranging and executing the actors. Since the shipbuilding process is a kind of event-based sequence, all simulation models were configured using Discrete EVent System Specification (DEVS formalism. The proposed method was applied to simulations of various operations in shipyards such as lifting and erection of a block and heavy load lifting operation using multiple cranes.
Limits on the efficiency of event-based algorithms for Monte Carlo neutron transport
Directory of Open Access Journals (Sweden)
Paul K. Romano
2017-09-01
Full Text Available The traditional form of parallelism in Monte Carlo particle transport simulations, wherein each individual particle history is considered a unit of work, does not lend itself well to data-level parallelism. Event-based algorithms, which were originally used for simulations on vector processors, may offer a path toward better utilizing data-level parallelism in modern computer architectures. In this study, a simple model is developed for estimating the efficiency of the event-based particle transport algorithm under two sets of assumptions. Data collected from simulations of four reactor problems using OpenMC was then used in conjunction with the models to calculate the speedup due to vectorization as a function of the size of the particle bank and the vector width. When each event type is assumed to have constant execution time, the achievable speedup is directly related to the particle bank size. We observed that the bank size generally needs to be at least 20 times greater than vector size to achieve vector efficiency greater than 90%. When the execution times for events are allowed to vary, the vector speedup is also limited by differences in the execution time for events being carried out in a single event-iteration.
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...
Risk-based ranking of dominant contributors to maritime pollution events
International Nuclear Information System (INIS)
Wheeler, T.A.
1993-01-01
This report describes a conceptual approach for identifying dominant contributors to risk from maritime shipping of hazardous materials. Maritime transportation accidents are relatively common occurrences compared to more frequently analyzed contributors to public risk. Yet research on maritime safety and pollution incidents has not been guided by a systematic, risk-based approach. Maritime shipping accidents can be analyzed using event trees to group the accidents into 'bins,' or groups, of similar characteristics such as type of cargo, location of accident (e.g., harbor, inland waterway), type of accident (e.g., fire, collision, grounding), and size of release. The importance of specific types of events to each accident bin can be quantified. Then the overall importance of accident events to risk can be estimated by weighting the events' individual bin importance measures by the risk associated with each accident bin. 4 refs., 3 figs., 6 tabs
Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo
2017-07-01
This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.
DEFF Research Database (Denmark)
Nguyen, Daniel Xuyen
This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models....... This retooling addresses several shortcomings. First, the imperfect correlation of demands reconciles the sales variation observed in and across destinations. Second, since demands for the firm's output are correlated across destinations, a firm can use previously realized demands to forecast unknown demands...... in untested destinations. The option to forecast demands causes firms to delay exporting in order to gather more information about foreign demand. Third, since uncertainty is resolved after entry, many firms enter a destination and then exit after learning that they cannot profit. This prediction reconciles...
Golias, Mihalis M.
2011-01-01
Berth scheduling is a critical function at marine container terminals and determining the best berth schedule depends on several factors including the type and function of the port, size of the port, location, nearby competition, and type of contractual agreement between the terminal and the carriers. In this paper we formulate the berth scheduling problem as a bi-objective mixed-integer problem with the objective to maximize customer satisfaction and reliability of the berth schedule under the assumption that vessel handling times are stochastic parameters following a discrete and known probability distribution. A combination of an exact algorithm, a Genetic Algorithms based heuristic and a simulation post-Pareto analysis is proposed as the solution approach to the resulting problem. Based on a number of experiments it is concluded that the proposed berth scheduling policy outperforms the berth scheduling policy where reliability is not considered.
Set-Based Approach to Design under Uncertainty and Applications to Shaping a Hydrofoil
2016-01-01
given requirements. This notion of set-based designwas pioneered by Toyota and adopted by the U.S. Navy [1]. It responds to most real-world design...in such a way that all desired shape variations are allowed both on the suction and pressure side. Figure 2 gives a schematic representation of the...of the hydrofoil. The control points of the pressure side have been changed in different ways to en- sure the overall hydrodynamic performance
Arun Kaintura; Tom Dhaene; Domenico Spina
2018-01-01
Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standard Monte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developm...
CMOS-based Stochastically Spiking Neural Network for Optimization under Uncertainties
2017-03-01
cost function/constraint variables are generated based on inverse transform on CDF. In Fig. 5, F-1(u) for uniformly distributed random number u [0, 1... Inverse transform on CDF to extract random sample of variable x. (b) Histogram of samples. Figure 6: (a) Successive approximation circuit for... inverse transform evaluation on CDF. (b) Inverse transform transients. 262 generator (RNG) generates a sample value u. SA circuit evaluates F(xin
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
International Nuclear Information System (INIS)
Bell, L R; Pogson, E M; Metcalfe, P; Holloway, L; Dowling, J A
2017-01-01
Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes. (paper)
Development of uncertainty-based work injury model using Bayesian structural equation modelling.
Chatterjee, Snehamoy
2014-01-01
This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.
Bayesian risk-based decision method for model validation under uncertainty
International Nuclear Information System (INIS)
Jiang Xiaomo; Mahadevan, Sankaran
2007-01-01
This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment
Uncertainty quantification in flood risk assessment
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.
Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.
Wang, Ding; He, Haibo; Liu, Derong
2017-10-01
In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.