WorldWideScience

Sample records for internal probabilistic model

  1. 14th International Probabilistic Workshop

    CERN Document Server

    Taerwe, Luc; Proske, Dirk

    2017-01-01

    This book presents the proceedings of the 14th International Probabilistic Workshop that was held in Ghent, Belgium in December 2016. Probabilistic methods are currently of crucial importance for research and developments in the field of engineering, which face challenges presented by new materials and technologies and rapidly changing societal needs and values. Contemporary needs related to, for example, performance-based design, service-life design, life-cycle analysis, product optimization, assessment of existing structures and structural robustness give rise to new developments as well as accurate and practically applicable probabilistic and statistical engineering methods to support these developments. These proceedings are a valuable resource for anyone interested in contemporary developments in the field of probabilistic engineering applications.

  2. Learning Probabilistic Logic Models from Probabilistic Examples.

    Science.gov (United States)

    Chen, Jianzhong; Muggleton, Stephen; Santos, José

    2008-10-01

    We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

  3. Development of Probabilistic Internal Dosimetry Computer Code

    Energy Technology Data Exchange (ETDEWEB)

    Noh, Siwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kwon, Tae-Eun [Korea Institute of Radiological and Medical Sciences, Seoul (Korea, Republic of); Lee, Jai-Ki [Korean Association for Radiation Protection, Seoul (Korea, Republic of)

    2017-02-15

    Internal radiation dose assessment involves biokinetic models, the corresponding parameters, measured data, and many assumptions. Every component considered in the internal dose assessment has its own uncertainty, which is propagated in the intake activity and internal dose estimates. For research or scientific purposes, and for retrospective dose reconstruction for accident scenarios occurring in workplaces having a large quantity of unsealed radionuclides, such as nuclear power plants, nuclear fuel cycle facilities, and facilities in which nuclear medicine is practiced, a quantitative uncertainty assessment of the internal dose is often required. However, no calculation tools or computer codes that incorporate all the relevant processes and their corresponding uncertainties, i.e., from the measured data to the committed dose, are available. Thus, the objective of the present study is to develop an integrated probabilistic internal-dose-assessment computer code. First, the uncertainty components in internal dosimetry are identified, and quantitative uncertainty data are collected. Then, an uncertainty database is established for each component. In order to propagate these uncertainties in an internal dose assessment, a probabilistic internal-dose-assessment system that employs the Bayesian and Monte Carlo methods. Based on the developed system, we developed a probabilistic internal-dose-assessment code by using MATLAB so as to estimate the dose distributions from the measured data with uncertainty. Using the developed code, we calculated the internal dose distribution and statistical values (e.g. the 2.5{sup th}, 5{sup th}, median, 95{sup th}, and 97.5{sup th} percentiles) for three sample scenarios. On the basis of the distributions, we performed a sensitivity analysis to determine the influence of each component on the resulting dose in order to identify the major component of the uncertainty in a bioassay. The results of this study can be applied to various

  4. Development of Probabilistic Internal Dosimetry Computer Code

    International Nuclear Information System (INIS)

    Noh, Siwan; Kwon, Tae-Eun; Lee, Jai-Ki

    2017-01-01

    Internal radiation dose assessment involves biokinetic models, the corresponding parameters, measured data, and many assumptions. Every component considered in the internal dose assessment has its own uncertainty, which is propagated in the intake activity and internal dose estimates. For research or scientific purposes, and for retrospective dose reconstruction for accident scenarios occurring in workplaces having a large quantity of unsealed radionuclides, such as nuclear power plants, nuclear fuel cycle facilities, and facilities in which nuclear medicine is practiced, a quantitative uncertainty assessment of the internal dose is often required. However, no calculation tools or computer codes that incorporate all the relevant processes and their corresponding uncertainties, i.e., from the measured data to the committed dose, are available. Thus, the objective of the present study is to develop an integrated probabilistic internal-dose-assessment computer code. First, the uncertainty components in internal dosimetry are identified, and quantitative uncertainty data are collected. Then, an uncertainty database is established for each component. In order to propagate these uncertainties in an internal dose assessment, a probabilistic internal-dose-assessment system that employs the Bayesian and Monte Carlo methods. Based on the developed system, we developed a probabilistic internal-dose-assessment code by using MATLAB so as to estimate the dose distributions from the measured data with uncertainty. Using the developed code, we calculated the internal dose distribution and statistical values (e.g. the 2.5 th , 5 th , median, 95 th , and 97.5 th percentiles) for three sample scenarios. On the basis of the distributions, we performed a sensitivity analysis to determine the influence of each component on the resulting dose in order to identify the major component of the uncertainty in a bioassay. The results of this study can be applied to various situations. In cases

  5. A probabilistic approach to quantify the uncertainties in internal dose assessment using response surface and neural network

    International Nuclear Information System (INIS)

    Baek, M.; Lee, S.K.; Lee, U.C.; Kang, C.S.

    1996-01-01

    A probabilistic approach is formulated to assess the internal radiation exposure following the intake of radioisotopes. This probabilistic approach consists of 4 steps as follows: (1) screening, (2) quantification of uncertainties, (3) propagation of uncertainties, and (4) analysis of output. The approach has been applied for Pu-induced internal dose assessment and a multi-compartment dosimetric model is used for internal transport. In this approach, surrogate models of original system are constructed using response and neural network. And the results of these surrogate models are compared with those of original model. Each surrogate model well approximates the original model. The uncertainty and sensitivity analysis of the model parameters are evaluated in this process. Dominant contributors to each organ are identified and the results show that this approach could serve a good tool of assessing the internal radiation exposure

  6. Probabilistic fracture mechanics applied for lbb case study: international benchmark

    International Nuclear Information System (INIS)

    Radu, V.

    2015-01-01

    An application of probabilistic fracture mechanics to evaluate the structural integrity for a case study chosen from experimental Mock-ups of FP7 STYLE project is described. The reliability model for probabilistic structural integrity, focused on the assessment of TWC in the pipe weld under complex loading (bending moment and residual stress) has been setup. The basic model is the model of fracture for through-wall cracked pipe under elastic-plastic conditions. The corresponding structural reliability approach is developed with the probabilities of failure associated with maximum load for crack initiation, net-section collapse but also the evaluation the instability loads. The probabilities of failure for a through-wall crack in a pipe subject to pure bending are evaluated by using crude Monte Carlo simulations. The results from the international benchmark are presented for the mentioned case in the context of ageing and lifetime management of pressure boundary/pressure circuit component. (authors)

  7. PROBABILISTIC RELATIONAL MODELS OF COMPLETE IL-SEMIRINGS

    OpenAIRE

    Tsumagari, Norihiro

    2012-01-01

    This paper studies basic properties of probabilistic multirelations which are generalized the semantic domain of probabilistic systems and then provides two probabilistic models of complete IL-semirings using probabilistic multirelations. Also it is shown that these models need not be models of complete idempotentsemirings.

  8. Probabilistic modeling of timber structures

    DEFF Research Database (Denmark)

    Köhler, Jochen; Sørensen, John Dalsgaard; Faber, Michael Havbro

    2007-01-01

    The present paper contains a proposal for the probabilistic modeling of timber material properties. It is produced in the context of the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS) [Joint Committee of Structural Safety. Probabilistic Model Code, Internet...... Publication: www.jcss.ethz.ch; 2001] and of the COST action E24 ‘Reliability of Timber Structures' [COST Action E 24, Reliability of timber structures. Several meetings and Publications, Internet Publication: http://www.km.fgg.uni-lj.si/coste24/coste24.htm; 2005]. The present proposal is based on discussions...... and comments from participants of the COST E24 action and the members of the JCSS. The paper contains a description of the basic reference properties for timber strength parameters and ultimate limit state equations for timber components. The recommended probabilistic model for these basic properties...

  9. Probabilistic Harmonic Modeling of Wind Power Plants

    DEFF Research Database (Denmark)

    Guest, Emerson; Jensen, Kim H.; Rasmussen, Tonny Wederberg

    2017-01-01

    A probabilistic sequence domain (SD) harmonic model of a grid-connected voltage-source converter is used to estimate harmonic emissions in a wind power plant (WPP) comprised of Type-IV wind turbines. The SD representation naturally partitioned converter generated voltage harmonics into those...... with deterministic phase and those with probabilistic phase. A case study performed on a string of ten 3MW, Type-IV wind turbines implemented in PSCAD was used to verify the probabilistic SD harmonic model. The probabilistic SD harmonic model can be employed in the planning phase of WPP projects to assess harmonic...

  10. Probabilistic reasoning for assembly-based 3D modeling

    KAUST Repository

    Chaudhuri, Siddhartha

    2011-01-01

    Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components. © 2011 ACM.

  11. International Space Station End-of-Life Probabilistic Risk Assessment

    Science.gov (United States)

    Duncan, Gary W.

    2014-01-01

    The International Space Station (ISS) end-of-life (EOL) cycle is currently scheduled for 2020, although there are ongoing efforts to extend ISS life cycle through 2028. The EOL for the ISS will require deorbiting the ISS. This will be the largest manmade object ever to be de-orbited therefore safely deorbiting the station will be a very complex problem. This process is being planned by NASA and its international partners. Numerous factors will need to be considered to accomplish this such as target corridors, orbits, altitude, drag, maneuvering capabilities etc. The ISS EOL Probabilistic Risk Assessment (PRA) will play a part in this process by estimating the reliability of the hardware supplying the maneuvering capabilities. The PRA will model the probability of failure of the systems supplying and controlling the thrust needed to aid in the de-orbit maneuvering.

  12. Probabilistic reasoning with graphical security models

    NARCIS (Netherlands)

    Kordy, Barbara; Pouly, Marc; Schweitzer, Patrick

    This work provides a computational framework for meaningful probabilistic evaluation of attack–defense scenarios involving dependent actions. We combine the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. In order

  13. Probabilistic Modeling of Timber Structures

    DEFF Research Database (Denmark)

    Köhler, J.D.; Sørensen, John Dalsgaard; Faber, Michael Havbro

    2005-01-01

    The present paper contains a proposal for the probabilistic modeling of timber material properties. It is produced in the context of the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS) and of the COST action E24 'Reliability of Timber Structures'. The present...... proposal is based on discussions and comments from participants of the COST E24 action and the members of the JCSS. The paper contains a description of the basic reference properties for timber strength parameters and ultimate limit state equations for components and connections. The recommended...

  14. A probabilistic graphical model based stochastic input model construction

    International Nuclear Information System (INIS)

    Wan, Jiang; Zabaras, Nicholas

    2014-01-01

    Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media

  15. Probabilistic modeling of discourse-aware sentence processing.

    Science.gov (United States)

    Dubey, Amit; Keller, Frank; Sturt, Patrick

    2013-07-01

    Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. Copyright © 2013 Cognitive Science Society, Inc.

  16. HMM_Model-Checker pour la vérification probabiliste HMM_Model ...

    African Journals Online (AJOL)

    ASSIA

    probabiliste –Télescope Hubble. Abstract. Probabilistic verification for embedded systems continues to attract more and more followers in the research community. Given a probabilistic model, a formula of temporal logic, describing a property of a system and an exploration algorithm to check whether the property is satisfied ...

  17. Insights from Guideline for Performance of Internal Flooding Probabilistic Risk Assessment (IFPRA)

    International Nuclear Information System (INIS)

    Choi, Sun Yeong; Yang, Joo Eon

    2009-01-01

    An internal flooding (IF) risk assessment refers to the quantitative probabilistic safety assessment (PSA) treatment of flooding as a result of pipe and tank breaks inside the plants, as well as from other recognized flood sources. The industry consensus standard for Internal Events Probabilistic Risk Assessment (ASME-RA-Sb-2005) includes high-level and supporting technical requirements for developing internal flooding probabilistic risk assessment (IFPRA). This industry standard is endorsed in Regulatory Guide 1.200, Revision 1 as an acceptable approach for addressing the risk contribution from IF events for risk informed applications that require U.S. Nuclear Regulatory commission (NRC) approval. In 2006, EPRI published a draft report for IFPRA that addresses the requirements of the ASME PRA consensus standard and have made efforts to refine and update the final EPRI IFPRA guideline. Westinghouse has performed an IFPRA analysis for several nuclear power plants (NPPs), such as Watts Bar and Fort Calhoun, using the draft EPRI guidelines for development of an IFPRA. Proprietary methodologies have been developed to apply the EPRI guidelines. The objectives of the draft report for IFPRA guideline are to: · Provide guidance for PSA practitioners in the performance of the elements of a PRA associated with internal flooding events consistent with the current state of the art for internal flooding PRA · Provide guidance regarding acceptable approaches that is sufficient to meeting the requirements of the ASME PRA Standard associated with internal flooding · Incorporate lessons learned in the performance of internal flooding PRAs including those identified as pilot applications of earlier drafts of this procedures guide The purpose of this paper is to present a vision for domestic nuclear power plants' IFPRA by comparing the method of the draft EPRI guidelines with the existing IFPRA method for domestic NPPs

  18. Insights from Guideline for Performance of Internal Flooding Probabilistic Risk Assessment (IFPRA)

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Sun Yeong; Yang, Joo Eon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2009-10-15

    An internal flooding (IF) risk assessment refers to the quantitative probabilistic safety assessment (PSA) treatment of flooding as a result of pipe and tank breaks inside the plants, as well as from other recognized flood sources. The industry consensus standard for Internal Events Probabilistic Risk Assessment (ASME-RA-Sb-2005) includes high-level and supporting technical requirements for developing internal flooding probabilistic risk assessment (IFPRA). This industry standard is endorsed in Regulatory Guide 1.200, Revision 1 as an acceptable approach for addressing the risk contribution from IF events for risk informed applications that require U.S. Nuclear Regulatory commission (NRC) approval. In 2006, EPRI published a draft report for IFPRA that addresses the requirements of the ASME PRA consensus standard and have made efforts to refine and update the final EPRI IFPRA guideline. Westinghouse has performed an IFPRA analysis for several nuclear power plants (NPPs), such as Watts Bar and Fort Calhoun, using the draft EPRI guidelines for development of an IFPRA. Proprietary methodologies have been developed to apply the EPRI guidelines. The objectives of the draft report for IFPRA guideline are to: {center_dot} Provide guidance for PSA practitioners in the performance of the elements of a PRA associated with internal flooding events consistent with the current state of the art for internal flooding PRA {center_dot} Provide guidance regarding acceptable approaches that is sufficient to meeting the requirements of the ASME PRA Standard associated with internal flooding {center_dot} Incorporate lessons learned in the performance of internal flooding PRAs including those identified as pilot applications of earlier drafts of this procedures guide The purpose of this paper is to present a vision for domestic nuclear power plants' IFPRA by comparing the method of the draft EPRI guidelines with the existing IFPRA method for domestic NPPs.

  19. Use of probabilistic relational model (PRM) for dependability analysis of complex systems

    OpenAIRE

    Medina-Oliva , Gabriela; Weber , Philippe; Levrat , Eric; Iung , Benoît

    2010-01-01

    International audience; This paper proposes a methodology to develop a aided decision-making tool for assessing the dependability and performances (i.e. reliability) of an industrial system. This tool is built on a model based on a new formalism, called the probabilistic relational model (PRM) which is adapted to deal with large and complex systems. The model is formalized from functional, dysfunctional and informational studies of the technical industrial systems. An application of this meth...

  20. The international probabilistic system assessment group. Background and results 1990

    International Nuclear Information System (INIS)

    1991-01-01

    The OECD Nuclear Energy Agency (NEA) devotes considerable effort to the further development of methodologies to assess the performance of radioactive waste disposal systems, and to increase confidence in their application and results. The NEA provides an international forum for the exchange of information and experience among national experts of its twenty-three Member countries and conducts joint studies of issues important for safety assessment. In 1985, the NEA Radioactive Waste Management Committee set up the Probabilistic System Assessment Code User Group (PSAC), in order to help coordinate the development of probabilistic system assessment codes. The activities of the Group include exchange of information, code and experience, discussion of relevant technical issues, and the conduct of code comparison (PSACOIN) exercises designed to build confidence in the correct operation of these tools for safety assessment. The Group is now known simply as the Probabilistic System Assessment Group (PSAG). This report has been prepared to inform interested parties, beyond the group of specialists directly involved, about probabilistic system assessment techniques as used for performance assessment of waste disposal systems, and to give a summary of the objectives and achievements of PSAG. The report is published under the responsibility of the Secretary General of the OECD

  1. CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

    KAUST Repository

    Zhang, Kai-Ke

    2016-04-11

    Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.

  2. CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

    KAUST Repository

    Zhang, Kai-Ke; Hu, Kai-Mo; Yin, Li-Cheng; Yan, Dongming; Wang, Bin

    2016-01-01

    Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.

  3. Probabilistic, meso-scale flood loss modelling

    Science.gov (United States)

    Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

    2016-04-01

    Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

  4. Transitive probabilistic CLIR models.

    NARCIS (Netherlands)

    Kraaij, W.; de Jong, Franciska M.G.

    2004-01-01

    Transitive translation could be a useful technique to enlarge the number of supported language pairs for a cross-language information retrieval (CLIR) system in a cost-effective manner. The paper describes several setups for transitive translation based on probabilistic translation models. The

  5. Efficient probabilistic model checking on general purpose graphic processors

    NARCIS (Netherlands)

    Bosnacki, D.; Edelkamp, S.; Sulewski, D.; Pasareanu, C.S.

    2009-01-01

    We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). For this purpose we exploit the fact that some of the basic algorithms for probabilistic model checking rely on matrix vector multiplication. Since this kind of linear algebraic

  6. A Probabilistic Graphical Model to Detect Chromosomal Domains

    Science.gov (United States)

    Heermann, Dieter; Hofmann, Andreas; Weber, Eva

    To understand the nature of a cell, one needs to understand the structure of its genome. For this purpose, experimental techniques such as Hi-C detecting chromosomal contacts are used to probe the three-dimensional genomic structure. These experiments yield topological information, consistently showing a hierarchical subdivision of the genome into self-interacting domains across many organisms. Current methods for detecting these domains using the Hi-C contact matrix, i.e. a doubly-stochastic matrix, are mostly based on the assumption that the domains are distinct, thus non-overlapping. For overcoming this simplification and for being able to unravel a possible nested domain structure, we developed a probabilistic graphical model that makes no a priori assumptions on the domain structure. Within this approach, the Hi-C contact matrix is analyzed using an Ising like probabilistic graphical model whose coupling constant is proportional to each lattice point (entry in the contact matrix). The results show clear boundaries between identified domains and the background. These domain boundaries are dependent on the coupling constant, so that one matrix yields several clusters of different sizes, which show the self-interaction of the genome on different scales. This work was supported by a Grant from the International Human Frontier Science Program Organization (RGP0014/2014).

  7. Probabilistic Models for Solar Particle Events

    Science.gov (United States)

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

    2009-01-01

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

  8. Probabilistic Model Development

    Science.gov (United States)

    Adam, James H., Jr.

    2010-01-01

    Objective: Develop a Probabilistic Model for the Solar Energetic Particle Environment. Develop a tool to provide a reference solar particle radiation environment that: 1) Will not be exceeded at a user-specified confidence level; 2) Will provide reference environments for: a) Peak flux; b) Event-integrated fluence; and c) Mission-integrated fluence. The reference environments will consist of: a) Elemental energy spectra; b) For protons, helium and heavier ions.

  9. PROBABILISTIC FINITE ELEMENT ANALYSIS OF A HEAVY DUTY RADIATOR UNDER INTERNAL PRESSURE LOADING

    Directory of Open Access Journals (Sweden)

    ROBIN ROY P.

    2017-09-01

    Full Text Available Engine cooling is vital in keeping the engine at most efficient temperature for the different vehicle speed and operating road conditions. Radiator is one of the key components in the heavy duty engine cooling system. Heavy duty radiator is subjected to various kinds of loading such as pressure, thermal, vibration, internal erosion, external corrosion, creep. Pressure cycle durability is one of the most important characteristic in the design of heavy duty radiator. Current design methodologies involve design of heavy duty radiator using the nominal finite element approach which does not take into account of the variations occurring in the geometry, material and boundary condition, leading to over conservative and uneconomical designs of radiator system. A new approach is presented in the paper to integrate traditional linear finite element method and probabilistic approach to design a heavy duty radiator by including the uncertainty in the computational model. As a first step, nominal run is performed with input design variables and desired responses are extracted. A probabilistic finite elementanalysis is performed to identify the robust designs and validated for reliability. Probabilistic finite element includes the uncertainty of the material thickness, dimensional and geometrical variation. Gaussian distribution is employed to define the random variation and uncertainty. Monte Carlo method is used to generate the random design points.Output response distributions of the random design points are post-processed using different statistical and probability technique to find the robust design. The above approach of systematic virtual modelling and analysis of the data helps to find efficient and reliable robust design.

  10. ISSUES ASSOCIATED WITH PROBABILISTIC FAILURE MODELING OF DIGITAL SYSTEMS

    International Nuclear Information System (INIS)

    CHU, T.L.; MARTINEZ-GURIDI, G.; LIHNER, J.; OVERLAND, D.

    2004-01-01

    The current U.S. Nuclear Regulatory Commission (NRC) licensing process of instrumentation and control (I and C) systems is based on deterministic requirements, e.g., single failure criteria, and defense in depth and diversity. Probabilistic considerations can be used as supplements to the deterministic process. The National Research Council has recommended development of methods for estimating failure probabilities of digital systems, including commercial off-the-shelf (COTS) equipment, for use in probabilistic risk assessment (PRA). NRC staff has developed informal qualitative and quantitative requirements for PRA modeling of digital systems. Brookhaven National Laboratory (BNL) has performed a review of the-state-of-the-art of the methods and tools that can potentially be used to model digital systems. The objectives of this paper are to summarize the review, discuss the issues associated with probabilistic modeling of digital systems, and identify potential areas of research that would enhance the state of the art toward a satisfactory modeling method that could be integrated with a typical probabilistic risk assessment

  11. Probabilistic reconstruction of internal exposure for nuclear power plant workers using air concentration measurements

    International Nuclear Information System (INIS)

    Linkov, I.; Burmistrov, D.

    2000-01-01

    Air surveys, whole-body counting, bioassays or combination of these measurements can be utilized for purposes or assessing internal doses to determine compliance with occupational dose equivalent limits. Air sampling with a little support provided by whole body counting and/or bioassays was often relied on in dose calculations. The utility of air sampling for internal dose reconstruction is addressed in this paper through the probabilistic analysis of environmental factors and their impact on dose estimates. In this paper we attempt to reconstruct an internal dose due to inhalation of beta + gamma emitting radionuclides for a contractual electrician, Mr. X. The data available for reconstruction of internal dose for Mr. X was found to be highly variable and uncertain. Uncertainty describes a lack of knowledge about a parameter, this lack of knowledge theoretically can be reduced, e.g., if more measurements were to be taken (for example, estimated activities for alpha-emitting radionuclides are uncertain due to the influence of naturally-occurring alpha-emitters). Variability describes the existence of different values that represent different environmental conditions (for example, the air concentrations of radionuclides may vary over time because of the different tasks performed by workers in the area). Variability can not be reduced by additional data collection because the varying values reflect the variable nature of the environment, not a lack of data. The high variability in measured air concentrations in the restricted areas of a LWR nuclear power plant where he worked do not allow adequate reconstruction of his individual internal dose using deterministic methods and therefore probabilistic methods are desirable. The guidance for probabilistic assessment developed by the United States Environmental Protection Agency as well as recommendations of the National Council of Radiation Protection provide an adequate framework for probabilistic reconstruction of

  12. Probabilistic Physics-Based Risk Tools Used to Analyze the International Space Station Electrical Power System Output

    Science.gov (United States)

    Patel, Bhogila M.; Hoge, Peter A.; Nagpal, Vinod K.; Hojnicki, Jeffrey S.; Rusick, Jeffrey J.

    2004-01-01

    This paper describes the methods employed to apply probabilistic modeling techniques to the International Space Station (ISS) power system. These techniques were used to quantify the probabilistic variation in the power output, also called the response variable, due to variations (uncertainties) associated with knowledge of the influencing factors called the random variables. These uncertainties can be due to unknown environmental conditions, variation in the performance of electrical power system components or sensor tolerances. Uncertainties in these variables, cause corresponding variations in the power output, but the magnitude of that effect varies with the ISS operating conditions, e.g. whether or not the solar panels are actively tracking the sun. Therefore, it is important to quantify the influence of these uncertainties on the power output for optimizing the power available for experiments.

  13. Probabilistic Model for Fatigue Crack Growth in Welded Bridge Details

    DEFF Research Database (Denmark)

    Toft, Henrik Stensgaard; Sørensen, John Dalsgaard; Yalamas, Thierry

    2013-01-01

    In the present paper a probabilistic model for fatigue crack growth in welded steel details in road bridges is presented. The probabilistic model takes the influence of bending stresses in the joints into account. The bending stresses can either be introduced by e.g. misalignment or redistribution...... of stresses in the structure. The fatigue stress ranges are estimated from traffic measurements and a generic bridge model. Based on the probabilistic models for the resistance and load the reliability is estimated for a typical welded steel detail. The results show that large misalignments in the joints can...

  14. Probabilistic escalation modelling

    Energy Technology Data Exchange (ETDEWEB)

    Korneliussen, G.; Eknes, M.L.; Haugen, K.; Selmer-Olsen, S. [Det Norske Veritas, Oslo (Norway)

    1997-12-31

    This paper describes how structural reliability methods may successfully be applied within quantitative risk assessment (QRA) as an alternative to traditional event tree analysis. The emphasis is on fire escalation in hydrocarbon production and processing facilities. This choice was made due to potential improvements over current QRA practice associated with both the probabilistic approach and more detailed modelling of the dynamics of escalating events. The physical phenomena important for the events of interest are explicitly modelled as functions of time. Uncertainties are represented through probability distributions. The uncertainty modelling enables the analysis to be simple when possible and detailed when necessary. The methodology features several advantages compared with traditional risk calculations based on event trees. (Author)

  15. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  16. Integration of Advanced Probabilistic Analysis Techniques with Multi-Physics Models

    Energy Technology Data Exchange (ETDEWEB)

    Cetiner, Mustafa Sacit; none,; Flanagan, George F. [ORNL; Poore III, Willis P. [ORNL; Muhlheim, Michael David [ORNL

    2014-07-30

    An integrated simulation platform that couples probabilistic analysis-based tools with model-based simulation tools can provide valuable insights for reactive and proactive responses to plant operating conditions. The objective of this work is to demonstrate the benefits of a partial implementation of the Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Detailed Framework Specification through the coupling of advanced PRA capabilities and accurate multi-physics plant models. Coupling a probabilistic model with a multi-physics model will aid in design, operations, and safety by providing a more accurate understanding of plant behavior. This represents the first attempt at actually integrating these two types of analyses for a control system used for operations, on a faster than real-time basis. This report documents the development of the basic communication capability to exchange data with the probabilistic model using Reliability Workbench (RWB) and the multi-physics model using Dymola. The communication pathways from injecting a fault (i.e., failing a component) to the probabilistic and multi-physics models were successfully completed. This first version was tested with prototypic models represented in both RWB and Modelica. First, a simple event tree/fault tree (ET/FT) model was created to develop the software code to implement the communication capabilities between the dynamic-link library (dll) and RWB. A program, written in C#, successfully communicates faults to the probabilistic model through the dll. A systems model of the Advanced Liquid-Metal Reactor–Power Reactor Inherently Safe Module (ALMR-PRISM) design developed under another DOE project was upgraded using Dymola to include proper interfaces to allow data exchange with the control application (ConApp). A program, written in C+, successfully communicates faults to the multi-physics model. The results of the example simulation were successfully plotted.

  17. Undecidability of model-checking branching-time properties of stateless probabilistic pushdown process

    OpenAIRE

    Lin, T.

    2014-01-01

    In this paper, we settle a problem in probabilistic verification of infinite--state process (specifically, {\\it probabilistic pushdown process}). We show that model checking {\\it stateless probabilistic pushdown process} (pBPA) against {\\it probabilistic computational tree logic} (PCTL) is undecidable.

  18. Probabilistic language models in cognitive neuroscience: Promises and pitfalls.

    Science.gov (United States)

    Armeni, Kristijan; Willems, Roel M; Frank, Stefan L

    2017-12-01

    Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Fatigue modelling according to the JCSS Probabilistic model code

    NARCIS (Netherlands)

    Vrouwenvelder, A.C.W.M.

    2007-01-01

    The Joint Committee on Structural Safety is working on a Model Code for full probabilistic design. The code consists out of three major parts: Basis of design, Load Models and Models for Material and Structural Properties. The code is intended as the operational counter part of codes like ISO,

  20. Does a more sophisticated storm erosion model improve probabilistic erosion estimates?

    NARCIS (Netherlands)

    Ranasinghe, R.W.M.R.J.B.; Callaghan, D.; Roelvink, D.

    2013-01-01

    The dependency between the accuracy/uncertainty of storm erosion exceedance estimates obtained via a probabilistic model and the level of sophistication of the structural function (storm erosion model) embedded in the probabilistic model is assessed via the application of Callaghan et al.'s (2008)

  1. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2018-04-01

    Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

  2. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  3. A generative, probabilistic model of local protein structure

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Mardia, Kanti V.; Taylor, Charles C.

    2008-01-01

    Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative...... conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state...

  4. Mastering probabilistic graphical models using Python

    CERN Document Server

    Ankan, Ankur

    2015-01-01

    If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.

  5. An Individual-based Probabilistic Model for Fish Stock Simulation

    Directory of Open Access Journals (Sweden)

    Federico Buti

    2010-08-01

    Full Text Available We define an individual-based probabilistic model of a sole (Solea solea behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA, a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.

  6. Biological sequence analysis: probabilistic models of proteins and nucleic acids

    National Research Council Canada - National Science Library

    Durbin, Richard

    1998-01-01

    ... analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference...

  7. Probabilistic Modeling of Wind Turbine Drivetrain Components

    DEFF Research Database (Denmark)

    Rafsanjani, Hesam Mirzaei

    Wind energy is one of several energy sources in the world and a rapidly growing industry in the energy sector. When placed in offshore or onshore locations, wind turbines are exposed to wave excitations, highly dynamic wind loads and/or the wakes from other wind turbines. Therefore, most components...... in a wind turbine experience highly dynamic and time-varying loads. These components may fail due to wear or fatigue, and this can lead to unplanned shutdown repairs that are very costly. The design by deterministic methods using safety factors is generally unable to account for the many uncertainties. Thus......, a reliability assessment should be based on probabilistic methods where stochastic modeling of failures is performed. This thesis focuses on probabilistic models and the stochastic modeling of the fatigue life of the wind turbine drivetrain. Hence, two approaches are considered for stochastic modeling...

  8. Branching bisimulation congruence for probabilistic systems

    NARCIS (Netherlands)

    Andova, S.; Georgievska, S.; Trcka, N.

    2012-01-01

    A notion of branching bisimilarity for the alternating model of probabilistic systems, compatible with parallel composition, is defined. For a congruence result, an internal transition immediately followed by a non-trivial probability distribution is not considered inert. A weaker definition of

  9. Probabilistically modeling lava flows with MOLASSES

    Science.gov (United States)

    Richardson, J. A.; Connor, L.; Connor, C.; Gallant, E.

    2017-12-01

    Modeling lava flows through Cellular Automata methods enables a computationally inexpensive means to quickly forecast lava flow paths and ultimate areal extents. We have developed a lava flow simulator, MOLASSES, that forecasts lava flow inundation over an elevation model from a point source eruption. This modular code can be implemented in a deterministic fashion with given user inputs that will produce a single lava flow simulation. MOLASSES can also be implemented in a probabilistic fashion where given user inputs define parameter distributions that are randomly sampled to create many lava flow simulations. This probabilistic approach enables uncertainty in input data to be expressed in the model results and MOLASSES outputs a probability map of inundation instead of a determined lava flow extent. Since the code is comparatively fast, we use it probabilistically to investigate where potential vents are located that may impact specific sites and areas, as well as the unconditional probability of lava flow inundation of sites or areas from any vent. We have validated the MOLASSES code to community-defined benchmark tests and to the real world lava flows at Tolbachik (2012-2013) and Pico do Fogo (2014-2015). To determine the efficacy of the MOLASSES simulator at accurately and precisely mimicking the inundation area of real flows, we report goodness of fit using both model sensitivity and the Positive Predictive Value, the latter of which is a Bayesian posterior statistic. Model sensitivity is often used in evaluating lava flow simulators, as it describes how much of the lava flow was successfully modeled by the simulation. We argue that the positive predictive value is equally important in determining how good a simulator is, as it describes the percentage of the simulation space that was actually inundated by lava.

  10. Diablo Canyon internal events PRA [Probabilistic Risk Assessment] review: Methodology and findings

    International Nuclear Information System (INIS)

    Fitzpatrick, R.G.; Bozoki, G.; Sabek, M.

    1990-01-01

    The review of the Diablo Canyon Probabilistic Risk Assessment (DCRPA) incorporated some new and innovative approaches. These were necessitated by the unprecedented size, scope and level of detail of the DCRPA, which was submitted to the NRC for licensing purposes. This paper outlines the elements of the internal events portion of the review citing selected findings to illustrate the various approaches employed. The paper also provides a description of the extensive and comprehensive importance analysis applied by BNL to the DCRPA model. Importance calculations included: top event/function level; individual split fractions; pair importances between frontline-support and support-support systems; system importance by initiator; and others. The paper concludes with a brief discussion of the effectiveness of the applied methodology. 3 refs., 5 tabs

  11. A physical probabilistic model to predict failure rates in buried PVC pipelines

    International Nuclear Information System (INIS)

    Davis, P.; Burn, S.; Moglia, M.; Gould, S.

    2007-01-01

    For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical

  12. Probabilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2004-01-01

    We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic ...

  13. Probabilistic evaluation of concrete containment capacity for beyond design basis internal pressures

    International Nuclear Information System (INIS)

    Tang, H.T.; Dameron, R.A.; Rashid, Y.R.

    1995-01-01

    For beyond design basis internal pressure loading, experimental studies have demonstrated that the most probable failure mode governing the ultimate functional capacity of concrete containments is leak rather than break. Based on leak rates measured in experiments, a prediction formula for leak rate as functions of containment liner size and internal pressure has been postulated. The determination of liner tear is cast in a probabilistic framework. In calculating leakage, particular attention is paid to the evaluation of leakage versus rupture and the loading rates that may be required to leapfrog over a leakage mode. (orig.)

  14. A probabilistic model of RNA conformational space

    DEFF Research Database (Denmark)

    Frellsen, Jes; Moltke, Ida; Thiim, Martin

    2009-01-01

    efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D......, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

  15. Modeling and control of an unstable system using probabilistic fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Sozhamadevi N.

    2015-09-01

    Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

  16. Probabilistic Modeling of the Renal Stone Formation Module

    Science.gov (United States)

    Best, Lauren M.; Myers, Jerry G.; Goodenow, Debra A.; McRae, Michael P.; Jackson, Travis C.

    2013-01-01

    The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously

  17. Modelling probabilistic fatigue crack propagation rates for a mild structural steel

    Directory of Open Access Journals (Sweden)

    J.A.F.O. Correia

    2015-01-01

    Full Text Available A class of fatigue crack growth models based on elastic–plastic stress–strain histories at the crack tip region and local strain-life damage models have been proposed in literature. The fatigue crack growth is regarded as a process of continuous crack initializations over successive elementary material blocks, which may be governed by smooth strain-life damage data. Some approaches account for the residual stresses developing at the crack tip in the actual crack driving force assessment, allowing mean stresses and loading sequential effects to be modelled. An extension of the fatigue crack propagation model originally proposed by Noroozi et al. (2005 to derive probabilistic fatigue crack propagation data is proposed, in particular concerning the derivation of probabilistic da/dN-ΔK-R fields. The elastic-plastic stresses at the vicinity of the crack tip, computed using simplified formulae, are compared with the stresses computed using an elasticplastic finite element analyses for specimens considered in the experimental program proposed to derive the fatigue crack propagation data. Using probabilistic strain-life data available for the S355 structural mild steel, probabilistic crack propagation fields are generated, for several stress ratios, and compared with experimental fatigue crack propagation data. A satisfactory agreement between the predicted probabilistic fields and experimental data is observed.

  18. Towards decision making via expressive probabilistic ontologies

    NARCIS (Netherlands)

    Acar, Erman; Thorne, Camilo; Stuckenschmidt, Heiner

    2015-01-01

    © Springer International Publishing Switzerland 2015. We propose a framework for automated multi-attribute deci- sion making, employing the probabilistic non-monotonic description log- ics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making

  19. Systems analysis approach to probabilistic modeling of fault trees

    International Nuclear Information System (INIS)

    Bartholomew, R.J.; Qualls, C.R.

    1985-01-01

    A method of probabilistic modeling of fault tree logic combined with stochastic process theory (Markov modeling) has been developed. Systems are then quantitatively analyzed probabilistically in terms of their failure mechanisms including common cause/common mode effects and time dependent failure and/or repair rate effects that include synergistic and propagational mechanisms. The modeling procedure results in a state vector set of first order, linear, inhomogeneous, differential equations describing the time dependent probabilities of failure described by the fault tree. The solutions of this Failure Mode State Variable (FMSV) model are cumulative probability distribution functions of the system. A method of appropriate synthesis of subsystems to form larger systems is developed and applied to practical nuclear power safety systems

  20. Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

    DEFF Research Database (Denmark)

    Valentin, Jan B.; Andreetta, Christian; Boomsma, Wouter

    2014-01-01

    We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length s....... The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. © 2013 Wiley Periodicals, Inc....

  1. Performance analysis of chi models using discrete-time probabilistic reward graphs

    NARCIS (Netherlands)

    Trcka, N.; Georgievska, S.; Markovski, J.; Andova, S.; Vink, de E.P.

    2008-01-01

    We propose the model of discrete-time probabilistic reward graphs (DTPRGs) for performance analysis of systems exhibiting discrete deterministic time delays and probabilistic behavior, via their interpretation as discrete-time Markov reward chains, full-fledged platform for qualitative and

  2. E-Area LLWF Vadose Zone Model: Probabilistic Model for Estimating Subsided-Area Infiltration Rates

    Energy Technology Data Exchange (ETDEWEB)

    Dyer, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Flach, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-12-12

    A probabilistic model employing a Monte Carlo sampling technique was developed in Python to generate statistical distributions of the upslope-intact-area to subsided-area ratio (AreaUAi/AreaSAi) for closure cap subsidence scenarios that differ in assumed percent subsidence and the total number of intact plus subsided compartments. The plan is to use this model as a component in the probabilistic system model for the E-Area Performance Assessment (PA), contributing uncertainty in infiltration estimates.

  3. Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

    Science.gov (United States)

    Hilbig, Benjamin E; Moshagen, Morten

    2014-12-01

    Model comparisons are a vital tool for disentangling which of several strategies a decision maker may have used--that is, which cognitive processes may have governed observable choice behavior. However, previous methodological approaches have been limited to models (i.e., decision strategies) with deterministic choice rules. As such, psychologically plausible choice models--such as evidence-accumulation and connectionist models--that entail probabilistic choice predictions could not be considered appropriately. To overcome this limitation, we propose a generalization of Bröder and Schiffer's (Journal of Behavioral Decision Making, 19, 361-380, 2003) choice-based classification method, relying on (1) parametric order constraints in the multinomial processing tree framework to implement probabilistic models and (2) minimum description length for model comparison. The advantages of the generalized approach are demonstrated through recovery simulations and an experiment. In explaining previous methods and our generalization, we maintain a nontechnical focus--so as to provide a practical guide for comparing both deterministic and probabilistic choice models.

  4. Probabilistic Radiological Performance Assessment Modeling and Uncertainty

    Science.gov (United States)

    Tauxe, J.

    2004-12-01

    A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A

  5. Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

    National Research Council Canada - National Science Library

    Sakhanenko, Nikita A; Luger, George F

    2008-01-01

    We propose a context-sensitive probabilistic modeling system (COSMOS) that reasons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information...

  6. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

  7. Toward a Probabilistic Phenological Model for Wheat Growing Degree Days (GDD)

    Science.gov (United States)

    Rahmani, E.; Hense, A.

    2017-12-01

    Are there deterministic relations between phenological and climate parameters? The answer is surely `No'. This answer motivated us to solve the problem through probabilistic theories. Thus, we developed a probabilistic phenological model which has the advantage of giving additional information in terms of uncertainty. To that aim, we turned to a statistical analysis named survival analysis. Survival analysis deals with death in biological organisms and failure in mechanical systems. In survival analysis literature, death or failure is considered as an event. By event, in this research we mean ripening date of wheat. We will assume only one event in this special case. By time, we mean the growing duration from sowing to ripening as lifetime for wheat which is a function of GDD. To be more precise we will try to perform the probabilistic forecast for wheat ripening. The probability value will change between 0 and 1. Here, the survivor function gives the probability that the not ripened wheat survives longer than a specific time or will survive to the end of its lifetime as a ripened crop. The survival function at each station is determined by fitting a normal distribution to the GDD as the function of growth duration. Verification of the models obtained is done using CRPS skill score (CRPSS). The positive values of CRPSS indicate the large superiority of the probabilistic phonologic survival model to the deterministic models. These results demonstrate that considering uncertainties in modeling are beneficial, meaningful and necessary. We believe that probabilistic phenological models have the potential to help reduce the vulnerability of agricultural production systems to climate change thereby increasing food security.

  8. Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for internal dosimetry. Volume 2: Appendices

    Energy Technology Data Exchange (ETDEWEB)

    Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Harrison, J.D. [National Radiological Protection Board (United Kingdom); Harper, F.T. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States)

    1998-04-01

    The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on internal dosimetry, (4) short biographies of the experts, and (5) the aggregated results of their responses.

  9. Generative probabilistic models extend the scope of inferential structure determination

    DEFF Research Database (Denmark)

    Olsson, Simon; Boomsma, Wouter; Frellsen, Jes

    2011-01-01

    demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure......Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically...

  10. Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows

    NARCIS (Netherlands)

    Talebi, A.; Uijlenhoet, R.; Troch, P.A.

    2008-01-01

    Recently, D'Odorico and Fagherazzi (2003) proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003). Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building

  11. Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

    Science.gov (United States)

    Chua, Matthew; Chui, Chee-Kong

    2015-04-01

    Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Probabilistic flood damage modelling at the meso-scale

    Science.gov (United States)

    Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

    2014-05-01

    Decisions on flood risk management and adaptation are usually based on risk analyses. Such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments. Most damage models have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood damage models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we show how the model BT-FLEMO (Bagging decision Tree based Flood Loss Estimation MOdel) can be applied on the meso-scale, namely on the basis of ATKIS land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany. The application of BT-FLEMO provides a probability distribution of estimated damage to residential buildings per municipality. Validation is undertaken on the one hand via a comparison with eight other damage models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official damage data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of damage estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation model BT-FLEMO is that it inherently provides quantitative information about the uncertainty of the prediction. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64.

  13. Probabilistic Modelling of Robustness and Resilience of Power Grid Systems

    DEFF Research Database (Denmark)

    Qin, Jianjun; Sansavini, Giovanni; Nielsen, Michael Havbro Faber

    2017-01-01

    The present paper proposes a framework for the modeling and analysis of resilience of networked power grid systems. A probabilistic systems model is proposed based on the JCSS Probabilistic Model Code (JCSS, 2001) and deterministic engineering systems modeling techniques such as the DC flow model...... cascading failure event scenarios (Nan and Sansavini, 2017). The concept of direct and indirect consequences proposed by the Joint Committee on Structural Safety (JCSS, 2008) is utilized to model the associated consequences. To facilitate a holistic modeling of robustness and resilience, and to identify how...... these characteristics may be optimized these characteristics, the power grid system is finally interlinked with its fundamental interdependent systems, i.e. a societal model, a regulatory system and control feedback loops. The proposed framework is exemplified with reference to optimal decision support for resilience...

  14. Financial Markets Analysis by Probabilistic Fuzzy Modelling

    NARCIS (Netherlands)

    J.H. van den Berg (Jan); W.-M. van den Bergh (Willem-Max); U. Kaymak (Uzay)

    2003-01-01

    textabstractFor successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno

  15. Financial markets analysis by probabilistic fuzzy modelling

    NARCIS (Netherlands)

    Berg, van den J.; Kaymak, U.; Bergh, van den W.M.

    2003-01-01

    For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS)

  16. Probabilistic Load Models for Simulating the Impact of Load Management

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Bak-Jensen, Birgitte; Chen, Zhe

    2009-01-01

    . It is concluded that the AR(12) model is favored with limited measurement data and that the joint-normal model may provide better results with a large data set. Both models can be applied in general to model load time series and used in time-sequential simulation of distribution system planning.......This paper analyzes a distribution system load time series through autocorrelation coefficient, power spectral density, probabilistic distribution and quantile value. Two probabilistic load models, i.e. the joint-normal model and the autoregressive model of order 12 (AR(12)), are proposed...... to simulate the impact of load management. The joint-normal model is superior in modeling the tail region of the hourly load distribution and implementing the change of hourly standard deviation. Whereas the AR(12) model requires much less parameter and is superior in modeling the autocorrelation...

  17. Probabilistic Modeling and Risk Assessment of Cable Icing

    DEFF Research Database (Denmark)

    Roldsgaard, Joan Hee

    This dissertation addresses the issues related to icing of structures with special emphasis on bridge cables. Cable supported bridges in cold climate suffers for ice accreting on the cables, this poses three different undesirable situations. Firstly the changed shape of the cable due to ice...... preliminary framework is modified for assessing the probability of occurrence of in-cloud and precipitation icing and its duration. Different probabilistic models are utilized for the representation of the meteorological variables and their appropriateness is evaluated both through goodness-of-fit tests...... are influencing the two icing mechanisms and their duration. The model is found to be more sensitive to changes in the discretization levels of the input variables. Thirdly the developed operational probabilistic framework for the assessment of the expected number of occurrences of ice/snow accretion on bridge...

  18. Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method.

    Science.gov (United States)

    Valentin, Jan B; Andreetta, Christian; Boomsma, Wouter; Bottaro, Sandro; Ferkinghoff-Borg, Jesper; Frellsen, Jes; Mardia, Kanti V; Tian, Pengfei; Hamelryck, Thomas

    2014-02-01

    We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length scale, which concern the dihedral angles in main chain and side chains, respectively. Conceptually, this constitutes a probabilistic and continuous alternative to the use of discrete fragment and rotamer libraries. The local model is combined with a nonlocal model that involves a small number of energy terms according to a physical force field, and some information on the overall secondary structure content. In this initial study we focus on the formulation of the joint model and the evaluation of the use of an energy vector as a descriptor of a protein's nonlocal structure; hence, we derive the parameters of the nonlocal model from the native structure without loss of generality. The local and nonlocal models are combined using the reference ratio method, which is a well-justified probabilistic construction. For evaluation, we use the resulting joint models to predict the structure of four proteins. The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. Copyright © 2013 Wiley Periodicals, Inc.

  19. Review of probabilistic models of the strength of composite materials

    International Nuclear Information System (INIS)

    Sutherland, L.S.; Guedes Soares, C.

    1997-01-01

    The available literature concerning probabilistic models describing the strength of composite materials has been reviewed to highlight the important aspects of this behaviour which will be of interest to the modelling and analysis of a complex system. The success with which these theories have been used to predict experimental results has been discussed. Since the brittle reinforcement phase largely controls the strength of composites, the probabilistic theories used to describe the strength of brittle materials, fibres and bundles of fibres have been detailed. The use of these theories to predict the strength of composite materials has been considered, along with further developments incorporating the damage accumulation observed in the failure of such materials. Probabilistic theories of the strength of short-fibre composites have been outlined. Emphasis has been placed throughout on straightforward engineering explanations of these theories and how they may be used, rather than providing comprehensive statistical descriptions

  20. Probabilistic record linkage.

    Science.gov (United States)

    Sayers, Adrian; Ben-Shlomo, Yoav; Blom, Ashley W; Steele, Fiona

    2016-06-01

    Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods. © The Author 2015; Published by Oxford University Press on behalf of the International Epidemiological Association.

  1. Probabilistic Logical Characterization

    DEFF Research Database (Denmark)

    Hermanns, Holger; Parma, Augusto; Segala, Roberto

    2011-01-01

    Probabilistic automata exhibit both probabilistic and non-deterministic choice. They are therefore a powerful semantic foundation for modeling concurrent systems with random phenomena arising in many applications ranging from artificial intelligence, security, systems biology to performance...... modeling. Several variations of bisimulation and simulation relations have proved to be useful as means to abstract and compare different automata. This paper develops a taxonomy of logical characterizations of these relations on image-finite and image-infinite probabilistic automata....

  2. Evaluation of seismic reliability of steel moment resisting frames rehabilitated by concentric braces with probabilistic models

    Directory of Open Access Journals (Sweden)

    Fateme Rezaei

    2017-08-01

    Full Text Available Probability of structure failure which has been designed by "deterministic methods" can be more than the one which has been designed in similar situation using probabilistic methods and models considering "uncertainties". The main purpose of this research was to evaluate the seismic reliability of steel moment resisting frames rehabilitated with concentric braces by probabilistic models. To do so, three-story and nine-story steel moment resisting frames were designed based on resistant criteria of Iranian code and then they were rehabilitated based on controlling drift limitations by concentric braces. Probability of frames failure was evaluated by probabilistic models of magnitude, location of earthquake, ground shaking intensity in the area of the structure, probabilistic model of building response (based on maximum lateral roof displacement and probabilistic methods. These frames were analyzed under subcrustal source by sampling probabilistic method "Risk Tools" (RT. Comparing the exceedance probability of building response curves (or selected points on it of the three-story and nine-story model frames (before and after rehabilitation, seismic response of rehabilitated frames, was reduced and their reliability was improved. Also the main effective variables in reducing the probability of frames failure were determined using sensitivity analysis by FORM probabilistic method. The most effective variables reducing the probability of frames failure are  in the magnitude model, ground shaking intensity model error and magnitude model error

  3. Approximating methods for intractable probabilistic models: Applications in neuroscience

    DEFF Research Database (Denmark)

    Højen-Sørensen, Pedro

    2002-01-01

    This thesis investigates various methods for carrying out approximate inference in intractable probabilistic models. By capturing the relationships between random variables, the framework of graphical models hints at which sets of random variables pose a problem to the inferential step. The appro...

  4. A probabilistic maintenance model for diesel engines

    Science.gov (United States)

    Pathirana, Shan; Abeygunawardane, Saranga Kumudu

    2018-02-01

    In this paper, a probabilistic maintenance model is developed for inspection based preventive maintenance of diesel engines based on the practical model concepts discussed in the literature. Developed model is solved using real data obtained from inspection and maintenance histories of diesel engines and experts' views. Reliability indices and costs were calculated for the present maintenance policy of diesel engines. A sensitivity analysis is conducted to observe the effect of inspection based preventive maintenance on the life cycle cost of diesel engines.

  5. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas

    2009-01-01

    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  6. Probabilistic Role Models and the Guarded Fragment

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2004-01-01

    We propose a uniform semantic framework for interpreting probabilistic concept subsumption and probabilistic role quantification through statistical sampling distributions. This general semantic principle serves as the foundation for the development of a probabilistic version of the guarded fragm...... fragment of first-order logic. A characterization of equivalence in that logic in terms of bisimulations is given....

  7. Probabilistic role models and the guarded fragment

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2006-01-01

    We propose a uniform semantic framework for interpreting probabilistic concept subsumption and probabilistic role quantification through statistical sampling distributions. This general semantic principle serves as the foundation for the development of a probabilistic version of the guarded fragm...... fragment of first-order logic. A characterization of equivalence in that logic in terms of bisimulations is given....

  8. Probabilistic Compositional Models: solution of an equivalence problem

    Czech Academy of Sciences Publication Activity Database

    Kratochvíl, Václav

    2013-01-01

    Roč. 54, č. 5 (2013), s. 590-601 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Probabilistic model * Compositional model * Independence * Equivalence Subject RIV: BA - General Mathematics Impact factor: 1.977, year: 2013 http://library.utia.cas.cz/separaty/2013/MTR/kratochvil-0391079.pdf

  9. Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models

    Directory of Open Access Journals (Sweden)

    Ludmila N. Turino

    2014-05-01

    Full Text Available Administration of exogenous progesterone is widely used in hormonal protocols for estrous (resynchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.

  10. A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

    Science.gov (United States)

    Rivas, Elena; Lang, Raymond; Eddy, Sean R

    2012-02-01

    The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

  11. Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

    Science.gov (United States)

    McClelland, James L

    2013-01-01

    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

  12. A probabilistic model of RNA conformational space

    DEFF Research Database (Denmark)

    Frellsen, Jes; Moltke, Ida; Thiim, Martin

    2009-01-01

    , the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows...... conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

  13. Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

    DEFF Research Database (Denmark)

    ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto

    2015-01-01

    We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...

  14. On the logical specification of probabilistic transition models

    CSIR Research Space (South Africa)

    Rens, G

    2013-05-01

    Full Text Available We investigate the requirements for specifying the behaviors of actions in a stochastic domain. That is, we propose how to write sentences in a logical language to capture a model of probabilistic transitions due to the execution of actions of some...

  15. A Probabilistic Model of Meter Perception: Simulating Enculturation

    Directory of Open Access Journals (Sweden)

    Bastiaan van der Weij

    2017-05-01

    Full Text Available Enculturation is known to shape the perception of meter in music but this is not explicitly accounted for by current cognitive models of meter perception. We hypothesize that the induction of meter is a result of predictive coding: interpreting onsets in a rhythm relative to a periodic meter facilitates prediction of future onsets. Such prediction, we hypothesize, is based on previous exposure to rhythms. As such, predictive coding provides a possible explanation for the way meter perception is shaped by the cultural environment. Based on this hypothesis, we present a probabilistic model of meter perception that uses statistical properties of the relation between rhythm and meter to infer meter from quantized rhythms. We show that our model can successfully predict annotated time signatures from quantized rhythmic patterns derived from folk melodies. Furthermore, we show that by inferring meter, our model improves prediction of the onsets of future events compared to a similar probabilistic model that does not infer meter. Finally, as a proof of concept, we demonstrate how our model can be used in a simulation of enculturation. From the results of this simulation, we derive a class of rhythms that are likely to be interpreted differently by enculturated listeners with different histories of exposure to rhythms.

  16. A methodology for acquiring qualitative knowledge for probabilistic graphical models

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders L.

    2004-01-01

    We present a practical and general methodology that simplifies the task of acquiring and formulating qualitative knowledge for constructing probabilistic graphical models (PGMs). The methodology efficiently captures and communicates expert knowledge, and has significantly eased the model...

  17. EREM: Parameter Estimation and Ancestral Reconstruction by Expectation-Maximization Algorithm for a Probabilistic Model of Genomic Binary Characters Evolution.

    Science.gov (United States)

    Carmel, Liran; Wolf, Yuri I; Rogozin, Igor B; Koonin, Eugene V

    2010-01-01

    Evolutionary binary characters are features of species or genes, indicating the absence (value zero) or presence (value one) of some property. Examples include eukaryotic gene architecture (the presence or absence of an intron in a particular locus), gene content, and morphological characters. In many studies, the acquisition of such binary characters is assumed to represent a rare evolutionary event, and consequently, their evolution is analyzed using various flavors of parsimony. However, when gain and loss of the character are not rare enough, a probabilistic analysis becomes essential. Here, we present a comprehensive probabilistic model to describe the evolution of binary characters on a bifurcating phylogenetic tree. A fast software tool, EREM, is provided, using maximum likelihood to estimate the parameters of the model and to reconstruct ancestral states (presence and absence in internal nodes) and events (gain and loss events along branches).

  18. Non-probabilistic defect assessment for structures with cracks based on interval model

    International Nuclear Information System (INIS)

    Dai, Qiao; Zhou, Changyu; Peng, Jian; Chen, Xiangwei; He, Xiaohua

    2013-01-01

    Highlights: • Non-probabilistic approach is introduced to defect assessment. • Definition and establishment of IFAC are put forward. • Determination of assessment rectangle is proposed. • Solution of non-probabilistic reliability index is presented. -- Abstract: Traditional defect assessment methods conservatively treat uncertainty of parameters as safety factors, while the probabilistic method is based on the clear understanding of detailed statistical information of parameters. In this paper, the non-probabilistic approach is introduced to the failure assessment diagram (FAD) to propose a non-probabilistic defect assessment method for structures with cracks. This novel defect assessment method contains three critical processes: establishment of the interval failure assessment curve (IFAC), determination of the assessment rectangle, and solution of the non-probabilistic reliability degree. Based on the interval theory, uncertain parameters such as crack sizes, material properties and loads are considered as interval variables. As a result, the failure assessment curve (FAC) will vary in a certain range, which is defined as IFAC. And the assessment point will vary within a rectangle zone which is defined as an assessment rectangle. Based on the interval model, the establishment of IFAC and the determination of the assessment rectangle are presented. Then according to the interval possibility degree method, the non-probabilistic reliability degree of IFAC can be determined. Meanwhile, in order to clearly introduce the non-probabilistic defect assessment method, a numerical example for the assessment of a pipe with crack is given. In addition, the assessment result of the proposed method is compared with that of the traditional probabilistic method, which confirms that this non-probabilistic defect assessment can reasonably resolve the practical problem with interval variables

  19. Non-probabilistic defect assessment for structures with cracks based on interval model

    Energy Technology Data Exchange (ETDEWEB)

    Dai, Qiao; Zhou, Changyu, E-mail: changyu_zhou@163.com; Peng, Jian; Chen, Xiangwei; He, Xiaohua

    2013-09-15

    Highlights: • Non-probabilistic approach is introduced to defect assessment. • Definition and establishment of IFAC are put forward. • Determination of assessment rectangle is proposed. • Solution of non-probabilistic reliability index is presented. -- Abstract: Traditional defect assessment methods conservatively treat uncertainty of parameters as safety factors, while the probabilistic method is based on the clear understanding of detailed statistical information of parameters. In this paper, the non-probabilistic approach is introduced to the failure assessment diagram (FAD) to propose a non-probabilistic defect assessment method for structures with cracks. This novel defect assessment method contains three critical processes: establishment of the interval failure assessment curve (IFAC), determination of the assessment rectangle, and solution of the non-probabilistic reliability degree. Based on the interval theory, uncertain parameters such as crack sizes, material properties and loads are considered as interval variables. As a result, the failure assessment curve (FAC) will vary in a certain range, which is defined as IFAC. And the assessment point will vary within a rectangle zone which is defined as an assessment rectangle. Based on the interval model, the establishment of IFAC and the determination of the assessment rectangle are presented. Then according to the interval possibility degree method, the non-probabilistic reliability degree of IFAC can be determined. Meanwhile, in order to clearly introduce the non-probabilistic defect assessment method, a numerical example for the assessment of a pipe with crack is given. In addition, the assessment result of the proposed method is compared with that of the traditional probabilistic method, which confirms that this non-probabilistic defect assessment can reasonably resolve the practical problem with interval variables.

  20. Probabilistic safety analysis procedures guide

    International Nuclear Information System (INIS)

    Papazoglou, I.A.; Bari, R.A.; Buslik, A.J.

    1984-01-01

    A procedures guide for the performance of probabilistic safety assessment has been prepared for interim use in the Nuclear Regulatory Commission programs. The probabilistic safety assessment studies performed are intended to produce probabilistic predictive models that can be used and extended by the utilities and by NRC to sharpen the focus of inquiries into a range of tissues affecting reactor safety. This guide addresses the determination of the probability (per year) of core damage resulting from accident initiators internal to the plant and from loss of offsite electric power. The scope includes analyses of problem-solving (cognitive) human errors, a determination of importance of the various core damage accident sequences, and an explicit treatment and display of uncertainties for the key accident sequences. Ultimately, the guide will be augmented to include the plant-specific analysis of in-plant processes (i.e., containment performance) and the risk associated with external accident initiators, as consensus is developed regarding suitable methodologies in these areas. This guide provides the structure of a probabilistic safety study to be performed, and indicates what products of the study are essential for regulatory decision making. Methodology is treated in the guide only to the extent necessary to indicate the range of methods which is acceptable; ample reference is given to alternative methodologies which may be utilized in the performance of the study

  1. Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

    Science.gov (United States)

    Pearce, Marcus T

    2018-05-11

    Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

  2. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  3. Tensit - a novel probabilistic simulation tool for safety assessments. Tests and verifications using biosphere models

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Jakob; Vahlund, Fredrik; Kautsky, Ulrik

    2004-06-01

    This report documents the verification of a new simulation tool for dose assessment put together in a package under the name Tensit (Technical Nuclide Simulation Tool). The tool is developed to solve differential equation systems describing transport and decay of radionuclides. It is capable of handling both deterministic and probabilistic simulations. The verifications undertaken shows good results. Exceptions exist only where the reference results are unclear. Tensit utilise and connects two separate commercial softwares. The equation solving capability is derived from the Matlab/Simulink software environment to which Tensit adds a library of interconnectable building blocks. Probabilistic simulations are provided through a statistical software named at{sub R}isk that communicates with Matlab/Simulink. More information about these softwares can be found at www.palisade.com and www.mathworks.com. The underlying intention of developing this new tool has been to make available a cost efficient and easy to use means for advanced dose assessment simulations. The mentioned benefits are gained both through the graphical user interface provided by Simulink and at{sub R}isk, and the use of numerical equation solving routines in Matlab. To verify Tensit's numerical correctness, an implementation was done of the biosphere modules for dose assessments used in the earlier safety assessment project SR 97. Acquired probabilistic results for deterministic as well as probabilistic simulations have been compared with documented values. Additional verification has been made both with another simulation tool named AMBER and also against the international test case from PSACOIN named Level 1B. This report documents the models used for verification with equations and parameter values so that the results can be recreated. For a background and a more detailed description of the underlying processes in the models, the reader is referred to the original references. Finally, in the

  4. Tensit - a novel probabilistic simulation tool for safety assessments. Tests and verifications using biosphere models

    International Nuclear Information System (INIS)

    Jones, Jakob; Vahlund, Fredrik; Kautsky, Ulrik

    2004-06-01

    This report documents the verification of a new simulation tool for dose assessment put together in a package under the name Tensit (Technical Nuclide Simulation Tool). The tool is developed to solve differential equation systems describing transport and decay of radionuclides. It is capable of handling both deterministic and probabilistic simulations. The verifications undertaken shows good results. Exceptions exist only where the reference results are unclear. Tensit utilise and connects two separate commercial softwares. The equation solving capability is derived from the Matlab/Simulink software environment to which Tensit adds a library of interconnectable building blocks. Probabilistic simulations are provided through a statistical software named at R isk that communicates with Matlab/Simulink. More information about these softwares can be found at www.palisade.com and www.mathworks.com. The underlying intention of developing this new tool has been to make available a cost efficient and easy to use means for advanced dose assessment simulations. The mentioned benefits are gained both through the graphical user interface provided by Simulink and at R isk, and the use of numerical equation solving routines in Matlab. To verify Tensit's numerical correctness, an implementation was done of the biosphere modules for dose assessments used in the earlier safety assessment project SR 97. Acquired probabilistic results for deterministic as well as probabilistic simulations have been compared with documented values. Additional verification has been made both with another simulation tool named AMBER and also against the international test case from PSACOIN named Level 1B. This report documents the models used for verification with equations and parameter values so that the results can be recreated. For a background and a more detailed description of the underlying processes in the models, the reader is referred to the original references. Finally, in the perspective of

  5. A review of the report ''IAEA safety targets and probabilistic risk assessment'' prepared for Greenpeace International

    International Nuclear Information System (INIS)

    1991-01-01

    At the request of the Director General, INSAG reviewed the report ''IAEA Safety Targets and Probabilistic Risk Assessment'' prepared for Greenpeace International by the Gesellschaft fuer Oekologische Forschung und Beratung mbH, Hannover, Germany. The conclusions of the report as well as the review results of INSAG experts are reproduced in this document

  6. Optimization and evaluation of probabilistic-logic sequence models

    DEFF Research Database (Denmark)

    Christiansen, Henning; Lassen, Ole Torp

    to, in principle, Turing complete languages. In general, such models are computationally far to complex for direct use, so optimization by pruning and approximation are needed. % The first steps are made towards a methodology for optimizing such models by approximations using auxiliary models......Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...

  7. Probabilistic Modeling of the Fatigue Crack Growth Rate for Ni-base Alloy X-750

    International Nuclear Information System (INIS)

    Yoon, J.Y.; Nam, H.O.; Hwang, I.S.; Lee, T.H.

    2012-01-01

    Extending the operating life of existing nuclear power plants (NPP's) beyond 60 years. Many aging problems of passive components such as PWSCC, IASCC, FAC and Corrosion Fatigue; Safety analysis: Deterministic analysis + Probabilistic analysis; Many uncertainties of parameters or relationship in general probabilistic analysis such as probabilistic safety assessment (PSA); Bayesian inference: Decreasing uncertainties by updating unknown parameter; Ensuring the reliability of passive components (e.g. pipes) as well as active components (e.g. valve, pump) in NPP's; Developing probabilistic model for failures; Updating the fatigue crack growth rate (FCGR)

  8. Trait-Dependent Biogeography: (Re)Integrating Biology into Probabilistic Historical Biogeographical Models.

    Science.gov (United States)

    Sukumaran, Jeet; Knowles, L Lacey

    2018-04-20

    The development of process-based probabilistic models for historical biogeography has transformed the field by grounding it in modern statistical hypothesis testing. However, most of these models abstract away biological differences, reducing species to interchangeable lineages. We present here the case for reintegration of biology into probabilistic historical biogeographical models, allowing a broader range of questions about biogeographical processes beyond ancestral range estimation or simple correlation between a trait and a distribution pattern, as well as allowing us to assess how inferences about ancestral ranges themselves might be impacted by differential biological traits. We show how new approaches to inference might cope with the computational challenges resulting from the increased complexity of these trait-based historical biogeographical models. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. A Probabilistic Typhoon Risk Model for Vietnam

    Science.gov (United States)

    Haseemkunju, A.; Smith, D. F.; Brolley, J. M.

    2017-12-01

    Annually, the coastal Provinces of low-lying Mekong River delta region in the southwest to the Red River Delta region in Northern Vietnam is exposed to severe wind and flood risk from landfalling typhoons. On average, about two to three tropical cyclones with a maximum sustained wind speed of >=34 knots make landfall along the Vietnam coast. Recently, Typhoon Wutip (2013) crossed Central Vietnam as a category 2 typhoon causing significant damage to properties. As tropical cyclone risk is expected to increase with increase in exposure and population growth along the coastal Provinces of Vietnam, insurance/reinsurance, and capital markets need a comprehensive probabilistic model to assess typhoon risk in Vietnam. In 2017, CoreLogic has expanded the geographical coverage of its basin-wide Western North Pacific probabilistic typhoon risk model to estimate the economic and insured losses from landfalling and by-passing tropical cyclones in Vietnam. The updated model is based on 71 years (1945-2015) of typhoon best-track data and 10,000 years of a basin-wide simulated stochastic tracks covering eight countries including Vietnam. The model is capable of estimating damage from wind, storm surge and rainfall flooding using vulnerability models, which relate typhoon hazard to building damageability. The hazard and loss models are validated against past historical typhoons affecting Vietnam. Notable typhoons causing significant damage in Vietnam are Lola (1993), Frankie (1996), Xangsane (2006), and Ketsana (2009). The central and northern coastal provinces of Vietnam are more vulnerable to wind and flood hazard, while typhoon risk in the southern provinces are relatively low.

  10. Probabilistic Data Modeling and Querying for Location-Based Data Warehouses

    DEFF Research Database (Denmark)

    Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach

    Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical and unc...

  11. Probabilistic Data Modeling and Querying for Location-Based Data Warehouses

    DEFF Research Database (Denmark)

    Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach

    2005-01-01

    Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical and unc...

  12. Failure probabilistic model of CNC lathes

    International Nuclear Information System (INIS)

    Wang Yiqiang; Jia Yazhou; Yu Junyi; Zheng Yuhua; Yi Shangfeng

    1999-01-01

    A field failure analysis of computerized numerical control (CNC) lathes is described. Field failure data was collected over a period of two years on approximately 80 CNC lathes. A coding system to code failure data was devised and a failure analysis data bank of CNC lathes was established. The failure position and subsystem, failure mode and cause were analyzed to indicate the weak subsystem of a CNC lathe. Also, failure probabilistic model of CNC lathes was analyzed by fuzzy multicriteria comprehensive evaluation

  13. Probabilistic transport models for fusion

    International Nuclear Information System (INIS)

    Milligen, B.Ph. van; Carreras, B.A.; Lynch, V.E.; Sanchez, R.

    2005-01-01

    A generalization of diffusive (Fickian) transport is considered, in which particle motion is described by probability distributions. We design a simple model that includes a critical mechanism to switch between two transport channels, and show that it exhibits various interesting characteristics, suggesting that the ideas of probabilistic transport might provide a framework for the description of a range of unusual transport phenomena observed in fusion plasmas. The model produces power degradation and profile consistency, as well as a scaling of the confinement time with system size reminiscent of the gyro-Bohm/Bohm scalings observed in fusion plasmas, and rapid propagation of disturbances. In the present work we show how this model may also produce on-axis peaking of the profiles with off-axis fuelling. It is important to note that the fluid limit of a simple model like this, characterized by two transport channels, does not correspond to the usual (Fickian) transport models commonly used for modelling transport in fusion plasmas, and behaves in a fundamentally different way. (author)

  14. Probabilistic modelling and analysis of stand-alone hybrid power systems

    International Nuclear Information System (INIS)

    Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

    2013-01-01

    As a part of the Hybrid Intelligent Algorithm, a model based on an ANN (artificial neural network) has been proposed in this paper to represent hybrid system behaviour considering the uncertainty related to wind speed and solar radiation, battery bank lifetime, and fuel prices. The Hybrid Intelligent Algorithm suggests a combination of probabilistic analysis based on a Monte Carlo simulation approach and artificial neural network training embedded in a genetic algorithm optimisation model. The installation of a typical hybrid system was analysed. Probabilistic analysis was used to generate an input–output dataset of 519 samples that was later used to train the ANNs to reduce the computational effort required. The generalisation ability of the ANNs was measured in terms of RMSE (Root Mean Square Error), MBE (Mean Bias Error), MAE (Mean Absolute Error), and R-squared estimators using another data group of 200 samples. The results obtained from the estimation of the expected energy not supplied, the probability of a determined reliability level, and the estimation of expected value of net present cost show that the presented model is able to represent the main characteristics of a typical hybrid power system under uncertain operating conditions. - Highlights: • This paper presents a probabilistic model for stand-alone hybrid power system. • The model considers the main sources of uncertainty related to renewable resources. • The Hybrid Intelligent Algorithm has been applied to represent hybrid system behaviour. • The installation of a typical hybrid system was analysed. • The results obtained from the study case validate the presented model

  15. EREM: Parameter Estimation and Ancestral Reconstruction by Expectation-Maximization Algorithm for a Probabilistic Model of Genomic Binary Characters Evolution

    Directory of Open Access Journals (Sweden)

    Liran Carmel

    2010-01-01

    Full Text Available Evolutionary binary characters are features of species or genes, indicating the absence (value zero or presence (value one of some property. Examples include eukaryotic gene architecture (the presence or absence of an intron in a particular locus, gene content, and morphological characters. In many studies, the acquisition of such binary characters is assumed to represent a rare evolutionary event, and consequently, their evolution is analyzed using various flavors of parsimony. However, when gain and loss of the character are not rare enough, a probabilistic analysis becomes essential. Here, we present a comprehensive probabilistic model to describe the evolution of binary characters on a bifurcating phylogenetic tree. A fast software tool, EREM, is provided, using maximum likelihood to estimate the parameters of the model and to reconstruct ancestral states (presence and absence in internal nodes and events (gain and loss events along branches.

  16. Implementation of probabilistic safety concepts in international codes

    International Nuclear Information System (INIS)

    Borges, J.F.

    1977-01-01

    Recent progress in the implementation of safety concepts in international structure codes is briefly presented. Special attention is paid to the work of the Joint-Committee on Structural Safety. The discussion is centered on some problems such as: safety differentiation, definition and combination of actions, spaces for checking safety and non-linear structural behaviour. When discussing safety differentiation it should be considered that the total probability of failure derives from a theoretical probability of failure and a probability of failure due to error and gross negligence. Optimization of design criteria should take into account both causes of failure. The quantification of reliability implies a probabilistic idealization of all basic variables. Steps taken to obtain an improved definition of different types of actions and rules for their combination are described. Safety checking can be carried out in terms of basic variables, action-effects, or any other suitable variable. However, the advantages and disadvantages of the different types of formulation should be discussed, particularly in the case of non-linear structural behaviour. (orig.) [de

  17. Probabilistic Modelling of Timber Material Properties

    DEFF Research Database (Denmark)

    Nielsen, Michael Havbro Faber; Köhler, Jochen; Sørensen, John Dalsgaard

    2001-01-01

    The probabilistic modeling of timber material characteristics is considered with special emphasis to the modeling of the effect of different quality control and selection procedures used as means for grading of timber in the production line. It is shown how statistical models may be established...... on the basis of the same type of information which is normally collected as a part of the quality control procedures and furthermore, how the efficiency of different control procedures may be compared. The tail behavior of the probability distributions of timber material characteristics play an important role...... such that they may readily be applied in structural reliability analysis and the format appears to be appropriate for codification purposes of quality control and selection for grading procedures...

  18. Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model.

    Science.gov (United States)

    Liu, Xueliang; Wang, Meng; Yin, Bao-Cai; Huet, Benoit; Li, Xuelong

    2015-11-01

    Nowadays, with the continual development of digital capture technologies and social media services, a vast number of media documents are captured and shared online to help attendees record their experience during events. In this paper, we present a method combining semantic inference and multimodal analysis for automatically finding media content to illustrate events using an adaptive probabilistic hypergraph model. In this model, media items are taken as vertices in the weighted hypergraph and the task of enriching media to illustrate events is formulated as a ranking problem. In our method, each hyperedge is constructed using the K-nearest neighbors of a given media document. We also employ a probabilistic representation, which assigns each vertex to a hyperedge in a probabilistic way, to further exploit the correlation among media data. Furthermore, we optimize the hypergraph weights in a regularization framework, which is solved as a second-order cone problem. The approach is initiated by seed media and then used to rank the media documents using a transductive inference process. The results obtained from validating the approach on an event dataset collected from EventMedia demonstrate the effectiveness of the proposed approach.

  19. Probabilistic Modelling of Information Propagation in Wireless Mobile Ad-Hoc Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Hansen, Martin Bøgsted; Schwefel, Hans-Peter

    2005-01-01

    In this paper the dynamics of broadcasting wireless ad-hoc networks is studied through probabilistic modelling. A randomized transmission discipline is assumed in accordance with existing MAC definitions such as WLAN with Decentralized Coordination or IEEE-802.15.4. Message reception is assumed...... to be governed by node power-down policies and is equivalently assumed to be randomized. Altogether randomization facilitates a probabilistic model in the shape of an integro-differential equation governing the propagation of information, where brownian node mobility may be accounted for by including an extra...... diffusion term. The established model is analyzed for transient behaviour and a travelling wave solution facilitates expressions for propagation speed as well as parametrized analysis of network reliability and node power consumption. Applications of the developed models for node localization and network...

  20. Economic Dispatch for Microgrid Containing Electric Vehicles via Probabilistic Modeling: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Yao, Yin; Gao, Wenzhong; Momoh, James; Muljadi, Eduard

    2016-02-11

    In this paper, an economic dispatch model with probabilistic modeling is developed for a microgrid. The electric power supply in a microgrid consists of conventional power plants and renewable energy power plants, such as wind and solar power plants. Because of the fluctuation in the output of solar and wind power plants, an empirical probabilistic model is developed to predict their hourly output. According to different characteristics of wind and solar power plants, the parameters for probabilistic distribution are further adjusted individually for both. On the other hand, with the growing trend in plug-in electric vehicles (PHEVs), an integrated microgrid system must also consider the impact of PHEVs. The charging loads from PHEVs as well as the discharging output via the vehicle-to-grid (V2G) method can greatly affect the economic dispatch for all of the micro energy sources in a microgrid. This paper presents an optimization method for economic dispatch in a microgrid considering conventional power plants, renewable power plants, and PHEVs. The simulation results reveal that PHEVs with V2G capability can be an indispensable supplement in a modern microgrid.

  1. A note on probabilistic models over strings: the linear algebra approach.

    Science.gov (United States)

    Bouchard-Côté, Alexandre

    2013-12-01

    Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

  2. Fully probabilistic design of hierarchical Bayesian models

    Czech Academy of Sciences Publication Activity Database

    Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine

    2016-01-01

    Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf

  3. Development of probabilistic seismic hazard analysis for international sites, challenges and guidelines

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez Ares, Antonio, E-mail: antonio.fernandez@rizzoassoc.com [Paul C. Rizzo Associates, Inc., 500 Penn Center Boulevard, Penn Center East, Suite 100, Pittsburgh, PA 15235 (United States); Fatehi, Ali, E-mail: ali.fatehi@rizzoassoc.com [Paul C. Rizzo Associates, Inc., 500 Penn Center Boulevard, Penn Center East, Suite 100, Pittsburgh, PA 15235 (United States)

    2013-06-15

    Research highlights: ► Site-specific seismic hazard study and suggestions for overcoming those challenges that are inherent to the significant amounts of epistemic uncertainty for sites at remote locations. ► Main aspects of probabilistic seismic hazard analysis (PSHA). ► Regional and site geology in the context of a probabilistic seismic hazard analysis (PSHA), including state-of-the-art ground motion estimation methods, and geophysical conditions. ► Senior seismic hazard analysis (SSHAC) as a mean to incorporate the opinions and contributions of the informed scientific community. -- Abstract: This article provides guidance to conduct a site-specific seismic hazard study, giving suggestions for overcoming those challenges that are inherent to the significant amounts of epistemic uncertainty for sites at remote locations. The text follows the general process of a seismic hazard study, describing both the deterministic and probabilistic approaches. Key and controversial items are identified in the areas of recorded seismicity, seismic sources, magnitude, ground motion models, and local site effects. A case history corresponding to a seismic hazard study in the Middle East for a Greenfield site in a remote location is incorporated along the development of the recommendations. Other examples of analysis case histories throughout the World are presented as well.

  4. The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes

    Science.gov (United States)

    Robusto, Egidio; Stefanutti, Luca; Anselmi, Pasquale

    2010-01-01

    Within the theoretical framework of knowledge space theory, a probabilistic skill multimap model for assessing learning processes is proposed. The learning process of a student is modeled as a function of the student's knowledge and of an educational intervention on the attainment of specific skills required to solve problems in a knowledge…

  5. Probabilistic Survivability Versus Time Modeling

    Science.gov (United States)

    Joyner, James J., Sr.

    2016-01-01

    This presentation documents Kennedy Space Center's Independent Assessment work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer during key programmatic reviews and provided the GSDO Program with analyses of how egress time affects the likelihood of astronaut and ground worker survival during an emergency. For each assessment, a team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedy's Vehicle Assembly Building.

  6. From sub-source to source: Interpreting results of biological trace investigations using probabilistic models

    NARCIS (Netherlands)

    Oosterman, W.T.; Kokshoorn, B.; Maaskant-van Wijk, P.A.; de Zoete, J.

    2015-01-01

    The current method of reporting a putative cell type is based on a non-probabilistic assessment of test results by the forensic practitioner. Additionally, the association between donor and cell type in mixed DNA profiles can be exceedingly complex. We present a probabilistic model for

  7. Probabilistic model for sterilization of food

    International Nuclear Information System (INIS)

    Chepurko, V.V.; Malinovskij, O.V.

    1986-01-01

    The probabilistic model for radiation sterilization is proposed based on the followng suppositions: (1) initial contamination of a volume unit of the sterilized product m is described by the distribution of the probabilities q(m), (2) inactivation of the population from m of microorganisms is approximated by Bernoulli test scheme, and (3) contamination of unit of the sterilized product is independent. The possibility of approximation q(m) by Poisson distribution is demonstrated. The diagrams are presented permitting to evaluate the dose which provides the defined reliability of sterilization of food for chicken-gnotobionts

  8. Report of the IPERS (International Peer Review Service) pre-review mission for the Cernavoda nuclear power plant probabilistic safety evaluation (CPSE - PHASE B) in Romania 31 October to 3 November 1994

    International Nuclear Information System (INIS)

    1994-01-01

    This report presents the results of the IAEA international peer review services pre-review mission which reviewed the status of the present version of the Cernavoda probabilistic safety evaluation, a Level 1 internal events Probabilistic Safety Assessment for the Cernavoda, Unit 1, nuclear power plant. 2 refs

  9. Advanced Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Technical Exchange Meeting

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Curtis [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2013-09-01

    During FY13, the INL developed an advanced SMR PRA framework which has been described in the report Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Detailed Technical Framework Specification, INL/EXT-13-28974 (April 2013). In this framework, the various areas are considered: Probabilistic models to provide information specific to advanced SMRs Representation of specific SMR design issues such as having co-located modules and passive safety features Use of modern open-source and readily available analysis methods Internal and external events resulting in impacts to safety All-hazards considerations Methods to support the identification of design vulnerabilities Mechanistic and probabilistic data needs to support modeling and tools In order to describe this framework more fully and obtain feedback on the proposed approaches, the INL hosted a technical exchange meeting during August 2013. This report describes the outcomes of that meeting.

  10. Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

    Science.gov (United States)

    Jaworske, D. A.; Myers, J. G.; Goodenow, D.; Young, M.; Arellano, J. D.

    2016-01-01

    Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

  11. Probabilistic Bandwidth Assignment in Wireless Sensor Networks

    OpenAIRE

    Khan , Dawood; Nefzi , Bilel; Santinelli , Luca; Song , Ye-Qiong

    2012-01-01

    International audience; With this paper we offer an insight in designing and analyzing wireless sensor networks in a versatile manner. Our framework applies probabilistic and component-based design principles for the wireless sensor network modeling and consequently analysis; while maintaining flexibility and accuracy. In particular, we address the problem of allocating and reconfiguring the available bandwidth. The framework has been successfully implemented in IEEE 802.15.4 using an Admissi...

  12. Development of the PRO-LOCA Probabilistic Fracture Mechanics Code, MERIT Final Report

    International Nuclear Information System (INIS)

    Scott, Paul; Kurth, Robert; Cox, Andrew; Olson, Rick; Rudland, Dave

    2010-12-01

    The MERIT project has been an internationally financed program with the main purpose of developing probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code named PRO-LOCA. The principal objective of the project has been to develop probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code. The MERIT program has produced a code named PRO-LOCA with the following features: - Crack initiation models for fatigue or stress corrosion cracking for previously unflawed material. - Subcritical crack growth models for fatigue and stress corrosion cracking for both initiated and pre-existing circumferential defects. - Models for flaw detection by inspections and leak detection. - Crack stability. The PRO-LOCA code can thus predict the leak or break frequency for the whole sequence of initiation, subcritical crack growth until wall penetration and leakage, instability of the through-wall crack (pipe rupture). The outcome of the PRO-LOCA code are a sequence of failure frequencies which represents the probability of surface crack developing, a through-wall crack developing and six different sizes of crack opening areas corresponding to different leak flow rates or LOCA categories. Note that the level of quality assurance of the PRO-LOCA code is such that the code in its current state of development is considered to be more of a research code than a regulatory tool.

  13. Development of the PRO-LOCA Probabilistic Fracture Mechanics Code, MERIT Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Scott, Paul; Kurth, Robert; Cox, Andrew; Olson, Rick (Battelle Columbus (United States)); Rudland, Dave (Nuclear Regulatory Commission (United States))

    2010-12-15

    The MERIT project has been an internationally financed program with the main purpose of developing probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code named PRO-LOCA. The principal objective of the project has been to develop probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code. The MERIT program has produced a code named PRO-LOCA with the following features: - Crack initiation models for fatigue or stress corrosion cracking for previously unflawed material. - Subcritical crack growth models for fatigue and stress corrosion cracking for both initiated and pre-existing circumferential defects. - Models for flaw detection by inspections and leak detection. - Crack stability. The PRO-LOCA code can thus predict the leak or break frequency for the whole sequence of initiation, subcritical crack growth until wall penetration and leakage, instability of the through-wall crack (pipe rupture). The outcome of the PRO-LOCA code are a sequence of failure frequencies which represents the probability of surface crack developing, a through-wall crack developing and six different sizes of crack opening areas corresponding to different leak flow rates or LOCA categories. Note that the level of quality assurance of the PRO-LOCA code is such that the code in its current state of development is considered to be more of a research code than a regulatory tool.

  14. A probabilistic model for US nuclear power construction times

    International Nuclear Information System (INIS)

    Shash, A.A.H.

    1988-01-01

    Construction time for nuclear power plants is an important element in planning for resources to meet future load demands. Analysis of actual versus estimated construction times for past US nuclear power plants indicates that utilities have continuously underestimated their power plants' construction durations. The analysis also indicates that the actual average construction time has been increasing upward, and the actual durations of power plants permitted to construct in the same year varied substantially. This study presents two probabilistic models for nuclear power construction time for use by the nuclear industry as estimating tool. The study also presents a detailed explanation of the factors that are responsible for increasing and varying nuclear power construction times. Observations on 91 complete nuclear units were involved in three interdependent analyses in the process of explanation and derivation of the probabilistic models. The historical data was first utilized in the data envelopment analysis (DEA) for the purpose of obtaining frontier index measures for project management achievement in building nuclear power plants

  15. Probabilistic disaggregation model with application to natural hazard risk assessment of portfolios

    DEFF Research Database (Denmark)

    Custer, Rocco; Nishijima, Kazuyoshi

    In natural hazard risk assessment, a resolution mismatch between hazard data and aggregated exposure data is often observed. A possible solution to this issue is the disaggregation of exposure data to match the spatial resolution of hazard data. Disaggregation models available in literature...... disaggregation model that considers the uncertainty in the disaggregation, taking basis in the scaled Dirichlet distribution. The proposed probabilistic disaggregation model is applied to a portfolio of residential buildings in the Canton Bern, Switzerland, subject to flood risk. Thereby, the model is verified...... are usually deterministic and make use of auxiliary indicator, such as land cover, to spatially distribute exposures. As the dependence between auxiliary indicator and disaggregated number of exposures is generally imperfect, uncertainty arises in disaggregation. This paper therefore proposes a probabilistic...

  16. A Practical Probabilistic Graphical Modeling Tool for Weighing ...

    Science.gov (United States)

    Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for ecological risk determinations. Probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure wasdeveloped for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors. We provide a flexible Bayesian network structure for weighing and integrating lines of evidence for ecological risk determinations

  17. An Empirical Study of Efficiency and Accuracy of Probabilistic Graphical Models

    DEFF Research Database (Denmark)

    Nielsen, Jens Dalgaard; Jaeger, Manfred

    2006-01-01

    In this paper we compare Na\\ii ve Bayes (NB) models, general Bayes Net (BN) models and Probabilistic Decision Graph (PDG) models w.r.t. accuracy and efficiency. As the basis for our analysis we use graphs of size vs. likelihood that show the theoretical capabilities of the models. We also measure...

  18. Lessons learned on probabilistic methodology for precursor analyses

    Energy Technology Data Exchange (ETDEWEB)

    Babst, Siegfried [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) gGmbH, Berlin (Germany); Wielenberg, Andreas; Gaenssmantel, Gerhard [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) gGmbH, Garching (Germany)

    2016-11-15

    Based on its experience in precursor assessment of operating experience from German NPP and related international activities in the field, GRS has identified areas for enhancing probabilistic methodology. These are related to improving the completeness of PSA models, to insufficiencies in probabilistic assessment approaches, and to enhancements of precursor assessment methods. Three examples from the recent practice in precursor assessments illustrating relevant methodological insights are provided and discussed in more detail. Our experience reinforces the importance of having full scope, current PSA models up to Level 2 PSA and including hazard scenarios for precursor analysis. Our lessons learned include that PSA models should be regularly updated regarding CCF data and inclusion of newly discovered CCF mechanisms or groups. Moreover, precursor classification schemes should be extended to degradations and unavailabilities of the containment function. Finally, PSA and precursor assessments should put more emphasis on the consideration of passive provisions for safety, e. g. by sensitivity cases.

  19. Lessons learned on probabilistic methodology for precursor analyses

    International Nuclear Information System (INIS)

    Babst, Siegfried; Wielenberg, Andreas; Gaenssmantel, Gerhard

    2016-01-01

    Based on its experience in precursor assessment of operating experience from German NPP and related international activities in the field, GRS has identified areas for enhancing probabilistic methodology. These are related to improving the completeness of PSA models, to insufficiencies in probabilistic assessment approaches, and to enhancements of precursor assessment methods. Three examples from the recent practice in precursor assessments illustrating relevant methodological insights are provided and discussed in more detail. Our experience reinforces the importance of having full scope, current PSA models up to Level 2 PSA and including hazard scenarios for precursor analysis. Our lessons learned include that PSA models should be regularly updated regarding CCF data and inclusion of newly discovered CCF mechanisms or groups. Moreover, precursor classification schemes should be extended to degradations and unavailabilities of the containment function. Finally, PSA and precursor assessments should put more emphasis on the consideration of passive provisions for safety, e. g. by sensitivity cases.

  20. Probabilistic logics and probabilistic networks

    CERN Document Server

    Haenni, Rolf; Wheeler, Gregory; Williamson, Jon; Andrews, Jill

    2014-01-01

    Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which various approaches to probabilistic logic naturally fit. Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.

  1. A study of probabilistic fatigue crack propagation models in Mg Al Zn alloys under different specimen thickness conditions by using the residual of a random variable

    International Nuclear Information System (INIS)

    Choi, Seon Soon

    2012-01-01

    The primary aim of this paper was to evaluate several probabilistic fatigue crack propagation models using the residual of a random variable, and to present the model fit for probabilistic fatigue behavior in Mg Al Zn alloys. The proposed probabilistic models are the probabilistic Paris Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman models. These models were prepared by applying a random variable to the empirical fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models vor describing fatigue crack propagation behavior in Mg Al Zn alloys were generally the probabilistic Paris Erdogan and probabilistic Walker models. The probabilistic Forman model was a good model only for a specimen with a thickness of 9.45mm

  2. Pipe fracture evaluations for leak-rate detection: Probabilistic models

    International Nuclear Information System (INIS)

    Rahman, S.; Wilkowski, G.; Ghadiali, N.

    1993-01-01

    This is the second in series of three papers generated from studies on nuclear pipe fracture evaluations for leak-rate detection. This paper focuses on the development of novel probabilistic models for stochastic performance evaluation of degraded nuclear piping systems. It was accomplished here in three distinct stages. First, a statistical analysis was conducted to characterize various input variables for thermo-hydraulic analysis and elastic-plastic fracture mechanics, such as material properties of pipe, crack morphology variables, and location of cracks found in nuclear piping. Second, a new stochastic model was developed to evaluate performance of degraded piping systems. It is based on accurate deterministic models for thermo-hydraulic and fracture mechanics analyses described in the first paper, statistical characterization of various input variables, and state-of-the-art methods of modem structural reliability theory. From this model. the conditional probability of failure as a function of leak-rate detection capability of the piping systems can be predicted. Third, a numerical example was presented to illustrate the proposed model for piping reliability analyses. Results clearly showed that the model provides satisfactory estimates of conditional failure probability with much less computational effort when compared with those obtained from Monte Carlo simulation. The probabilistic model developed in this paper will be applied to various piping in boiling water reactor and pressurized water reactor plants for leak-rate detection applications

  3. Quantification of Wave Model Uncertainties Used for Probabilistic Reliability Assessments of Wave Energy Converters

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

    2015-01-01

    Wave models used for site assessments are subjected to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Four different wave models are considered, and validation...... data are collected from published scientific research. The bias and the root-mean-square error, as well as the scatter index, are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example, this paper presents how the quantified...... uncertainties can be implemented in probabilistic reliability assessments....

  4. Determination of Wave Model Uncertainties used for Probabilistic Reliability Assessments of Wave Energy Devices

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

    2014-01-01

    Wave models used for site assessments are subject to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Considered are four different wave models and validation...... data is collected from published scientific research. The bias, the root-mean-square error as well as the scatter index are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example it is shown how the estimated uncertainties can...... be implemented in probabilistic reliability assessments....

  5. Probabilistic analysis of a materially nonlinear structure

    Science.gov (United States)

    Millwater, H. R.; Wu, Y.-T.; Fossum, A. F.

    1990-01-01

    A probabilistic finite element program is used to perform probabilistic analysis of a materially nonlinear structure. The program used in this study is NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), under development at Southwest Research Institute. The cumulative distribution function (CDF) of the radial stress of a thick-walled cylinder under internal pressure is computed and compared with the analytical solution. In addition, sensitivity factors showing the relative importance of the input random variables are calculated. Significant plasticity is present in this problem and has a pronounced effect on the probabilistic results. The random input variables are the material yield stress and internal pressure with Weibull and normal distributions, respectively. The results verify the ability of NESSUS to compute the CDF and sensitivity factors of a materially nonlinear structure. In addition, the ability of the Advanced Mean Value (AMV) procedure to assess the probabilistic behavior of structures which exhibit a highly nonlinear response is shown. Thus, the AMV procedure can be applied with confidence to other structures which exhibit nonlinear behavior.

  6. Towards port sustainability through probabilistic models: Bayesian networks

    Directory of Open Access Journals (Sweden)

    B. Molina

    2018-04-01

    Full Text Available It is necessary that a manager of an infrastructure knows relations between variables. Using Bayesian networks, variables can be classified, predicted and diagnosed, being able to estimate posterior probability of the unknown ones based on known ones. The proposed methodology has generated a database with port variables, which have been classified as economic, social, environmental and institutional, as addressed in of smart ports studies made in all Spanish Port System. Network has been developed using an acyclic directed graph, which have let us know relationships in terms of parents and sons. In probabilistic terms, it can be concluded from the constructed network that the most decisive variables for port sustainability are those that are part of the institutional dimension. It has been concluded that Bayesian networks allow modeling uncertainty probabilistically even when the number of variables is high as it occurs in port planning and exploitation.

  7. Probabilistic systems coalgebraically: A survey

    Science.gov (United States)

    Sokolova, Ana

    2011-01-01

    We survey the work on both discrete and continuous-space probabilistic systems as coalgebras, starting with how probabilistic systems are modeled as coalgebras and followed by a discussion of their bisimilarity and behavioral equivalence, mentioning results that follow from the coalgebraic treatment of probabilistic systems. It is interesting to note that, for different reasons, for both discrete and continuous probabilistic systems it may be more convenient to work with behavioral equivalence than with bisimilarity. PMID:21998490

  8. Quantitative Risk Modeling of Fire on the International Space Station

    Science.gov (United States)

    Castillo, Theresa; Haught, Megan

    2014-01-01

    The International Space Station (ISS) Program has worked to prevent fire events and to mitigate their impacts should they occur. Hardware is designed to reduce sources of ignition, oxygen systems are designed to control leaking, flammable materials are prevented from flying to ISS whenever possible, the crew is trained in fire response, and fire response equipment improvements are sought out and funded. Fire prevention and mitigation are a top ISS Program priority - however, programmatic resources are limited; thus, risk trades are made to ensure an adequate level of safety is maintained onboard the ISS. In support of these risk trades, the ISS Probabilistic Risk Assessment (PRA) team has modeled the likelihood of fire occurring in the ISS pressurized cabin, a phenomenological event that has never before been probabilistically modeled in a microgravity environment. This paper will discuss the genesis of the ISS PRA fire model, its enhancement in collaboration with fire experts, and the results which have informed ISS programmatic decisions and will continue to be used throughout the life of the program.

  9. Probabilistic Programming (Invited Talk)

    OpenAIRE

    Yang, Hongseok

    2017-01-01

    Probabilistic programming refers to the idea of using standard programming constructs for specifying probabilistic models from machine learning and statistics, and employing generic inference algorithms for answering various queries on these models, such as posterior inference and estimation of model evidence. Although this idea itself is not new and was, in fact, explored by several programming-language and statistics researchers in the early 2000, it is only in the last few years that proba...

  10. Probabilistic Modelling of Fatigue Life of Composite Laminates Using Bayesian Inference

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der

    2014-01-01

    A probabilistic model for estimating the fatigue life of laminated composite plates subjected to constant-amplitude or variable-amplitude loading is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configuratio...

  11. Global/local methods for probabilistic structural analysis

    Science.gov (United States)

    Millwater, H. R.; Wu, Y.-T.

    1993-04-01

    A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.

  12. Consideration of aging in probabilistic safety assessment

    International Nuclear Information System (INIS)

    Titina, B.; Cepin, M.

    2007-01-01

    Probabilistic safety assessment is a standardised tool for assessment of safety of nuclear power plants. It is a complement to the safety analyses. Standard probabilistic models of safety equipment assume component failure rate as a constant. Ageing of systems, structures and components can theoretically be included in new age-dependent probabilistic safety assessment, which generally causes the failure rate to be a function of age. New age-dependent probabilistic safety assessment models, which offer explicit calculation of the ageing effects, are developed. Several groups of components are considered which require their unique models: e.g. operating components e.g. stand-by components. The developed models on the component level are inserted into the models of the probabilistic safety assessment in order that the ageing effects are evaluated for complete systems. The preliminary results show that the lack of necessary data for consideration of ageing causes highly uncertain models and consequently the results. (author)

  13. Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

    NARCIS (Netherlands)

    Engström, Kerstin; Olin, Stefan; Rounsevell, Mark D A; Brogaard, Sara; Van Vuuren, Detlef P.; Alexander, Peter; Murray-Rust, Dave; Arneth, Almut

    2016-01-01

    We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS

  14. Multiple sequential failure model: A probabilistic approach to quantifying human error dependency

    International Nuclear Information System (INIS)

    Samanta

    1985-01-01

    This paper rpesents a probabilistic approach to quantifying human error dependency when multiple tasks are performed. Dependent human failures are dominant contributors to risks from nuclear power plants. An overview of the Multiple Sequential Failure (MSF) model developed and its use in probabilistic risk assessments (PRAs) depending on the available data are discussed. A small-scale psychological experiment was conducted on the nature of human dependency and the interpretation of the experimental data by the MSF model show remarkable accommodation of the dependent failure data. The model, which provides an unique method for quantification of dependent failures in human reliability analysis, can be used in conjunction with any of the general methods currently used for performing the human reliability aspect in PRAs

  15. A probabilistic model for the persistence of early planar fabrics in polydeformed pelitic schists

    Science.gov (United States)

    Ferguson, C.C.

    1984-01-01

    Although early planar fabrics are commonly preserved within microlithons in low-grade pelites, in higher-grade (amphibolite facies) pelitic schists fabric regeneration often appears complete. Evidence for early fabrics may be preserved within porphyroblasts but, within the matrix, later deformation often appears to totally obliterate or reorient earlier fabrics. However, examination of several hundred Dalradian pelites from Connemara, western Ireland, reveals that preservation of early fabrics is by no means uncommon; relict matrix domains, although volumetrically insignificant, are remarkably persistent even when inferred later strains are very large and fabric regeneration appears, at first sight, complete. Deterministic plasticity theories are ill-suited to the analysis of such an inhomogeneous material response, and a probabilistic model is proposed instead. It assumes that ductile polycrystal deformation is controlled by elementary flow units which can be activated once their associated stress barrier is overcome. Bulk flow propensity is related to the proportion of simultaneous activations, and a measure of this is derived from the probabilistic interaction between a stress-barrier spectrum and an internal stress spectrum (the latter determined by the external loading and the details of internal stress transfer). The spectra are modelled as Gaussian distributions although the treatment is very general and could be adapted for other distributions. Using the time rate of change of activation probability it is predicted that, initially, fabric development will be rapid but will then slow down dramatically even though stress increases at a constant rate. This highly non-linear response suggests that early fabrics persist because they comprise unfavourable distributions of stress-barriers which remain unregenerated at the time bulk stress is stabilized by steady-state flow. Relict domains will, however, bear the highest stress and are potential upper

  16. Can model weighting improve probabilistic projections of climate change?

    Energy Technology Data Exchange (ETDEWEB)

    Raeisaenen, Jouni; Ylhaeisi, Jussi S. [Department of Physics, P.O. Box 48, University of Helsinki (Finland)

    2012-10-15

    Recently, Raeisaenen and co-authors proposed a weighting scheme in which the relationship between observable climate and climate change within a multi-model ensemble determines to what extent agreement with observations affects model weights in climate change projection. Within the Third Coupled Model Intercomparison Project (CMIP3) dataset, this scheme slightly improved the cross-validated accuracy of deterministic projections of temperature change. Here the same scheme is applied to probabilistic temperature change projection, under the strong limiting assumption that the CMIP3 ensemble spans the actual modeling uncertainty. Cross-validation suggests that probabilistic temperature change projections may also be improved by this weighting scheme. However, the improvement relative to uniform weighting is smaller in the tail-sensitive logarithmic score than in the continuous ranked probability score. The impact of the weighting on projection of real-world twenty-first century temperature change is modest in most parts of the world. However, in some areas mainly over the high-latitude oceans, the mean of the distribution is substantially changed and/or the distribution is considerably narrowed. The weights of individual models vary strongly with location, so that a model that receives nearly zero weight in some area may still get a large weight elsewhere. Although the details of this variation are method-specific, it suggests that the relative strengths of different models may be difficult to harness by weighting schemes that use spatially uniform model weights. (orig.)

  17. Probabilistic model of random uncertainties in structural dynamics for mis-tuned bladed disks; Modele probabiliste des incertitudes en dynamique des structures pour le desaccordage des roues aubagees

    Energy Technology Data Exchange (ETDEWEB)

    Capiez-Lernout, E.; Soize, Ch. [Universite de Marne la Vallee, Lab. de Mecanique, 77 (France)

    2003-10-01

    The mis-tuning of blades is frequently the cause of spatial localizations for the dynamic forced response in turbomachinery industry. The random character of mis-tuning requires the construction of probabilistic models of random uncertainties. A usual parametric probabilistic description considers the mis-tuning through the Young modulus of each blade. This model consists in mis-tuning blade eigenfrequencies, assuming the blade modal shapes unchanged. Recently a new approach known as a non-parametric model of random uncertainties has been introduced for modelling random uncertainties in elasto-dynamics. This paper proposes the construction of a non-parametric model which is coherent with all the uncertainties which characterize mis-tuning. As mis-tuning is a phenomenon which is independent from one blade to another one, the structure is considered as an assemblage of substructures. The mean reduced matrix model required by the non-parametric approach is thus constructed by dynamic sub-structuring. A comparative approach is also needed to study the influence of the non-parametric approach for a usual parametric model adapted to mis-tuning. A numerical example is presented. (authors)

  18. Developing Pavement Distress Deterioration Models for Pavement Management System Using Markovian Probabilistic Process

    Directory of Open Access Journals (Sweden)

    Promothes Saha

    2017-01-01

    Full Text Available In the state of Colorado, the Colorado Department of Transportation (CDOT utilizes their pavement management system (PMS to manage approximately 9,100 miles of interstate, highways, and low-volume roads. Three types of deterioration models are currently being used in the existing PMS: site-specific, family, and expert opinion curves. These curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to traffic and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for five distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated that longitudinal, fatigue, and rut indices had very slow deterioration over time, whereas transverse and ride indices showed faster deterioration. The developed deterioration models had the coefficient of determination (R2 above 0.84. As probabilistic models provide more accurate results, it is recommended that these models be used as the family curves in the CDOT PMS for low-volume roads.

  19. Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

    Science.gov (United States)

    Tan, Ping; Tan, Guan-Zheng; Cai, Zi-Xing; Sa, Wei-Ping; Zou, Yi-Qun

    2017-01-01

    Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

  20. Application of probabilistic seismic hazard models with special calculation for the waste storage sites in Egypt

    International Nuclear Information System (INIS)

    Othman, A.A.; El-Hemamy, S.T.

    2000-01-01

    Probabilistic strong motion maps of Egypt are derived by applying Gumbel models and likelihood method to 8 earthquake source zones in Egypt and adjacent regions. Peak horizontal acceleration is mapped. Seismic data are collected from Helwan Catalog (1900-1997), regional catalog of earthquakes from the International Seismological Center (ISC,1910-1993) and earthquake data reports of US Department of International Geological Survey (USCGS, 1900-1994). Iso-seismic maps are also available for some events, which occurred in Egypt. Some earthquake source zones are well defined on the basis of both tectonics and average seismicity rates, but a lack of understanding of the near field effects of the large earthquakes prohibits accurate estimates of ground motion in their vicinity. Some source zones have no large-scale crustal features or zones of weakness that can explain the seismicity and must, therefore, be defined simply as concentrations of seismic activity with no geological or geophysical controls on the boundaries. Other source zones lack information on low-magnitude seismicity that would be representative of longer periods of time. Comparisons of the new probabilistic ground motion estimates in Egypt with equivalent estimates made in 1990 have been done. The new ground motion estimates are used to produce a new peak ground acceleration map to replace the 1990 peak acceleration zoning maps in the Building code of Egypt. (author)

  1. Participation in the international comparison of probabilistic consequence assessment codes organized by OECD/NEA and CEC. Final Report

    International Nuclear Information System (INIS)

    Rossi, J.

    1994-02-01

    Probabilistic Consequence Assessment (PCA) methods are exploited not only in risk evaluation but also to study alternative design features, reactor siting recommendations and to obtain acceptable dose criteria by the radiation safety authorities. The models are programmed into computer codes for these kind of assessment. To investigate the quality and competence of different models, OECD/NEA and CEC organized the international code comparison exercise, which was participated by the organizations from 15 countries. There were seven codes participating in the exercise. The objectives of the code comparison exercise were to compare the results by the codes, to contribute to PCA code quality assurance, to harmonize the codes, to provide a forum for discussion on various approaches and to produce the report on the exercise. The project started in 1991 and the results of the calculations were completed in autumn 1992. The international report consists of two parts: the Overview Report for decision makers and the supporting detailed Technical Report. The results of the project are reviewed as an user of the ARANO-programme of VTT and trends of it's further development are indicated in this report. (orig.) (11 refs., 13 figs., 4 tabs.)

  2. Probabilistic Modeling of Graded Timber Material Properties

    DEFF Research Database (Denmark)

    Faber, M. H.; Köhler, J.; Sørensen, John Dalsgaard

    2004-01-01

    The probabilistic modeling of timber material characteristics is considered with special emphasis to the modeling of the effect of different quality control and selection procedures used as means for quality grading in the production line. It is shown how statistical models may be established...... on the basis of the same type of information which is normally collected as a part of the quality control procedures and furthermore, how the efficiency of different control procedures may be quantified and compared. The tail behavior of the probability distributions of timber material characteristics plays...... such that they may readily be applied in structural reliability analysis and their format appears to be appropriate for codification purposes of quality control and selection for grading procedures....

  3. Modeling and analysis of cell membrane systems with probabilistic model checking

    Science.gov (United States)

    2011-01-01

    Background Recently there has been a growing interest in the application of Probabilistic Model Checking (PMC) for the formal specification of biological systems. PMC is able to exhaustively explore all states of a stochastic model and can provide valuable insight into its behavior which are more difficult to see using only traditional methods for system analysis such as deterministic and stochastic simulation. In this work we propose a stochastic modeling for the description and analysis of sodium-potassium exchange pump. The sodium-potassium pump is a membrane transport system presents in all animal cell and capable of moving sodium and potassium ions against their concentration gradient. Results We present a quantitative formal specification of the pump mechanism in the PRISM language, taking into consideration a discrete chemistry approach and the Law of Mass Action aspects. We also present an analysis of the system using quantitative properties in order to verify the pump reversibility and understand the pump behavior using trend labels for the transition rates of the pump reactions. Conclusions Probabilistic model checking can be used along with other well established approaches such as simulation and differential equations to better understand pump behavior. Using PMC we can determine if specific events happen such as the potassium outside the cell ends in all model traces. We can also have a more detailed perspective on its behavior such as determining its reversibility and why its normal operation becomes slow over time. This knowledge can be used to direct experimental research and make it more efficient, leading to faster and more accurate scientific discoveries. PMID:22369714

  4. Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

    Science.gov (United States)

    Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

    2015-01-01

    Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

  5. Bayesian uncertainty analyses of probabilistic risk models

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1989-01-01

    Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

  6. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  7. Probabilistic models for steel corrosion loss and pitting of marine infrastructure

    International Nuclear Information System (INIS)

    Melchers, R.E.; Jeffrey, R.J.

    2008-01-01

    With the increasing emphasis on attempting to retain in service ageing infrastructure models for the description and prediction of corrosion losses and for maximum pit depth are of increasing interest. In most cases assessment and prediction will be done in a probabilistic risk assessment framework and this then requires probabilistic corrosion models. Recently, novel models for corrosion loss and maximum pit depth under marine immersion conditions have been developed. The models show that both corrosion loss and pit depth progress in a non-linear fashion with increased exposure time and do so in a non-monotonic manner as a result of the controlling corrosion process changing from oxidation to being influenced by bacterial action. For engineers the importance of this lies in the fact that conventional 'corrosion rates' have no validity, particularly for the long-term corrosion effects as relevant to deteriorated infrastructure. The models are consistent with corrosion science principles as well as current understanding of the considerable influence of bacterial processes on corrosion loss and pitting. The considerable practical implications of this are described

  8. Understanding onsets of rainfall in Southern Africa using temporal probabilistic modelling

    CSIR Research Space (South Africa)

    Cheruiyot, D

    2010-12-01

    Full Text Available This research investigates an alternative approach to automatically evolve the hidden temporal distribution of onset of rainfall directly from multivariate time series (MTS) data in the absence of domain experts. Temporal probabilistic modelling...

  9. The Terrestrial Investigation Model: A probabilistic risk assessment model for birds exposed to pesticides

    Science.gov (United States)

    One of the major recommendations of the National Academy of Science to the USEPA, NMFS and USFWS was to utilize probabilistic methods when assessing the risks of pesticides to federally listed endangered and threatened species. The Terrestrial Investigation Model (TIM, version 3....

  10. Probabilistic modelling of the high-pressure arc cathode spot displacement dynamic

    International Nuclear Information System (INIS)

    Coulombe, Sylvain

    2003-01-01

    A probabilistic modelling approach for the study of the cathode spot displacement dynamic in high-pressure arc systems is developed in an attempt to interpret the observed voltage fluctuations. The general framework of the model allows to define simple, probabilistic displacement rules, the so-called cathode spot dynamic rules, for various possible surface states (un-arced metal, arced, contaminated) and to study the resulting dynamic of the cathode spot displacements over one or several arc passages. The displacements of the type-A cathode spot (macro-spot) in a magnetically rotating arc using concentric electrodes made up of either clean or contaminated metal surfaces is considered. Experimental observations for this system revealed a 1/f -tilde1 signature in the frequency power spectrum (FPS) of the arc voltage for anchoring arc conditions on the cathode (e.g. clean metal surface), while it shows a 'white noise' signature for conditions favouring a smooth movement (e.g. oxide-contaminated cathode surface). Through an appropriate choice of the local probabilistic displacement rules, the model is able to correctly represent the dynamic behaviours of the type-A cathode spot, including the FPS for the arc elongation (i.e. voltage) and the arc erosion trace formation. The model illustrates that the cathode spot displacements between re-strikes can be seen as a diffusion process with a diffusion constant which depends on the surface structure. A physical interpretation for the jumping probability associated with the re-strike event is given in terms of the electron emission processes across dielectric contaminants present on the cathode surface

  11. Branching bisimulation congruence for probabilistic systems

    NARCIS (Netherlands)

    Trcka, N.; Georgievska, S.; Aldini, A.; Baier, C.

    2008-01-01

    The notion of branching bisimulation for the alternating model of probabilistic systems is not a congruence with respect to parallel composition. In this paper we first define another branching bisimulation in the more general model allowing consecutive probabilistic transitions, and we prove that

  12. Probabilistic programming in Python using PyMC3

    Directory of Open Access Journals (Sweden)

    John Salvatier

    2016-04-01

    Full Text Available Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.

  13. Psacoin level 1A intercomparison probabilistic system assessment code (PSAC) user group

    International Nuclear Information System (INIS)

    Nies, A.; Laurens, J.M.; Galson, D.A.; Webster, S.

    1990-01-01

    This report describes an international code intercomparison exercise conducted by the NEA Probabilistic System Assessment Code (PSAC) User Group. The PSACOIN Level 1A exercise is the third of a series designed to contribute to the verification of probabilistic codes that may be used in assessing the safety of radioactive waste disposal systems or concepts. Level 1A is based on a more realistic system model than that used in the two previous exercises, and involves deep geological disposal concepts with a relatively complex structure of the repository vault. The report compares results and draws conclusions with regard to the use of different modelling approaches and the possible importance to safety of various processes within and around a deep geological repository. In particular, the relative significance of model uncertainty and data variability is discussed

  14. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  15. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  16. Reasoning with probabilistic and deterministic graphical models exact algorithms

    CERN Document Server

    Dechter, Rina

    2013-01-01

    Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well

  17. Probabilistic safety assessment model in consideration of human factors based on object-oriented bayesian networks

    International Nuclear Information System (INIS)

    Zhou Zhongbao; Zhou Jinglun; Sun Quan

    2007-01-01

    Effect of Human factors on system safety is increasingly serious, which is often ignored in traditional probabilistic safety assessment methods however. A new probabilistic safety assessment model based on object-oriented Bayesian networks is proposed in this paper. Human factors are integrated into the existed event sequence diagrams. Then the classes of the object-oriented Bayesian networks are constructed which are converted to latent Bayesian networks for inference. Finally, the inference results are integrated into event sequence diagrams for probabilistic safety assessment. The new method is applied to the accident of loss of coolant in a nuclear power plant. the results show that the model is not only applicable to real-time situation assessment, but also applicable to situation assessment based certain amount of information. The modeling complexity is kept down and the new method is appropriate to large complex systems due to the thoughts of object-oriented. (authors)

  18. MODELING PROBABILISTIC CONFLICT OF TECHNOLOGICAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    D. B. Desyatov

    2015-01-01

    Full Text Available Recently for the study of conflict increasingly used method of mathematical optical modeling. Its importance stems from the fact that experimental research such conflicts rather time-consuming and complex. However, existing approaches to the study of conflict do not take into account the stochastic nature of the systems, suffers from conceptual incompleteness. There is a need to develop models, algorithms and principles, in order to assess the conflict, to choose conflict resolution to ensure that not the worst of conditions. For stochastic technological systems as a utility function, we consider the probability of achieving a given objective. We assume that some system S1 is in conflict with the system S2, (SR2R К SR1R, if q(SR1R,SR2Rprobabilistic conflict of the first kind (А К1 B, if P(A/Bprobabilistic conflict of the second kind (А К2 B, if P(A/B

  19. Overview of Future of Probabilistic Methods and RMSL Technology and the Probabilistic Methods Education Initiative for the US Army at the SAE G-11 Meeting

    Science.gov (United States)

    Singhal, Surendra N.

    2003-01-01

    The SAE G-11 RMSL Division and Probabilistic Methods Committee meeting sponsored by the Picatinny Arsenal during March 1-3, 2004 at Westin Morristown, will report progress on projects for probabilistic assessment of Army system and launch an initiative for probabilistic education. The meeting features several Army and industry Senior executives and Ivy League Professor to provide an industry/government/academia forum to review RMSL technology; reliability and probabilistic technology; reliability-based design methods; software reliability; and maintainability standards. With over 100 members including members with national/international standing, the mission of the G-11s Probabilistic Methods Committee is to enable/facilitate rapid deployment of probabilistic technology to enhance the competitiveness of our industries by better, faster, greener, smarter, affordable and reliable product development.

  20. A probabilistic model for component-based shape synthesis

    KAUST Repository

    Kalogerakis, Evangelos

    2012-07-01

    We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

  1. Disruption of the Right Temporoparietal Junction Impairs Probabilistic Belief Updating.

    Science.gov (United States)

    Mengotti, Paola; Dombert, Pascasie L; Fink, Gereon R; Vossel, Simone

    2017-05-31

    Generating and updating probabilistic models of the environment is a fundamental modus operandi of the human brain. Although crucial for various cognitive functions, the neural mechanisms of these inference processes remain to be elucidated. Here, we show the causal involvement of the right temporoparietal junction (rTPJ) in updating probabilistic beliefs and we provide new insights into the chronometry of the process by combining online transcranial magnetic stimulation (TMS) with computational modeling of behavioral responses. Female and male participants performed a modified location-cueing paradigm, where false information about the percentage of cue validity (%CV) was provided in half of the experimental blocks to prompt updating of prior expectations. Online double-pulse TMS over rTPJ 300 ms (but not 50 ms) after target appearance selectively decreased participants' updating of false prior beliefs concerning %CV, reflected in a decreased learning rate of a Rescorla-Wagner model. Online TMS over rTPJ also impacted on participants' explicit beliefs, causing them to overestimate %CV. These results confirm the involvement of rTPJ in updating of probabilistic beliefs, thereby advancing our understanding of this area's function during cognitive processing. SIGNIFICANCE STATEMENT Contemporary views propose that the brain maintains probabilistic models of the world to minimize surprise about sensory inputs. Here, we provide evidence that the right temporoparietal junction (rTPJ) is causally involved in this process. Because neuroimaging has suggested that rTPJ is implicated in divergent cognitive domains, the demonstration of an involvement in updating internal models provides a novel unifying explanation for these findings. We used computational modeling to characterize how participants change their beliefs after new observations. By interfering with rTPJ activity through online transcranial magnetic stimulation, we showed that participants were less able to update

  2. Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure

    International Nuclear Information System (INIS)

    Praks, Pavel; Kopustinskas, Vytis; Masera, Marcelo

    2015-01-01

    The paper presents a probabilistic model to study security of supply in a gas network. The model is based on Monte-Carlo simulations with graph theory, and is implemented in the software tool ProGasNet. The software allows studying gas networks in various aspects including identification of weakest links and nodes, vulnerability analysis, bottleneck analysis, evaluation of new infrastructure etc. In this paper ProGasNet is applied to a benchmark network based on a real EU gas transmission network of several countries with the purpose of evaluating the security of supply effects of new infrastructure, either under construction, recently completed or under planning. The probabilistic model enables quantitative evaluations by comparing the reliability of gas supply in each consuming node of the network. - Highlights: • A Monte-Carlo algorithm for stochastic flow networks is presented. • Network elements can fail according to a given probabilistic model. • Priority supply pattern of gas transmission networks is assumed. • A real-world EU gas transmission network is presented and analyzed. • A risk ratio is used for security of supply quantification of a new infrastructure.

  3. Chiefly Symmetric: Results on the Scalability of Probabilistic Model Checking for Operating-System Code

    Directory of Open Access Journals (Sweden)

    Marcus Völp

    2012-11-01

    Full Text Available Reliability in terms of functional properties from the safety-liveness spectrum is an indispensable requirement of low-level operating-system (OS code. However, with evermore complex and thus less predictable hardware, quantitative and probabilistic guarantees become more and more important. Probabilistic model checking is one technique to automatically obtain these guarantees. First experiences with the automated quantitative analysis of low-level operating-system code confirm the expectation that the naive probabilistic model checking approach rapidly reaches its limits when increasing the numbers of processes. This paper reports on our work-in-progress to tackle the state explosion problem for low-level OS-code caused by the exponential blow-up of the model size when the number of processes grows. We studied the symmetry reduction approach and carried out our experiments with a simple test-and-test-and-set lock case study as a representative example for a wide range of protocols with natural inter-process dependencies and long-run properties. We quickly see a state-space explosion for scenarios where inter-process dependencies are insignificant. However, once inter-process dependencies dominate the picture models with hundred and more processes can be constructed and analysed.

  4. On synchronous parallel computations with independent probabilistic choice

    International Nuclear Information System (INIS)

    Reif, J.H.

    1984-01-01

    This paper introduces probabilistic choice to synchronous parallel machine models; in particular parallel RAMs. The power of probabilistic choice in parallel computations is illustrate by parallelizing some known probabilistic sequential algorithms. The authors characterize the computational complexity of time, space, and processor bounded probabilistic parallel RAMs in terms of the computational complexity of probabilistic sequential RAMs. They show that parallelism uniformly speeds up time bounded probabilistic sequential RAM computations by nearly a quadratic factor. They also show that probabilistic choice can be eliminated from parallel computations by introducing nonuniformity

  5. Probabilistic inversion for chicken processing lines

    International Nuclear Information System (INIS)

    Cooke, Roger M.; Nauta, Maarten; Havelaar, Arie H.; Fels, Ine van der

    2006-01-01

    We discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts' distributions (after combination) are then pulled back onto the parameter space of the model, a process termed 'probabilistic inversion'. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism

  6. Probabilistic topic modeling for the analysis and classification of genomic sequences

    Science.gov (United States)

    2015-01-01

    Background Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. Methods The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. Results and conclusions We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased. PMID:25916734

  7. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

    Science.gov (United States)

    Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

    2017-05-01

    Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  8. Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling.

    Science.gov (United States)

    Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

    2016-01-01

    Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  9. Unified Probabilistic Models for Face Recognition from a Single Example Image per Person

    Institute of Scientific and Technical Information of China (English)

    Pin Liao; Li Shen

    2004-01-01

    This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.

  10. Competing probabilistic models for catch-effort relationships in wildlife censuses

    Energy Technology Data Exchange (ETDEWEB)

    Skalski, J.R.; Robson, D.S.; Matsuzaki, C.L.

    1983-01-01

    Two probabilistic models are presented for describing the chance that an animal is captured during a wildlife census, as a function of trapping effort. The models in turn are used to propose relationships between sampling intensity and catch-per-unit-effort (C.P.U.E.) that were field tested on small mammal populations. Capture data suggests a model of diminshing C.P.U.E. with increasing levels of trapping intensity. The catch-effort model is used to illustrate optimization procedures in the design of mark-recapture experiments for censusing wild populations. 14 references, 2 tables.

  11. Up-gradient transport in a probabilistic transport model

    DEFF Research Database (Denmark)

    Gavnholt, J.; Juul Rasmussen, J.; Garcia, O.E.

    2005-01-01

    The transport of particles or heat against the driving gradient is studied by employing a probabilistic transport model with a characteristic particle step length that depends on the local concentration or heat gradient. When this gradient is larger than a prescribed critical value, the standard....... These results supplement recent works by van Milligen [Phys. Plasmas 11, 3787 (2004)], which applied Levy distributed step sizes in the case of supercritical gradients to obtain the up-gradient transport. (c) 2005 American Institute of Physics....

  12. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

    Science.gov (United States)

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-01-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

  13. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Dejan Pecevski

    2011-12-01

    Full Text Available An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away" and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

  14. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Science.gov (United States)

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-12-01

    An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

  15. Probabilistic Structural Analysis Program

    Science.gov (United States)

    Pai, Shantaram S.; Chamis, Christos C.; Murthy, Pappu L. N.; Stefko, George L.; Riha, David S.; Thacker, Ben H.; Nagpal, Vinod K.; Mital, Subodh K.

    2010-01-01

    NASA/NESSUS 6.2c is a general-purpose, probabilistic analysis program that computes probability of failure and probabilistic sensitivity measures of engineered systems. Because NASA/NESSUS uses highly computationally efficient and accurate analysis techniques, probabilistic solutions can be obtained even for extremely large and complex models. Once the probabilistic response is quantified, the results can be used to support risk-informed decisions regarding reliability for safety-critical and one-of-a-kind systems, as well as for maintaining a level of quality while reducing manufacturing costs for larger-quantity products. NASA/NESSUS has been successfully applied to a diverse range of problems in aerospace, gas turbine engines, biomechanics, pipelines, defense, weaponry, and infrastructure. This program combines state-of-the-art probabilistic algorithms with general-purpose structural analysis and lifting methods to compute the probabilistic response and reliability of engineered structures. Uncertainties in load, material properties, geometry, boundary conditions, and initial conditions can be simulated. The structural analysis methods include non-linear finite-element methods, heat-transfer analysis, polymer/ceramic matrix composite analysis, monolithic (conventional metallic) materials life-prediction methodologies, boundary element methods, and user-written subroutines. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. NASA/NESSUS 6.2c is structured in a modular format with 15 elements.

  16. Probabilistic modeling of crack networks in thermal fatigue

    International Nuclear Information System (INIS)

    Malesys, N.

    2007-11-01

    Thermal superficial crack networks have been detected in mixing zone of cooling system in nuclear power plants. Numerous experimental works have already been led to characterize initiation and propagation of these cracks. The random aspect of initiation led to propose a probabilistic model for the formation and propagation of crack networks in thermal fatigue. In a first part, uniaxial mechanical test were performed on smooth and slightly notched specimens in order to characterize the initiation of multiple cracks, their arrest due to obscuration and the coalescence phenomenon by recovery of amplification stress zones. In a second time, the probabilistic model was established under two assumptions: the continuous cracks initiation on surface, described by a Poisson point process law with threshold, and the shielding phenomenon which prohibits the initiation or the propagation of a crack if this one is in the relaxation stress zone of another existing crack. The crack propagation is assumed to follow a Paris' law based on the computation of stress intensity factors at the top and the bottom of crack. The evolution of multiaxial cracks on the surface can be followed thanks to three quantities: the shielding probability, comparable to a damage variable of the structure, the initiated crack density, representing the total number of cracks per unit surface which can be compared to experimental observations, and the propagating crack density, representing the number per unit surface of active cracks in the network. The crack sizes distribution is also computed by the model allowing an easier comparison with experimental results. (author)

  17. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  18. Probabilistic Failure Analysis of Bone Using a Finite Element Model of Mineral-Collagen Composites

    OpenAIRE

    Dong, X. Neil; Guda, Teja; Millwater, Harry R.; Wang, Xiaodu

    2008-01-01

    Microdamage accumulation is a major pathway for energy dissipation during the post-yield deformation of bone. In this study, a two-dimensional probabilistic finite element model of a mineral-collagen composite was developed to investigate the influence of the tissue and ultrastructural properties of bone on the evolution of microdamage from an initial defect in tension. The probabilistic failure analyses indicated that the microdamage progression would be along the plane of the initial defect...

  19. Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling

    Directory of Open Access Journals (Sweden)

    Moritz eBoos

    2016-05-01

    Full Text Available Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities by two (likelihoods design. Five computational models of cognitive processes were compared with the observed behaviour. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model’s success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modelling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modelling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  20. A logic for inductive probabilistic reasoning

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2005-01-01

    Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from '70% of As are Bs" and "a is an A" infer...... that a is a B with probability 0.7. Direct inference is generalized by Jeffrey's rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have...... to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework...

  1. Probabilistic modelling of the high-pressure arc cathode spot displacement dynamic

    CERN Document Server

    Coulombe, S

    2003-01-01

    A probabilistic modelling approach for the study of the cathode spot displacement dynamic in high-pressure arc systems is developed in an attempt to interpret the observed voltage fluctuations. The general framework of the model allows to define simple, probabilistic displacement rules, the so-called cathode spot dynamic rules, for various possible surface states (un-arced metal, arced, contaminated) and to study the resulting dynamic of the cathode spot displacements over one or several arc passages. The displacements of the type-A cathode spot (macro-spot) in a magnetically rotating arc using concentric electrodes made up of either clean or contaminated metal surfaces is considered. Experimental observations for this system revealed a 1/f sup - sup t sup i sup l sup d sup e sup 1 signature in the frequency power spectrum (FPS) of the arc voltage for anchoring arc conditions on the cathode (e.g. clean metal surface), while it shows a 'white noise' signature for conditions favouring a smooth movement (e.g. ox...

  2. Probabilistic Reversible Automata and Quantum Automata

    OpenAIRE

    Golovkins, Marats; Kravtsev, Maksim

    2002-01-01

    To study relationship between quantum finite automata and probabilistic finite automata, we introduce a notion of probabilistic reversible automata (PRA, or doubly stochastic automata). We find that there is a strong relationship between different possible models of PRA and corresponding models of quantum finite automata. We also propose a classification of reversible finite 1-way automata.

  3. Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks

    Science.gov (United States)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

    Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.

  4. Probabilistic analysis of fires in nuclear plants

    International Nuclear Information System (INIS)

    Unione, A.; Teichmann, T.

    1985-01-01

    The aim of this paper is to describe a multilevel (i.e., staged) probabilistic analysis of fire risks in nuclear plants (as part of a general PRA) which maximizes the benefits of the FRA (fire risk assessment) in a cost effective way. The approach uses several stages of screening, physical modeling of clearly dominant risk contributors, searches for direct (e.g., equipment dependences) and secondary (e.g., fire induced internal flooding) interactions, and relies on lessons learned and available data from and surrogate FRAs. The general methodology is outlined. 6 figs., 10 tabs

  5. The management of subsurface uncertainty using probabilistic modeling of life cycle production forecasts and cash flows

    International Nuclear Information System (INIS)

    Olatunbosun, O. O.

    1998-01-01

    The subject pertains to the implementation of the full range of subsurface uncertainties in life cycle probabilistic forecasting and its extension to project cash flows using the methodology of probabilities. A new tool has been developed in the probabilistic application of Crystal-Ball which can model reservoir volumetrics, life cycle production forecasts and project cash flows in a single environment. The tool is modular such that the volumetrics and cash flow modules are optional. Production forecasts are often generated by applying a decline equation to single best estimate values of input parameters such as initial potential, decline rate, abandonment rate etc -or sometimes by results of reservoir simulation. This new tool provides a means of implementing the full range of uncertainties and interdependencies of the input parameters into the production forecasts by defining the input parameters as probability density functions, PDFs and performing several iterations to generate an expectation curve forecast. Abandonment rate is implemented in each iteration via a link to an OPEX model. The expectation curve forecast is input into a cash flow model to generate a probabilistic NPV. Base case and sensitivity runs from reservoir simulation can likewise form the basis for a probabilistic production forecast from which a probabilistic cash flow can be generated. A good illustration of the application of this tool is in the modelling of the production forecast for a well that encounters its target reservoirs in OUT/ODT situation and thus has significant uncertainties. The uncertainty in presence and size (if present) of gas cap and dependency between ultimate recovery and initial potential amongst other uncertainties can be easily implemented in the production forecast with this tool. From the expectation curve forecast, a probabilistic NPV can be easily generated. Possible applications of this tool include: i. estimation of range of actual recoverable volumes based

  6. Probabilistic-Stochastic Model of Distribution of Physical and Mechanical Properties of Soft Mineral Rocks

    Directory of Open Access Journals (Sweden)

    O.O. Sdvizhkova

    2017-12-01

    Full Text Available The physical and mechanical characteristics of soils and soft rocks obtained as a result of laboratory tests are important initial parameters for assessing the stability of natural and artificial slopes. Such properties of rocks as adhesion and the angle of internal friction are due to the influence of a number of natural and technogenic factors. At the same time, from the set of factors influencing the stability of the slope, the most significant ones are singled out, which to a greater extent determine the properties of the rocks. The more factors are taken into account in the geotechnical model, the more closely the properties of the rocks are studied, which increases the accuracy of the scientific forecast of the landslide danger of the slope. On the other hand, an increase in the number of factors involved in the model complicates it and causes a decrease in the reliability of geotechnical calculations. The aim of the work is to construct a statistical distribution of the studied physical and mechanical properties of soft rocks and to substantiate a probabilistic statistical model. Based on the results of laboratory tests of rocks, the statistical distributions of the quantitative traits studied, the angle of internal friction φ and the cohesion, were constructed. It was established that the statistical distribution of physical mechanical properties of rocks is close to a uniform law.

  7. Probabilistic delay differential equation modeling of event-related potentials.

    Science.gov (United States)

    Ostwald, Dirk; Starke, Ludger

    2016-08-01

    "Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  9. Example Plant Model for an International Benchmark Study on DI and C PSA

    International Nuclear Information System (INIS)

    Shin, Sung Min; Park, Jinkyun; Jang, Wondea; Kang, Hyun Gook

    2016-01-01

    In this context the risk quantification due to these digitalized safety systems became more important. Although there are many challenges to address about this issue, many countries agreed with the necessity of research on reliability quantification of DI and C system. Based on the agreement of several countries, one of internal research association is planning a benchmark study on this issue by sharing an example digitalized plant model and let each participating member develop its own probabilistic safety assessment (PSA) model of digital I and C systems. Although the DI and C systems are being applied to NPPs, of which modeling method to quantify its reliability still ambiguous. Therefore, an internal research association is planning a benchmark study to address this issue by sharing an example digitalized plant model and let each member develop their own PSA model for DI and C systems

  10. Risk Management Technologies With Logic and Probabilistic Models

    CERN Document Server

    Solozhentsev, E D

    2012-01-01

    This book presents intellectual, innovative, information technologies (I3-technologies) based on logical and probabilistic (LP) risk models. The technologies presented here consider such models for structurally complex systems and processes with logical links and with random events in economics and technology.  The volume describes the following components of risk management technologies: LP-calculus; classes of LP-models of risk and efficiency; procedures for different classes; special software for different classes; examples of applications; methods for the estimation of probabilities of events based on expert information. Also described are a variety of training courses in these topics. The classes of risk models treated here are: LP-modeling, LP-classification, LP-efficiency, and LP-forecasting. Particular attention is paid to LP-models of risk of failure to resolve difficult economic and technical problems. Amongst the  discussed  procedures of I3-technologies  are the construction of  LP-models,...

  11. Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

    KAUST Repository

    Chikalov, Igor; Yao, Peggy; Moshkov, Mikhail; Latombe, Jean-Claude

    2011-01-01

    . The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor.Methods: This paper describes inductive learning methods to train protein-independent probabilistic models of H

  12. Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

    Science.gov (United States)

    Nagarajan, Mahesh B; Raman, Steven S; Lo, Pechin; Lin, Wei-Chan; Khoshnoodi, Pooria; Sayre, James W; Ramakrishna, Bharath; Ahuja, Preeti; Huang, Jiaoti; Margolis, Daniel J A; Lu, David S K; Reiter, Robert E; Goldin, Jonathan G; Brown, Matthew S; Enzmann, Dieter R

    2018-02-19

    We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

  13. A Probabilistic Model for Diagnosing Misconceptions by a Pattern Classification Approach.

    Science.gov (United States)

    Tatsuoka, Kikumi K.

    A probabilistic approach is introduced to classify and diagnose erroneous rules of operation resulting from a variety of misconceptions ("bugs") in a procedural domain of arithmetic. The model is contrasted with the deterministic approach which has commonly been used in the field of artificial intelligence, and the advantage of treating the…

  14. Results of the Level 1 probabilistic risk assessment (PRA) of internal events for heavy water production reactors (U)

    International Nuclear Information System (INIS)

    Tinnes, S.P.; Cramer, D.S.; Logan, V.E.; Topp, S.V.; Smith, J.A.; Brandyberry, M.D.

    1990-01-01

    This paper reports on a full-scope probabilistic risk assessment (PRA) performed for the Savannah River Site (SRS) production reactors. The Level 1 PRA for the K Reactor has been completed and includes the assessment of reactor systems response to accidents and estimates of the severe core melt frequency (SCMF). The internal events spectrum includes those events related directly to plant systems and safety functions for which transients or failures may initiate an accident

  15. Probabilistic graphical models to deal with age estimation of living persons.

    Science.gov (United States)

    Sironi, Emanuele; Gallidabino, Matteo; Weyermann, Céline; Taroni, Franco

    2016-03-01

    Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

  16. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents

    International Nuclear Information System (INIS)

    Chang, Y.H.J.; Mosleh, A.

    2007-01-01

    This is the last in a series of five papers that discuss the Information Decision and Action in Crew (IDAC) context for human reliability analysis (HRA) and example application. The model is developed to probabilistically predict the responses of the control room operating crew in nuclear power plants during an accident, for use in probabilistic risk assessments (PRA). The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper describes a dynamic PRA computer simulation program, accident dynamics simulator (ADS), developed in part to implement the IDAC model. This paper also provides a detailed example of implementing a simpler version of IDAC, compared with the IDAC model discussed in the first four papers of this series, to demonstrate the practicality of integrating a detailed cognitive HRA model within a dynamic PRA framework

  17. Approach to modeling of human performance for purposes of probabilistic risk assessment

    International Nuclear Information System (INIS)

    Swain, A.D.

    1983-01-01

    This paper describes the general approach taken in NUREG/CR-1278 to model human performance in sufficienct detail to permit probabilistic risk assessments of nuclear power plant operations. To show the basis for the more specific models in the above NUREG, a simplified model of the human component in man-machine systems is presented, the role of performance shaping factors is discussed, and special problems in modeling the cognitive aspect of behavior are described

  18. A Probabilistic Model of the LMAC Protocol for Concurrent Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Esparza, Luz Judith R; Zeng, Kebin; Nielsen, Bo Friis

    2011-01-01

    We present a probabilistic model for the network setup phase of the Lightweight Medium Access Protocol (LMAC) for concurrent Wireless Sensor Networks. In the network setup phase, time slots are allocated to the individual sensors through resolution of successive collisions. The setup phase...

  19. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1991-06-01

    The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. 3 refs., 1 fig

  20. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1991-01-01

    The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. (Author)

  1. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1992-01-01

    The double contingency principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative and intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive for a search for a quantitative and probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies, as functions of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess the effectiveness of the DCP. (Author)

  2. Probabilistic Mu-Calculus

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Mardare, Radu Iulian; Xue, Bingtian

    2016-01-01

    We introduce a version of the probabilistic µ-calculus (PMC) built on top of a probabilistic modal logic that allows encoding n-ary inequational conditions on transition probabilities. PMC extends previously studied calculi and we prove that, despite its expressiveness, it enjoys a series of good...... metaproperties. Firstly, we prove the decidability of satisfiability checking by establishing the small model property. An algorithm for deciding the satisfiability problem is developed. As a second major result, we provide a complete axiomatization for the alternation-free fragment of PMC. The completeness proof...

  3. Validation analysis of probabilistic models of dietary exposure to food additives.

    Science.gov (United States)

    Gilsenan, M B; Thompson, R L; Lambe, J; Gibney, M J

    2003-10-01

    The validity of a range of simple conceptual models designed specifically for the estimation of food additive intakes using probabilistic analysis was assessed. Modelled intake estimates that fell below traditional conservative point estimates of intake and above 'true' additive intakes (calculated from a reference database at brand level) were considered to be in a valid region. Models were developed for 10 food additives by combining food intake data, the probability of an additive being present in a food group and additive concentration data. Food intake and additive concentration data were entered as raw data or as a lognormal distribution, and the probability of an additive being present was entered based on the per cent brands or the per cent eating occasions within a food group that contained an additive. Since the three model components assumed two possible modes of input, the validity of eight (2(3)) model combinations was assessed. All model inputs were derived from the reference database. An iterative approach was employed in which the validity of individual model components was assessed first, followed by validation of full conceptual models. While the distribution of intake estimates from models fell below conservative intakes, which assume that the additive is present at maximum permitted levels (MPLs) in all foods in which it is permitted, intake estimates were not consistently above 'true' intakes. These analyses indicate the need for more complex models for the estimation of food additive intakes using probabilistic analysis. Such models should incorporate information on market share and/or brand loyalty.

  4. Probabilistic linguistics

    NARCIS (Netherlands)

    Bod, R.; Heine, B.; Narrog, H.

    2010-01-01

    Probabilistic linguistics takes all linguistic evidence as positive evidence and lets statistics decide. It allows for accurate modelling of gradient phenomena in production and perception, and suggests that rule-like behaviour is no more than a side effect of maximizing probability. This chapter

  5. Precise Quantitative Analysis of Probabilistic Business Process Model and Notation Workflows

    DEFF Research Database (Denmark)

    Herbert, Luke Thomas; Sharp, Robin

    2013-01-01

    We present a framework for modeling and analysis of real-world business workflows. We present a formalized core subset of the business process modeling and notation (BPMN) and then proceed to extend this language with probabilistic nondeterministic branching and general-purpose reward annotations...... the entire BPMN language, allow for more complex annotations and ultimately to automatically synthesize workflows by composing predefined subprocesses, in order to achieve a configuration that is optimal for parameters of interest....

  6. Propagating Water Quality Analysis Uncertainty Into Resource Management Decisions Through Probabilistic Modeling

    Science.gov (United States)

    Gronewold, A. D.; Wolpert, R. L.; Reckhow, K. H.

    2007-12-01

    Most probable number (MPN) and colony-forming-unit (CFU) are two estimates of fecal coliform bacteria concentration commonly used as measures of water quality in United States shellfish harvesting waters. The MPN is the maximum likelihood estimate (or MLE) of the true fecal coliform concentration based on counts of non-sterile tubes in serial dilution of a sample aliquot, indicating bacterial metabolic activity. The CFU is the MLE of the true fecal coliform concentration based on the number of bacteria colonies emerging on a growth plate after inoculation from a sample aliquot. Each estimating procedure has intrinsic variability and is subject to additional uncertainty arising from minor variations in experimental protocol. Several versions of each procedure (using different sized aliquots or different numbers of tubes, for example) are in common use, each with its own levels of probabilistic and experimental error and uncertainty. It has been observed empirically that the MPN procedure is more variable than the CFU procedure, and that MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies. We construct a probabilistic model that provides a clear theoretical explanation for the observed variability in, and discrepancy between, MPN and CFU measurements. We then explore how this variability and uncertainty might propagate into shellfish harvesting area management decisions through a two-phased modeling strategy. First, we apply our probabilistic model in a simulation-based analysis of future water quality standard violation frequencies under alternative land use scenarios, such as those evaluated under guidelines of the total maximum daily load (TMDL) program. Second, we apply our model to water quality data from shellfish harvesting areas which at present are closed (either conditionally or permanently) to shellfishing, to determine if alternative laboratory analysis procedures might have led to different

  7. Probabilistic Design and Analysis Framework

    Science.gov (United States)

    Strack, William C.; Nagpal, Vinod K.

    2010-01-01

    PRODAF is a software package designed to aid analysts and designers in conducting probabilistic analysis of components and systems. PRODAF can integrate multiple analysis programs to ease the tedious process of conducting a complex analysis process that requires the use of multiple software packages. The work uses a commercial finite element analysis (FEA) program with modules from NESSUS to conduct a probabilistic analysis of a hypothetical turbine blade, disk, and shaft model. PRODAF applies the response surface method, at the component level, and extrapolates the component-level responses to the system level. Hypothetical components of a gas turbine engine are first deterministically modeled using FEA. Variations in selected geometrical dimensions and loading conditions are analyzed to determine the effects of the stress state within each component. Geometric variations include the cord length and height for the blade, inner radius, outer radius, and thickness, which are varied for the disk. Probabilistic analysis is carried out using developing software packages like System Uncertainty Analysis (SUA) and PRODAF. PRODAF was used with a commercial deterministic FEA program in conjunction with modules from the probabilistic analysis program, NESTEM, to perturb loads and geometries to provide a reliability and sensitivity analysis. PRODAF simplified the handling of data among the various programs involved, and will work with many commercial and opensource deterministic programs, probabilistic programs, or modules.

  8. Arbitrage and Hedging in a non probabilistic framework

    OpenAIRE

    Alvarez, Alexander; Ferrando, Sebastian; Olivares, Pablo

    2011-01-01

    The paper studies the concepts of hedging and arbitrage in a non probabilistic framework. It provides conditions for non probabilistic arbitrage based on the topological structure of the trajectory space and makes connections with the usual notion of arbitrage. Several examples illustrate the non probabilistic arbitrage as well perfect replication of options under continuous and discontinuous trajectories, the results can then be applied in probabilistic models path by path. The approach is r...

  9. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Multimedia

    CERN. Geneva; NACHMAN, Ben

    2015-01-01

    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  10. Probabilistic safety analysis procedures guide, Sections 8-12. Volume 2, Rev. 1

    International Nuclear Information System (INIS)

    McCann, M.; Reed, J.; Ruger, C.; Shiu, K.; Teichmann, T.; Unione, A.; Youngblood, R.

    1985-08-01

    A procedures guide for the performance of probabilistic safety assessment has been prepared for interim use in the Nuclear Regulatory Commission programs. It will be revised as comments are received, and as experience is gained from its use. The probabilistic safety assessment studies performed are intended to produce probabilistic predictive models that can be used and extended by the utilities and by NRC to sharpen the focus of inquiries into a range of issues affecting reactor safety. The first volume of the guide describes the determination of the probability (per year) of core damage resulting from accident initiators internal to the plant (i.e., intrinsic to plant operation) and from loss of off-site electric power. The scope includes human reliability analysis, a determination of the importance of various core damage accident sequences, and an explicit treatment and display of uncertainties for key accident sequences. This second volume deals with the treatment of the so-called external events including seismic disturbances, fires, floods, etc. Ultimately, the guide will be augmented to include the plant-specific analysis of in-plant processes (i.e., containment performance). This guide provides the structure of a probabilistic safety study to be performed, and indicates what products of the study are valuable for regulatory decision making. For internal events, methodology is treated in the guide only to the extent necessary to indicate the range of methods which is acceptable; ample reference is given to alternative methodologies which may be utilized in the performance of the study. For external events, more explicit guidance is given

  11. Deliverable D74.2. Probabilistic analysis methods for support structures

    DEFF Research Database (Denmark)

    Gintautas, Tomas

    2018-01-01

    Relevant Description: Report describing the probabilistic analysis for offshore substructures and results attained. This includes comparison with experimental data and with conventional design. Specific targets: 1) Estimate current reliability level of support structures 2) Development of basis...... for probabilistic calculations and evaluation of reliability for offshore support structures (substructures) 3) Development of a probabilistic model for stiffness and strength of soil parameters and for modeling geotechnical load bearing capacity 4) Comparison between probabilistic analysis and deterministic...

  12. Multi-model approach to petroleum resource appraisal using analytic methodologies for probabilistic systems

    Science.gov (United States)

    Crovelli, R.A.

    1988-01-01

    The geologic appraisal model that is selected for a petroleum resource assessment depends upon purpose of the assessment, basic geologic assumptions of the area, type of available data, time available before deadlines, available human and financial resources, available computer facilities, and, most importantly, the available quantitative methodology with corresponding computer software and any new quantitative methodology that would have to be developed. Therefore, different resource assessment projects usually require different geologic models. Also, more than one geologic model might be needed in a single project for assessing different regions of the study or for cross-checking resource estimates of the area. Some geologic analyses used in the past for petroleum resource appraisal involved play analysis. The corresponding quantitative methodologies of these analyses usually consisted of Monte Carlo simulation techniques. A probabilistic system of petroleum resource appraisal for play analysis has been designed to meet the following requirements: (1) includes a variety of geologic models, (2) uses an analytic methodology instead of Monte Carlo simulation, (3) possesses the capacity to aggregate estimates from many areas that have been assessed by different geologic models, and (4) runs quickly on a microcomputer. Geologic models consist of four basic types: reservoir engineering, volumetric yield, field size, and direct assessment. Several case histories and present studies by the U.S. Geological Survey are discussed. ?? 1988 International Association for Mathematical Geology.

  13. Probabilistic Modeling of Intracranial Pressure Effects on Optic Nerve Biomechanics

    Science.gov (United States)

    Ethier, C. R.; Feola, Andrew J.; Raykin, Julia; Myers, Jerry G.; Nelson, Emily S.; Samuels, Brian C.

    2016-01-01

    Altered intracranial pressure (ICP) is involved/implicated in several ocular conditions: papilledema, glaucoma and Visual Impairment and Intracranial Pressure (VIIP) syndrome. The biomechanical effects of altered ICP on optic nerve head (ONH) tissues in these conditions are uncertain but likely important. We have quantified ICP-induced deformations of ONH tissues, using finite element (FE) and probabilistic modeling (Latin Hypercube Simulations (LHS)) to consider a range of tissue properties and relevant pressures.

  14. Some thoughts on the future of probabilistic structural design of nuclear components

    International Nuclear Information System (INIS)

    Stancampiano, P.A.

    1978-01-01

    This paper presents some views on the future role of probabilistic methods in the structural design of nuclear components. The existing deterministic design approach is discussed and compared to the probabilistic approach. Some of the objections to both deterministic and probabilistic design are listed. Extensive research and development activities are required to mature the probabilistic approach suficiently to make it cost-effective and competitive with current deterministic design practices. The required research activities deal with probabilistic methods development, more realistic casual failure mode models development, and statistical data models development. A quasi-probabilistic structural design approach is recommended which accounts for the random error in the design models. (Auth.)

  15. Probabilistic Modeling of Aircraft Trajectories for Dynamic Separation Volumes

    Science.gov (United States)

    Lewis, Timothy A.

    2016-01-01

    With a proliferation of new and unconventional vehicles and operations expected in the future, the ab initio airspace design will require new approaches to trajectory prediction for separation assurance and other air traffic management functions. This paper presents an approach to probabilistic modeling of the trajectory of an aircraft when its intent is unknown. The approach uses a set of feature functions to constrain a maximum entropy probability distribution based on a set of observed aircraft trajectories. This model can be used to sample new aircraft trajectories to form an ensemble reflecting the variability in an aircraft's intent. The model learning process ensures that the variability in this ensemble reflects the behavior observed in the original data set. Computational examples are presented.

  16. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

    DEFF Research Database (Denmark)

    David, M.; Ramahatana, F.; Trombe, Pierre-Julien

    2016-01-01

    Forecasting of the solar irradiance is a key feature in order to increase the penetration rate of solar energy into the energy grids. Indeed, the anticipation of the fluctuations of the solar renewables allows a better management of the production means of electricity and a better operation...... sky index show some similarities with that of financial time series. The aim of this paper is to assess the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance. In addition, a recursive...... regarding the statistical distribution of the error, the reliability of the probabilistic forecasts stands in the same order of magnitude as other works done in the field of solar forecasting....

  17. Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data

    Directory of Open Access Journals (Sweden)

    Seyyed Mohammad Sadat Hoseini

    2011-07-01

    Full Text Available The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.

  18. Probabilistic Mobility Models for Mobile and Wireless Networks

    DEFF Research Database (Denmark)

    Song, Lei; Godskesen, Jens Christian

    2010-01-01

    In this paper we present a probabilistic broadcast calculus for mobile and wireless networks whose connections are unreliable. In our calculus broadcasted messages can be lost with a certain probability, and due to mobility the connection probabilities may change. If a network broadcasts a message...... from a location it will evolve to a network distribution depending on whether nodes at other locations receive the message or not. Mobility of locations is not arbitrary but guarded by a probabilistic mobility function (PMF) and we also define the notion of a weak bisimulation given a PMF...

  19. Probabilistic brains: knowns and unknowns

    Science.gov (United States)

    Pouget, Alexandre; Beck, Jeffrey M; Ma, Wei Ji; Latham, Peter E

    2015-01-01

    There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference. PMID:23955561

  20. Probabilistic Design of Wind Turbines

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Toft, H.S.

    2010-01-01

    Probabilistic design of wind turbines requires definition of the structural elements to be included in the probabilistic basis: e.g., blades, tower, foundation; identification of important failure modes; careful stochastic modeling of the uncertain parameters; recommendations for target reliability....... It is described how uncertainties in wind turbine design related to computational models, statistical data from test specimens, results from a few full-scale tests and from prototype wind turbines can be accounted for using the Maximum Likelihood Method and a Bayesian approach. Assessment of the optimal...... reliability level by cost-benefit optimization is illustrated by an offshore wind turbine example. Uncertainty modeling is illustrated by an example where physical, statistical and model uncertainties are estimated....

  1. Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ferson, Scott [Applied Biomathematics, Setauket, NY (United States); Nelsen, Roger B. [Lewis & Clark College, Portland OR (United States); Hajagos, Janos [Applied Biomathematics, Setauket, NY (United States); Berleant, Daniel J. [Iowa State Univ., Ames, IA (United States); Zhang, Jianzhong [Iowa State Univ., Ames, IA (United States); Tucker, W. Troy [Applied Biomathematics, Setauket, NY (United States); Ginzburg, Lev R. [Applied Biomathematics, Setauket, NY (United States); Oberkampf, William L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-05-01

    This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.

  2. Probabilistic estimation of residential air exchange rates for population-based human exposure modeling

    Science.gov (United States)

    Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER meas...

  3. Probabilistic risk models for multiple disturbances: an example of forest insects and wildfires

    Science.gov (United States)

    Haiganoush K. Preisler; Alan A. Ager; Jane L. Hayes

    2010-01-01

    Building probabilistic risk models for highly random forest disturbances like wildfire and forest insect outbreaks is a challenging. Modeling the interactions among natural disturbances is even more difficult. In the case of wildfire and forest insects, we looked at the probability of a large fire given an insect outbreak and also the incidence of insect outbreaks...

  4. A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

    DEFF Research Database (Denmark)

    Have, Christian Theil; Mørk, Søren

    We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... and are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames...

  5. Applying Probabilistic Decision Models to Clinical Trial Design

    Science.gov (United States)

    Smith, Wade P; Phillips, Mark H

    2018-01-01

    Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance.

  6. Bisimulations meet PCTL equivalences for probabilistic automata

    DEFF Research Database (Denmark)

    Song, Lei; Zhang, Lijun; Godskesen, Jens Chr.

    2013-01-01

    Probabilistic automata (PAs) have been successfully applied in formal verification of concurrent and stochastic systems. Efficient model checking algorithms have been studied, where the most often used logics for expressing properties are based on probabilistic computation tree logic (PCTL) and its...

  7. Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

    Science.gov (United States)

    Caglar, Mehmet Umut; Pal, Ranadip

    2011-03-01

    Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

  8. PROBABILISTIC MODEL FOR AIRPORT RUNWAY SAFETY AREAS

    Directory of Open Access Journals (Sweden)

    Stanislav SZABO

    2017-06-01

    Full Text Available The Laboratory of Aviation Safety and Security at CTU in Prague has recently started a project aimed at runway protection zones. The probability of exceeding by a certain distance from the runway in common incident/accident scenarios (take-off/landing overrun/veer-off, landing undershoot is being identified relative to the runway for any airport. As a result, the size and position of safety areas around runways are defined for the chosen probability. The basis for probability calculation is a probabilistic model using statistics from more than 1400 real-world cases where jet airplanes have been involved over the last few decades. Other scientific studies have contributed to understanding the issue and supported the model’s application to different conditions.

  9. Optimization (Alara) and probabilistic exposures: the application of optimization criteria to the control of risks due to exposures of a probabilistic nature

    International Nuclear Information System (INIS)

    Gonzalez, A.J.

    1989-01-01

    The paper described the application of the principles of optimization recommended by the International Commission on Radiological Protection (ICRP) to the restrain of radiation risks due to exposures that may or may not be incurred and to which a probability of occurrence can be assigned. After describing the concept of probabilistic exposures, it proposes a basis for a converging policy of control for both certain and probabilistic exposures, namely the dose-risk relationship adopted for radiation protection purposes. On that basis some coherent approaches for dealing with probabilistic exposures, such as the limitation of individual risks, are discussed. The optimization of safety for reducing all risks from probabilistic exposures to as-low-as-reasonably-achievable (ALARA) levels is reviewed in full. The principles of optimization of protection are used as a basic framework and the relevant factors to be taken into account when moving to probabilistic exposures are presented. The paper also reviews the decision-aiding techniques suitable for performing optimization with particular emphasis to the multi-attribute utility-analysis technique. Finally, there is a discussion on some practical application of decision-aiding multi-attribute utility analysis to probabilistic exposures including the use of probabilistic utilities. In its final outlook, the paper emphasizes the need for standardization and solutions to generic problems, if optimization of safety is to be successful

  10. Probabilistic Learning by Rodent Grid Cells.

    Science.gov (United States)

    Cheung, Allen

    2016-10-01

    Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation can tie together grid fragmentation and grid rescaling, which show very different forms of flexibility in grid responses when the environment is varied. Other properties such as attractor dynamics and grid anisotropy seem to be at odds with one another unless additional properties are assumed such as a varying velocity gain. Modelling efforts have largely ignored the breadth of response patterns, while also failing to account for the disastrous effects of sensory noise during spatial learning and recall, especially in darkness. Here, published electrophysiological evidence from a range of experiments are reinterpreted using a novel probabilistic learning model, which shows that grid cell responses are accurately predicted by a probabilistic learning process. Diverse response properties of probabilistic grid cells are statistically indistinguishable from rat grid cells across key manipulations. A simple coherent set of probabilistic computations explains stable grid fields in darkness, partial grid rescaling in resized arenas, low-dimensional attractor grid cell dynamics, and grid fragmentation in hairpin mazes. The same computations also reconcile oscillatory dynamics at the single cell level with attractor dynamics at the cell ensemble level. Additionally, a clear functional role for boundary cells is proposed for spatial learning. These findings provide a parsimonious and unified explanation of grid cell function, and implicate grid cells as an accessible neuronal population

  11. Convex models and probabilistic approach of nonlinear fatigue failure

    International Nuclear Information System (INIS)

    Qiu Zhiping; Lin Qiang; Wang Xiaojun

    2008-01-01

    This paper is concerned with the nonlinear fatigue failure problem with uncertainties in the structural systems. In the present study, in order to solve the nonlinear problem by convex models, the theory of ellipsoidal algebra with the help of the thought of interval analysis is applied. In terms of the inclusion monotonic property of ellipsoidal functions, the nonlinear fatigue failure problem with uncertainties can be solved. A numerical example of 25-bar truss structures is given to illustrate the efficiency of the presented method in comparison with the probabilistic approach

  12. Probabilistic migration modelling focused on functional barrier efficiency and low migration concepts in support of risk assessment.

    Science.gov (United States)

    Brandsch, Rainer

    2017-10-01

    Migration modelling provides reliable migration estimates from food-contact materials (FCM) to food or food simulants based on mass-transfer parameters like diffusion and partition coefficients related to individual materials. In most cases, mass-transfer parameters are not readily available from the literature and for this reason are estimated with a given uncertainty. Historically, uncertainty was accounted for by introducing upper limit concepts first, turning out to be of limited applicability due to highly overestimated migration results. Probabilistic migration modelling gives the possibility to consider uncertainty of the mass-transfer parameters as well as other model inputs. With respect to a functional barrier, the most important parameters among others are the diffusion properties of the functional barrier and its thickness. A software tool that accepts distribution as inputs and is capable of applying Monte Carlo methods, i.e., random sampling from the input distributions of the relevant parameters (i.e., diffusion coefficient and layer thickness), predicts migration results with related uncertainty and confidence intervals. The capabilities of probabilistic migration modelling are presented in the view of three case studies (1) sensitivity analysis, (2) functional barrier efficiency and (3) validation by experimental testing. Based on the predicted migration by probabilistic migration modelling and related exposure estimates, safety evaluation of new materials in the context of existing or new packaging concepts is possible. Identifying associated migration risk and potential safety concerns in the early stage of packaging development is possible. Furthermore, dedicated material selection exhibiting required functional barrier efficiency under application conditions becomes feasible. Validation of the migration risk assessment by probabilistic migration modelling through a minimum of dedicated experimental testing is strongly recommended.

  13. Probabilistic Model for Integrated Assessment of the Behavior at the T.D.P. Version 2

    International Nuclear Information System (INIS)

    Hurtado, A.; Eguilior, S.; Recreo, F

    2015-01-01

    This report documents the completion of the first phase of the implementation of the methodology ABACO2G (Bayes Application to Geological Storage of CO2) and the final version of the ABACO2G probabilistic model for the injection phase before its future validation in the experimental field of the Technology Development Plant in Hontom (Burgos). The model, which is based on the determination of the probabilistic risk component of a geological storage of CO2 using the formalism of Bayesian networks and Monte Carlo probability yields quantitative probability functions of the total system CO2 storage and of each one of their subsystems (storage subsystem and the primary seal; secondary containment subsystem and dispersion subsystem or tertiary one); the implementation of the stochastic time evolution of the CO2 plume during the injection period, the stochastic time evolution of the drying front, the probabilistic evolution of the pressure front, decoupled from the CO2 plume progress front, and the implementation of submodels and leakage probability functions through major leakage risk elements (fractures / faults and wells / deep boreholes) which together define the space of events to estimate the risks associated with the CO2 geological storage system. The activities included in this report have been to replace the previous qualitative estimation submodels of former ABACO2G version developed during Phase I of the project ALM-10-017, by analytical, semi-analytical or numerical submodels for the main elements of risk (wells and fractures), to obtain an integrated probabilistic model of a CO2 storage complex in carbonate formations that meets the needs of the integrated behavior evaluation of the Technology Development Plant in Hontomín

  14. A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

    Science.gov (United States)

    Knoops, Paul G M; Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W F; Jeelani, Owase; Dunaway, David J; Schievano, Silvia

    2018-01-01

    Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

  15. Probabilistic estimation of the constitutive parameters of polymers

    Directory of Open Access Journals (Sweden)

    Siviour C.R.

    2012-08-01

    Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.

  16. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

    Science.gov (United States)

    Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome. Chave

    2014-01-01

    We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

  17. Enhanced probabilistic decision analysis for radiological confinement barriers of the International Thermonuclear Experimental Reactor

    International Nuclear Information System (INIS)

    Golinescu, R.P.; Kazimi, M.S.

    1998-01-01

    To ensure a defence-in-depth approach, several radiological confinement barriers surrounding a tokamak plant can be employed. A methodology using probabilistic risk assessment (PRA) techniques is a useful tool for evaluating the performance of each confinement barrier within the context of a limited allowable risk of accidental radioactivity releases. Such a methodology was developed and applied to the confinement strategy for the International Thermonuclear Experimental Reactor (ITER). Accident sequence models were constructed for each of the confinement barriers to evaluate the probabilities of events leading to radioactive releases from the corresponding confinement barrier. The current ITER design requirements set radioactive release and dose limits for individual event sequences grouped in categories by frequency. To limit the plant's overall risk and account for event uncertainties in both frequency and consequence, an analytical form for a limit line is derived here as a complementary cumulative frequency (CCF) of radioactive releases to the environment. By comparing the releases from each confinement barrier against the limit line, a decision can be made about the number of barriers required to comply with the design requirements. The first barrier is the vacuum vessel (VV) and the primary heat transfer systems. The second confinement barrier consists of the cryostat vessel (CV) and the heat transfer system vaults. In case the outer building is needed to act as a third barrier for ITER, a decision model using the multi-attribute utility theory was constructed to help the designer choose the best type of tokamak building. The decision model allows for performing sensitivity analysis on relevant parameters and for design features of new options for the ITER tokamak building. (orig.)

  18. Probabilistic model for fatigue crack growth and fracture of welded joints in civil engineering structures

    NARCIS (Netherlands)

    Maljaars, J.; Steenbergen, H.M.G.M.; Vrouwenvelder, A.C.W.M.

    2012-01-01

    This paper presents a probabilistic assessment model for linear elastic fracture mechanics (LEFM). The model allows the determination of the failure probability of a structure subjected to fatigue loading. The distributions of the random variables for civil engineering structures are provided, and

  19. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  20. A deterministic-probabilistic model for contaminant transport. User manual

    Energy Technology Data Exchange (ETDEWEB)

    Schwartz, F W; Crowe, A

    1980-08-01

    This manual describes a deterministic-probabilistic contaminant transport (DPCT) computer model designed to simulate mass transfer by ground-water movement in a vertical section of the earth's crust. The model can account for convection, dispersion, radioactive decay, and cation exchange for a single component. A velocity is calculated from the convective transport of the ground water for each reference particle in the modeled region; dispersion is accounted for in the particle motion by adding a readorn component to the deterministic motion. The model is sufficiently general to enable the user to specify virtually any type of water table or geologic configuration, and a variety of boundary conditions. A major emphasis in the model development has been placed on making the model simple to use, and information provided in the User Manual will permit changes to the computer code to be made relatively easily for those that might be required for specific applications. (author)

  1. Surrogate reservoir models for CSI well probabilistic production forecast

    Directory of Open Access Journals (Sweden)

    Saúl Buitrago

    2017-09-01

    Full Text Available The aim of this work is to present the construction and use of Surrogate Reservoir Models capable of accurately predicting cumulative oil production for every well stimulated with cyclic steam injection at any given time in a heavy oil reservoir in Mexico considering uncertain variables. The central composite experimental design technique was selected to capture the maximum amount of information from the model response with a minimum number of reservoir models simulations. Four input uncertain variables (the dead oil viscosity with temperature, the reservoir pressure, the reservoir permeability and oil sand thickness hydraulically connected to the well were selected as the ones with more impact on the initial hot oil production rate according to an analytical production prediction model. Twenty five runs were designed and performed with the STARS simulator for each well type on the reservoir model. The results show that the use of Surrogate Reservoir Models is a fast viable alternative to perform probabilistic production forecasting of the reservoir.

  2. Statistical analysis of probabilistic models of software product lines with quantitative constraints

    DEFF Research Database (Denmark)

    Beek, M.H. ter; Legay, A.; Lluch Lafuente, Alberto

    2015-01-01

    We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra...... of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study....

  3. Process for computing geometric perturbations for probabilistic analysis

    Science.gov (United States)

    Fitch, Simeon H. K. [Charlottesville, VA; Riha, David S [San Antonio, TX; Thacker, Ben H [San Antonio, TX

    2012-04-10

    A method for computing geometric perturbations for probabilistic analysis. The probabilistic analysis is based on finite element modeling, in which uncertainties in the modeled system are represented by changes in the nominal geometry of the model, referred to as "perturbations". These changes are accomplished using displacement vectors, which are computed for each node of a region of interest and are based on mean-value coordinate calculations.

  4. Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

    Science.gov (United States)

    Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

    2018-01-01

    Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

  5. A Probabilistic Asteroid Impact Risk Model

    Science.gov (United States)

    Mathias, Donovan L.; Wheeler, Lorien F.; Dotson, Jessie L.

    2016-01-01

    Asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data has little effect on the metrics of interest.

  6. Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling.

    Science.gov (United States)

    Cheng, Yi-Hsien; Riviere, Jim E; Monteiro-Riviere, Nancy A; Lin, Zhoumeng

    2018-04-14

    This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.

  7. A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

    Science.gov (United States)

    Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

    2018-05-01

    Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

  8. Developing probabilistic models to predict amphibian site occupancy in a patchy landscape

    Science.gov (United States)

    R. A. Knapp; K.R. Matthews; H. K. Preisler; R. Jellison

    2003-01-01

    Abstract. Human-caused fragmentation of habitats is threatening an increasing number of animal and plant species, making an understanding of the factors influencing patch occupancy ever more important. The overall goal of the current study was to develop probabilistic models of patch occupancy for the mountain yellow-legged frog (Rana muscosa). This once-common species...

  9. Development of probabilistic assessment methodology for geologic disposal of radioactive wastes

    International Nuclear Information System (INIS)

    Kimura, H.; Takahashi, T.

    1998-01-01

    The probabilistic assessment methodology is essential to evaluate uncertainties of long-term radiological consequences associated with geologic disposal of radioactive wastes. We have developed a probabilistic assessment methodology to estimate the influences of parameter uncertainties/variabilities. An exposure scenario considered here is based on a groundwater migration scenario. A computer code system GSRW-PSA thus developed is based on a non site-specific model, and consists of a set of sub modules for sampling of model parameters, calculating the release of radionuclides from engineered barriers, calculating the transport of radionuclides through the geosphere, calculating radiation exposures of the public, and calculating the statistical values relating the uncertainties and sensitivities. The results of uncertainty analyses for α-nuclides quantitatively indicate that natural uranium ( 238 U) concentration is suitable for an alternative safety indicator of long-lived radioactive waste disposal, because the estimated range of individual dose equivalent due to 238 U decay chain is narrower that that due to other decay chain ( 237 Np decay chain). It is internationally necessary to have detailed discussion on the PDF of model parameters and the PSA methodology to evaluated the uncertainties due to conceptual models and scenarios. (author)

  10. A Probabilistic Model to Evaluate the Optimal Density of Stations Measuring Snowfall.

    Science.gov (United States)

    Schneebeli, Martin; Laternser, Martin

    2004-05-01

    Daily new snow measurements are very important for avalanche forecasting and tourism. A dense network of manual or automatic stations measuring snowfall is necessary to have spatially reliable data. Snow stations in Switzerland were built at partially subjective locations. A probabilistic model based on the frequency and spatial extent of areas covered by heavy snowfalls was developed to quantify the probability that snowfall events are measured by the stations. Area probability relations were calculated for different thresholds of daily accumulated snowfall. A probabilistic model, including autocorrelation, was used to calculate the optimal spacing of stations based on simulated triangular grids and to compare the capture probability of different networks and snowfall thresholds. The Swiss operational snow-stations network captured snowfall events with high probability, but the distribution of the stations could be optimized. The spatial variability increased with higher thresholds of daily accumulated snowfall, and the capture probability decreased with increasing thresholds. The method can be used for other areas where the area probability relation for threshold values of snow or rain can be calculated.

  11. Probabilistic safety goals. Phase 3 - Status report

    Energy Technology Data Exchange (ETDEWEB)

    Holmberg, J.-E. (VTT (Finland)); Knochenhauer, M. (Relcon Scandpower AB, Sundbyberg (Sweden))

    2009-07-15

    The first phase of the project (2006) described the status, concepts and history of probabilistic safety goals for nuclear power plants. The second and third phases (2007-2008) have provided guidance related to the resolution of some of the problems identified, and resulted in a common understanding regarding the definition of safety goals. The basic aim of phase 3 (2009) has been to increase the scope and level of detail of the project, and to start preparations of a guidance document. Based on the conclusions from the previous project phases, the following issues have been covered: 1) Extension of international overview. Analysis of results from the questionnaire performed within the ongoing OECD/NEA WGRISK activity on probabilistic safety criteria, including participation in the preparation of the working report for OECD/NEA/WGRISK (to be finalised in phase 4). 2) Use of subsidiary criteria and relations between these (to be finalised in phase 4). 3) Numerical criteria when using probabilistic analyses in support of deterministic safety analysis (to be finalised in phase 4). 4) Guidance for the formulation, application and interpretation of probabilistic safety criteria (to be finalised in phase 4). (LN)

  12. Probabilistic safety goals. Phase 3 - Status report

    International Nuclear Information System (INIS)

    Holmberg, J.-E.; Knochenhauer, M.

    2009-07-01

    The first phase of the project (2006) described the status, concepts and history of probabilistic safety goals for nuclear power plants. The second and third phases (2007-2008) have provided guidance related to the resolution of some of the problems identified, and resulted in a common understanding regarding the definition of safety goals. The basic aim of phase 3 (2009) has been to increase the scope and level of detail of the project, and to start preparations of a guidance document. Based on the conclusions from the previous project phases, the following issues have been covered: 1) Extension of international overview. Analysis of results from the questionnaire performed within the ongoing OECD/NEA WGRISK activity on probabilistic safety criteria, including participation in the preparation of the working report for OECD/NEA/WGRISK (to be finalised in phase 4). 2) Use of subsidiary criteria and relations between these (to be finalised in phase 4). 3) Numerical criteria when using probabilistic analyses in support of deterministic safety analysis (to be finalised in phase 4). 4) Guidance for the formulation, application and interpretation of probabilistic safety criteria (to be finalised in phase 4). (LN)

  13. Improved transformer protection using probabilistic neural network ...

    African Journals Online (AJOL)

    This article presents a novel technique to distinguish between magnetizing inrush current and internal fault current of power transformer. An algorithm has been developed around the theme of the conventional differential protection method in which parallel combination of Probabilistic Neural Network (PNN) and Power ...

  14. Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification.

    Science.gov (United States)

    Li, Haoxiang; Hua, Gang

    2018-04-01

    Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic part model. We extract local descriptors (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each descriptor with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of the face parts of all face images in the training corpus, namely the probabilistic elastic part (PEP) model. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms, which naturally defines a part. Given one or multiple face images of the same subject, the PEP-model builds its PEP representation by sequentially concatenating descriptors identified by each Gaussian component in a maximum likelihood sense. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that we achieve state-of-the-art face verification accuracy with the proposed representations on the Labeled Face in the Wild (LFW) dataset, the YouTube video face database, and the CMU MultiPIE dataset.

  15. A probabilistic model of the electron transport in films of nanocrystals arranged in a cubic lattice

    Energy Technology Data Exchange (ETDEWEB)

    Kriegel, Ilka [Department of Nanochemistry, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova (Italy); Scotognella, Francesco, E-mail: francesco.scotognella@polimi.it [Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Center for Nano Science and Technology@PoliMi, Istituto Italiano di Tecnologia, Via Giovanni Pascoli, 70/3, 20133 Milan (Italy)

    2016-08-01

    The fabrication of nanocrystal (NC) films, starting from colloidal dispersion, is a very attractive topic in condensed matter physics community. NC films can be employed for transistors, light emitting diodes, lasers, and solar cells. For this reason the understanding of the film conductivity is of major importance. In this paper we describe a probabilistic model that allows the prediction of the conductivity of NC films, in this case of a cubic lattice of Lead Selenide or Cadmium Selenide NCs. The model is based on the hopping probability between NCs. The results are compared to experimental data reported in literature. - Highlights: • Colloidal nanocrystal (NC) film conductivity is a topic of major importance. • We present a probabilistic model to predict the electron conductivity in NC films. • The model is based on the hopping probability between NCs. • We found a good agreement between the model and data reported in literature.

  16. Probabilistic inversion in priority setting of emerging zoonoses.

    NARCIS (Netherlands)

    Kurowicka, D.; Bucura, C.; Cooke, R.; Havelaar, A.H.

    2010-01-01

    This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi-criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a

  17. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    2014-02-14

    Feb 14, 2014 ... Application of probabilistic precipitation forecasts from a deterministic model ... aim of this paper is to investigate the increase in the lead-time of flash flood warnings of the SAFFG using probabilistic precipitation forecasts ... The procedure is applied to a real flash flood event and the ensemble-based.

  18. Using Probabilistic Models to Appraise and Decide on Sovereign Disaster Risk Financing and Insurance

    OpenAIRE

    Ley-Borrás, Roberto; Fox, Benjamin D.

    2015-01-01

    This paper presents an overview of the structure of probabilistic catastrophe risk models, discusses their importance for appraising sovereign disaster risk financing and insurance instruments and strategy, and puts forward a model and a process for improving decision making on the linked disaster risk management strategy and sovereign disaster risk financing and insurance strategy. The pa...

  19. Probabilistic safety analysis procedures guide. Sections 1-7 and appendices. Volume 1, Revision 1

    International Nuclear Information System (INIS)

    Bari, R.A.; Buslik, A.J.; Cho, N.Z.

    1985-08-01

    A procedures guide for the performance of probabilistic safety assessment has been prepared for interim use in the Nuclear Regulatory Commission programs. It will be revised as comments are received, and as experience is gained from its use. The probabilistic safety assessment studies performed are intended to produce probabilistic predictive models that can be used and extended by the utilities and by NRC to sharpen the focus of inquiries into a range of issues affecting reactor safety. This first volume of the guide describes the determination of the probability (per year) of core damage resulting from accident initiators internal to the plant (i.e., intrinsic to plant operation) and from loss of off-site electric power. The scope includes human reliability analysis, a determination of the importance of various core damage accident sequences, and an explicit treatment and display of uncertainties for key accident sequences. The second volume deals with the treatment of the so-called external events including seismic disturbances, fires, floods, etc. Ultimately, the guide will be augmented to include the plant-specific analysis of in-plant processes (i.e., containment performance). This guide provides the structure of a probabilistic safety study to be performed, and indicates what products of the study are valuable for regulatory decision making. For internal events, methodology is treated in the guide only to the extent necessary to indicate the range of methods which is acceptable; ample reference is given to alternative methodologies which may be utilized in the performance of the study. For external events, more explicit guidance is given

  20. Probabilistic structural analysis of aerospace components using NESSUS

    Science.gov (United States)

    Shiao, Michael C.; Nagpal, Vinod K.; Chamis, Christos C.

    1988-01-01

    Probabilistic structural analysis of a Space Shuttle main engine turbopump blade is conducted using the computer code NESSUS (numerical evaluation of stochastic structures under stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade geometry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted assuming different failure models.

  1. Probabilistic calculation of dose commitment from uranium mill tailings

    International Nuclear Information System (INIS)

    1983-10-01

    The report discusses in a general way considerations of uncertainty in relation to probabilistic modelling. An example of a probabilistic calculation applied to the behaviour of uranium mill tailings is given

  2. Probabilistic forward model for electroencephalography source analysis

    International Nuclear Information System (INIS)

    Plis, Sergey M; George, John S; Jun, Sung C; Ranken, Doug M; Volegov, Petr L; Schmidt, David M

    2007-01-01

    Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates

  3. Financial and Real Sector Leading Indicators of Recessions in Brazil Using Probabilistic Models

    Directory of Open Access Journals (Sweden)

    Fernando Nascimento de Oliveira

    Full Text Available We examine the usefulness of various financial and real sector variables to forecast recessions in Brazil between one and eight quarters ahead. We estimate probabilistic models of recession and select models based on their outof-sample forecasts, using the Receiver Operating Characteristic (ROC function. We find that the predictive out-of-sample ability of several models vary depending on the numbers of quarters ahead to forecast and on the number of regressors used in the model specification. The models selected seem to be relevant to give early warnings of recessions in Brazil.

  4. Next-generation probabilistic seismicity forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Hiemer, S.

    2014-07-01

    The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

  5. Next-generation probabilistic seismicity forecasting

    International Nuclear Information System (INIS)

    Hiemer, S.

    2014-01-01

    The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

  6. Probabilistic Determination of Green Infrastructure Pollutant Removal Rates from the International Stormwater BMP Database

    Science.gov (United States)

    Gilliom, R.; Hogue, T. S.; McCray, J. E.

    2017-12-01

    There is a need for improved parameterization of stormwater best management practices (BMP) performance estimates to improve modeling of urban hydrology, planning and design of green infrastructure projects, and water quality crediting for stormwater management. Percent removal is commonly used to estimate BMP pollutant removal efficiency, but there is general agreement that this approach has significant uncertainties and is easily affected by site-specific factors. Additionally, some fraction of monitored BMPs have negative percent removal, so it is important to understand the probability that a BMP will provide the desired water quality function versus exacerbating water quality problems. The widely used k-C* equation has shown to provide a more adaptable and accurate method to model BMP contaminant attenuation, and previous work has begun to evaluate the strengths and weaknesses of the k-C* method. However, no systematic method exists for obtaining first-order removal rate constants needed to use the k-C* equation for stormwater BMPs; thus there is minimal application of the method. The current research analyzes existing water quality data in the International Stormwater BMP Database to provide screening-level parameterization of the k-C* equation for selected BMP types and analysis of factors that skew the distribution of efficiency estimates from the database. Results illustrate that while certain BMPs are more likely to provide desired contaminant removal than others, site- and design-specific factors strongly influence performance. For example, bioretention systems show both the highest and lowest removal rates of dissolved copper, total phosphorous, and total nitrogen. Exploration and discussion of this and other findings will inform the application of the probabilistic pollutant removal rate constants. Though data limitations exist, this research will facilitate improved accuracy of BMP modeling and ultimately aid decision-making for stormwater quality

  7. Review of the Diablo Canyon probabilistic risk assessment

    International Nuclear Information System (INIS)

    Bozoki, G.E.; Fitzpatrick, R.G.; Bohn, M.P.; Sabek, M.G.; Ravindra, M.K.; Johnson, J.J.

    1994-08-01

    This report details the review of the Diablo Canyon Probabilistic Risk Assessment (DCPRA). The study was performed under contract from the Probabilistic Risk Analysis Branch, Office of Nuclear Reactor Research, USNRC by Brookhaven National Laboratory. The DCPRA is a full scope Level I effort and although the review touched on all aspects of the PRA, the internal events and seismic events received the vast majority of the review effort. The report includes a number of independent systems analyses sensitivity studies, importance analyses as well as conclusions on the adequacy of the DCPRA for use in the Diablo Canyon Long Term Seismic Program

  8. Probabilistic insurance

    OpenAIRE

    Wakker, P.P.; Thaler, R.H.; Tversky, A.

    1997-01-01

    textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these preferences are intuitively appealing they are difficult to reconcile with expected utility theory. Under highly plausible assumptions about the utility function, willingness to pay for probabilistic i...

  9. Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

    Energy Technology Data Exchange (ETDEWEB)

    Soltanzadeh, I. [Tehran Univ. (Iran, Islamic Republic of). Inst. of Geophysics; Azadi, M.; Vakili, G.A. [Atmospheric Science and Meteorological Research Center (ASMERC), Teheran (Iran, Islamic Republic of)

    2011-07-01

    Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast. (orig.)

  10. Using Bayesian Model Averaging (BMA to calibrate probabilistic surface temperature forecasts over Iran

    Directory of Open Access Journals (Sweden)

    I. Soltanzadeh

    2011-07-01

    Full Text Available Using Bayesian Model Averaging (BMA, an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM, with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP Global Forecast System (GFS and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009 over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

  11. International status of application of probabilistic risk analysis

    International Nuclear Information System (INIS)

    Cullingford, M.C.

    1984-01-01

    Probabilistic Risk Assessment (PRA) having been practised for about ten years and with more than twenty studies completed has reached a level of maturity such that the insights and other products derived from specific studies may be assessed. The first full-scale PRA studies were designed to develop the methodology and assess the overall risk from nuclear power. At present PRA is performed mostly for individual plants to identify core damage accident sequences and significant contributors to such sequences. More than 25 countries are utilizing insights from PRA, some from full-scale PRA studies and other countries by performing reliability analyses on safety systems identified as important contributors to one or more core melt sequences. Many Member States of the IAEA fall into one of three groups: those having (a) a large, (b) a medium number of reactor-years of operating experience and (c) those countries in the planning or feasibility study stages of a nuclear power programme. Of the many potential uses of PRA the decision areas of safety improvement by backfitting, development of operating procedures and as the basis of standards are felt to be important by countries of all three groups. The use of PRA in showing compliance with safety goals and for plant availability studies is held to be important only by those countries which have operating experience. The evolution of the PRA methodology has led to increased attention to quantification of uncertainties both in the probabilities and consequences. Although many products from performing a PRA do not rely upon overall risk numbers, increasing emphasis is being placed on the interpretation of uncertainties in risk numbers for use in decisions. International co-operation through exchange of information regarding experience with PRA methodology and its application to nuclear safety decisions will greatly enhance the widespread use of PRA. (author)

  12. Growing hierarchical probabilistic self-organizing graphs.

    Science.gov (United States)

    López-Rubio, Ezequiel; Palomo, Esteban José

    2011-07-01

    Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

  13. Probabilistic simple sticker systems

    Science.gov (United States)

    Selvarajoo, Mathuri; Heng, Fong Wan; Sarmin, Nor Haniza; Turaev, Sherzod

    2017-04-01

    A model for DNA computing using the recombination behavior of DNA molecules, known as a sticker system, was introduced by by L. Kari, G. Paun, G. Rozenberg, A. Salomaa, and S. Yu in the paper entitled DNA computing, sticker systems and universality from the journal of Acta Informatica vol. 35, pp. 401-420 in the year 1998. A sticker system uses the Watson-Crick complementary feature of DNA molecules: starting from the incomplete double stranded sequences, and iteratively using sticking operations until a complete double stranded sequence is obtained. It is known that sticker systems with finite sets of axioms and sticker rules generate only regular languages. Hence, different types of restrictions have been considered to increase the computational power of sticker systems. Recently, a variant of restricted sticker systems, called probabilistic sticker systems, has been introduced [4]. In this variant, the probabilities are initially associated with the axioms, and the probability of a generated string is computed by multiplying the probabilities of all occurrences of the initial strings in the computation of the string. Strings for the language are selected according to some probabilistic requirements. In this paper, we study fundamental properties of probabilistic simple sticker systems. We prove that the probabilistic enhancement increases the computational power of simple sticker systems.

  14. Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

    Directory of Open Access Journals (Sweden)

    Shengchun Yang

    2016-01-01

    Full Text Available Demand response (DR programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL, plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

  15. Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

    Science.gov (United States)

    Mørk, Søren; Holmes, Ian

    2012-03-01

    Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

  16. Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

    Science.gov (United States)

    Strauch, R. L.; Istanbulluoglu, E.

    2017-12-01

    We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

  17. Probabilistic Graphical Models for the Analysis and Synthesis of Musical Audio

    Science.gov (United States)

    Hoffmann, Matthew Douglas

    Content-based Music Information Retrieval (MIR) systems seek to automatically extract meaningful information from musical audio signals. This thesis applies new and existing generative probabilistic models to several content-based MIR tasks: timbral similarity estimation, semantic annotation and retrieval, and latent source discovery and separation. In order to estimate how similar two songs sound to one another, we employ a Hierarchical Dirichlet Process (HDP) mixture model to discover a shared representation of the distribution of timbres in each song. Comparing songs under this shared representation yields better query-by-example retrieval quality and scalability than previous approaches. To predict what tags are likely to apply to a song (e.g., "rap," "happy," or "driving music"), we develop the Codeword Bernoulli Average (CBA) model, a simple and fast mixture-of-experts model. Despite its simplicity, CBA performs at least as well as state-of-the-art approaches at automatically annotating songs and finding to what songs in a database a given tag most applies. Finally, we address the problem of latent source discovery and separation by developing two Bayesian nonparametric models, the Shift-Invariant HDP and Gamma Process NMF. These models allow us to discover what sounds (e.g. bass drums, guitar chords, etc.) are present in a song or set of songs and to isolate or suppress individual source. These models' ability to decide how many latent sources are necessary to model the data is particularly valuable in this application, since it is impossible to guess a priori how many sounds will appear in a given song or set of songs. Once they have been fit to data, probabilistic models can also be used to drive the synthesis of new musical audio, both for creative purposes and to qualitatively diagnose what information a model does and does not capture. We also adapt the SIHDP model to create new versions of input audio with arbitrary sample sets, for example, to create

  18. Review of the Brunswick Steam Electric Plant Probabilistic Risk Assessment

    International Nuclear Information System (INIS)

    Sattison, M.B.; Davis, P.R.; Satterwhite, D.G.; Gilmore, W.E.; Gregg, R.E.

    1989-11-01

    A review of the Brunswick Steam Electric Plant probabilistic risk Assessment was conducted with the objective of confirming the safety perspectives brought to light by the probabilistic risk assessment. The scope of the review included the entire Level I probabilistic risk assessment including external events. This is consistent with the scope of the probabilistic risk assessment. The review included an assessment of the assumptions, methods, models, and data used in the study. 47 refs., 14 figs., 15 tabs

  19. Probabilistic modelling of human exposure to intense sweeteners in Italian teenagers: validation and sensitivity analysis of a probabilistic model including indicators of market share and brand loyalty.

    Science.gov (United States)

    Arcella, D; Soggiu, M E; Leclercq, C

    2003-10-01

    For the assessment of exposure to food-borne chemicals, the most commonly used methods in the European Union follow a deterministic approach based on conservative assumptions. Over the past few years, to get a more realistic view of exposure to food chemicals, risk managers are getting more interested in the probabilistic approach. Within the EU-funded 'Monte Carlo' project, a stochastic model of exposure to chemical substances from the diet and a computer software program were developed. The aim of this paper was to validate the model with respect to the intake of saccharin from table-top sweeteners and cyclamate from soft drinks by Italian teenagers with the use of the software and to evaluate the impact of the inclusion/exclusion of indicators on market share and brand loyalty through a sensitivity analysis. Data on food consumption and the concentration of sweeteners were collected. A food frequency questionnaire aimed at identifying females who were high consumers of sugar-free soft drinks and/or of table top sweeteners was filled in by 3982 teenagers living in the District of Rome. Moreover, 362 subjects participated in a detailed food survey by recording, at brand level, all foods and beverages ingested over 12 days. Producers were asked to provide the intense sweeteners' concentration of sugar-free products. Results showed that consumer behaviour with respect to brands has an impact on exposure assessments. Only probabilistic models that took into account indicators of market share and brand loyalty met the validation criteria.

  20. Probabilistic Fatigue Model for Reinforced Concrete Onshore Wind Turbine Foundations

    DEFF Research Database (Denmark)

    Marquez-Dominguez, Sergio; Sørensen, John Dalsgaard

    2013-01-01

    Reinforced Concrete Slab Foundation (RCSF) is the most common onshore wind turbine foundation type installed by the wind industry around the world. Fatigue cracks in a RCSF are an important issue to be considered by the designers. Causes and consequences of the cracks due to fatigue damage in RCSFs...... are discussed in this paper. A probabilistic fatigue model for a RCSF is established which makes a rational treatment of the uncertainties involved in the complex interaction between fatigue cyclic loads and reinforced concrete. Design and limit state equations are established considering concrete shear...

  1. Probabilistic analysis of glass elements with three-parameter Weibull distribution

    International Nuclear Information System (INIS)

    Ramos, A.; Muniz-Calvente, M.; Fernandez, P.; Fernandez Cantel, A.; Lamela, M. J.

    2015-01-01

    Glass and ceramics present a brittle behaviour so a large scatter in the test results is obtained. This dispersion is mainly due to the inevitable presence of micro-cracks on its surface, edge defects or internal defects, which must be taken into account using an appropriate failure criteria non-deterministic but probabilistic. Among the existing probability distributions, the two or three parameter Weibull distribution is generally used in adjusting material resistance results, although the method of use thereof is not always correct. Firstly, in this work, the results of a large experimental programme using annealed glass specimens of different dimensions based on four-point bending and coaxial double ring tests was performed. Then, the finite element models made for each type of test, the adjustment of the parameters of the three-parameter Weibull distribution function (cdf) (λ: location, β: shape, d: scale) for a certain failure criterion and the calculation of the effective areas from the cumulative distribution function are presented. Summarizing, this work aims to generalize the use of the three-parameter Weibull function in structural glass elements with stress distributions not analytically described, allowing to apply the probabilistic model proposed in general loading distributions. (Author)

  2. On probabilistic forecasting of wind power time-series

    DEFF Research Database (Denmark)

    Pinson, Pierre

    power dynamics. In both cases, the model parameters are adaptively and recursively estimated, time-adaptativity being the result of exponential forgetting of past observations. The probabilistic forecasting methodology is applied at the Horns Rev wind farm in Denmark, for 10-minute ahead probabilistic...... forecasting of wind power generation. Probabilistic forecasts generated from the proposed methodology clearly have higher skill than those obtained from a classical Gaussian assumption about wind power predictive densities. Corresponding point forecasts also exhibit significantly lower error criteria....

  3. Effects of varying the step particle distribution on a probabilistic transport model

    International Nuclear Information System (INIS)

    Bouzat, S.; Farengo, R.

    2005-01-01

    The consequences of varying the step particle distribution on a probabilistic transport model, which captures the basic features of transport in plasmas and was recently introduced in Ref. 1 [B. Ph. van Milligen et al., Phys. Plasmas 11, 2272 (2004)], are studied. Different superdiffusive transport mechanisms generated by a family of distributions with algebraic decays (Tsallis distributions) are considered. It is observed that the possibility of changing the superdiffusive transport mechanism improves the flexibility of the model for describing different situations. The use of the model to describe the low (L) and high (H) confinement modes is also analyzed

  4. Probabilistic numerical discrimination in mice.

    Science.gov (United States)

    Berkay, Dilara; Çavdaroğlu, Bilgehan; Balcı, Fuat

    2016-03-01

    Previous studies showed that both human and non-human animals can discriminate between different quantities (i.e., time intervals, numerosities) with a limited level of precision due to their endogenous/representational uncertainty. In addition, other studies have shown that subjects can modulate their temporal categorization responses adaptively by incorporating information gathered regarding probabilistic contingencies into their time-based decisions. Despite the psychophysical similarities between the interval timing and nonverbal counting functions, the sensitivity of count-based decisions to probabilistic information remains an unanswered question. In the current study, we investigated whether exogenous probabilistic information can be integrated into numerosity-based judgments by mice. In the task employed in this study, reward was presented either after few (i.e., 10) or many (i.e., 20) lever presses, the last of which had to be emitted on the lever associated with the corresponding trial type. In order to investigate the effect of probabilistic information on performance in this task, we manipulated the relative frequency of different trial types across different experimental conditions. We evaluated the behavioral performance of the animals under models that differed in terms of their assumptions regarding the cost of responding (e.g., logarithmically increasing vs. no response cost). Our results showed for the first time that mice could adaptively modulate their count-based decisions based on the experienced probabilistic contingencies in directions predicted by optimality.

  5. Comparing Categorical and Probabilistic Fingerprint Evidence.

    Science.gov (United States)

    Garrett, Brandon; Mitchell, Gregory; Scurich, Nicholas

    2018-04-23

    Fingerprint examiners traditionally express conclusions in categorical terms, opining that impressions do or do not originate from the same source. Recently, probabilistic conclusions have been proposed, with examiners estimating the probability of a match between recovered and known prints. This study presented a nationally representative sample of jury-eligible adults with a hypothetical robbery case in which an examiner opined on the likelihood that a defendant's fingerprints matched latent fingerprints in categorical or probabilistic terms. We studied model language developed by the U.S. Defense Forensic Science Center to summarize results of statistical analysis of the similarity between prints. Participant ratings of the likelihood the defendant left prints at the crime scene and committed the crime were similar when exposed to categorical and strong probabilistic match evidence. Participants reduced these likelihoods when exposed to the weaker probabilistic evidence, but did not otherwise discriminate among the prints assigned different match probabilities. © 2018 American Academy of Forensic Sciences.

  6. Report of the IPERS (International Peer Review Service) review mission for the Cernavoda nuclear power plant probabilistic safety evaluation (CPSE -PHASE B) in Romania 3 to 14 July 1995

    International Nuclear Information System (INIS)

    1995-01-01

    This report presents the results of the IAEA International Peer Review Services review of the internal events Level 1 probabilistic safety assessment (PSA) for the Cernavoda, Unit 1 NPP. The review was based on the PSA documentation available and on intensive communications with the analysis team and representatives from the utility and the plant operator. 7 refs, figs, tabs

  7. Development and Application of a Probabilistic Risk-Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components.

    Science.gov (United States)

    Boué, Géraldine; Cummins, Enda; Guillou, Sandrine; Antignac, Jean-Philippe; Le Bizec, Bruno; Membré, Jeanne-Marie

    2017-12-01

    A probabilistic and interdisciplinary risk-benefit assessment (RBA) model integrating microbiological, nutritional, and chemical components was developed for infant milk, with the objective of predicting the health impact of different scenarios of consumption. Infant feeding is a particular concern of interest in RBA as breast milk and powder infant formula have both been associated with risks and benefits related to chemicals, bacteria, and nutrients, hence the model considers these three facets. Cronobacter sakazakii, dioxin-like polychlorinated biphenyls (dl-PCB), and docosahexaenoic acid (DHA) were three risk/benefit factors selected as key issues in microbiology, chemistry, and nutrition, respectively. The present model was probabilistic with variability and uncertainty separated using a second-order Monte Carlo simulation process. In this study, advantages and limitations of undertaking probabilistic and interdisciplinary RBA are discussed. In particular, the probabilistic technique was found to be powerful in dealing with missing data and to translate assumptions into quantitative inputs while taking uncertainty into account. In addition, separation of variability and uncertainty strengthened the interpretation of the model outputs by enabling better consideration and distinction of natural heterogeneity from lack of knowledge. Interdisciplinary RBA is necessary to give more structured conclusions and avoid contradictory messages to policymakers and also to consumers, leading to more decisive food recommendations. This assessment provides a conceptual development of the RBA methodology and is a robust basis on which to build upon. © 2017 Society for Risk Analysis.

  8. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Science.gov (United States)

    Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

    2018-03-01

    Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  9. A probabilistic model for component-based shape synthesis

    KAUST Repository

    Kalogerakis, Evangelos; Chaudhuri, Siddhartha; Koller, Daphne; Koltun, Vladlen

    2012-01-01

    represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation

  10. Probabilistic modelling of the damage of geological barriers of the nuclear waste deep storage - ENDOSTON project, final report

    International Nuclear Information System (INIS)

    2010-01-01

    As the corrosion of metallic casings of radioactive waste storage packages releases hydrogen under pressure, and as the overpressure disturbs the stress fields, the authors report the development of methodologies and numerical simulation tools aimed at a better understanding of the mechanisms of development and propagation of crack networks in the geological barrier due to this overpressure. They present a probabilistic model of the formation of crack networks in rocks, with the probabilistic post-processing of a finite element calculation. They describe the modelling of crack propagation and damage in quasi-brittle materials. They present the ENDO-HETEROGENE model for the formation and propagation of cracks in heterogeneous media, describe the integration of the model into the Aster code, and report the model validation (calculation of the stress intensity factor, grid dependence). They finally report a test case of the ENDO-HETEROGENE model

  11. Uncertainty estimation in nuclear power plant probabilistic safety assessment

    International Nuclear Information System (INIS)

    Guarro, S.B.; Cummings, G.E.

    1989-01-01

    Probabilistic Risk Assessment (PRA) was introduced in the nuclear industry and the nuclear regulatory process in 1975 with the publication of the Reactor Safety Study by the U.S. Nuclear Regulatory Commission. Almost fifteen years later, the state-of-the-art in this field has been expanded and sharpened in many areas, and about thirty-five plant-specific PRAs (Probabilistic Risk Assessments) have been performed by the nuclear utility companies or by the U.S. Nuclear Regulatory commission. Among the areas where the most evident progress has been made in PRA and PSA (Probabilistic Safety Assessment, as these studies are more commonly referred to in the international community outside the U.S.) is the development of a consistent framework for the identification of sources of uncertainty and the estimation of their magnitude as it impacts various risk measures. Techniques to propagate uncertainty in reliability data through the risk models and display its effect on the top level risk estimates were developed in the early PRAs. The Seismic Safety Margin Research Program (SSMRP) study was the first major risk study to develop an approach to deal explicitly with uncertainty in risk estimates introduced not only by uncertainty in component reliability data, but by the incomplete state of knowledge of the assessor(s) with regard to basic phenomena that may trigger and drive a severe accident. More recently NUREG-1150, another major study of reactor risk sponsored by the NRC, has expanded risk uncertainty estimation and analysis into the realm of model uncertainty related to the relatively poorly known post-core-melt phenomena which determine the behavior of the molten core and of the rector containment structures

  12. A probabilistic topic model for clinical risk stratification from electronic health records.

    Science.gov (United States)

    Huang, Zhengxing; Dong, Wei; Duan, Huilong

    2015-12-01

    Risk stratification aims to provide physicians with the accurate assessment of a patient's clinical risk such that an individualized prevention or management strategy can be developed and delivered. Existing risk stratification techniques mainly focus on predicting the overall risk of an individual patient in a supervised manner, and, at the cohort level, often offer little insight beyond a flat score-based segmentation from the labeled clinical dataset. To this end, in this paper, we propose a new approach for risk stratification by exploring a large volume of electronic health records (EHRs) in an unsupervised fashion. Along this line, this paper proposes a novel probabilistic topic modeling framework called probabilistic risk stratification model (PRSM) based on Latent Dirichlet Allocation (LDA). The proposed PRSM recognizes a patient clinical state as a probabilistic combination of latent sub-profiles, and generates sub-profile-specific risk tiers of patients from their EHRs in a fully unsupervised fashion. The achieved stratification results can be easily recognized as high-, medium- and low-risk, respectively. In addition, we present an extension of PRSM, called weakly supervised PRSM (WS-PRSM) by incorporating minimum prior information into the model, in order to improve the risk stratification accuracy, and to make our models highly portable to risk stratification tasks of various diseases. We verify the effectiveness of the proposed approach on a clinical dataset containing 3463 coronary heart disease (CHD) patient instances. Both PRSM and WS-PRSM were compared with two established supervised risk stratification algorithms, i.e., logistic regression and support vector machine, and showed the effectiveness of our models in risk stratification of CHD in terms of the Area Under the receiver operating characteristic Curve (AUC) analysis. As well, in comparison with PRSM, WS-PRSM has over 2% performance gain, on the experimental dataset, demonstrating that

  13. Probabilistic modeling of caprock leakage from seismic reflection data

    DEFF Research Database (Denmark)

    Zunino, Andrea; Hansen, Thomas Mejer; Bergjofd-Kitterød, Ingjerd

    We illustrate a methodology which helps to perform a leakage risk analysis for a CO2 reservoir based on a consistent, probabilistic approach to geophysical and geostatistical inversion. Generally, risk assessments of storage complexes are based on geological models and simulations of CO2 movement...... within the storage complexes. The geological models are built on top of geophysical data such as seismic surveys, geological information and well logs from the reservoir or nearby regions. The risk assessment of CO2 storage requires a careful analysis which accounts for all sources of uncertainty....... However, at present, no well-defined and consistent method for mapping the true uncertainty related to the geophysical data and how that uncertainty affects the overall risk assessment for the potential storage site is available. To properly quantify the uncertainties and to avoid unrealistic...

  14. Review of the Shoreham Nuclear Power Station Probabilistic Risk Assessment: internal events and core damage frequency

    International Nuclear Information System (INIS)

    Ilberg, D.; Shiu, K.; Hanan, N.; Anavim, E.

    1985-11-01

    A review of the Probabilistic Risk Assessment of the Shoreham Nuclear Power Station was conducted with the broad objective of evaluating its risks in relation to those identified in the Reactor Safety Study (WASH-1400). The scope of the review was limited to the ''front end'' part, i.e., to the evaluation of the frequencies of states in which core damage may occur. Furthermore, the review considered only internally generated accidents, consistent with the scope of the PRA. The review included an assessment of the assumptions and methods used in the Shoreham study. It also encompassed a reevaluation of the main results within the scope and general methodological framework of the Shoreham PRA, including both qualitative and quantitative analyses of accident initiators, data bases, and accident sequences which result in initiation of core damage. Specific comparisons are given between the Shoreham study, the results of the present review, and the WASH-1400 BWR, for the core damage frequency. The effect of modeling uncertainties was considered by a limited sensitivity study so as to show how the results would change if other assumptions were made. This review provides an independently assessed point value estimate of core damage frequency and describes the major contributors, by frontline systems and by accident sequences. 17 figs., 81 tabs

  15. Probabilistic Approaches to Video Retrieval

    NARCIS (Netherlands)

    Ianeva, Tzvetanka; Boldareva, L.; Westerveld, T.H.W.; Cornacchia, Roberto; Hiemstra, Djoerd; de Vries, A.P.

    Our experiments for TRECVID 2004 further investigate the applicability of the so-called “Generative Probabilistic Models to video retrieval��?. TRECVID 2003 results demonstrated that mixture models computed from video shot sequences improve the precision of “query by examples��? results when

  16. Transitions in a probabilistic interface growth model

    International Nuclear Information System (INIS)

    Alves, S G; Moreira, J G

    2011-01-01

    We study a generalization of the Wolf–Villain (WV) interface growth model based on a probabilistic growth rule. In the WV model, particles are randomly deposited onto a substrate and subsequently move to a position nearby where the binding is strongest. We introduce a growth probability which is proportional to a power of the number n i of bindings of the site i: p i ∝n i ν . Through extensive simulations, in (1 + 1) dimensions, we find three behaviors depending on the ν value: (i) if ν is small, a crossover from the Mullins–Herring to the Edwards–Wilkinson (EW) universality class; (ii) for intermediate values of ν, a crossover from the EW to the Kardar–Parisi–Zhang (KPZ) universality class; and, finally, (iii) for large ν values, the system is always in the KPZ class. In (2 + 1) dimensions, we obtain three different behaviors: (i) a crossover from the Villain–Lai–Das Sarma to the EW universality class for small ν values; (ii) the EW class is always present for intermediate ν values; and (iii) a deviation from the EW class is observed for large ν values

  17. Tractable approximations for probabilistic models: The adaptive Thouless-Anderson-Palmer mean field approach

    DEFF Research Database (Denmark)

    Opper, Manfred; Winther, Ole

    2001-01-01

    We develop an advanced mean held method for approximating averages in probabilistic data models that is based on the Thouless-Anderson-Palmer (TAP) approach of disorder physics. In contrast to conventional TAP. where the knowledge of the distribution of couplings between the random variables...... is required. our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is so far restricted to models with nonglassy behavior? by replica calculations for a wide class of models as well as by simulations for a real data set....

  18. Probabilistic models of population evolution scaling limits, genealogies and interactions

    CERN Document Server

    Pardoux, Étienne

    2016-01-01

    This expository book presents the mathematical description of evolutionary models of populations subject to interactions (e.g. competition) within the population. The author includes both models of finite populations, and limiting models as the size of the population tends to infinity. The size of the population is described as a random function of time and of the initial population (the ancestors at time 0). The genealogical tree of such a population is given. Most models imply that the population is bound to go extinct in finite time. It is explained when the interaction is strong enough so that the extinction time remains finite, when the ancestral population at time 0 goes to infinity. The material could be used for teaching stochastic processes, together with their applications. Étienne Pardoux is Professor at Aix-Marseille University, working in the field of Stochastic Analysis, stochastic partial differential equations, and probabilistic models in evolutionary biology and population genetics. He obtai...

  19. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

    Science.gov (United States)

    Orhan, A Emin; Ma, Wei Ji

    2017-07-26

    Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

  20. Probabilistic Structural Analysis of SSME Turbopump Blades: Probabilistic Geometry Effects

    Science.gov (United States)

    Nagpal, V. K.

    1985-01-01

    A probabilistic study was initiated to evaluate the precisions of the geometric and material properties tolerances on the structural response of turbopump blades. To complete this study, a number of important probabilistic variables were identified which are conceived to affect the structural response of the blade. In addition, a methodology was developed to statistically quantify the influence of these probabilistic variables in an optimized way. The identified variables include random geometric and material properties perturbations, different loadings and a probabilistic combination of these loadings. Influences of these probabilistic variables are planned to be quantified by evaluating the blade structural response. Studies of the geometric perturbations were conducted for a flat plate geometry as well as for a space shuttle main engine blade geometry using a special purpose code which uses the finite element approach. Analyses indicate that the variances of the perturbations about given mean values have significant influence on the response.

  1. Probabilistic modeling of dietary intake of substances - The risk management question governs the method

    NARCIS (Netherlands)

    Pieters MN; Ossendorp BC; Bakker MI; Slob W; SIR

    2005-01-01

    In this report the discussion on the use of probabilistic modeling in relation to pesticide use in food crops is analyzed. Due to different policy questions the current discussion is complex and considers safety of an MRL as well as probability of a health risk. The question regarding the use of

  2. A Level 1+ Probabilistic Safety Assessment of the High Flux Australian Reactor. Vol 3: Appendices

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-01-01

    The third volume of the Probabilistic Safety Assessment contains supporting information for the PSA as follows: Appendix C (continued) with details of the system analysis and reports for the system/top event models; Appendix D with results of the specific engineering analyses of internal initiating events; Appendix E, containing supporting data for the human performance assessment,; Appendix F with details of the estimation of the frequency of leaks at HIFAR and Appendix G, containing event sequence model and quantification results

  3. A Probabilistic Framework for Security Scenarios with Dependent Actions

    NARCIS (Netherlands)

    Kordy, Barbara; Pouly, Marc; Schweizer, Patrick; Albert, Elvira; Sekereinsk, Emil

    2014-01-01

    This work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform

  4. A state-based probabilistic model for tumor respiratory motion prediction

    International Nuclear Information System (INIS)

    Kalet, Alan; Sandison, George; Schmitz, Ruth; Wu Huanmei

    2010-01-01

    This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more

  5. Human-Guided Learning for Probabilistic Logic Models

    Directory of Open Access Journals (Sweden)

    Phillip Odom

    2018-06-01

    Full Text Available Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final structure of the model.

  6. Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Kieran R Campbell

    2017-03-01

    Full Text Available Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.

  7. On Probabilistic Alpha-Fuzzy Fixed Points and Related Convergence Results in Probabilistic Metric and Menger Spaces under Some Pompeiu-Hausdorff-Like Probabilistic Contractive Conditions

    OpenAIRE

    De la Sen, M.

    2015-01-01

    In the framework of complete probabilistic metric spaces and, in particular, in probabilistic Menger spaces, this paper investigates some relevant properties of convergence of sequences to probabilistic α-fuzzy fixed points under some types of probabilistic contractive conditions.

  8. Probabilistic model for untargeted peak detection in LC-MS using Bayesian statistics

    NARCIS (Netherlands)

    Woldegebriel, M.; Vivó-Truyols, G.

    2015-01-01

    We introduce a novel Bayesian probabilistic peak detection algorithm for liquid chromatography mass spectroscopy (LC-MS). The final probabilistic result allows the user to make a final decision about which points in a 2 chromatogram are affected by a chromatographic peak and which ones are only

  9. Probabilistic image processing by means of the Bethe approximation for the Q-Ising model

    International Nuclear Information System (INIS)

    Tanaka, Kazuyuki; Inoue, Jun-ichi; Titterington, D M

    2003-01-01

    The framework of Bayesian image restoration for multi-valued images by means of the Q-Ising model with nearest-neighbour interactions is presented. Hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. A practical algorithm is described for multi-valued image restoration based on the Bethe approximation. The algorithm corresponds to loopy belief propagation in artificial intelligence. We conclude that, in real world grey-level images, the Q-Ising model can give us good results

  10. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India); Gupta, Shikha; Rai, Premanjali [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India)

    2013-10-15

    Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive

  11. Probabilistic Insurance

    NARCIS (Netherlands)

    Wakker, P.P.; Thaler, R.H.; Tversky, A.

    1997-01-01

    Probabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in premium to compensate for a 1% default risk. These observations cannot be

  12. Probabilistic Insurance

    NARCIS (Netherlands)

    P.P. Wakker (Peter); R.H. Thaler (Richard); A. Tversky (Amos)

    1997-01-01

    textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these

  13. Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

    Science.gov (United States)

    Francis, Andrew; Moulton, Vincent

    2018-06-07

    Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

    KAUST Repository

    Chikalov, Igor

    2011-02-15

    Background: Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor.Methods: This paper describes inductive learning methods to train protein-independent probabilistic models of H-bond stability from molecular dynamics (MD) simulation trajectories of various proteins. The training data contains 32 input attributes (predictors) that describe an H-bond and its local environment in a conformation c and the output attribute is the probability that the H-bond will be present in an arbitrary conformation of this protein achievable from c within a time duration ?. We model dependence of the output variable on the predictors by a regression tree.Results: Several models are built using 6 MD simulation trajectories containing over 4000 distinct H-bonds (millions of occurrences). Experimental results demonstrate that such models can predict H-bond stability quite well. They perform roughly 20% better than models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a conformation. In most tests, about 80% of the 10% H-bonds predicted as the least stable are actually among the 10% truly least stable. The important attributes identified during the tree construction are consistent with previous findings.Conclusions: We use inductive learning methods to build protein-independent probabilistic models to study H-bond stability, and demonstrate that the models perform better than H-bond energy alone. 2011 Chikalov et al; licensee BioMed Central Ltd.

  15. Probabilistic Modeling of Seismic Risk Based Design for a Dual System Structure

    OpenAIRE

    Sidi, Indra Djati

    2017-01-01

    The dual system structure concept has gained popularity in the construction of high-rise buildings over the last decades. Meanwhile, earthquake engineering design provisions for buildings have moved from the uniform hazard concept to the uniform risk concept upon recognizing the uncertainties involved in the earthquake resistance of concrete structures. In this study, a probabilistic model for the evaluation of such risk is proposed for a dual system structure consisting of shear walls or cor...

  16. Shear-wave velocity-based probabilistic and deterministic assessment of seismic soil liquefaction potential

    Science.gov (United States)

    Kayen, R.; Moss, R.E.S.; Thompson, E.M.; Seed, R.B.; Cetin, K.O.; Der Kiureghian, A.; Tanaka, Y.; Tokimatsu, K.

    2013-01-01

    Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand and the soil capacity were estimated and included in the analysis, resulting in greatly reduced overall model uncertainty relative to previous studies. The presented data set and probabilistic analysis also help resolve the ancillary issues of adjustment for soil fines content and magnitude scaling factors.

  17. PBDE exposure from food in Ireland: optimising data exploitation in probabilistic exposure modelling.

    Science.gov (United States)

    Trudel, David; Tlustos, Christina; Von Goetz, Natalie; Scheringer, Martin; Hungerbühler, Konrad

    2011-01-01

    Polybrominated diphenyl ethers (PBDEs) are a class of brominated flame retardants added to plastics, polyurethane foam, electronics, textiles, and other products. These products release PBDEs into the indoor and outdoor environment, thus causing human exposure through food and dust. This study models PBDE dose distributions from ingestion of food for Irish adults on congener basis by using two probabilistic and one semi-deterministic method. One of the probabilistic methods was newly developed and is based on summary statistics of food consumption combined with a model generating realistic daily energy supply from food. Median (intermediate) doses of total PBDEs are in the range of 0.4-0.6 ng/kg(bw)/day for Irish adults. The 97.5th percentiles of total PBDE doses lie in a range of 1.7-2.2 ng/kg(bw)/day, which is comparable to doses derived for Belgian and Dutch adults. BDE-47 and BDE-99 were identified as the congeners contributing most to estimated intakes, accounting for more than half of the total doses. The most influential food groups contributing to this intake are lean fish and salmon which together account for about 22-25% of the total doses.

  18. Probabilistic safety assessment - regulatory perspective

    International Nuclear Information System (INIS)

    Solanki, R.B.; Paul, U.K.; Hajra, P.; Agarwal, S.K.

    2002-01-01

    Full text: Nuclear power plants (NPPs) have been designed, constructed and operated mainly based on deterministic safety analysis philosophy. In this approach, a substantial amount of safety margin is incorporated in the design and operational requirements. Additional margin is incorporated by applying the highest quality engineering codes, standards and practices, and the concept of defence-in-depth in design and operating procedures, by including conservative assumptions and acceptance criteria in plant response analysis of postulated initiating events (PIEs). However, as the probabilistic approach has been improved and refined over the years, it is possible for the designer, operator and regulator to get a more detailed and realistic picture of the safety importance of plant design features, operating procedures and operational practices by using probabilistic safety assessment (PSA) along with the deterministic methodology. At present, many countries including USA, UK and France are using PSA insights in their decision making along with deterministic basis. India has also made substantial progress in the development of methods for carrying out PSA. However, consensus on the use of PSA in regulatory decision-making has not been achieved yet. This paper emphasises on the requirements (e.g.,level of details, key modelling assumptions, data, modelling aspects, success criteria, sensitivity and uncertainty analysis) for improving the quality and consistency in performance and use of PSA that can facilitate meaningful use of the PSA insights in the regulatory decision-making in India. This paper also provides relevant information on international scenario and various application areas of PSA along with progress made in India. The PSA perspective presented in this paper may help in achieving consensus on the use of PSA for regulatory / utility decision-making in design and operation of NPPs

  19. Procedures for conducting probabilistic safety assessments of nuclear power plants (Level 1)

    International Nuclear Information System (INIS)

    1992-01-01

    This report provides guidance for conducting a Level 1 of probabilistic safety assessment (PSA), that is a PSA concerned with events leading to core damage. The scope of this report is confined to internal initiating events (excluding internal fires and floods). A particular aim is to promote a standardized framework, terminology and form of documentation for PSAs so as to facilitate external review of the results of such studies. The report is divided into the following major sections: management and organization; identification of sources of radioactive releases and accident initiators; accident sequence modelling; data assessment and parameter estimation; accident sequence quantification; documentation of the analysis: display and interpretation of result. 45 refs, 7 figs, 23 tabs

  20. DYNAMIC SOFTWARE TESTING MODELS WITH PROBABILISTIC PARAMETERS FOR FAULT DETECTION AND ERLANG DISTRIBUTION FOR FAULT RESOLUTION DURATION

    Directory of Open Access Journals (Sweden)

    A. D. Khomonenko

    2016-07-01

    Full Text Available Subject of Research.Software reliability and test planning models are studied taking into account the probabilistic nature of error detection and discovering. Modeling of software testing enables to plan the resources and final quality at early stages of project execution. Methods. Two dynamic models of processes (strategies are suggested for software testing, using error detection probability for each software module. The Erlang distribution is used for arbitrary distribution approximation of fault resolution duration. The exponential distribution is used for approximation of fault resolution discovering. For each strategy, modified labeled graphs are built, along with differential equation systems and their numerical solutions. The latter makes it possible to compute probabilistic characteristics of the test processes and states: probability states, distribution functions for fault detection and elimination, mathematical expectations of random variables, amount of detected or fixed errors. Evaluation of Results. Probabilistic characteristics for software development projects were calculated using suggested models. The strategies have been compared by their quality indexes. Required debugging time to achieve the specified quality goals was calculated. The calculation results are used for time and resources planning for new projects. Practical Relevance. The proposed models give the possibility to use the reliability estimates for each individual module. The Erlang approximation removes restrictions on the use of arbitrary time distribution for fault resolution duration. It improves the accuracy of software test process modeling and helps to take into account the viability (power of the tests. With the use of these models we can search for ways to improve software reliability by generating tests which detect errors with the highest probability.

  1. A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue

    International Nuclear Information System (INIS)

    Chookah, M.; Nuhi, M.; Modarres, M.

    2011-01-01

    A combined probabilistic physics-of-failure-based model for pitting and corrosion-fatigue degradation mechanisms is proposed to estimate the reliability of structures and to perform prognosis and health management. A mechanistic superposition model for corrosion-fatigue mechanism was used as a benchmark model to propose the simple model. The proposed model describes the degradation of the structures as a function of physical and critical environmental stresses, such as amplitude and frequency of mechanical loads (for example caused by the internal piping pressure) and the concentration of corrosive chemical agents. The parameters of the proposed model are represented by the probability density functions and estimated through a Bayesian approach based on the data taken from the experiments performed as part of this research. For demonstrating applications, the proposed model provides prognostic information about the reliability of aging of structures and is helpful in developing inspection and replacement strategies. - Highlights: ► We model an inventory system under static–dynamic uncertainty strategy. ► The demand is stochastic and non-stationary. ► The optimal ordering policy is proven to be a base stock policy. ► A solution algorithm for finding an optimal solution is provided. ► Two heuristics developed produce high quality solutions and scale-up efficiently.

  2. COMPONENT SUPPLY MODEL FOR REPAIR ACTIVITIES NETWORK UNDER CONDITIONS OF PROBABILISTIC INDEFINITENESS.

    Directory of Open Access Journals (Sweden)

    Victor Yurievich Stroganov

    2017-02-01

    Full Text Available This article contains the systematization of the major production functions of repair activities network and the list of planning and control functions, which are described in the form of business processes (BP. Simulation model for analysis of the delivery effectiveness of components under conditions of probabilistic uncertainty was proposed. It has been shown that a significant portion of the total number of business processes is represented by the management and planning of the parts and components movement. Questions of construction of experimental design techniques on the simulation model in the conditions of non-stationarity were considered.

  3. Application of probabilistic modelling for the uncertainty evaluation of alignment measurements of large accelerator magnets assemblies

    Science.gov (United States)

    Doytchinov, I.; Tonnellier, X.; Shore, P.; Nicquevert, B.; Modena, M.; Mainaud Durand, H.

    2018-05-01

    Micrometric assembly and alignment requirements for future particle accelerators, and especially large assemblies, create the need for accurate uncertainty budgeting of alignment measurements. Measurements and uncertainties have to be accurately stated and traceable, to international standards, for metre-long sized assemblies, in the range of tens of µm. Indeed, these hundreds of assemblies will be produced and measured by several suppliers around the world, and will have to be integrated into a single machine. As part of the PACMAN project at CERN, we proposed and studied a practical application of probabilistic modelling of task-specific alignment uncertainty by applying a simulation by constraints calibration method. Using this method, we calibrated our measurement model using available data from ISO standardised tests (10360 series) for the metrology equipment. We combined this model with reference measurements and analysis of the measured data to quantify the actual specific uncertainty of each alignment measurement procedure. Our methodology was successfully validated against a calibrated and traceable 3D artefact as part of an international inter-laboratory study. The validated models were used to study the expected alignment uncertainty and important sensitivity factors in measuring the shortest and longest of the compact linear collider study assemblies, 0.54 m and 2.1 m respectively. In both cases, the laboratory alignment uncertainty was within the targeted uncertainty budget of 12 µm (68% confidence level). It was found that the remaining uncertainty budget for any additional alignment error compensations, such as the thermal drift error due to variation in machine operation heat load conditions, must be within 8.9 µm and 9.8 µm (68% confidence level) respectively.

  4. bayesPop: Probabilistic Population Projections

    Directory of Open Access Journals (Sweden)

    Hana Ševčíková

    2016-12-01

    Full Text Available We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects.

  5. bayesPop: Probabilistic Population Projections

    Science.gov (United States)

    Ševčíková, Hana; Raftery, Adrian E.

    2016-01-01

    We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects. PMID:28077933

  6. Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

    Science.gov (United States)

    Cruse, T. A.

    1987-01-01

    The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

  7. Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

    Science.gov (United States)

    Cruse, T. A.; Burnside, O. H.; Wu, Y.-T.; Polch, E. Z.; Dias, J. B.

    1988-01-01

    The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

  8. Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization

    NARCIS (Netherlands)

    Voet, van der H.; Slob, W.

    2007-01-01

    A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a

  9. Methodology for Developing a Probabilistic Risk Assessment Model of Spacecraft Rendezvous and Dockings

    Science.gov (United States)

    Farnham, Steven J., II; Garza, Joel, Jr.; Castillo, Theresa M.; Lutomski, Michael

    2011-01-01

    In 2007 NASA was preparing to send two new visiting vehicles carrying logistics and propellant to the International Space Station (ISS). These new vehicles were the European Space Agency s (ESA) Automated Transfer Vehicle (ATV), the Jules Verne, and the Japanese Aerospace and Explorations Agency s (JAXA) H-II Transfer Vehicle (HTV). The ISS Program wanted to quantify the increased risk to the ISS from these visiting vehicles. At the time, only the Shuttle, the Soyuz, and the Progress vehicles rendezvoused and docked to the ISS. The increased risk to the ISS was from an increase in vehicle traffic, thereby, increasing the potential catastrophic collision during the rendezvous and the docking or berthing of the spacecraft to the ISS. A universal method of evaluating the risk of rendezvous and docking or berthing was created by the ISS s Risk Team to accommodate the increasing number of rendezvous and docking or berthing operations due to the increasing number of different spacecraft, as well as the future arrival of commercial spacecraft. Before the first docking attempt of ESA's ATV and JAXA's HTV to the ISS, a probabilistic risk model was developed to quantitatively calculate the risk of collision of each spacecraft with the ISS. The 5 rendezvous and docking risk models (Soyuz, Progress, Shuttle, ATV, and HTV) have been used to build and refine the modeling methodology for rendezvous and docking of spacecrafts. This risk modeling methodology will be NASA s basis for evaluating the addition of future ISS visiting spacecrafts hazards, including SpaceX s Dragon, Orbital Science s Cygnus, and NASA s own Orion spacecraft. This paper will describe the methodology used for developing a visiting vehicle risk model.

  10. Probabilistic fatigue life of balsa cored sandwich composites subjected to transverse shear

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Berggreen, Christian

    2015-01-01

    A probabilistic fatigue life model for end-grain balsa cored sandwich composites subjectedto transverse shear is proposed. The model is calibrated to measured three-pointbending constant-amplitude fatigue test data using the maximum likelihood method. Some possible applications of the probabilistic...

  11. Probabilistic Networks

    DEFF Research Database (Denmark)

    Jensen, Finn Verner; Lauritzen, Steffen Lilholt

    2001-01-01

    This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs.......This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs....

  12. Normally occurring environmental and behavioral influences on gene activity: from central dogma to probabilistic epigenesis.

    Science.gov (United States)

    Gottlieb, G

    1998-10-01

    The central dogma of molecular biology holds that "information" flows from the genes to the structure of the proteins that the genes bring about through the formula DNA-->RNA-->Protein. In this view, a set of master genes activates the DNA necessary to produce the appropriate proteins that the organism needs during development. In contrast to this view, probabilistic epigenesis holds that necessarily there are signals from the internal and external environment that activate DNA to produce the appropriate proteins. To support this view, a substantial body of evidence is reviewed showing that external environmental influences on gene activation are normally occurring events in a large variety of organisms, including humans. This demonstrates how genes and environments work together to produce functional organisms, thus extending the author's model of probabilistic epigenesis.

  13. Probabilistic modeling of the flows and environmental risks of nano-silica

    International Nuclear Information System (INIS)

    Wang, Yan; Kalinina, Anna; Sun, Tianyin; Nowack, Bernd

    2016-01-01

    Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12 μg/l in the EU (0.053–3.3 μg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43 mg/kg·y in the EU (0.19–12 mg/kg·y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data. - Highlights: • We quantify the exposure of nano-silica to technical systems and the environment. • The median concentration in surface waters is predicted to be 0.12 μg/L in the EU. • Probabilistic species sensitivity distributions were computed for surface waters. • The risk assessment suggests that nano-silica poses no risk to aquatic organisms.

  14. a Probabilistic Embedding Clustering Method for Urban Structure Detection

    Science.gov (United States)

    Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

    2017-09-01

    Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  15. A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

    Directory of Open Access Journals (Sweden)

    X. Lin

    2017-09-01

    Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  16. Review of models for use in probabilistic assessments of the disposal of high level radioactive wastes on or beneath the seabed

    International Nuclear Information System (INIS)

    Hooper, A.G.; Gibson, A.E.

    1985-08-01

    The report describes a review of computer models applicable to probabilistic assessments of the ocean disposal of vitrified high level waste. Computer models were identified and each evaluated against a common set of criteria. The review covers each stage of the chain of radionuclide transport: canister deterioration and leaching, migration through the bottom sediments, dispersion in the ocean, suspended sediment effects, biological aspects and uptake by man. The models identified are generally at a relatively early stage of development and documentation was often only of a general nature. These overall systems models and sub-models considered to be most suitable for probabilistic assessments were identified and areas of further development identified. (author)

  17. A Stochastic Lagrangian Basis for a Probabilistic Parameterization of Moisture Condensation in Eulerian Models

    OpenAIRE

    Tsang, Yue-Kin; Vallis, Geoffrey K.

    2018-01-01

    In this paper we describe the construction of an efficient probabilistic parameterization that could be used in a coarse-resolution numerical model in which the variation of moisture is not properly resolved. An Eulerian model using a coarse-grained field on a grid cannot properly resolve regions of saturation---in which condensation occurs---that are smaller than the grid boxes. Thus, in the absence of a parameterization scheme, either the grid box must become saturated or condensation will ...

  18. Convolution product construction of interactions in probabilistic physical models

    International Nuclear Information System (INIS)

    Ratsimbarison, H.M.; Raboanary, R.

    2007-01-01

    This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.

  19. A probabilistic model-based soft sensor to monitor lactic acid bacteria fermentations

    DEFF Research Database (Denmark)

    Spann, Robert; Roca, Christophe; Kold, David

    2018-01-01

    A probabilistic soft sensor based on a mechanistic model was designed to monitor S. thermophilus fermentations, and validated with experimental lab-scale data. It considered uncertainties in the initial conditions, on-line measurements, and model parameters by performing Monte Carlo simulations...... the model parameters that were then used as input to the mechanistic model. The soft sensor predicted both the current state variables, as well as the future course of the fermentation, e.g. with a relative mean error of the biomass concentration of 8 %. This successful implementation of a process...... within the monitoring system. It predicted, therefore, the probability distributions of the unmeasured states, such as biomass, lactose, and lactic acid concentrations. To this end, a mechanistic model was developed first, and a statistical parameter estimation was performed in order to assess parameter...

  20. Probabilistic assessment of dry transport with burnup credit

    International Nuclear Information System (INIS)

    Lake, W.H.

    2003-01-01

    The general concept of probabilistic analysis and its application to the use of burnup credit in spent fuel transport is explored. Discussion of the probabilistic analysis method is presented. The concepts of risk and its perception are introduced, and models are suggested for performing probability and risk estimates. The general probabilistic models are used for evaluating the application of burnup credit for dry spent nuclear fuel transport. Two basic cases are considered. The first addresses the question of the relative likelihood of exceeding an established criticality safety limit with and without burnup credit. The second examines the effect of using burnup credit on the overall risk for dry spent fuel transport. Using reasoned arguments and related failure probability and consequence data analysis is performed to estimate the risks of using burnup credit for dry transport of spent nuclear fuel. (author)

  1. On Probabilistic Automata in Continuous Time

    DEFF Research Database (Denmark)

    Eisentraut, Christian; Hermanns, Holger; Zhang, Lijun

    2010-01-01

    We develop a compositional behavioural model that integrates a variation of probabilistic automata into a conservative extension of interactive Markov chains. The model is rich enough to embody the semantics of generalised stochastic Petri nets. We define strong and weak bisimulations and discuss...

  2. Probabilistic metric spaces

    CERN Document Server

    Schweizer, B

    2005-01-01

    Topics include special classes of probabilistic metric spaces, topologies, and several related structures, such as probabilistic normed and inner-product spaces. 1983 edition, updated with 3 new appendixes. Includes 17 illustrations.

  3. Probabilistic cellular automata.

    Science.gov (United States)

    Agapie, Alexandru; Andreica, Anca; Giuclea, Marius

    2014-09-01

    Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case-connecting the probability of a configuration in the stationary distribution to its number of zero-one borders-the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata.

  4. Attributing Asymmetric Productivity Responses to Internal Ecosystem Dynamics and External Drivers Using Probabilistic Models

    Science.gov (United States)

    Parolari, A.; Goulden, M.

    2017-12-01

    A major challenge to interpreting asymmetric changes in ecosystem productivity is the attribution of these changes to external climate forcing or to internal ecophysiological processes that respond to these drivers (e.g., photosynthesis response to drying soil). For example, positive asymmetry in productivity can result from either positive skewness in the distribution of annual rainfall amount or from negative curvature in the productivity response to annual rainfall. To analyze the relative influences of climate and ecosystem dynamics on both positive and negative asymmetry in multi-year ANPP experiments, we use a multi-scale coupled ecosystem water-carbon model to interpret field experimental results that span gradients of rainfall skewness and ANPP response curvature. The model integrates rainfall variability, soil moisture dynamics, and net carbon assimilation from the daily to inter-annual scales. From the underlying physical basis of the model, we compute the joint probability distribution of the minimum and maximum ANPP for an annual ANPP experiment of N years. The distribution is used to estimate the likelihood that either positive or negative asymmetry will be observed in an experiment, given the annual rainfall distribution and the ANPP response curve. We estimate the total asymmetry as the mode of this joint distribution and the relative contribution attributable to rainfall skewness as the mode for a linear ANPP response curve. Applied to data from several long-term ANPP experiments, we find that there is a wide range of observed ANPP asymmetry (positive and negative) and a spectrum of contributions from internal and external factors. We identify the soil water holding capacity relative to the mean rain event depth as a critical ecosystem characteristic that controls the non-linearity of the ANPP response and positive curvature at high rainfall. Further, the seasonal distribution of rainfall is shown to control the presence or absence of negative

  5. Ambient Surveillance by Probabilistic-Possibilistic Perception

    NARCIS (Netherlands)

    Bittermann, M.S.; Ciftcioglu, O.

    2013-01-01

    A method for quantifying ambient surveillance is presented, which is based on probabilistic-possibilistic perception. The human surveillance of a scene through observing camera sensed images on a monitor is modeled in three steps. First immersion of the observer is simulated by modeling perception

  6. Probabilistic risk assessment model for allergens in food: sensitivity analysis of the minimum eliciting dose and food consumption

    NARCIS (Netherlands)

    Kruizinga, A.G.; Briggs, D.; Crevel, R.W.R.; Knulst, A.C.; Bosch, L.M.C.v.d.; Houben, G.F.

    2008-01-01

    Previously, TNO developed a probabilistic model to predict the likelihood of an allergic reaction, resulting in a quantitative assessment of the risk associated with unintended exposure to food allergens. The likelihood is estimated by including in the model the proportion of the population who is

  7. ToPS: a framework to manipulate probabilistic models of sequence data.

    Directory of Open Access Journals (Sweden)

    André Yoshiaki Kashiwabara

    Full Text Available Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i independent and identically distributed process; (ii variable-length Markov chain; (iii inhomogeneous Markov chain; (iv hidden Markov model; (v profile hidden Markov model; (vi pair hidden Markov model; (vii generalized hidden Markov model; and (viii similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC. The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

  8. Probabilistic hypergraph based hash codes for social image search

    Institute of Scientific and Technical Information of China (English)

    Yi XIE; Hui-min YU; Roland HU

    2014-01-01

    With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.

  9. Probabilistic graphs as a conceptual and computational tool in hydrology and water management

    Science.gov (United States)

    Schoups, Gerrit

    2014-05-01

    Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

  10. Probabilistic method/techniques of evaluation/modeling that permits to optimize/reduce the necessary resources

    International Nuclear Information System (INIS)

    Florescu, G.; Apostol, M.; Farcasiu, M.; Luminita Bedreaga, M.; Nitoi, M.; Turcu, I.

    2004-01-01

    Fault tree/event tree modeling approach is widely used in modeling and behavior simulation of nuclear structures, systems and components (NSSCs), during different condition of operation. Evaluation of NSSCs reliability, availability, risk or safety, during operation, by using probabilistic techniques, is also largely used. Development of computer capabilities offered new possibilities for large NSSCs models designing, processing and using. There are situations where large, complex and correct NSSC models are desired to be associated with rapid results/solutions/decisions or with multiple processing in order to obtain specific results. Large fault/event trees are hardly to be developed, reviewed and processed. During operation of NSSCs, the time, especially, is an important factor in taking decision. The paper presents a probabilistic method that permits evaluation/modeling of NSSCs and intents to solve the above problems by adopting appropriate techniques. The method is stated for special applications and is based on specific PSA analysis steps, information, algorithms, criteria and relations, in correspondence with the fault tree/event tree modeling and similar techniques, in order to obtain appropriate results for NSSC model analysis. Special classification of NSSCs is stated in order to reflect aspects of use of the method. Also the common reliability databases are part of information necessary to complete the analysis process. Special data and information bases contribute to state the proposed method/techniques. The paper also presents the specific steps of the method, its applicability, the main advantages and problems to be furthermore studied. The method permits optimization/reducing of resources used to perform the PSA activities. (author)

  11. Probabilistic Criterion for the Economical Assessment of Nuclear Reactors

    International Nuclear Information System (INIS)

    Juanico, L; Florido, Pablo; Bergallo, Juan

    2000-01-01

    In this paper a MonteCarlo probabilistic model for the economic evaluation of nuclear power plants is presented.The probabilistic results have shown a wide spread on the economic performance due to the schedule complexity and coupling if tasks.This spread increasing to the discount rate, end hence, it becomes more important for developing countries

  12. Psychological Plausibility of the Theory of Probabilistic Mental Models and the Fast and Frugal Heuristics

    Science.gov (United States)

    Dougherty, Michael R.; Franco-Watkins, Ana M.; Thomas, Rick

    2008-01-01

    The theory of probabilistic mental models (PMM; G. Gigerenzer, U. Hoffrage, & H. Kleinbolting, 1991) has had a major influence on the field of judgment and decision making, with the most recent important modifications to PMM theory being the identification of several fast and frugal heuristics (G. Gigerenzer & D. G. Goldstein, 1996). These…

  13. Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing

    International Nuclear Information System (INIS)

    Tanaka, Kazuyuki; Shouno, Hayaru; Okada, Masato; Titterington, D M

    2004-01-01

    We investigate the accuracy of statistical-mechanical approximations for the estimation of hyperparameters from observable data in probabilistic image processing, which is based on Bayesian statistics and maximum likelihood estimation. Hyperparameters in statistical science correspond to interactions or external fields in the statistical-mechanics context. In this paper, hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood. A practical algorithm is described for grey-level image restoration based on a Gaussian graphical model and the Bethe approximation. The algorithm corresponds to loopy belief propagation in artificial intelligence. We examine the accuracy of hyperparameter estimation when we use the Bethe approximation. It is well known that a practical algorithm for probabilistic image processing can be prescribed analytically when a Gaussian graphical model is adopted as a prior probabilistic model in Bayes' formula. We are therefore able to compare, in a numerical study, results obtained through mean-field-type approximations with those based on exact calculation

  14. Probabilistic common cause failure modeling for auxiliary feedwater system after the introduction of flood barriers

    International Nuclear Information System (INIS)

    Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi

    2013-01-01

    Causal inference is capable of assessing common cause failure (CCF) events from the viewpoint of causes' risk significance. Authors proposed the alpha decomposition method for probabilistic CCF analysis, in which the classical alpha factor model and causal inference are integrated to conduct a quantitative assessment of causes' CCF risk significance. The alpha decomposition method includes a hybrid Bayesian network for revealing the relationship between component failures and potential causes, and a regression model in which CCF parameters (global alpha factors) are expressed by explanatory variables (causes' occurrence frequencies) and parameters (decomposed alpha factors). This article applies this method and associated databases needed to predict CCF parameters of auxiliary feedwater (AFW) system when defense barriers against internal flood are introduced. There is scarce operation data for functionally modified safety systems and the utilization of generic CCF databases is of unknown uncertainty. The alpha decomposition method has the potential of analyzing the CCF risk of modified AFW system reasonably based on generic CCF databases. Moreover, the sources of uncertainty in parameter estimation can be studied. An example is presented to demonstrate the process of applying Bayesian inference in the alpha decomposition process. The results show that the system-specific posterior distributions for CCF parameters can be predicted. (author)

  15. A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

    Directory of Open Access Journals (Sweden)

    S. Zhang

    2018-03-01

    Full Text Available Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

  16. Cooperation in an evolutionary prisoner’s dilemma game with probabilistic strategies

    International Nuclear Information System (INIS)

    Li Haihong; Dai Qionglin; Cheng Hongyan; Yang Junzhong

    2012-01-01

    Highlights: ► Introducing probabilistic strategies instead of the pure C/D in the PDG. ► The strategies patterns depends on interaction structures and updating rules. ► There exists an optimal increment of the probabilistic strategy. - Abstract: In this work, we investigate an evolutionary prisoner’s dilemma game in structured populations with probabilistic strategies instead of the pure strategies of cooperation and defection. We explore the model in details by considering different strategy update rules and different population structures. We find that the distribution of probabilistic strategies patterns is dependent on both the interaction structures and the updating rules. We also find that, when an individual updates her strategy by increasing or decreasing her probabilistic strategy a certain amount towards that of her opponent, there exists an optimal increment of the probabilistic strategy at which the cooperator frequency reaches its maximum.

  17. Bayesian statistic methods and theri application in probabilistic simulation models

    Directory of Open Access Journals (Sweden)

    Sergio Iannazzo

    2007-03-01

    Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

  18. On the Probabilistic Characterization of Robustness and Resilience

    DEFF Research Database (Denmark)

    Faber, Michael Havbro; Qin, J.; Miraglia, Simona

    2017-01-01

    Over the last decade significant research efforts have been devoted to the probabilistic modeling and analysis of system characteristics. Especially performance characteristics of systems subjected to random disturbances, such as robustness and resilience have been in the focus of these efforts...... in the modeling of robustness and resilience in the research areas of natural disaster risk management, socio-ecological systems and social systems and we propose a generic decision analysis framework for the modeling and analysis of systems across application areas. The proposed framework extends the concept...... of direct and indirect consequences and associated risks in probabilistic systems modeling formulated by the Joint Committee on Structural Safety (JCSS) to facilitate the modeling and analysis of resilience in addition to robustness and vulnerability. Moreover, based on recent insights in the modeling...

  19. Probabilistic dual heuristic programming-based adaptive critic

    Science.gov (United States)

    Herzallah, Randa

    2010-02-01

    Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.

  20. Disjunctive Probabilistic Modal Logic is Enough for Bisimilarity on Reactive Probabilistic Systems

    OpenAIRE

    Bernardo, Marco; Miculan, Marino

    2016-01-01

    Larsen and Skou characterized probabilistic bisimilarity over reactive probabilistic systems with a logic including true, negation, conjunction, and a diamond modality decorated with a probabilistic lower bound. Later on, Desharnais, Edalat, and Panangaden showed that negation is not necessary to characterize the same equivalence. In this paper, we prove that the logical characterization holds also when conjunction is replaced by disjunction, with negation still being not necessary. To this e...

  1. Effects of shipping on marine acoustic habitats in Canadian Arctic estimated via probabilistic modeling and mapping.

    Science.gov (United States)

    Aulanier, Florian; Simard, Yvan; Roy, Nathalie; Gervaise, Cédric; Bandet, Marion

    2017-12-15

    Canadian Arctic and Subarctic regions experience a rapid decrease of sea ice accompanied with increasing shipping traffic. The resulting time-space changes in shipping noise are studied for four key regions of this pristine environment, for 2013 traffic conditions and a hypothetical tenfold traffic increase. A probabilistic modeling and mapping framework, called Ramdam, which integrates the intrinsic variability and uncertainties of shipping noise and its effects on marine habitats, is developed and applied. A substantial transformation of soundscapes is observed in areas where shipping noise changes from present occasional-transient contributor to a dominant noise source. Examination of impacts on low-frequency mammals within ecologically and biologically significant areas reveals that shipping noise has the potential to trigger behavioral responses and masking in the future, although no risk of temporary or permanent hearing threshold shifts is noted. Such probabilistic modeling and mapping is strategic in marine spatial planning of this emerging noise issues. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

  2. Probabilistic modeling of fatigue crack growth in Ti-6Al-4V

    International Nuclear Information System (INIS)

    Soboyejo, W.O.; Shen, W.; Soboyejo, A.B.O.

    2001-01-01

    This paper presents the results of a combined experimental and analytical study of the probabilistic nature of fatigue crack growth in Ti-6Al-4V. A simple experimental fracture mechanics framework is presented for the determination of statistical fatigue crack growth parameters from two fatigue tests. The experimental studies show that the variabilities in long fatigue crack growth rate data and the Paris coefficient are well described by the log-normal distributions. The variabilities in the Paris exponent are also shown to be well characterized by a normal distribution. The measured statistical distributions are incorporated into a probabilistic fracture mechanics framework for the estimation of material reliability. The implications of the results are discussed for the probabilistic analysis of fatigue crack growth in engineering components and structures. (orig.)

  3. Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows

    Directory of Open Access Journals (Sweden)

    A. Talebi

    2008-07-01

    Full Text Available Recently, D'Odorico and Fagherazzi (2003 proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003. Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building blocks of the model are: an infinite-slope stability analysis; a steady-state kinematic wave model (KW of hollow groundwater hydrology; and a statistical model relating intensity, duration, and frequency of extreme precipitation. Here we extend the work of D'Odorico and Fagherazzi (2003 by incorporating a more realistic description of hollow hydrology (hillslope storage Boussinesq model, HSB such that this model can also be applied to more gentle slopes and hollows with different plan shapes. We show that results obtained using the KW and HSB models are significantly different as in the KW model the diffusion term is ignored. We generalize our results by examining the stability of several hollow types with different plan shapes (different convergence degree. For each hollow type, the minimum value of the landslide-triggering saturated depth corresponding to the triggering precipitation (critical recharge rate is computed for steep and gentle hollows. Long term analysis of shallow landslides by the presented model illustrates that all hollows show a quite different behavior from the stability view point. In hollows with more convergence, landslide occurrence is limited by the supply of deposits (supply limited regime or rainfall events (event limited regime while hollows with low convergence degree are unconditionally stable regardless of the soil thickness or rainfall intensity. Overall, our results show that in addition to the effect of slope angle, plan shape (convergence degree also controls the subsurface flow and this process affects the probability distribution of landslide occurrence in different hollows. Finally, we conclude that

  4. Sensitivity analysis in multi-parameter probabilistic systems

    International Nuclear Information System (INIS)

    Walker, J.R.

    1987-01-01

    Probabilistic methods involving the use of multi-parameter Monte Carlo analysis can be applied to a wide range of engineering systems. The output from the Monte Carlo analysis is a probabilistic estimate of the system consequence, which can vary spatially and temporally. Sensitivity analysis aims to examine how the output consequence is influenced by the input parameter values. Sensitivity analysis provides the necessary information so that the engineering properties of the system can be optimized. This report details a package of sensitivity analysis techniques that together form an integrated methodology for the sensitivity analysis of probabilistic systems. The techniques have known confidence limits and can be applied to a wide range of engineering problems. The sensitivity analysis methodology is illustrated by performing the sensitivity analysis of the MCROC rock microcracking model

  5. Comparison of Four Probabilistic Models (CARES, Calendex, ConsEspo, SHEDS) to Estimate Aggregate Residential Exposures to Pesticides

    Science.gov (United States)

    Two deterministic models (US EPA’s Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children’s Non-Occupational Exposure to Pesticides by all Relevant Pathways (Draft Protocol)) and four probabilistic mo...

  6. Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

    DEFF Research Database (Denmark)

    Wan, Can; Lin, Jin; Song, Yonghua

    2017-01-01

    This letter proposes a novel efficient probabilistic forecasting approach to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for P...... power generation is proposed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.......This letter proposes a novel efficient probabilistic forecasting approach to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV...

  7. Probabilistic Material Strength Degradation Model for Inconel 718 Components Subjected to High Temperature, Mechanical Fatigue, Creep and Thermal Fatigue Effects

    Science.gov (United States)

    Bast, Callie Corinne Scheidt

    1994-01-01

    This thesis presents the on-going development of methodology for a probabilistic material strength degradation model. The probabilistic model, in the form of a postulated randomized multifactor equation, provides for quantification of uncertainty in the lifetime material strength of aerospace propulsion system components subjected to a number of diverse random effects. This model is embodied in the computer program entitled PROMISS, which can include up to eighteen different effects. Presently, the model includes four effects that typically reduce lifetime strength: high temperature, mechanical fatigue, creep, and thermal fatigue. Statistical analysis was conducted on experimental Inconel 718 data obtained from the open literature. This analysis provided regression parameters for use as the model's empirical material constants, thus calibrating the model specifically for Inconel 718. Model calibration was carried out for four variables, namely, high temperature, mechanical fatigue, creep, and thermal fatigue. Methodology to estimate standard deviations of these material constants for input into the probabilistic material strength model was developed. Using the current version of PROMISS, entitled PROMISS93, a sensitivity study for the combined effects of mechanical fatigue, creep, and thermal fatigue was performed. Results, in the form of cumulative distribution functions, illustrated the sensitivity of lifetime strength to any current value of an effect. In addition, verification studies comparing a combination of mechanical fatigue and high temperature effects by model to the combination by experiment were conducted. Thus, for Inconel 718, the basic model assumption of independence between effects was evaluated. Results from this limited verification study strongly supported this assumption.

  8. Uniform and localized corrosion modelling by means of probabilistic cellular automata

    International Nuclear Information System (INIS)

    Perez-Brokate, Cristian

    2016-01-01

    Numerical modelling is complementary tool for corrosion prediction. The objective of this work is to develop a corrosion model by means of a probabilistic cellular automata approach at a mesoscopic scale. In this work, we study the morphological evolution and kinetics of corrosion. This model couples electrochemical oxidation and reduction reactions. Regarding kinetics, cellular automata models are able to describe current as a function of the applied potential for a redox reaction on an inert electrode. The inclusion of probabilities allows the description of the stochastic nature of anodic and cathodic reactions. Corrosion morphology has been studied in different context: generalised corrosion, pitting corrosion and corrosion in an occluded environment. a general tendency of two regimes is found. a first regime of uniform corrosion where the anodic and cathodic reactions occur homogeneously over the surface. a second regime of localized corrosion when there is a spatial separation of anodic and cathodic zones, with an increase of anodic reaction rate. (author) [fr

  9. Probabilistic risk assessment, Volume I

    International Nuclear Information System (INIS)

    Anon.

    1982-01-01

    This book contains 158 papers presented at the International Topical Meeting on Probabilistic Risk Assessment held by the American Nuclear Society (ANS) and the European Nuclear Society (ENS) in Port Chester, New York in 1981. The meeting was second in a series of three. The main focus of the meeting was on the safety of light water reactors. The papers discuss safety goals and risk assessment. Quantitative safety goals, risk assessment in non-nuclear technologies, and operational experience and data base are also covered. Included is an address by Dr. Chauncey Starr

  10. Probabilistic safety analysis using microcomputer

    International Nuclear Information System (INIS)

    Futuro Filho, F.L.F.; Mendes, J.E.S.; Santos, M.J.P. dos

    1990-01-01

    The main steps of execution of a Probabilistic Safety Assessment (PSA) are presented in this report, as the study of the system description, construction of event trees and fault trees, and the calculation of overall unavailability of the systems. It is also presented the use of microcomputer in performing some tasks, highlightning the main characteristics of a software to perform adequately the job. A sample case of fault tree construction and calculation is presented, using the PSAPACK software, distributed by the IAEA (International Atomic Energy Agency) for training purpose. (author)

  11. Probabilistic risk assessment modeling of digital instrumentation and control systems using two dynamic methodologies

    Energy Technology Data Exchange (ETDEWEB)

    Aldemir, T., E-mail: aldemir.1@osu.ed [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Guarro, S. [ASCA, Inc., 1720 S. Catalina Avenue, Suite 220, Redondo Beach, CA 90277-5501 (United States); Mandelli, D. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Kirschenbaum, J. [Ohio State University, Department of Computer Science and Engineering, Columbus, OH 43210 (United States); Mangan, L.A. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Bucci, P. [Ohio State University, Department of Computer Science and Engineering, Columbus, OH 43210 (United States); Yau, M. [ASCA, Inc., 1720 S. Catalina Avenue, Suite 220, Redondo Beach, CA 90277-5501 (United States); Ekici, E. [Ohio State University, Department of Electrical and Computer Engineering, Columbus, OH 43210 (United States); Miller, D.W.; Sun, X. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Arndt, S.A. [U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001 (United States)

    2010-10-15

    The Markov/cell-to-cell mapping technique (CCMT) and the dynamic flowgraph methodology (DFM) are two system logic modeling methodologies that have been proposed to address the dynamic characteristics of digital instrumentation and control (I and C) systems and provide risk-analytical capabilities that supplement those provided by traditional probabilistic risk assessment (PRA) techniques for nuclear power plants. Both methodologies utilize a discrete state, multi-valued logic representation of the digital I and C system. For probabilistic quantification purposes, both techniques require the estimation of the probabilities of basic system failure modes, including digital I and C software failure modes, that appear in the prime implicants identified as contributors to a given system event of interest. As in any other system modeling process, the accuracy and predictive value of the models produced by the two techniques, depend not only on the intrinsic features of the modeling paradigm, but also and to a considerable extent on information and knowledge available to the analyst, concerning the system behavior and operation rules under normal and off-nominal conditions, and the associated controlled/monitored process dynamics. The application of the two methodologies is illustrated using a digital feedwater control system (DFWCS) similar to that of an operating pressurized water reactor. This application was carried out to demonstrate how the use of either technique, or both, can facilitate the updating of an existing nuclear power plant PRA model following an upgrade of the instrumentation and control system from analog to digital. Because of scope limitations, the focus of the demonstration of the methodologies was intentionally limited to aspects of digital I and C system behavior for which probabilistic data was on hand or could be generated within the existing project bounds of time and resources. The data used in the probabilistic quantification portion of the

  12. Probabilistic design framework for sustainable repari and rehabilitation of civil infrastructure

    DEFF Research Database (Denmark)

    Lepech, Michael; Geiker, Mette Rica; Stang, Henrik

    2011-01-01

    This paper presents a probabilistic-based framework for the design of civil infrastructure repair and rehabilitation to achieve targeted improvements in sustainability indicators. The framework consists of two types of models: (i) service life prediction models combining one or several deteriorat......This paper presents a probabilistic-based framework for the design of civil infrastructure repair and rehabilitation to achieve targeted improvements in sustainability indicators. The framework consists of two types of models: (i) service life prediction models combining one or several...

  13. Development of Probabilistic Flood Inundation Mapping For Flooding Induced by Dam Failure

    Science.gov (United States)

    Tsai, C.; Yeh, J. J. J.

    2017-12-01

    A primary function of flood inundation mapping is to forecast flood hazards and assess potential losses. However, uncertainties limit the reliability of inundation hazard assessments. Major sources of uncertainty should be taken into consideration by an optimal flood management strategy. This study focuses on the 20km reach downstream of the Shihmen Reservoir in Taiwan. A dam failure induced flood herein provides the upstream boundary conditions of flood routing. The two major sources of uncertainty that are considered in the hydraulic model and the flood inundation mapping herein are uncertainties in the dam break model and uncertainty of the roughness coefficient. The perturbance moment method is applied to a dam break model and the hydro system model to develop probabilistic flood inundation mapping. Various numbers of uncertain variables can be considered in these models and the variability of outputs can be quantified. The probabilistic flood inundation mapping for dam break induced floods can be developed with consideration of the variability of output using a commonly used HEC-RAS model. Different probabilistic flood inundation mappings are discussed and compared. Probabilistic flood inundation mappings are hoped to provide new physical insights in support of the evaluation of concerning reservoir flooded areas.

  14. Probabilistic Power Flow Simulation allowing Temporary Current Overloading

    NARCIS (Netherlands)

    W.S. Wadman (Wander); G. Bloemhof; D.T. Crommelin (Daan); J.E. Frank (Jason)

    2012-01-01

    htmlabstractThis paper presents a probabilistic power flow model subject to connection temperature constraints. Renewable power generation is included and modelled stochastically in order to reflect its intermittent nature. In contrast to conventional models that enforce connection current

  15. Exploiting Tensor Rank-One Decomposition in Probabilistic Inference

    Czech Academy of Sciences Publication Activity Database

    Savický, Petr; Vomlel, Jiří

    2007-01-01

    Roč. 43, č. 5 (2007), s. 747-764 ISSN 0023-5954 R&D Projects: GA MŠk 1M0545; GA MŠk 1M0572; GA ČR GA201/04/0393 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z10750506 Keywords : graphical probabilistic models * probabilistic inference * tensor rank Subject RIV: BD - Theory of Information Impact factor: 0.552, year: 2007 http://dml.cz/handle/10338.dmlcz/135810

  16. Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Zugno, Marco; Madsen, Henrik

    2016-01-01

    The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known...... that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic-type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state......-dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non-parametric modelling to explore conditional correlation...

  17. Duplicate Detection in Probabilistic Data

    NARCIS (Netherlands)

    Panse, Fabian; van Keulen, Maurice; de Keijzer, Ander; Ritter, Norbert

    2009-01-01

    Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused

  18. A Probabilistic Design Methodology for a Turboshaft Engine Overall Performance Analysis

    Directory of Open Access Journals (Sweden)

    Min Chen

    2014-05-01

    Full Text Available In reality, the cumulative effect of the many uncertainties in engine component performance may stack up to affect the engine overall performance. This paper aims to quantify the impact of uncertainty in engine component performance on the overall performance of a turboshaft engine based on Monte-Carlo probabilistic design method. A novel probabilistic model of turboshaft engine, consisting of a Monte-Carlo simulation generator, a traditional nonlinear turboshaft engine model, and a probability statistical model, was implemented to predict this impact. One of the fundamental results shown herein is that uncertainty in component performance has a significant impact on the engine overall performance prediction. This paper also shows that, taking into consideration the uncertainties in component performance, the turbine entry temperature and overall pressure ratio based on the probabilistic design method should increase by 0.76% and 8.33%, respectively, compared with the ones of deterministic design method. The comparison shows that the probabilistic approach provides a more credible and reliable way to assign the design space for a target engine overall performance.

  19. Geothermal probabilistic cost study

    Energy Technology Data Exchange (ETDEWEB)

    Orren, L.H.; Ziman, G.M.; Jones, S.C.; Lee, T.K.; Noll, R.; Wilde, L.; Sadanand, V.

    1981-08-01

    A tool is presented to quantify the risks of geothermal projects, the Geothermal Probabilistic Cost Model (GPCM). The GPCM model is used to evaluate a geothermal reservoir for a binary-cycle electric plant at Heber, California. Three institutional aspects of the geothermal risk which can shift the risk among different agents are analyzed. The leasing of geothermal land, contracting between the producer and the user of the geothermal heat, and insurance against faulty performance are examined. (MHR)

  20. Probabilistic Matching of Deidentified Data From a Trauma Registry and a Traumatic Brain Injury Model System Center: A Follow-up Validation Study.

    Science.gov (United States)

    Kumar, Raj G; Wang, Zhensheng; Kesinger, Matthew R; Newman, Mark; Huynh, Toan T; Niemeier, Janet P; Sperry, Jason L; Wagner, Amy K

    2018-04-01

    In a previous study, individuals from a single Traumatic Brain Injury Model Systems and trauma center were matched using a novel probabilistic matching algorithm. The Traumatic Brain Injury Model Systems is a multicenter prospective cohort study containing more than 14,000 participants with traumatic brain injury, following them from inpatient rehabilitation to the community over the remainder of their lifetime. The National Trauma Databank is the largest aggregation of trauma data in the United States, including more than 6 million records. Linking these two databases offers a broad range of opportunities to explore research questions not otherwise possible. Our objective was to refine and validate the previous protocol at another independent center. An algorithm generation and validation data set were created, and potential matches were blocked by age, sex, and year of injury; total probabilistic weight was calculated based on of 12 common data fields. Validity metrics were calculated using a minimum probabilistic weight of 3. The positive predictive value was 98.2% and 97.4% and sensitivity was 74.1% and 76.3%, in the algorithm generation and validation set, respectively. These metrics were similar to the previous study. Future work will apply the refined probabilistic matching algorithm to the Traumatic Brain Injury Model Systems and the National Trauma Databank to generate a merged data set for clinical traumatic brain injury research use.

  1. Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

    NARCIS (Netherlands)

    Rosas-Carbajal, M.; Linde, N.; Kalscheuer, T.; Vrugt, J.A.

    2014-01-01

    Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space

  2. Use of limited data to construct Bayesian networks for probabilistic risk assessment.

    Energy Technology Data Exchange (ETDEWEB)

    Groth, Katrina M.; Swiler, Laura Painton

    2013-03-01

    Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.

  3. International Seminar on Stability Problems for Stochastic Models

    CERN Document Server

    Zolatarev, Vladimir

    1993-01-01

    The subject of this book is a new direction in the field of probability theory and mathematical statistics which can be called "stability theory": it deals with evaluating the effects of perturbing initial probabilistic models and embraces quite varied subtopics: limit theorems, queueing models, statistical inference, probability metrics, etc. The contributions are original research articles developing new ideas and methods of stability analysis.

  4. Compression of Probabilistic XML Documents

    Science.gov (United States)

    Veldman, Irma; de Keijzer, Ander; van Keulen, Maurice

    Database techniques to store, query and manipulate data that contains uncertainty receives increasing research interest. Such UDBMSs can be classified according to their underlying data model: relational, XML, or RDF. We focus on uncertain XML DBMS with as representative example the Probabilistic XML model (PXML) of [10,9]. The size of a PXML document is obviously a factor in performance. There are PXML-specific techniques to reduce the size, such as a push down mechanism, that produces equivalent but more compact PXML documents. It can only be applied, however, where possibilities are dependent. For normal XML documents there also exist several techniques for compressing a document. Since Probabilistic XML is (a special form of) normal XML, it might benefit from these methods even more. In this paper, we show that existing compression mechanisms can be combined with PXML-specific compression techniques. We also show that best compression rates are obtained with a combination of PXML-specific technique with a rather simple generic DAG-compression technique.

  5. Confluence reduction for probabilistic systems

    NARCIS (Netherlands)

    Timmer, Mark; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette

    In this presentation we introduce a novel technique for state space reduction of probabilistic specifications, based on a newly developed notion of confluence for probabilistic automata. We proved that this reduction preserves branching probabilistic bisimulation and can be applied on-the-fly. To

  6. Resolution and Probabilistic Models of Components in CryoEM Maps of Mature P22 Bacteriophage

    Science.gov (United States)

    Pintilie, Grigore; Chen, Dong-Hua; Haase-Pettingell, Cameron A.; King, Jonathan A.; Chiu, Wah

    2016-01-01

    CryoEM continues to produce density maps of larger and more complex assemblies with multiple protein components of mixed symmetries. Resolution is not always uniform throughout a cryoEM map, and it can be useful to estimate the resolution in specific molecular components of a large assembly. In this study, we present procedures to 1) estimate the resolution in subcomponents by gold-standard Fourier shell correlation (FSC); 2) validate modeling procedures, particularly at medium resolutions, which can include loop modeling and flexible fitting; and 3) build probabilistic models that combine high-accuracy priors (such as crystallographic structures) with medium-resolution cryoEM densities. As an example, we apply these methods to new cryoEM maps of the mature bacteriophage P22, reconstructed without imposing icosahedral symmetry. Resolution estimates based on gold-standard FSC show the highest resolution in the coat region (7.6 Å), whereas other components are at slightly lower resolutions: portal (9.2 Å), hub (8.5 Å), tailspike (10.9 Å), and needle (10.5 Å). These differences are indicative of inherent structural heterogeneity and/or reconstruction accuracy in different subcomponents of the map. Probabilistic models for these subcomponents provide new insights, to our knowledge, and structural information when taking into account uncertainty given the limitations of the observed density. PMID:26743049

  7. Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling

    Science.gov (United States)

    Papaioannou, George; Vasiliades, Lampros; Loukas, Athanasios; Aronica, Giuseppe T.

    2017-04-01

    Probabilistic flood inundation mapping is performed and analysed at the ungauged Xerias stream reach, Volos, Greece. The study evaluates the uncertainty introduced by the roughness coefficient values on hydraulic models in flood inundation modelling and mapping. The well-established one-dimensional (1-D) hydraulic model, HEC-RAS is selected and linked to Monte-Carlo simulations of hydraulic roughness. Terrestrial Laser Scanner data have been used to produce a high quality DEM for input data uncertainty minimisation and to improve determination accuracy on stream channel topography required by the hydraulic model. Initial Manning's n roughness coefficient values are based on pebble count field surveys and empirical formulas. Various theoretical probability distributions are fitted and evaluated on their accuracy to represent the estimated roughness values. Finally, Latin Hypercube Sampling has been used for generation of different sets of Manning roughness values and flood inundation probability maps have been created with the use of Monte Carlo simulations. Historical flood extent data, from an extreme historical flash flood event, are used for validation of the method. The calibration process is based on a binary wet-dry reasoning with the use of Median Absolute Percentage Error evaluation metric. The results show that the proposed procedure supports probabilistic flood hazard mapping at ungauged rivers and provides water resources managers with valuable information for planning and implementing flood risk mitigation strategies.

  8. Probabilistic simulation applications to reliability assessments

    International Nuclear Information System (INIS)

    Miller, Ian; Nutt, Mark W.; Hill, Ralph S. III

    2003-01-01

    Probabilistic risk/reliability (PRA) analyses for engineered systems are conventionally based on fault-tree methods. These methods are mature and efficient, and are well suited to systems consisting of interacting components with known, low probabilities of failure. Even complex systems, such as nuclear power plants or aircraft, are modeled by the careful application of these approaches. However, for systems that may evolve in complex and nonlinear ways, and where the performance of components may be a sensitive function of the history of their working environments, fault-tree methods can be very demanding. This paper proposes an alternative method of evaluating such systems, based on probabilistic simulation using intelligent software objects to represent the components of such systems. Using a Monte Carlo approach, simulation models can be constructed from relatively simple interacting objects that capture the essential behavior of the components that they represent. Such models are capable of reflecting the complex behaviors of the systems that they represent in a natural and realistic way. (author)

  9. Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models

    Energy Technology Data Exchange (ETDEWEB)

    Galan, S.F. [Dpto. de Inteligencia Artificial, E.T.S.I. Informatica (UNED), Juan del Rosal, 16, 28040 Madrid (Spain)]. E-mail: seve@dia.uned.es; Mosleh, A. [2100A Marie Mount Hall, Materials and Nuclear Engineering Department, University of Maryland, College Park, MD 20742 (United States)]. E-mail: mosleh@umd.edu; Izquierdo, J.M. [Area de Modelado y Simulacion, Consejo de Seguridad Nuclear, Justo Dorado, 11, 28040 Madrid (Spain)]. E-mail: jmir@csn.es

    2007-08-15

    The {omega}-factor approach is a method that explicitly incorporates organizational factors into Probabilistic safety assessment of nuclear power plants. Bayesian networks (BNs) are the underlying formalism used in this approach. They have a structural part formed by a graph whose nodes represent organizational variables, and a parametric part that consists of conditional probabilities, each of them quantifying organizational influences between one variable and its parents in the graph. The aim of this paper is twofold. First, we discuss some important limitations of current procedures in the {omega}-factor approach for either assessing conditional probabilities from experts or estimating them from data. We illustrate the discussion with an example that uses data from Licensee Events Reports of nuclear power plants for the estimation task. Second, we introduce significant improvements in the way BNs for the {omega}-factor approach can be constructed, so that parameter acquisition becomes easier and more intuitive. The improvements are based on the use of noisy-OR gates as model of multicausal interaction between each BN node and its parents.

  10. Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models

    International Nuclear Information System (INIS)

    Galan, S.F.; Mosleh, A.; Izquierdo, J.M.

    2007-01-01

    The ω-factor approach is a method that explicitly incorporates organizational factors into Probabilistic safety assessment of nuclear power plants. Bayesian networks (BNs) are the underlying formalism used in this approach. They have a structural part formed by a graph whose nodes represent organizational variables, and a parametric part that consists of conditional probabilities, each of them quantifying organizational influences between one variable and its parents in the graph. The aim of this paper is twofold. First, we discuss some important limitations of current procedures in the ω-factor approach for either assessing conditional probabilities from experts or estimating them from data. We illustrate the discussion with an example that uses data from Licensee Events Reports of nuclear power plants for the estimation task. Second, we introduce significant improvements in the way BNs for the ω-factor approach can be constructed, so that parameter acquisition becomes easier and more intuitive. The improvements are based on the use of noisy-OR gates as model of multicausal interaction between each BN node and its parents

  11. Results of the Level 1 probabilistic risk assessment (PRA) of internal events for heavy water production reactors

    International Nuclear Information System (INIS)

    Tinnes, S.P.; Cramer, D.S.; Logan, V.E.; Topp, S.V.; Smith, J.A.; Brandyberry, M.D.

    1990-01-01

    A full-scope probabilistic risk assessment (PRA) is being performed for the Savannah River site (SRS) production reactors. The Level 1 PRA for the K Reactor has been completed and includes the assessment of reactor systems response to accidents and estimates of the severe core melt frequency (SCMF). The internal events spectrum includes those events related directly to plant systems and safety functions for which transients or failures may initiate an accident. The SRS PRA has three principal objectives: improved understanding of SRS reactor safety issues through discovery and understanding of the mechanisms involved. Improved risk management capability through tools for assessing the safety impact of both current standard operations and proposed revisions. A quantitative measure of the risks posed by SRS reactor operation to employees and the general public, to allow comparison with declared goals and other societal risks

  12. Upgrade of internal events PSA model using the AESJ level-1 PSA standard for operating state

    International Nuclear Information System (INIS)

    Sato, Teruyoshi; Yoneyama, Mitsuru; Hirokawa, Naoki; Sato, Chikahiro; Sato, Eisuke; Tomizawa, Shigeatsu

    2009-01-01

    In 2003, the Atomic Energy Society of Japan (AESJ) started to develop the Level-1 Probabilistic Safety Assessment (PSA) standard of internal events for operating state (AESJ standard). The AESJ standard has been finished to be asked for public comment. Using the AESJ standard (draft version), the authors have upgraded the PSA model for Tokyo Electric Power Company (TEPCO) BWR-5 plant not only to reflect latest knowledge but also to ensure high quality of PSA model (not yet peer-reviewed) for the purpose of better operation and maintenance management of TEPCO BWR plants. For example, the categorization of structures, systems and components (SSCs) will be performed to improve nuclear reactor safety using information of risk importance. (author)

  13. Computer codes for level 1 probabilistic safety assessment

    International Nuclear Information System (INIS)

    1990-06-01

    Probabilistic Safety Assessment (PSA) entails several laborious tasks suitable for computer codes assistance. This guide identifies these tasks, presents guidelines for selecting and utilizing computer codes in the conduct of the PSA tasks and for the use of PSA results in safety management and provides information on available codes suggested or applied in performing PSA in nuclear power plants. The guidance is intended for use by nuclear power plant system engineers, safety and operating personnel, and regulators. Large efforts are made today to provide PC-based software systems and PSA processed information in a way to enable their use as a safety management tool by the nuclear power plant overall management. Guidelines on the characteristics of software needed for management to prepare a software that meets their specific needs are also provided. Most of these computer codes are also applicable for PSA of other industrial facilities. The scope of this document is limited to computer codes used for the treatment of internal events. It does not address other codes available mainly for the analysis of external events (e.g. seismic analysis) flood and fire analysis. Codes discussed in the document are those used for probabilistic rather than for phenomenological modelling. It should be also appreciated that these guidelines are not intended to lead the user to selection of one specific code. They provide simply criteria for the selection. Refs and tabs

  14. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, Warren C.; Scherbov, Sergei; O'Neill, Brian C.; Lutz, Wolfgang

    2004-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because...

  15. An application of probabilistic safety assessment methods to model aircraft systems and accidents

    Energy Technology Data Exchange (ETDEWEB)

    Martinez-Guridi, G.; Hall, R.E.; Fullwood, R.R.

    1998-08-01

    A case study modeling the thrust reverser system (TRS) in the context of the fatal accident of a Boeing 767 is presented to illustrate the application of Probabilistic Safety Assessment methods. A simplified risk model consisting of an event tree with supporting fault trees was developed to represent the progression of the accident, taking into account the interaction between the TRS and the operating crew during the accident, and the findings of the accident investigation. A feasible sequence of events leading to the fatal accident was identified. Several insights about the TRS and the accident were obtained by applying PSA methods. Changes proposed for the TRS also are discussed.

  16. A Probabilistic Approach for Robustness Evaluation of Timber Structures

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard

    of Structures and a probabilistic modelling of the timber material proposed in the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS). Due to the framework in the Danish Code the timber structure has to be evaluated with respect to the following criteria where at least one shall...... to criteria a) and b) the timber frame structure has one column with a reliability index a bit lower than an assumed target level. By removal three columns one by one no significant extensive failure of the entire structure or significant parts of it are obatined. Therefore the structure can be considered......A probabilistic based robustness analysis has been performed for a glulam frame structure supporting the roof over the main court in a Norwegian sports centre. The robustness analysis is based on the framework for robustness analysis introduced in the Danish Code of Practice for the Safety...

  17. From equilibrium spin models to probabilistic cellular automata

    International Nuclear Information System (INIS)

    Georges, A.; Le Doussal, P.

    1989-01-01

    The general equivalence between D-dimensional probabilistic cellular automata (PCA) and (D + 1)-dimensional equilibrium spin models satisfying a disorder condition is first described in a pedagogical way and then used to analyze the phase diagrams, the critical behavior, and the universality classes of some automato. Diagrammatic representations of time-dependent correlation functions PCA are introduced. Two important classes of PCA are singled out for which these correlation functions simplify: (1) Quasi-Hamiltonian automata, which have a current-carrying steady state, and for which some correlation functions are those of a D-dimensional static model PCA satisfying the detailed balance condition appear as a particular case of these rules for which the current vanishes. (2) Linear (and more generally affine) PCA for which the diagrammatics reduces to a random walk problem closely related to (D + 1)-dimensional directed SAWs: both problems display a critical behavior with mean-field exponents in any dimension. The correlation length and effective velocity of propagation of excitations can be calculated for affine PCA, as is shown on an explicit D = 1 example. The authors conclude with some remarks on nonlinear PCA, for which the diagrammatics is related to reaction-diffusion processes, and which belong in some cases to the universality class of Reggeon field theory

  18. Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

    Science.gov (United States)

    Herzallah, Randa

    2015-03-01

    Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Probabilistic estimates of drought impacts on agricultural production

    Science.gov (United States)

    Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

    2017-08-01

    Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

  20. Probabilistic model for fluences and peak fluxes of solar energetic particles

    International Nuclear Information System (INIS)

    Nymmik, R.A.

    1999-01-01

    The model is intended for calculating the probability for solar energetic particles (SEP), i.e., protons and Z=2-28 ions, to have an effect on hardware and on biological and other objects in the space. The model describes the probability for the ≥10 MeV/nucleon SEP fluences and peak fluxes to occur in the near-Earth space beyond the Earth magnetosphere under varying solar activity. The physical prerequisites of the model are as follows. The occurrence of SEP is a probabilistic process. The mean SEP occurrence frequency is a power-law function of solar activity (sunspot number). The SEP size (taken to be the ≥30 MeV proton fluence size) distribution is a power-law function within a 10 5 -10 11 proton/cm 2 range. The SEP event particle energy spectra are described by a common function whose parameters are distributed log-normally. The SEP mean composition is energy-dependent and suffers fluctuations described by log-normal functions in separate events

  1. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, W.C.; Scherbov, S.; O'Neill, B.C.; Lutz, W.

    2003-01-01

    Since policy makers often prefer to think in terms of scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy makers it allows them to answer "what if"...

  2. Conditional probabilistic population forecasting

    OpenAIRE

    Sanderson, Warren; Scherbov, Sergei; O'Neill, Brian; Lutz, Wolfgang

    2003-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them...

  3. Report of the IPERS (International Peer Review Service) pre-review workshop for the Zaporozhye, Unit 5 nuclear power plant probabilistic safety assessment in Vienna, Austria 12 to 16 June 1995

    International Nuclear Information System (INIS)

    1995-01-01

    This report presents the results of the IAEA international peer review services pre-review workshop held in Vienna, 12 to 16 June 1995, which reviewed the status of the present version of the Probabilistic Safety Assessment (PSA) for the Zaporozhye, Unit 5, nuclear power plant. 3 refs, 1 fig., 1 tab

  4. Students’ difficulties in probabilistic problem-solving

    Science.gov (United States)

    Arum, D. P.; Kusmayadi, T. A.; Pramudya, I.

    2018-03-01

    There are many errors can be identified when students solving mathematics problems, particularly in solving the probabilistic problem. This present study aims to investigate students’ difficulties in solving the probabilistic problem. It focuses on analyzing and describing students errors during solving the problem. This research used the qualitative method with case study strategy. The subjects in this research involve ten students of 9th grade that were selected by purposive sampling. Data in this research involve students’ probabilistic problem-solving result and recorded interview regarding students’ difficulties in solving the problem. Those data were analyzed descriptively using Miles and Huberman steps. The results show that students have difficulties in solving the probabilistic problem and can be divided into three categories. First difficulties relate to students’ difficulties in understanding the probabilistic problem. Second, students’ difficulties in choosing and using appropriate strategies for solving the problem. Third, students’ difficulties with the computational process in solving the problem. Based on the result seems that students still have difficulties in solving the probabilistic problem. It means that students have not able to use their knowledge and ability for responding probabilistic problem yet. Therefore, it is important for mathematics teachers to plan probabilistic learning which could optimize students probabilistic thinking ability.

  5. Probabilistic liver atlas construction.

    Science.gov (United States)

    Dura, Esther; Domingo, Juan; Ayala, Guillermo; Marti-Bonmati, Luis; Goceri, E

    2017-01-13

    Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step. We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

  6. Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling

    DEFF Research Database (Denmark)

    Opper, Manfred; Winther, Ole

    2001-01-01

    We develop a generalization of the Thouless-Anderson-Palmer (TAP) mean-field approach of disorder physics. which makes the method applicable to the computation of approximate averages in probabilistic models for real data. In contrast to the conventional TAP approach, where the knowledge...... of the distribution of couplings between the random variables is required, our method adapts to the concrete set of couplings. We show the significance of the approach in two ways: Our approach reproduces replica symmetric results for a wide class of toy models (assuming a nonglassy phase) with given disorder...... distributions in the thermodynamic limit. On the other hand, simulations on a real data model demonstrate that the method achieves more accurate predictions as compared to conventional TAP approaches....

  7. A Probabilistic Palimpsest Model of Visual Short-term Memory

    Science.gov (United States)

    Matthey, Loic; Bays, Paul M.; Dayan, Peter

    2015-01-01

    Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ. PMID:25611204

  8. Probabilistic consequence model of accidenal or intentional chemical releases.

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Y.-S.; Samsa, M. E.; Folga, S. M.; Hartmann, H. M.

    2008-06-02

    In this work, general methodologies for evaluating the impacts of large-scale toxic chemical releases are proposed. The potential numbers of injuries and fatalities, the numbers of hospital beds, and the geographical areas rendered unusable during and some time after the occurrence and passage of a toxic plume are estimated on a probabilistic basis. To arrive at these estimates, historical accidental release data, maximum stored volumes, and meteorological data were used as inputs into the SLAB accidental chemical release model. Toxic gas footprints from the model were overlaid onto detailed population and hospital distribution data for a given region to estimate potential impacts. Output results are in the form of a generic statistical distribution of injuries and fatalities associated with specific toxic chemicals and regions of the United States. In addition, indoor hazards were estimated, so the model can provide contingency plans for either shelter-in-place or evacuation when an accident occurs. The stochastic distributions of injuries and fatalities are being used in a U.S. Department of Homeland Security-sponsored decision support system as source terms for a Monte Carlo simulation that evaluates potential measures for mitigating terrorist threats. This information can also be used to support the formulation of evacuation plans and to estimate damage and cleanup costs.

  9. Characterizing the topology of probabilistic biological networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

    Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the joint-degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for node-degree distribution computation than the deterministic ones. all the data sets used, the software

  10. A General Framework for Probabilistic Characterizing Formulae

    DEFF Research Database (Denmark)

    Sack, Joshua; Zhang, Lijun

    2012-01-01

    Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide...... a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward...

  11. A probabilistic Hu-Washizu variational principle

    Science.gov (United States)

    Liu, W. K.; Belytschko, T.; Besterfield, G. H.

    1987-01-01

    A Probabilistic Hu-Washizu Variational Principle (PHWVP) for the Probabilistic Finite Element Method (PFEM) is presented. This formulation is developed for both linear and nonlinear elasticity. The PHWVP allows incorporation of the probabilistic distributions for the constitutive law, compatibility condition, equilibrium, domain and boundary conditions into the PFEM. Thus, a complete probabilistic analysis can be performed where all aspects of the problem are treated as random variables and/or fields. The Hu-Washizu variational formulation is available in many conventional finite element codes thereby enabling the straightforward inclusion of the probabilistic features into present codes.

  12. Memristive Probabilistic Computing

    KAUST Repository

    Alahmadi, Hamzah

    2017-10-01

    In the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.

  13. Predicting coastal cliff erosion using a Bayesian probabilistic model

    Science.gov (United States)

    Hapke, Cheryl J.; Plant, Nathaniel G.

    2010-01-01

    Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.

  14. A Methodology for Probabilistic Accident Management

    International Nuclear Information System (INIS)

    Munteanu, Ion; Aldemir, Tunc

    2003-01-01

    While techniques have been developed to tackle different tasks in accident management, there have been very few attempts to develop an on-line operator assistance tool for accident management and none that can be found in the literature that uses probabilistic arguments, which are important in today's licensing climate. The state/parameter estimation capability of the dynamic system doctor (DSD) approach is combined with the dynamic event-tree generation capability of the integrated safety assessment (ISA) methodology to address this issue. The DSD uses the cell-to-cell mapping technique for system representation that models the system evolution in terms of probability of transitions in time between sets of user-defined parameter/state variable magnitude intervals (cells) within a user-specified time interval (e.g., data sampling interval). The cell-to-cell transition probabilities are obtained from the given system model. The ISA follows the system dynamics in tree form and braches every time a setpoint for system/operator intervention is exceeded. The combined approach (a) can automatically account for uncertainties in the monitored system state, inputs, and modeling uncertainties through the appropriate choice of the cells, as well as providing a probabilistic measure to rank the likelihood of possible system states in view of these uncertainties; (b) allows flexibility in system representation; (c) yields the lower and upper bounds on the estimated values of state variables/parameters as well as their expected values; and (d) leads to fewer branchings in the dynamic event-tree generation. Using a simple but realistic pressurizer model, the potential use of the DSD-ISA methodology for on-line probabilistic accident management is illustrated

  15. PRECIS -- A probabilistic risk assessment system

    International Nuclear Information System (INIS)

    Peterson, D.M.; Knowlton, R.G. Jr.

    1996-01-01

    A series of computer tools has been developed to conduct the exposure assessment and risk characterization phases of human health risk assessments within a probabilistic framework. The tools are collectively referred to as the Probabilistic Risk Evaluation and Characterization Investigation System (PRECIS). With this system, a risk assessor can calculate the doses and risks associated with multiple environmental and exposure pathways, for both chemicals and radioactive contaminants. Exposure assessment models in the system account for transport of contaminants to receptor points from a source zone originating in unsaturated soils above the water table. In addition to performing calculations of dose and risk based on initial concentrations, PRECIS can also be used in an inverse manner to compute soil concentrations in the source area that must not be exceeded if prescribed limits on dose or risk are to be met. Such soil contaminant levels, referred to as soil guidelines, are computed for both single contaminants and chemical mixtures and can be used as action levels or cleanup levels. Probabilistic estimates of risk, dose and soil guidelines are derived using Monte Carlo techniques

  16. Ignorability in Statistical and Probabilistic Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2005-01-01

    When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...

  17. International Conference on Risk Analysis

    CERN Document Server

    Oliveira, Teresa; Rigas, Alexandros; Gulati, Sneh

    2015-01-01

    This book covers the latest results in the field of risk analysis. Presented topics include probabilistic models in cancer research, models and methods in longevity, epidemiology of cancer risk, engineering reliability and economical risk problems. The contributions of this volume originate from the 5th International Conference on Risk Analysis (ICRA 5). The conference brought together researchers and practitioners working in the field of risk analysis in order to present new theoretical and computational methods with applications in biology, environmental sciences, public health, economics and finance.

  18. Analytical probabilistic modeling of RBE-weighted dose for ion therapy

    Science.gov (United States)

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

    2017-12-01

    Particle therapy is especially prone to uncertainties. This issue is usually addressed with uncertainty quantification and minimization techniques based on scenario sampling. For proton therapy, however, it was recently shown that it is also possible to use closed-form computations based on analytical probabilistic modeling (APM) for this purpose. APM yields unique features compared to sampling-based approaches, motivating further research in this context. This paper demonstrates the application of APM for intensity-modulated carbon ion therapy to quantify the influence of setup and range uncertainties on the RBE-weighted dose. In particular, we derive analytical forms for the nonlinear computations of the expectation value and variance of the RBE-weighted dose by propagating linearly correlated Gaussian input uncertainties through a pencil beam dose calculation algorithm. Both exact and approximation formulas are presented for the expectation value and variance of the RBE-weighted dose and are subsequently studied in-depth for a one-dimensional carbon ion spread-out Bragg peak. With V and B being the number of voxels and pencil beams, respectively, the proposed approximations induce only a marginal loss of accuracy while lowering the computational complexity from order O(V × B^2) to O(V × B) for the expectation value and from O(V × B^4) to O(V × B^2) for the variance of the RBE-weighted dose. Moreover, we evaluated the approximated calculation of the expectation value and standard deviation of the RBE-weighted dose in combination with a probabilistic effect-based optimization on three patient cases considering carbon ions as radiation modality against sampled references. The resulting global γ-pass rates (2 mm,2%) are > 99.15% for the expectation value and > 94.95% for the standard deviation of the RBE-weighted dose, respectively. We applied the derived analytical model to carbon ion treatment planning, although the concept is in general applicable to other

  19. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

    Science.gov (United States)

    Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

    2017-10-01

    Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

  20. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

    Directory of Open Access Journals (Sweden)

    Marcello Benedetti

    2017-11-01

    Full Text Available Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

  1. An individual-based probabilistic model for simulating fisheries population dynamics

    Directory of Open Access Journals (Sweden)

    Jie Cao

    2016-12-01

    Full Text Available The purpose of stock assessment is to support managers to provide intelligent decisions regarding removal from fish populations. Errors in assessment models may have devastating impacts on the population fitness and negative impacts on the economy of the resource users. Thus, accuracte estimations of population size, growth rates are critical for success. Evaluating and testing the behavior and performance of stock assessment models and assessing the consequences of model mis-specification and the impact of management strategies requires an operating model that accurately describe the dynamics of the target species, and can resolve spatial and seasonal changes. In addition, the most thorough evaluations of assessment models use an operating model that takes a different form than the assessment model. This paper presents an individual-based probabilistic model used to simulate the complex dynamics of populations and their associated fisheries. Various components of population dynamics are expressed as random Bernoulli trials in the model and detailed life and fishery histories of each individual are tracked over their life span. The simulation model is designed to be flexible so it can be used for different species and fisheries. It can simulate mixing among multiple stocks and link stock-recruit relationships to environmental factors. Furthermore, the model allows for flexibility in sub-models (e.g., growth and recruitment and model assumptions (e.g., age- or size-dependent selectivity. This model enables the user to conduct various simulation studies, including testing the performance of assessment models under different assumptions, assessing the impacts of model mis-specification and evaluating management strategies.

  2. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.

    Science.gov (United States)

    Kreibich, Heidi; Botto, Anna; Merz, Bruno; Schröter, Kai

    2017-04-01

    Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study. © 2016 Society for Risk Analysis.

  3. Probabilistic Durability Analysis in Advanced Engineering Design

    Directory of Open Access Journals (Sweden)

    A. Kudzys

    2000-01-01

    Full Text Available Expedience of probabilistic durability concepts and approaches in advanced engineering design of building materials, structural members and systems is considered. Target margin values of structural safety and serviceability indices are analyzed and their draft values are presented. Analytical methods of the cumulative coefficient of correlation and the limit transient action effect for calculation of reliability indices are given. Analysis can be used for probabilistic durability assessment of carrying and enclosure metal, reinforced concrete, wood, plastic, masonry both homogeneous and sandwich or composite structures and some kinds of equipments. Analysis models can be applied in other engineering fields.

  4. Probabilistic generation assessment system of renewable energy in Korea

    Directory of Open Access Journals (Sweden)

    Yeonchan Lee

    2016-01-01

    Full Text Available This paper proposes probabilistic generation assessment system introduction of renewable energy generators. This paper is focused on wind turbine generator and solar cell generator. The proposed method uses an assessment model based on probabilistic model considering uncertainty of resources (wind speed and solar radiation. Equivalent generation function of the wind and solar farms are evaluated. The equivalent generation curves of wind farms and solar farms are assessed using regression analysis method using typical least square method from last actual generation data for wind farms. The proposed model is applied to Korea Renewable Generation System of 8 grouped 41 wind farms and 9 grouped around 600 solar farms in South Korea.

  5. A probabilistic approach to crack instability

    Science.gov (United States)

    Chudnovsky, A.; Kunin, B.

    1989-01-01

    A probabilistic model of brittle fracture is examined with reference to two-dimensional problems. The model is illustrated by using experimental data obtained for 25 macroscopically identical specimens made of short-fiber-reinforced composites. It is shown that the model proposed here provides a predictive formalism for the probability distributions of critical crack depth, critical loads, and crack arrest depths. It also provides similarity criteria for small-scale testing.

  6. Probabilistic Logic and Probabilistic Networks

    NARCIS (Netherlands)

    Haenni, R.; Romeijn, J.-W.; Wheeler, G.; Williamson, J.

    2009-01-01

    While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches

  7. From Cyclone Tracks to the Costs of European Winter Storms: A Probabilistic Loss Assessment Model

    Science.gov (United States)

    Orwig, K.; Renggli, D.; Corti, T.; Reese, S.; Wueest, M.; Viktor, E.; Zimmerli, P.

    2014-12-01

    European winter storms cause billions of dollars of insured losses every year. Therefore, it is essential to understand potential impacts of future events, and the role reinsurance can play to mitigate the losses. The authors will present an overview on natural catastrophe risk assessment modeling in the reinsurance industry, and the development of a new innovative approach for modeling the risk associated with European winter storms.The new innovative approach includes the development of physically meaningful probabilistic (i.e. simulated) events for European winter storm loss assessment. The meteorological hazard component of the new model is based on cyclone and windstorm tracks identified in the 20thCentury Reanalysis data. The knowledge of the evolution of winter storms both in time and space allows the physically meaningful perturbation of historical event properties (e.g. track, intensity, etc.). The perturbation includes a random element but also takes the local climatology and the evolution of the historical event into account.The low-resolution wind footprints taken from the 20thCentury Reanalysis are processed by a statistical-dynamical downscaling to generate high-resolution footprints for both the simulated and historical events. Downscaling transfer functions are generated using ENSEMBLES regional climate model data. The result is a set of reliable probabilistic events representing thousands of years. The event set is then combined with country and site-specific vulnerability functions and detailed market- or client-specific information to compute annual expected losses.

  8. A probabilistic quantitative risk assessment model for the long-term work zone crashes.

    Science.gov (United States)

    Meng, Qiang; Weng, Jinxian; Qu, Xiaobo

    2010-11-01

    Work zones especially long-term work zones increase traffic conflicts and cause safety problems. Proper casualty risk assessment for a work zone is of importance for both traffic safety engineers and travelers. This paper develops a novel probabilistic quantitative risk assessment (QRA) model to evaluate the casualty risk combining frequency and consequence of all accident scenarios triggered by long-term work zone crashes. The casualty risk is measured by the individual risk and societal risk. The individual risk can be interpreted as the frequency of a driver/passenger being killed or injured, and the societal risk describes the relation between frequency and the number of casualties. The proposed probabilistic QRA model consists of the estimation of work zone crash frequency, an event tree and consequence estimation models. There are seven intermediate events--age (A), crash unit (CU), vehicle type (VT), alcohol (AL), light condition (LC), crash type (CT) and severity (S)--in the event tree. Since the estimated value of probability for some intermediate event may have large uncertainty, the uncertainty can thus be characterized by a random variable. The consequence estimation model takes into account the combination effects of speed and emergency medical service response time (ERT) on the consequence of work zone crash. Finally, a numerical example based on the Southeast Michigan work zone crash data is carried out. The numerical results show that there will be a 62% decrease of individual fatality risk and 44% reduction of individual injury risk if the mean travel speed is slowed down by 20%. In addition, there will be a 5% reduction of individual fatality risk and 0.05% reduction of individual injury risk if ERT is reduced by 20%. In other words, slowing down speed is more effective than reducing ERT in the casualty risk mitigation. 2010 Elsevier Ltd. All rights reserved.

  9. Site-specific Probabilistic Analysis of DCGLs Using RESRAD Code

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jeongju; Yoon, Suk Bon; Sohn, Wook [KHNP CRI, Daejeon (Korea, Republic of)

    2016-10-15

    In general, DCGLs can be conservative (screening DCGL) if they do not take into account site specific factors. Use of such conservative DCGLs can lead to additional remediation that would not be required if the effort was made to develop site-specific DCGLs. Therefore, the objective of this work is to provide an example on the use of the RESRAD 6.0 probabilistic (site-specific) dose analysis to compare with the screening DCGL. Site release regulations state that a site will be considered acceptable for unrestricted use if the residual radioactivity that is distinguishable from background radiation results in a Total Effective Dose Equivalent (TEDE) to an average member of the critical group of less than the site release criteria, for example 0.25 mSv per year in U.S. Utilities use computer dose modeling codes to establish an acceptable level of contamination, the derived concentration guideline level (DCGL) that will meet this regulatory limit. Since the DCGL value is the principal measure of residual radioactivity, it is critical to understand the technical basis of these dose modeling codes. The objective this work was to provide example on nuclear power plant decommissioning dose analysis in a probabilistic analysis framework. The focus was on the demonstration of regulatory compliance for surface soil contamination using the RESRAD 6.0 code. Both the screening and site-specific probabilistic dose analysis methodologies were examined. Example analyses performed with the screening probabilistic dose analysis confirmed the conservatism of the NRC screening values and indicated the effectiveness of probabilistic dose analysis in reducing the conservatism in DCGL derivation.

  10. Probabilistic modeling of the flows and environmental risks of nano-silica.

    Science.gov (United States)

    Wang, Yan; Kalinina, Anna; Sun, Tianyin; Nowack, Bernd

    2016-03-01

    Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12 μg/l in the EU (0.053-3.3 μg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43 mg/kg · y in the EU (0.19-12 mg/kg · y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

    Science.gov (United States)

    Forman, Jason L.; Kent, Richard W.; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

    2012-01-01

    This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5–7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992–2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction. PMID:23169122

  12. Probabilistic Design and Management of Sustainable Concrete Infrastructure Using Multi-Physics Service Life Models

    DEFF Research Database (Denmark)

    Lepech, Michael; Geiker, Mette; Michel, Alexander

    This paper looks to address the grand challenge of integrating construction materials engineering research within a multi-scale, inter-disciplinary research and management framework for sustainable concrete infrastructure. The ultimate goal is to drive sustainability-focused innovation and adoption...... cycles in the broader architecture, engineering, construction (AEC) industry. Specifically, a probabilistic design framework for sustainable concrete infrastructure and a multi-physics service life model for reinforced concrete are presented as important points of integration for innovation between...... design, consists of concrete service life models and life cycle assessment (LCA) models. Both types of models (service life and LCA) are formulated stochastically so that the service life and time(s) to repair, as well as total sustainability impact, are described by a probability distribution. A central...

  13. Probabilistic risk assessment methodology

    International Nuclear Information System (INIS)

    Shinaishin, M.A.

    1988-06-01

    The objective of this work is to provide the tools necessary for clear identification of: the purpose of a Probabilistic Risk Study, the bounds and depth of the study, the proper modeling techniques to be used, the failure modes contributing to the analysis, the classical and baysian approaches for manipulating data necessary for quantification, ways for treating uncertainties, and available computer codes that may be used in performing such probabilistic analysis. In addition, it provides the means for measuring the importance of a safety feature to maintaining a level of risk at a Nuclear Power Plant and the worth of optimizing a safety system in risk reduction. In applying these techniques so that they accommodate our national resources and needs it was felt that emphasis should be put on the system reliability analysis level of PRA. Objectives of such studies could include: comparing systems' designs of the various vendors in the bedding stage, and performing grid reliability and human performance analysis using national specific data. (author)

  14. Probabilistic risk assessment methodology

    Energy Technology Data Exchange (ETDEWEB)

    Shinaishin, M A

    1988-06-15

    The objective of this work is to provide the tools necessary for clear identification of: the purpose of a Probabilistic Risk Study, the bounds and depth of the study, the proper modeling techniques to be used, the failure modes contributing to the analysis, the classical and baysian approaches for manipulating data necessary for quantification, ways for treating uncertainties, and available computer codes that may be used in performing such probabilistic analysis. In addition, it provides the means for measuring the importance of a safety feature to maintaining a level of risk at a Nuclear Power Plant and the worth of optimizing a safety system in risk reduction. In applying these techniques so that they accommodate our national resources and needs it was felt that emphasis should be put on the system reliability analysis level of PRA. Objectives of such studies could include: comparing systems' designs of the various vendors in the bedding stage, and performing grid reliability and human performance analysis using national specific data. (author)

  15. Probabilistic models for reactive behaviour in heterogeneous condensed phase media

    Science.gov (United States)

    Baer, M. R.; Gartling, D. K.; DesJardin, P. E.

    2012-02-01

    This work presents statistically-based models to describe reactive behaviour in heterogeneous energetic materials. Mesoscale effects are incorporated in continuum-level reactive flow descriptions using probability density functions (pdfs) that are associated with thermodynamic and mechanical states. A generalised approach is presented that includes multimaterial behaviour by treating the volume fraction as a random kinematic variable. Model simplifications are then sought to reduce the complexity of the description without compromising the statistical approach. Reactive behaviour is first considered for non-deformable media having a random temperature field as an initial state. A pdf transport relationship is derived and an approximate moment approach is incorporated in finite element analysis to model an example application whereby a heated fragment impacts a reactive heterogeneous material which leads to a delayed cook-off event. Modelling is then extended to include deformation effects associated with shock loading of a heterogeneous medium whereby random variables of strain, strain-rate and temperature are considered. A demonstrative mesoscale simulation of a non-ideal explosive is discussed that illustrates the joint statistical nature of the strain and temperature fields during shock loading to motivate the probabilistic approach. This modelling is derived in a Lagrangian framework that can be incorporated in continuum-level shock physics analysis. Future work will consider particle-based methods for a numerical implementation of this modelling approach.

  16. Dynamic supplier selection problem considering full truck load in probabilistic environment

    Science.gov (United States)

    Sutrisno, Wicaksono, Purnawan Adi

    2017-11-01

    In this paper, we propose a mathematical model in a probabilistic dynamic optimization to solve a dynamic supplier selection problem considering full truck load in probabilistic environment where some parameters are uncertain. We determine the optimal strategy for this problem by using stochastic dynamic programming. We give some numerical experiments to evaluate and analyze the model. From the results, the optimal supplier and the optimal product volume from the optimal supplier were determined for each time period.

  17. Probabilistic programmable quantum processors

    International Nuclear Information System (INIS)

    Buzek, V.; Ziman, M.; Hillery, M.

    2004-01-01

    We analyze how to improve performance of probabilistic programmable quantum processors. We show how the probability of success of the probabilistic processor can be enhanced by using the processor in loops. In addition, we show that an arbitrary SU(2) transformations of qubits can be encoded in program state of a universal programmable probabilistic quantum processor. The probability of success of this processor can be enhanced by a systematic correction of errors via conditional loops. Finally, we show that all our results can be generalized also for qudits. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  18. Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

    Science.gov (United States)

    Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

    2017-01-01

    The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This

  19. Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing.

    Science.gov (United States)

    Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

    2017-01-01

    The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This

  20. Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

    Directory of Open Access Journals (Sweden)

    Andrew Denovan

    2017-10-01

    Full Text Available The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy, the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT, the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual

  1. Enhanced CANDU 6 design assist probabilistic safety assessment results and insights

    International Nuclear Information System (INIS)

    Torabi, T.; Bettig, R.; Iliescu, P.; Robinson, J.; Santamaura, P.; Skorupska, B.; Tyagi, A.K.; Vencel, I.

    2013-01-01

    The Enhanced CANDU 6(EC6) is a 700 MWe reactor, which has evolved from the well-established CANDU line of reactors, which are heavy-water moderated, and heavy-water cooled horizontal pressure tube reactors, using natural uranium fuel. The EC6 design retains the generic CANDU design features, while incorporating innovations and state-of-the-art technologies to ensure competitiveness with other design with respect to operation, performance and economics. A design assist probabilistic safety assessment (PSA) was conducted during the design change phase of the project. The purpose of the assessment was to assess internal events during at-power operation and identify the design improvements and additional features needed to comply with the latest regulatory requirements in Canada and compete with other reactor designs, internationally. The PSA results show that the EC6 plant response to the postulated initiating events is well balanced, and the design meets its safety objectives. This paper summarizes the results and insights gained during the development of the PSA models for at-power internal events. (author)

  2. A probabilistic model estimating oil spill clean-up costs – A case study for the Gulf of Finland

    International Nuclear Information System (INIS)

    Montewka, Jakub; Weckström, Mia; Kujala, Pentti

    2013-01-01

    Highlights: • A model evaluating oil spill cleanup-costs for the Gulf of Finland is presented. • Bayesian Belief Networks are used to develop the model in a probabilistic fashion. • The results are compared with existing models and good agreement is found. • The model can be applicable for cost-benefit analysis in risk framework. -- Abstract: Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA)

  3. Cognitive Development Effects of Teaching Probabilistic Decision Making to Middle School Students

    Science.gov (United States)

    Mjelde, James W.; Litzenberg, Kerry K.; Lindner, James R.

    2011-01-01

    This study investigated the comprehension and effectiveness of teaching formal, probabilistic decision-making skills to middle school students. Two specific objectives were to determine (1) if middle school students can comprehend a probabilistic decision-making approach, and (2) if exposure to the modeling approaches improves middle school…

  4. Sound Probabilistic #SAT with Projection

    Directory of Open Access Journals (Sweden)

    Vladimir Klebanov

    2016-10-01

    Full Text Available We present an improved method for a sound probabilistic estimation of the model count of a boolean formula under projection. The problem solved can be used to encode a variety of quantitative program analyses, such as concerning security of resource consumption. We implement the technique and discuss its application to quantifying information flow in programs.

  5. Probabilistic safety analysis vs probabilistic fracture mechanics -relation and necessary merging

    International Nuclear Information System (INIS)

    Nilsson, Fred

    1997-01-01

    A comparison is made between some general features of probabilistic fracture mechanics (PFM) and probabilistic safety assessment (PSA) in its standard form. We conclude that: Result from PSA is a numerically expressed level of confidence in the system based on the state of current knowledge. It is thus not any objective measure of risk. It is important to carefully define the precise nature of the probabilistic statement and relate it to a well defined situation. Standardisation of PFM methods is necessary. PFM seems to be the only way to obtain estimates of the pipe break probability. Service statistics are of doubtful value because of scarcity of data and statistical inhomogeneity. Collection of service data should be directed towards the occurrence of growing cracks

  6. Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics.

    Science.gov (United States)

    Hattori, Masasi

    2016-12-01

    This paper presents a new theory of syllogistic reasoning. The proposed model assumes there are probabilistic representations of given signature situations. Instead of conducting an exhaustive search, the model constructs an individual-based "logical" mental representation that expresses the most probable state of affairs, and derives a necessary conclusion that is not inconsistent with the model using heuristics based on informativeness. The model is a unification of previous influential models. Its descriptive validity has been evaluated against existing empirical data and two new experiments, and by qualitative analyses based on previous empirical findings, all of which supported the theory. The model's behavior is also consistent with findings in other areas, including working memory capacity. The results indicate that people assume the probabilities of all target events mentioned in a syllogism to be almost equal, which suggests links between syllogistic reasoning and other areas of cognition. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

  7. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

    Directory of Open Access Journals (Sweden)

    Francesca Gagliardi

    2017-07-01

    Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

  8. Probabilistic Accident Consequence Uncertainty - A Joint CEC/USNRC Study

    International Nuclear Information System (INIS)

    Gregory, Julie J.; Harper, Frederick T.

    1999-01-01

    The joint USNRC/CEC consequence uncertainty study was chartered after the development of two new probabilistic accident consequence codes, MACCS in the U.S. and COSYMA in Europe. Both the USNRC and CEC had a vested interest in expanding the knowledge base of the uncertainty associated with consequence modeling, and teamed up to co-sponsor a consequence uncertainty study. The information acquired from the study was expected to provide understanding of the strengths and weaknesses of current models as well as a basis for direction of future research. This paper looks at the elicitation process implemented in the joint study and discusses some of the uncertainty distributions provided by eight panels of experts from the U.S. and Europe that were convened to provide responses to the elicitation. The phenomenological areas addressed by the expert panels include atmospheric dispersion and deposition, deposited material and external doses, food chain, early health effects, late health effects and internal dosimetry

  9. Probabilistic Accident Consequence Uncertainty - A Joint CEC/USNRC Study

    Energy Technology Data Exchange (ETDEWEB)

    Gregory, Julie J.; Harper, Frederick T.

    1999-07-28

    The joint USNRC/CEC consequence uncertainty study was chartered after the development of two new probabilistic accident consequence codes, MACCS in the U.S. and COSYMA in Europe. Both the USNRC and CEC had a vested interest in expanding the knowledge base of the uncertainty associated with consequence modeling, and teamed up to co-sponsor a consequence uncertainty study. The information acquired from the study was expected to provide understanding of the strengths and weaknesses of current models as well as a basis for direction of future research. This paper looks at the elicitation process implemented in the joint study and discusses some of the uncertainty distributions provided by eight panels of experts from the U.S. and Europe that were convened to provide responses to the elicitation. The phenomenological areas addressed by the expert panels include atmospheric dispersion and deposition, deposited material and external doses, food chain, early health effects, late health effects and internal dosimetry.

  10. Comprehensive probabilistic modelling of environmental emissions of engineered nanomaterials.

    Science.gov (United States)

    Sun, Tian Yin; Gottschalk, Fadri; Hungerbühler, Konrad; Nowack, Bernd

    2014-02-01

    Concerns about the environmental risks of engineered nanomaterials (ENM) are growing, however, currently very little is known about their concentrations in the environment. Here, we calculate the concentrations of five ENM (nano-TiO2, nano-ZnO, nano-Ag, CNT and fullerenes) in environmental and technical compartments using probabilistic material-flow modelling. We apply the newest data on ENM production volumes, their allocation to and subsequent release from different product categories, and their flows into and within those compartments. Further, we compare newly predicted ENM concentrations to estimates from 2009 and to corresponding measured concentrations of their conventional materials, e.g. TiO2, Zn and Ag. We show that the production volume and the compounds' inertness are crucial factors determining final concentrations. ENM production estimates are generally higher than a few years ago. In most cases, the environmental concentrations of corresponding conventional materials are between one and seven orders of magnitude higher than those for ENM. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Probabilistic and sensitivity analysis of Botlek Bridge structures

    Directory of Open Access Journals (Sweden)

    Králik Juraj

    2017-01-01

    Full Text Available This paper deals with the probabilistic and sensitivity analysis of the largest movable lift bridge of the world. The bridge system consists of six reinforced concrete pylons and two steel decks 4000 tons weight each connected through ropes with counterweights. The paper focuses the probabilistic and sensitivity analysis as the base of dynamic study in design process of the bridge. The results had a high importance for practical application and design of the bridge. The model and resistance uncertainties were taken into account in LHS simulation method.

  12. Probabilistic risk assessment as an aid to risk management

    International Nuclear Information System (INIS)

    Garrick, B.J.

    1982-01-01

    Probabilistic risk assessments are providing important insights into nuclear power plant safety. Their value is two-fold: first as a means of quantifying nuclear plant risk including contributors to risk, and second as an aid to risk management. A risk assessment provides an analytical plant model that can be the basis for performing meaningful decision analyses for controlling safety. It is the aspect of quantitative risk management that makes probabilistic risk assessment an important technical discipline of the future

  13. Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution.

    Directory of Open Access Journals (Sweden)

    Xin He

    2009-03-01

    Full Text Available Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii binding sites in distal bound sequences (relative to transcription start sites tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis-regulatory Module Analysis, ready to be applied in a broad biological context.

  14. Exact and approximate probabilistic symbolic execution for nondeterministic programs

    DEFF Research Database (Denmark)

    Luckow, Kasper Søe; Păsăreanu, Corina S.; Dwyer, Matthew B.

    2014-01-01

    Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probab...... Java programs. We show that our algorithms significantly improve upon a state-of-the-art statistical model checking algorithm, originally developed for Markov Decision Processes....... probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also...

  15. A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

    Science.gov (United States)

    Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

    2011-07-01

    The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

  16. Use of a probabilistic safety study in the design of the Italian reference PWR

    International Nuclear Information System (INIS)

    Richardson, D.C.; Russino, G.; Valentini, V.

    1985-01-01

    The intent of this paper is to provide a description of the experience gained in having performed a Probabilistic Safety Study (PSS) on the proposed Italian reference pressurized water reactor. The experience revealed that through careful application of probabilistic techniques, Probabilistic Risk Assessment (PRA) can be used as a tool to develop an optimum plant design in terms of safety and cost. Furthermore, the PSS can also be maintained as a living document and a tool to assess additional regulatory requirements that may be imposed during the construction and operational life of the plant. Through the use of flexible probabilistic techniques, the probabilistic safety model can provide a living safety assessment starting from the conceptual design and continuing through the construction, testing and operational phases. Moreover, the probabilistic safety model can be used during the operational phase of the plant as a method to evaluate the operational experience and identify potential problems before they occur. The experience, overall, provided additional insights into the various aspects of the plants design and operation that would not have been identified through the use of traditional safety evaluation techniques

  17. PROBABILISTIC MODEL OF LASER RANGE FINDER FOR THREE DIMENSIONAL GRID CELL IN CLOSE RANGE ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Hafiz b Iman

    2016-04-01

    Full Text Available The probabilistic model of a laser scanner presents an important aspect for simultaneous localization and map-building (SLAM. However, the characteristic of the beam of the laser range finder under extreme incident angles approaching 900 has not been thoroughly investigated. This research paper reports the characteristic of the density of the range value coming from a laser range finder under close range circumstances where the laser is imposed with a high incident angle. The laser was placed in a controlled environment consisting of walls at a close range and 1000 iteration of scans was collected. The assumption of normal density of the metrical data collapses when the beam traverses across sharp edges in this environment. The data collected also shows multimodal density at instances where the range has discontinuity. The standard deviation of the laser range finder is reported to average at 10.54 mm, with 0.96 of accuracy. This significance suggests that under extreme incident angles, a laser range finder reading behaves differently compared to normal distribution. The use of this information is crucial for SLAM activity in enclosed environments such as inside piping grid or other cluttered environments.KEYWORDS:   Hokuyo UTM-30LX; kernel density estimation; probabilistic model  

  18. Probabilistic Structural Analysis Theory Development

    Science.gov (United States)

    Burnside, O. H.

    1985-01-01

    The objective of the Probabilistic Structural Analysis Methods (PSAM) project is to develop analysis techniques and computer programs for predicting the probabilistic response of critical structural components for current and future space propulsion systems. This technology will play a central role in establishing system performance and durability. The first year's technical activity is concentrating on probabilistic finite element formulation strategy and code development. Work is also in progress to survey critical materials and space shuttle mian engine components. The probabilistic finite element computer program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) is being developed. The final probabilistic code will have, in the general case, the capability of performing nonlinear dynamic of stochastic structures. It is the goal of the approximate methods effort to increase problem solving efficiency relative to finite element methods by using energy methods to generate trial solutions which satisfy the structural boundary conditions. These approximate methods will be less computer intensive relative to the finite element approach.

  19. Probabilistic safety assessment activities at Ignalina NPP

    International Nuclear Information System (INIS)

    Bagdonas, A.

    1999-01-01

    The Barselina Project was initiated in the summer 1991. The project was a multilateral co-operation between Lithuania, Russia and Sweden up until phase 3, and phase 4 has been performed as a bilateral between Lithuania and Sweden. The long-range objective is to establish common perspectives and unified bases for assessment of severe accident risks and needs for remedial measures for the RBMK reactors. During phase 3, from 1993 to 1994, a full scope Probabilistic Safety Analysis (PSA) model of the Ignalina Nuclear Power Plant unit 2 was developed to identify possible safety improvement of risk importance. The probabilistic methodology was applied on a plant specific basis for a channel type reactor of RBMK design. During phase 4, from 1994 to 1996, the PSA was further developed, taking into account plant changes, improved modelling methods and extended plant information concerning dependencies (area events, dynamic effects, electrical and signal dependencies). The model reflected the plant status before the outage 1996. During phase 4+, 1998 to 1999 the PSA model was upgraded taking into account the newest plant modifications. The new PSA model of CPS/AZRT was developed. Modelling was based on the Single Failure Analysis

  20. Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy.

    Science.gov (United States)

    Wahl, Niklas; Hennig, Philipp; Wieser, Hans-Peter; Bangert, Mark

    2018-04-01

    We show that it is possible to explicitly incorporate fractionation effects into closed-form probabilistic treatment plan analysis and optimization for intensity-modulated proton therapy with analytical probabilistic modeling (APM). We study the impact of different fractionation schemes on the dosimetric uncertainty induced by random and systematic sources of range and setup uncertainty for treatment plans that were optimized with and without consideration of the number of treatment fractions. The APM framework is capable of handling arbitrarily correlated uncertainty models including systematic and random errors in the context of fractionation. On this basis, we construct an analytical dose variance computation pipeline that explicitly considers the number of treatment fractions for uncertainty quantitation and minimization during treatment planning. We evaluate the variance computation model in comparison to random sampling of 100 treatments for conventional and probabilistic treatment plans under different fractionation schemes (1, 5, 30 fractions) for an intracranial, a paraspinal and a prostate case. The impact of neglecting the fractionation scheme during treatment planning is investigated by applying treatment plans that were generated with probabilistic optimization for 1 fraction in a higher number of fractions and comparing them to the probabilistic plans optimized under explicit consideration of the number of fractions. APM enables the construction of an analytical variance computation model for dose uncertainty considering fractionation at negligible computational overhead. It is computationally feasible (a) to simultaneously perform a robustness analysis for all possible fraction numbers and (b) to perform a probabilistic treatment plan optimization for a specific fraction number. The incorporation of fractionation assumptions for robustness analysis exposes a dose to uncertainty trade-off, i.e., the dose in the organs at risk is increased for a

  1. Probabilistic safety analysis for control rod drive system of ET-RR-1

    International Nuclear Information System (INIS)

    Nasr, M.; Nasser, O.

    1988-01-01

    The International Atomic Energy Agency (IAEA) co-ordinated a Research programme on Probabilistic Safety Analysis (PSA) for research reactors; with the participation of several countries. In the framework of this project (Project Int. 9/063) the Egyptian Atomic Energy Authority decided to perform a PSA study on the ET-RR-1 (Egypt Thermal Research Reactor). The study is conducted in collaboration between the nuclear regulatory and safety centre (NRSC) and the reactor department of the nuclear research centre at Inchass. The present work is a part of the PSA study on ET-RR- it is concerning a probabilistic safety analysis of the control rod drive mechanism

  2. Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models.

    Science.gov (United States)

    Li, Zhixi; Peck, Kyung K; Brennan, Nicole P; Jenabi, Mehrnaz; Hsu, Meier; Zhang, Zhigang; Holodny, Andrei I; Young, Robert J

    2013-02-01

    The purpose of this study was to compare the deterministic and probabilistic tracking methods of diffusion tensor white matter fiber tractography in patients with brain tumors. We identified 29 patients with left brain tumors probabilistic method based on an extended Monte Carlo Random Walk algorithm. Tracking was controlled using two ROIs corresponding to Broca's and Wernicke's areas. Tracts in tumoraffected hemispheres were examined for extension between Broca's and Wernicke's areas, anterior-posterior length and volume, and compared with the normal contralateral tracts. Probabilistic tracts displayed more complete anterior extension to Broca's area than did FACT tracts on the tumor-affected and normal sides (p probabilistic tracts than FACT tracts (p probabilistic tracts than FACT tracts (p = 0.01). Probabilistic tractography reconstructs the arcuate fasciculus more completely and performs better through areas of tumor and/or edema. The FACT algorithm tends to underestimate the anterior-most fibers of the arcuate fasciculus, which are crossed by primary motor fibers.

  3. On the Origins of Suboptimality in Human Probabilistic Inference

    Science.gov (United States)

    Acerbi, Luigi; Vijayakumar, Sethu; Wolpert, Daniel M.

    2014-01-01

    Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by

  4. On the origins of suboptimality in human probabilistic inference.

    Directory of Open Access Journals (Sweden)

    Luigi Acerbi

    2014-06-01

    Full Text Available Humans have been shown to combine noisy sensory information with previous experience (priors, in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal. Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the

  5. Some probabilistic aspects of fracture

    International Nuclear Information System (INIS)

    Thomas, J.M.

    1982-01-01

    Some probabilistic aspects of fracture in structural and mechanical components are examined. The principles of fracture mechanics, material quality and inspection uncertainty are formulated into a conceptual and analytical framework for prediction of failure probability. The role of probabilistic fracture mechanics in a more global context of risk and optimization of decisions is illustrated. An example, where Monte Carlo simulation was used to implement a probabilistic fracture mechanics analysis, is discussed. (orig.)

  6. A study on the fatigue life prediction of tire belt-layers using probabilistic method

    International Nuclear Information System (INIS)

    Lee, Dong Woo; Park, Jong Sang; Lee, Tae Won; Kim, Seong Rae; Sung, Ki Deug; Huh, Sun Chul

    2013-01-01

    Tire belt separation failure is occurred by internal cracks generated in *1 and *2 belt layers and by its growth. And belt failure seriously affects tire endurance. Therefore, to improve the tire endurance, it is necessary to analyze tire crack growth behavior and predict fatigue life. Generally, the prediction of tire endurance is performed by the experimental method using tire test machine. But it takes much cost and time to perform experiment. In this paper, to predict tire fatigue life, we applied deterministic fracture mechanics approach, based on finite element analysis. Also, probabilistic analysis method based on statistics using Monte Carlo simulation is presented. Above mentioned two methods include a global-local finite element analysis to provide the detail necessary to model explicitly an internal crack and calculate the J-integral for tire life prediction.

  7. Comparison of MACCS users calculations for the international comparison exercise on probabilistic accident consequence assessment code, October 1989--June 1993

    International Nuclear Information System (INIS)

    Neymotin, L.

    1994-04-01

    Over the past several years, the OECD/NEA and CEC sponsored an international program intercomparing a group of six probabilistic consequence assessment (PCA) codes designed to simulate health and economic consequences of radioactive releases into atmosphere of radioactive materials following severe accidents at nuclear power plants (NPPs): ARANO (Finland), CONDOR (UK), COSYMA (CEC), LENA (Sweden), MACCS (USA), and OSCAAR (Japan). In parallel with this effort, two separate groups performed similar calculations using the MACCS and COSYMA codes. Results produced in the MACCS Users Group (Greece, Italy, Spain, and USA) calculations and their comparison are contained in the present report. Version 1.5.11.1 of the MACCS code was used for the calculations. Good agreement between the results produced in the four participating calculations has been reached, with the exception of the results related to the ingestion pathway dose predictions. The main reason for the scatter in those particular results is attributed to the lack of a straightforward implementation of the specifications for agricultural production and counter-measures criteria provided for the exercise. A significantly smaller scatter in predictions of other consequences was successfully explained by differences in meteorological files and weather sampling, grids, rain distance intervals, dispersion model options, and population distributions

  8. Failure probability assessment of wall-thinned nuclear pipes using probabilistic fracture mechanics

    International Nuclear Information System (INIS)

    Lee, Sang-Min; Chang, Yoon-Suk; Choi, Jae-Boong; Kim, Young-Jin

    2006-01-01

    The integrity of nuclear piping system has to be maintained during operation. In order to maintain the integrity, reliable assessment procedures including fracture mechanics analysis, etc., are required. Up to now, this has been performed using conventional deterministic approaches even though there are many uncertainties to hinder a rational evaluation. In this respect, probabilistic approaches are considered as an appropriate method for piping system evaluation. The objectives of this paper are to estimate the failure probabilities of wall-thinned pipes in nuclear secondary systems and to propose limited operating conditions under different types of loadings. To do this, a probabilistic assessment program using reliability index and simulation techniques was developed and applied to evaluate failure probabilities of wall-thinned pipes subjected to internal pressure, bending moment and combined loading of them. The sensitivity analysis results as well as prototypal integrity assessment results showed a promising applicability of the probabilistic assessment program, necessity of practical evaluation reflecting combined loading condition and operation considering limited condition

  9. Report of the IPERS (International Peer Review Service) phase 1 review mission for the Temelin nuclear power plant Level 1 probabilistic safety assessment in the Czech Republic 24 April to 5 May 1995

    International Nuclear Information System (INIS)

    1995-01-01

    This report presents the results of the IAEA International Peer Review Services Phase 1 review of the internal events, Level 1 probabilistic safety assessment (PSA) for the Temelin Unit 1 and 2 NPP. The review was based on the PSA documentation available and on intensive communications with the analysis team and representatives from the utility and future plant operator. The results presented herein reflect the views of the international experts carrying out the review. They are provided for consideration by the responsible authorities of the Czech Republic. 2 refs, 1 fig., 5 tabs

  10. Validation of Neutron Calculation Codes and Models by means of benchmark cases in the frame of the Binational Commission of Nuclear Energy. Probabilistic Models

    International Nuclear Information System (INIS)

    Dos Santos, Adimir; Siqueira, Paulo de Tarso D.; Andrade e Silva, Graciete Simões; Grant, Carlos; Tarazaga, Ariel E.; Barberis, Claudia

    2013-01-01

    In year 2008 the Atomic Energy National Commission (CNEA) of Argentina, and the Brazilian Institute of Energetic and Nuclear Research (IPEN), under the frame of Nuclear Energy Argentine Brazilian Agreement (COBEN), among many others, included the project “Validation and Verification of Calculation Methods used for Research and Experimental Reactors . At this time, it was established that the validation was to be performed with models implemented in the deterministic codes HUEMUL and PUMA (cell and reactor codes) developed by CNEA and those ones implemented in MCNP by CNEA and IPEN. The necessary data for these validations would correspond to theoretical-experimental reference cases in the research reactor IPEN/MB-01 located in São Paulo, Brazil. The staff of the group Reactor and Nuclear Power Studies (SERC) of CNEA, from the argentine side, performed calculations with deterministic models (HUEMUL-PUMA) and probabilistic methods (MCNP) modeling a great number of physical situations of de reactor, which previously have been studied and modeled by members of the Center of Nuclear Engineering of the IPEN, whose results were extensively provided to CNEA. In this paper results of comparison of calculated and experimental results for critical configurations, temperature coefficients, kinetic parameters and fission rates evaluated with probabilistic models spatial distributions are shown. (author)

  11. Probabilistic pipe fracture evaluations for applications to leak-rate detection

    Energy Technology Data Exchange (ETDEWEB)

    Rahman, S; Wilkowski, G; Ghadiali, N [Battelle Columbus Labs., OH (United States)

    1993-12-31

    Stochastic pipe fracture evaluations are conducted for applications to leak-rate detection. A state-of-the-art review was first conducted to evaluate the adequacy of current deterministic models for thermo-hydraulic and elastic-plastic fracture analyses. Then a new probabilistic model was developed with the above deterministic models for structural reliability analysis of cracked piping systems and statistical characterization of crack morphology parameters, material properties of pipe, and crack location. The proposed models are then applied for computing conditional probability of failure for various nuclear piping systems in BWR and PWR plants. The PRAISE code was not used, and the probabilistic model is based on modern methods of stochastic mechanics, computationally far superior to Monte Carlo and Stratified Sampling methods used in PRAISE. 10 refs., 9 figs., 1 tab.

  12. Probabilistic pipe fracture evaluations for applications to leak-rate detection

    International Nuclear Information System (INIS)

    Rahman, S.; Wilkowski, G.; Ghadiali, N.

    1992-01-01

    Stochastic pipe fracture evaluations are conducted for applications to leak-rate detection. A state-of-the-art review was first conducted to evaluate the adequacy of current deterministic models for thermo-hydraulic and elastic-plastic fracture analyses. Then a new probabilistic model was developed with the above deterministic models for structural reliability analysis of cracked piping systems and statistical characterization of crack morphology parameters, material properties of pipe, and crack location. The proposed models are then applied for computing conditional probability of failure for various nuclear piping systems in BWR and PWR plants. The PRAISE code was not used, and the probabilistic model is based on modern methods of stochastic mechanics, computationally far superior to Monte Carlo and Stratified Sampling methods used in PRAISE. 10 refs., 9 figs., 1 tab

  13. BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

    Science.gov (United States)

    Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

    2017-12-01

    Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made

  14. Probabilistic forecasting for extreme NO2 pollution episodes

    International Nuclear Information System (INIS)

    Aznarte, José L.

    2017-01-01

    In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. - Highlights: • A new probabilistic forecasting system is presented to predict NO 2 concentrations. • While predicting the full distribution, it also outperforms other point-forecasting models. • Forecasts show good properties and peak concentrations are properly predicted. • It forecasts the probability of exceedance of thresholds, key to decision makers. • Relative forecasting importance of the variables is obtained as a by-product.

  15. Standardized approach for developing probabilistic exposure factor distributions

    Energy Technology Data Exchange (ETDEWEB)

    Maddalena, Randy L.; McKone, Thomas E.; Sohn, Michael D.

    2003-03-01

    The effectiveness of a probabilistic risk assessment (PRA) depends critically on the quality of input information that is available to the risk assessor and specifically on the probabilistic exposure factor distributions that are developed and used in the exposure and risk models. Deriving probabilistic distributions for model inputs can be time consuming and subjective. The absence of a standard approach for developing these distributions can result in PRAs that are inconsistent and difficult to review by regulatory agencies. We present an approach that reduces subjectivity in the distribution development process without limiting the flexibility needed to prepare relevant PRAs. The approach requires two steps. First, we analyze data pooled at a population scale to (1) identify the most robust demographic variables within the population for a given exposure factor, (2) partition the population data into subsets based on these variables, and (3) construct archetypal distributions for each subpopulation. Second, we sample from these archetypal distributions according to site- or scenario-specific conditions to simulate exposure factor values and use these values to construct the scenario-specific input distribution. It is envisaged that the archetypal distributions from step 1 will be generally applicable so risk assessors will not have to repeatedly collect and analyze raw data for each new assessment. We demonstrate the approach for two commonly used exposure factors--body weight (BW) and exposure duration (ED)--using data for the U.S. population. For these factors we provide a first set of subpopulation based archetypal distributions along with methodology for using these distributions to construct relevant scenario-specific probabilistic exposure factor distributions.

  16. Probabilistic methods used in NUSS

    International Nuclear Information System (INIS)

    Fischer, J.; Giuliani, P.

    1985-01-01

    Probabilistic considerations are used implicitly or explicitly in all technical areas. In the NUSS codes and guides the two areas of design and siting are those where more use is made of these concepts. A brief review of the relevant documents in these two areas is made in this paper. It covers the documents where either probabilistic considerations are implied or where probabilistic approaches are recommended in the evaluation of situations and of events. In the siting guides the review mainly covers the area of seismic hydrological and external man-made events analysis, as well as some aspects of meteorological extreme events analysis. Probabilistic methods are recommended in the design guides but they are not made a requirement. There are several reasons for this, mainly lack of reliable data and the absence of quantitative safety limits or goals against which to judge the design analysis. As far as practical, engineering judgement should be backed up by quantitative probabilistic analysis. Examples are given and the concept of design basis as used in NUSS design guides is explained. (author)

  17. A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation - With Application to Tumor and Stroke

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial

    2016-01-01

    jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model......), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions...

  18. The Quest for Minimal Quotients for Probabilistic Automata

    DEFF Research Database (Denmark)

    Eisentraut, Christian; Hermanns, Holger; Schuster, Johann

    2013-01-01

    One of the prevailing ideas in applied concurrency theory and verification is the concept of automata minimization with respect to strong or weak bisimilarity. The minimal automata can be seen as canonical representations of the behaviour modulo the bisimilarity considered. Together with congruence...... results wrt. process algebraic operators, this can be exploited to alleviate the notorious state space explosion problem. In this paper, we aim at identifying minimal automata and canonical representations for concurrent probabilistic models. We present minimality and canonicity results for probabilistic...... automata wrt. strong and weak bisimilarity, together with polynomial time minimization algorithms....

  19. Specifying design conservatism: Worst case versus probabilistic analysis

    Science.gov (United States)

    Miles, Ralph F., Jr.

    1993-01-01

    Design conservatism is the difference between specified and required performance, and is introduced when uncertainty is present. The classical approach of worst-case analysis for specifying design conservatism is presented, along with the modern approach of probabilistic analysis. The appropriate degree of design conservatism is a tradeoff between the required resources and the probability and consequences of a failure. A probabilistic analysis properly models this tradeoff, while a worst-case analysis reveals nothing about the probability of failure, and can significantly overstate the consequences of failure. Two aerospace examples will be presented that illustrate problems that can arise with a worst-case analysis.

  20. Probabilistic Flood Defence Assessment Tools

    Directory of Open Access Journals (Sweden)

    Slomp Robert

    2016-01-01

    institutions managing flood the defences, and not by just a small number of experts in probabilistic assessment. Therefore, data management and use of software are main issues that have been covered in courses and training in 2016 and 2017. All in all, this is the largest change in the assessment of Dutch flood defences since 1996. In 1996 probabilistic techniques were first introduced to determine hydraulic boundary conditions (water levels and waves (wave height, wave period and direction for different return periods. To simplify the process, the assessment continues to consist of a three-step approach, moving from simple decision rules, to the methods for semi-probabilistic assessment, and finally to a fully probabilistic analysis to compare the strength of flood defences with the hydraulic loads. The formal assessment results are thus mainly based on the fully probabilistic analysis and the ultimate limit state of the strength of a flood defence. For complex flood defences, additional models and software were developed. The current Hydra software suite (for policy analysis, formal flood defence assessment and design will be replaced by the model Ringtoets. New stand-alone software has been developed for revetments, geotechnical analysis and slope stability of the foreshore. Design software and policy analysis software, including the Delta model, will be updated in 2018. A fully probabilistic method results in more precise assessments and more transparency in the process of assessment and reconstruction of flood defences. This is of increasing importance, as large-scale infrastructural projects in a highly urbanized environment are increasingly subject to political and societal pressure to add additional features. For this reason, it is of increasing importance to be able to determine which new feature really adds to flood protection, to quantify how much its adds to the level of flood protection and to evaluate if it is really worthwhile. Please note: The Netherlands