Probabilistic structural analysis methods of hot engine structures

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Hopkins, D. A.

1989-01-01

Development of probabilistic structural analysis methods for hot engine structures at Lewis Research Center is presented. Three elements of the research program are: (1) composite load spectra methodology; (2) probabilistic structural analysis methodology; and (3) probabilistic structural analysis application. Recent progress includes: (1) quantification of the effects of uncertainties for several variables on high pressure fuel turbopump (HPFT) turbine blade temperature, pressure, and torque of the space shuttle main engine (SSME); (2) the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; and (3) evaluation of the failure probability. Collectively, the results demonstrate that the structural durability of hot engine structural components can be effectively evaluated in a formal probabilistic/reliability framework.

NESSUS (Numerical Evaluation of Stochastic Structures Under Stress)/EXPERT: Bridging the gap between artificial intelligence and FORTRAN

NASA Technical Reports Server (NTRS)

Fink, Pamela K.; Palmer, Karol K.

1988-01-01

The development of a probabilistic structural analysis methodology (PSAM) is described. In the near-term, the methodology will be applied to designing critical components of the next generation space shuttle main engine. In the long-term, PSAM will be applied very broadly, providing designers with a new technology for more effective design of structures whose character and performance are significantly affected by random variables. The software under development to implement the ideas developed in PSAM resembles, in many ways, conventional deterministic structural analysis code. However, several additional capabilities regarding the probabilistic analysis makes the input data requirements and the resulting output even more complex. As a result, an intelligent front- and back-end to the code is being developed to assist the design engineer in providing the input data in a correct and appropriate manner. The type of knowledge that this entails is, in general, heuristically-based, allowing the fairly well-understood technology of production rules to apply with little difficulty. However, the PSAM code, called NESSUS, is written in FORTRAN-77 and runs on a DEC VAX. Thus, the associated expert system, called NESSUS/EXPERT, must run on a DEC VAX as well, and integrate effectively and efficiently with the existing FORTRAN code. This paper discusses the process undergone to select a suitable tool, identify an appropriate division between the functions that should be performed in FORTRAN and those that should be performed by production rules, and how integration of the conventional and AI technologies was achieved.

Environmental probabilistic quantitative assessment methodologies

USGS Publications Warehouse

Crovelli, R.A.

1995-01-01

In this paper, four petroleum resource assessment methodologies are presented as possible pollution assessment methodologies, even though petroleum as a resource is desirable, whereas pollution is undesirable. A methodology is defined in this paper to consist of a probability model and a probabilistic method, where the method is used to solve the model. The following four basic types of probability models are considered: 1) direct assessment, 2) accumulation size, 3) volumetric yield, and 4) reservoir engineering. Three of the four petroleum resource assessment methodologies were written as microcomputer systems, viz. TRIAGG for direct assessment, APRAS for accumulation size, and FASPU for reservoir engineering. A fourth microcomputer system termed PROBDIST supports the three assessment systems. The three assessment systems have different probability models but the same type of probabilistic method. The type of advantages of the analytic method are in computational speed and flexibility, making it ideal for a microcomputer. -from Author

Development of Probabilistic Life Prediction Methodologies and Testing Strategies for MEMS and CMC's

NASA Technical Reports Server (NTRS)

Jadaan, Osama

2003-01-01

This effort is to investigate probabilistic life prediction methodologies for ceramic matrix composites and MicroElectroMechanical Systems (MEMS) and to analyze designs that determine stochastic properties of MEMS. For CMC's this includes a brief literature survey regarding lifing methodologies. Also of interest for MEMS is the design of a proper test for the Weibull size effect in thin film (bulge test) specimens. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. A main objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structures/Life (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads. A second set of objectives is to determine the applicability/suitability of the CARES/Life methodology for CMC analysis, what changes would be needed to the methodology and software, and if feasible, run a demonstration problem. Also important is an evaluation of CARES/Life coupled to the ANSYS Probabilistic Design System (PDS) and the potential of coupling transient reliability analysis to the ANSYS PDS.

Probabilistic Rock Slope Engineering.

DTIC Science & Technology

1984-06-01

4 U rmy Corps PROBABILISTIC ROCK SLOPE ENGINEERING by Stanley M. Miller jGeotechnical Engineer 509 E. Calle Avenue Tucson, Arizona 85705 Co N 00 IFI...NUMBERS Geological Engineer CW71 1ork Unit 31755 509 E. Calle Avenue, Tucson, Arizona 85705 11. CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATE...communication, J. P. Sa,.-1Iy, Inspiration Consolidated Copper Co., Inspiration, Ariz., 1980. Personal communication, R. D. Call, Pincock, Allen, and

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples, volume 1

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

The Stag Hunt Game: An Example of an Excel-Based Probabilistic Game

ERIC Educational Resources Information Center

Bridge, Dave

2016-01-01

With so many role-playing simulations already in the political science education literature, the recent repeated calls for new games is both timely and appropriate. This article answers and extends those calls by advocating the creation of probabilistic games using Microsoft Excel. I introduce the example of the Stag Hunt Game--a short, effective,…

Probabilistic Based Modeling and Simulation Assessment

DTIC Science & Technology

2010-06-01

different crash and blast scenarios. With the integration of the high fidelity neck and head model, a methodology to calculate the probability of injury...variability, correlation, and multiple (often competing) failure metrics. Important scenarios include vehicular collisions, blast /fragment impact, and...first area of focus is to develop a methodology to integrate probabilistic analysis into finite element analysis of vehicle collisions and blast . The

Reliability-Based Stability Analysis of Rock Slopes Using Numerical Analysis and Response Surface Method

NASA Astrophysics Data System (ADS)

Dadashzadeh, N.; Duzgun, H. S. B.; Yesiloglu-Gultekin, N.

2017-08-01

While advanced numerical techniques in slope stability analysis are successfully used in deterministic studies, they have so far found limited use in probabilistic analyses due to their high computation cost. The first-order reliability method (FORM) is one of the most efficient probabilistic techniques to perform probabilistic stability analysis by considering the associated uncertainties in the analysis parameters. However, it is not possible to directly use FORM in numerical slope stability evaluations as it requires definition of a limit state performance function. In this study, an integrated methodology for probabilistic numerical modeling of rock slope stability is proposed. The methodology is based on response surface method, where FORM is used to develop an explicit performance function from the results of numerical simulations. The implementation of the proposed methodology is performed by considering a large potential rock wedge in Sumela Monastery, Turkey. The accuracy of the developed performance function to truly represent the limit state surface is evaluated by monitoring the slope behavior. The calculated probability of failure is compared with Monte Carlo simulation (MCS) method. The proposed methodology is found to be 72% more efficient than MCS, while the accuracy is decreased with an error of 24%.

Software for Probabilistic Risk Reduction

NASA Technical Reports Server (NTRS)

Hensley, Scott; Michel, Thierry; Madsen, Soren; Chapin, Elaine; Rodriguez, Ernesto

2004-01-01

A computer program implements a methodology, denoted probabilistic risk reduction, that is intended to aid in planning the development of complex software and/or hardware systems. This methodology integrates two complementary prior methodologies: (1) that of probabilistic risk assessment and (2) a risk-based planning methodology, implemented in a prior computer program known as Defect Detection and Prevention (DDP), in which multiple requirements and the beneficial effects of risk-mitigation actions are taken into account. The present methodology and the software are able to accommodate both process knowledge (notably of the efficacy of development practices) and product knowledge (notably of the logical structure of a system, the development of which one seeks to plan). Estimates of the costs and benefits of a planned development can be derived. Functional and non-functional aspects of software can be taken into account, and trades made among them. It becomes possible to optimize the planning process in the sense that it becomes possible to select the best suite of process steps and design choices to maximize the expectation of success while remaining within budget.

An improved approach for flight readiness certification: Methodology for failure risk assessment and application examples. Volume 3: Structure and listing of programs

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.

Development of Probabilistic Rigid Pavement Design Methodologies for Military Airfields.

DTIC Science & Technology

1983-12-01

4A161102AT22, Task AO, Work Unit 009, "Methodology for Considering Material Variability in Pavement Design." OCE Project Monitor was Mr. S. S. Gillespie. The...PREFACE. .. ............................. VOLUME 1: STATE OF THE ART VARIABILITY OF AIRFIELD PAVEMENT MATERIALS VOLUME 11: MATHEMATICAL FORMULATION OF...VOLUME IV: PROBABILISTIC ANALYSIS OF RIGID AIRFIELD DESIGN BY ELASTIC LAYERED THEORY VOLUME I STATE OF THE ART VARIABILITY OF AIRFIELD PAVEMENT MATERIALS

Method and system for dynamic probabilistic risk assessment

NASA Technical Reports Server (NTRS)

Dugan, Joanne Bechta (Inventor); Xu, Hong (Inventor)

2013-01-01

The DEFT methodology, system and computer readable medium extends the applicability of the PRA (Probabilistic Risk Assessment) methodology to computer-based systems, by allowing DFT (Dynamic Fault Tree) nodes as pivot nodes in the Event Tree (ET) model. DEFT includes a mathematical model and solution algorithm, supports all common PRA analysis functions and cutsets. Additional capabilities enabled by the DFT include modularization, phased mission analysis, sequence dependencies, and imperfect coverage.

Probabilistic fatigue methodology for six nines reliability

NASA Technical Reports Server (NTRS)

Everett, R. A., Jr.; Bartlett, F. D., Jr.; Elber, Wolf

1990-01-01

Fleet readiness and flight safety strongly depend on the degree of reliability that can be designed into rotorcraft flight critical components. The current U.S. Army fatigue life specification for new rotorcraft is the so-called six nines reliability, or a probability of failure of one in a million. The progress of a round robin which was established by the American Helicopter Society (AHS) Subcommittee for Fatigue and Damage Tolerance is reviewed to investigate reliability-based fatigue methodology. The participants in this cooperative effort are in the U.S. Army Aviation Systems Command (AVSCOM) and the rotorcraft industry. One phase of the joint activity examined fatigue reliability under uniquely defined conditions for which only one answer was correct. The other phases were set up to learn how the different industry methods in defining fatigue strength affected the mean fatigue life and reliability calculations. Hence, constant amplitude and spectrum fatigue test data were provided so that each participant could perform their standard fatigue life analysis. As a result of this round robin, the probabilistic logic which includes both fatigue strength and spectrum loading variability in developing a consistant reliability analysis was established. In this first study, the reliability analysis was limited to the linear cumulative damage approach. However, it is expected that superior fatigue life prediction methods will ultimately be developed through this open AHS forum. To that end, these preliminary results were useful in identifying some topics for additional study.

Accounting for Uncertainties in Strengths of SiC MEMS Parts

NASA Technical Reports Server (NTRS)

Nemeth, Noel; Evans, Laura; Beheim, Glen; Trapp, Mark; Jadaan, Osama; Sharpe, William N., Jr.

2007-01-01

A methodology has been devised for accounting for uncertainties in the strengths of silicon carbide structural components of microelectromechanical systems (MEMS). The methodology enables prediction of the probabilistic strengths of complexly shaped MEMS parts using data from tests of simple specimens. This methodology is intended to serve as a part of a rational basis for designing SiC MEMS, supplementing methodologies that have been borrowed from the art of designing macroscopic brittle material structures. The need for this or a similar methodology arises as a consequence of the fundamental nature of MEMS and the brittle silicon-based materials of which they are typically fabricated. When tested to fracture, MEMS and structural components thereof show wide part-to-part scatter in strength. The methodology involves the use of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) software in conjunction with the ANSYS Probabilistic Design System (PDS) software to simulate or predict the strength responses of brittle material components while simultaneously accounting for the effects of variability of geometrical features on the strength responses. As such, the methodology involves the use of an extended version of the ANSYS/CARES/PDS software system described in Probabilistic Prediction of Lifetimes of Ceramic Parts (LEW-17682-1/4-1), Software Tech Briefs supplement to NASA Tech Briefs, Vol. 30, No. 9 (September 2006), page 10. The ANSYS PDS software enables the ANSYS finite-element-analysis program to account for uncertainty in the design-and analysis process. The ANSYS PDS software accounts for uncertainty in material properties, dimensions, and loading by assigning probabilistic distributions to user-specified model parameters and performing simulations using various sampling techniques.

Observations and Bayesian location methodology of transient acoustic signals (likely blue whales) in the Indian Ocean, using a hydrophone triplet.

PubMed

Le Bras, Ronan J; Kuzma, Heidi; Sucic, Victor; Bokelmann, Götz

2016-05-01

A notable sequence of calls was encountered, spanning several days in January 2003, in the central part of the Indian Ocean on a hydrophone triplet recording acoustic data at a 250 Hz sampling rate. This paper presents signal processing methods applied to the waveform data to detect, group, extract amplitude and bearing estimates for the recorded signals. An approximate location for the source of the sequence of calls is inferred from extracting the features from the waveform. As the source approaches the hydrophone triplet, the source level (SL) of the calls is estimated at 187 ± 6 dB re: 1 μPa-1 m in the 15-60 Hz frequency range. The calls are attributed to a subgroup of blue whales, Balaenoptera musculus, with a characteristic acoustic signature. A Bayesian location method using probabilistic models for bearing and amplitude is demonstrated on the calls sequence. The method is applied to the case of detection at a single triad of hydrophones and results in a probability distribution map for the origin of the calls. It can be extended to detections at multiple triads and because of the Bayesian formulation, additional modeling complexity can be built-in as needed.

Probabilistic load simulation: Code development status

NASA Astrophysics Data System (ADS)

Newell, J. F.; Ho, H.

1991-05-01

The objective of the Composite Load Spectra (CLS) project is to develop generic load models to simulate the composite load spectra that are included in space propulsion system components. The probabilistic loads thus generated are part of the probabilistic design analysis (PDA) of a space propulsion system that also includes probabilistic structural analyses, reliability, and risk evaluations. Probabilistic load simulation for space propulsion systems demands sophisticated probabilistic methodology and requires large amounts of load information and engineering data. The CLS approach is to implement a knowledge based system coupled with a probabilistic load simulation module. The knowledge base manages and furnishes load information and expertise and sets up the simulation runs. The load simulation module performs the numerical computation to generate the probabilistic loads with load information supplied from the CLS knowledge base.

Probabilistic Design Storm Method for Improved Flood Estimation in Ungauged Catchments

NASA Astrophysics Data System (ADS)

Berk, Mario; Å pačková, Olga; Straub, Daniel

2017-12-01

The design storm approach with event-based rainfall-runoff models is a standard method for design flood estimation in ungauged catchments. The approach is conceptually simple and computationally inexpensive, but the underlying assumptions can lead to flawed design flood estimations. In particular, the implied average recurrence interval (ARI) neutrality between rainfall and runoff neglects uncertainty in other important parameters, leading to an underestimation of design floods. The selection of a single representative critical rainfall duration in the analysis leads to an additional underestimation of design floods. One way to overcome these nonconservative approximations is the use of a continuous rainfall-runoff model, which is associated with significant computational cost and requires rainfall input data that are often not readily available. As an alternative, we propose a novel Probabilistic Design Storm method that combines event-based flood modeling with basic probabilistic models and concepts from reliability analysis, in particular the First-Order Reliability Method (FORM). The proposed methodology overcomes the limitations of the standard design storm approach, while utilizing the same input information and models without excessive computational effort. Additionally, the Probabilistic Design Storm method allows deriving so-called design charts, which summarize representative design storm events (combinations of rainfall intensity and other relevant parameters) for floods with different return periods. These can be used to study the relationship between rainfall and runoff return periods. We demonstrate, investigate, and validate the method by means of an example catchment located in the Bavarian Pre-Alps, in combination with a simple hydrological model commonly used in practice.

Development of Probabilistic Structural Analysis Integrated with Manufacturing Processes

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.; Nagpal, Vinod K.

2007-01-01

An effort has been initiated to integrate manufacturing process simulations with probabilistic structural analyses in order to capture the important impacts of manufacturing uncertainties on component stress levels and life. Two physics-based manufacturing process models (one for powdered metal forging and the other for annular deformation resistance welding) have been linked to the NESSUS structural analysis code. This paper describes the methodology developed to perform this integration including several examples. Although this effort is still underway, particularly for full integration of a probabilistic analysis, the progress to date has been encouraging and a software interface that implements the methodology has been developed. The purpose of this paper is to report this preliminary development.

Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction

PubMed Central

Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

2015-01-01

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error. PMID:27081304

Probabilistic models of eukaryotic evolution: time for integration

PubMed Central

Lartillot, Nicolas

2015-01-01

In spite of substantial work and recent progress, a global and fully resolved picture of the macroevolutionary history of eukaryotes is still under construction. This concerns not only the phylogenetic relations among major groups, but also the general characteristics of the underlying macroevolutionary processes, including the patterns of gene family evolution associated with endosymbioses, as well as their impact on the sequence evolutionary process. All these questions raise formidable methodological challenges, calling for a more powerful statistical paradigm. In this direction, model-based probabilistic approaches have played an increasingly important role. In particular, improved models of sequence evolution accounting for heterogeneities across sites and across lineages have led to significant, although insufficient, improvement in phylogenetic accuracy. More recently, one main trend has been to move away from simple parametric models and stepwise approaches, towards integrative models explicitly considering the intricate interplay between multiple levels of macroevolutionary processes. Such integrative models are in their infancy, and their application to the phylogeny of eukaryotes still requires substantial improvement of the underlying models, as well as additional computational developments. PMID:26323768

The Study of the Relationship between Probabilistic Design and Axiomatic Design Methodology. Volume 2

NASA Technical Reports Server (NTRS)

Onwubiko, Chin-Yere; Onyebueke, Landon

1996-01-01

The structural design, or the design of machine elements, has been traditionally based on deterministic design methodology. The deterministic method considers all design parameters to be known with certainty. This methodology is, therefore, inadequate to design complex structures that are subjected to a variety of complex, severe loading conditions. A nonlinear behavior that is dependent on stress, stress rate, temperature, number of load cycles, and time is observed on all components subjected to complex conditions. These complex conditions introduce uncertainties; hence, the actual factor of safety margin remains unknown. In the deterministic methodology, the contingency of failure is discounted; hence, there is a use of a high factor of safety. It may be most useful in situations where the design structures are simple. The probabilistic method is concerned with the probability of non-failure performance of structures or machine elements. It is much more useful in situations where the design is characterized by complex geometry, possibility of catastrophic failure, sensitive loads and material properties. Also included: Comparative Study of the use of AGMA Geometry Factors and Probabilistic Design Methodology in the Design of Compact Spur Gear Set.

Risk-Informed Safety Assurance and Probabilistic Assessment of Mission-Critical Software-Intensive Systems

NASA Technical Reports Server (NTRS)

Guarro, Sergio B.

2010-01-01

This report validates and documents the detailed features and practical application of the framework for software intensive digital systems risk assessment and risk-informed safety assurance presented in the NASA PRA Procedures Guide for Managers and Practitioner. This framework, called herein the "Context-based Software Risk Model" (CSRM), enables the assessment of the contribution of software and software-intensive digital systems to overall system risk, in a manner which is entirely compatible and integrated with the format of a "standard" Probabilistic Risk Assessment (PRA), as currently documented and applied for NASA missions and applications. The CSRM also provides a risk-informed path and criteria for conducting organized and systematic digital system and software testing so that, within this risk-informed paradigm, the achievement of a quantitatively defined level of safety and mission success assurance may be targeted and demonstrated. The framework is based on the concept of context-dependent software risk scenarios and on the modeling of such scenarios via the use of traditional PRA techniques - i.e., event trees and fault trees - in combination with more advanced modeling devices such as the Dynamic Flowgraph Methodology (DFM) or other dynamic logic-modeling representations. The scenarios can be synthesized and quantified in a conditional logic and probabilistic formulation. The application of the CSRM method documented in this report refers to the MiniAERCam system designed and developed by the NASA Johnson Space Center.

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.

1992-01-01

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Methodology for assessing quantities of water and proppant injection, and water production associated with development of continuous petroleum accumulations

USGS Publications Warehouse

Haines, Seth S.

2015-07-13

The quantities of water and hydraulic fracturing proppant required for producing petroleum (oil, gas, and natural gas liquids) from continuous accumulations, and the quantities of water extracted during petroleum production, can be quantitatively assessed using a probabilistic approach. The water and proppant assessment methodology builds on the U.S. Geological Survey methodology for quantitative assessment of undiscovered technically recoverable petroleum resources in continuous accumulations. The U.S. Geological Survey assessment methodology for continuous petroleum accumulations includes fundamental concepts such as geologically defined assessment units, and probabilistic input values including well-drainage area, sweet- and non-sweet-spot areas, and success ratio within the untested area of each assessment unit. In addition to petroleum-related information, required inputs for the water and proppant assessment methodology include probabilistic estimates of per-well water usage for drilling, cementing, and hydraulic-fracture stimulation; the ratio of proppant to water for hydraulic fracturing; the percentage of hydraulic fracturing water that returns to the surface as flowback; and the ratio of produced water to petroleum over the productive life of each well. Water and proppant assessments combine information from recent or current petroleum assessments with water- and proppant-related input values for the assessment unit being studied, using Monte Carlo simulation, to yield probabilistic estimates of the volume of water for drilling, cementing, and hydraulic fracture stimulation; the quantity of proppant for hydraulic fracture stimulation; and the volumes of water produced as flowback shortly after well completion, and produced over the life of the well.

Probabilistic lifetime strength of aerospace materials via computational simulation

NASA Technical Reports Server (NTRS)

Boyce, Lola; Keating, Jerome P.; Lovelace, Thomas B.; Bast, Callie C.

1991-01-01

The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.

Assessing the Ability of Vegetation Indices to Identify Shallow Subsurface Water Flow Pathways from Hyperspectral Imagery Using Machine Learning: Methodology

NASA Astrophysics Data System (ADS)

Byers, J. M.; Doctor, K.

2017-12-01

A common application of the satellite and airborne acquired hyperspectral imagery in the visible and NIR spectrum is the assessment of vegetation. Various absorption features of plants related to both water and chlorophyll content can be used to measure the vigor and access to underlying water sources of the vegetation. The typical strategy is to form hand-crafted features from the hyperspectral data cube by selecting two wavelengths to form difference or ratio images in the pixel space. The new image attempts to provide greater contrast for some feature of the vegetation. The Normalized Difference Vegetation Index (NDVI) is a widely used example formed from the ratio of differences and sums at two different wavelengths. There are dozens of these indices that are ostensibly formed using insights about the underlying physics of the spectral absorption with claims to efficacy in representing various properties of vegetation. In the language of machine learning these vegetation indices are features that can be used as a useful data representation within an algorithm. In this work we use a powerful approach from machine learning, probabilistic graphical models (PGM), to balance the competing needs of using existing hydrological classifications of terrain while finding statistically reliable features within hyperspectral data for identifying the generative process of the data. The algorithm in its simplest form is called a Naïve Bayes (NB) classifier and can be constructed in a data-driven estimation procedure of the conditional probability distributions that form the PGM. The Naïve Bayes model assumes that all vegetation indices (VI) are independent of one another given the hydrological class label. We seek to test its validity in a pilot study of detecting subsurface water flow pathways from VI. A more sophisticated PGM will also be explored called a tree-augmented NB that accounts for the probabilistic dependence between VI features. This methodology provides a general approach for classifying hydrological structures from hyperspectral data.

Probabilistic evaluation of uncertainties and risks in aerospace components

NASA Technical Reports Server (NTRS)

Shah, A. R.; Shiao, M. C.; Nagpal, V. K.; Chamis, C. C.

1992-01-01

A methodology is presented for the computational simulation of primitive variable uncertainties, and attention is given to the simulation of specific aerospace components. Specific examples treated encompass a probabilistic material behavior model, as well as static, dynamic, and fatigue/damage analyses of a turbine blade in a mistuned bladed rotor in the SSME turbopumps. An account is given of the use of the NESSES probabilistic FEM analysis CFD code.

Incorporating linguistic, probabilistic, and possibilistic information in a risk-based approach for ranking contaminated sites.

PubMed

Zhang, Kejiang; Achari, Gopal; Pei, Yuansheng

2010-10-01

Different types of uncertain information-linguistic, probabilistic, and possibilistic-exist in site characterization. Their representation and propagation significantly influence the management of contaminated sites. In the absence of a framework with which to properly represent and integrate these quantitative and qualitative inputs together, decision makers cannot fully take advantage of the available and necessary information to identify all the plausible alternatives. A systematic methodology was developed in the present work to incorporate linguistic, probabilistic, and possibilistic information into the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), a subgroup of Multi-Criteria Decision Analysis (MCDA) methods for ranking contaminated sites. The identification of criteria based on the paradigm of comparative risk assessment provides a rationale for risk-based prioritization. Uncertain linguistic, probabilistic, and possibilistic information identified in characterizing contaminated sites can be properly represented as numerical values, intervals, probability distributions, and fuzzy sets or possibility distributions, and linguistic variables according to their nature. These different kinds of representation are first transformed into a 2-tuple linguistic representation domain. The propagation of hybrid uncertainties is then carried out in the same domain. This methodology can use the original site information directly as much as possible. The case study shows that this systematic methodology provides more reasonable results. © 2010 SETAC.

Analytical simulation and PROFAT II: a new methodology and a computer automated tool for fault tree analysis in chemical process industries.

PubMed

Khan, F I; Abbasi, S A

2000-07-10

Fault tree analysis (FTA) is based on constructing a hypothetical tree of base events (initiating events) branching into numerous other sub-events, propagating the fault and eventually leading to the top event (accident). It has been a powerful technique used traditionally in identifying hazards in nuclear installations and power industries. As the systematic articulation of the fault tree is associated with assigning probabilities to each fault, the exercise is also sometimes called probabilistic risk assessment. But powerful as this technique is, it is also very cumbersome and costly, limiting its area of application. We have developed a new algorithm based on analytical simulation (named as AS-II), which makes the application of FTA simpler, quicker, and cheaper; thus opening up the possibility of its wider use in risk assessment in chemical process industries. Based on the methodology we have developed a computer-automated tool. The details are presented in this paper.

Probabilistic Structural Analysis of SSME Turbopump Blades: Probabilistic Geometry Effects

NASA Technical Reports Server (NTRS)

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.

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

PubMed Central

2011-01-01

Background The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call. The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient. Methods The ontology-based Nurse Call System (oNCS) was developed as an extension of a Context-Aware Service Platform. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient. Results The oNCS system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the oNCS system and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed. Conclusions The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the oNCS system significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves. PMID:21294860

A probabilistic assessment of health risks associated with short-term exposure to tropospheric ozone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitfield, R.G; Biller, W.F.; Jusko, M.J.

1996-06-01

The work described in this report is part of a larger risk assessment sponsored by the U.S. Environmental Protection Agency. Earlier efforts developed exposure-response relationships for acute health effects among populations engaged in heavy exertion. Those efforts also developed a probabilistic national ambient air quality standards exposure model and a general methodology for integrating probabilistic exposure-response relation- ships and exposure estimates to calculate overall risk results. Recently published data make it possible to model additional health endpoints (for exposure at moderate exertion), including hospital admissions. New air quality and exposure estimates for alternative national ambient air quality standards for ozonemore » are combined with exposure-response models to produce the risk results for hospital admissions and acute health effects. Sample results explain the methodology and introduce risk output formats.« less

Probabilistic failure assessment with application to solid rocket motors

NASA Technical Reports Server (NTRS)

Jan, Darrell L.; Davidson, Barry D.; Moore, Nicholas R.

1990-01-01

A quantitative methodology is being developed for assessment of risk of failure of solid rocket motors. This probabilistic methodology employs best available engineering models and available information in a stochastic framework. The framework accounts for incomplete knowledge of governing parameters, intrinsic variability, and failure model specification error. Earlier case studies have been conducted on several failure modes of the Space Shuttle Main Engine. Work in progress on application of this probabilistic approach to large solid rocket boosters such as the Advanced Solid Rocket Motor for the Space Shuttle is described. Failure due to debonding has been selected as the first case study for large solid rocket motors (SRMs) since it accounts for a significant number of historical SRM failures. Impact of incomplete knowledge of governing parameters and failure model specification errors is expected to be important.

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

NASA Technical Reports Server (NTRS)

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.

Documentation of probabilistic fracture mechanics codes used for reactor pressure vessels subjected to pressurized thermal shock loading: Parts 1 and 2. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balkey, K.; Witt, F.J.; Bishop, B.A.

1995-06-01

Significant attention has been focused on the issue of reactor vessel pressurized thermal shock (PTS) for many years. Pressurized thermal shock transient events are characterized by a rapid cooldown at potentially high pressure levels that could lead to a reactor vessel integrity concern for some pressurized water reactors. As a result of regulatory and industry efforts in the early 1980`s, a probabilistic risk assessment methodology has been established to address this concern. Probabilistic fracture mechanics analyses are performed as part of this methodology to determine conditional probability of significant flaw extension for given pressurized thermal shock events. While recent industrymore » efforts are underway to benchmark probabilistic fracture mechanics computer codes that are currently used by the nuclear industry, Part I of this report describes the comparison of two independent computer codes used at the time of the development of the original U.S. Nuclear Regulatory Commission (NRC) pressurized thermal shock rule. The work that was originally performed in 1982 and 1983 to compare the U.S. NRC - VISA and Westinghouse (W) - PFM computer codes has been documented and is provided in Part I of this report. Part II of this report describes the results of more recent industry efforts to benchmark PFM computer codes used by the nuclear industry. This study was conducted as part of the USNRC-EPRI Coordinated Research Program for reviewing the technical basis for pressurized thermal shock (PTS) analyses of the reactor pressure vessel. The work focused on the probabilistic fracture mechanics (PFM) analysis codes and methods used to perform the PTS calculations. An in-depth review of the methodologies was performed to verify the accuracy and adequacy of the various different codes. The review was structured around a series of benchmark sample problems to provide a specific context for discussion and examination of the fracture mechanics methodology.« less

Probabilistic structural analysis to quantify uncertainties associated with turbopump blades

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.; Rubinstein, Robert; Chamis, Christos C.

1988-01-01

A probabilistic study of turbopump blades has been in progress at NASA Lewis Research Center for over the last two years. The objectives of this study are to evaluate the effects of uncertainties in geometry and material properties on the structural response of the turbopump blades to evaluate the tolerance limits on the design. A methodology based on probabilistic approach was developed to quantify the effects of the random uncertainties. The results indicate that only the variations in geometry have significant effects.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

This report presents the results of a fourth year effort of a research program, conducted for NASA-LeRC by the University of Texas at San Antonio (UTSA). The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subject to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 has been analyzed using the developed methodology.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep, and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

The results of a fourth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA) are presented. The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue, or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation was randomized and is included in the computer program, PROMISC. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

An object-oriented approach to risk and reliability analysis : methodology and aviation safety applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dandini, Vincent John; Duran, Felicia Angelica; Wyss, Gregory Dane

2003-09-01

This article describes how features of event tree analysis and Monte Carlo-based discrete event simulation can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology, with some of the best features of each. The resultant object-based event scenario tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible. Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST methodology is then applied to anmore » aviation safety problem that considers mechanisms by which an aircraft might become involved in a runway incursion incident. The resulting OBEST model demonstrates how a close link between human reliability analysis and probabilistic risk assessment methods can provide important insights into aviation safety phenomenology.« less

SCAP: a new methodology for safety management based on feedback from credible accident-probabilistic fault tree analysis system.

PubMed

Khan, F I; Iqbal, A; Ramesh, N; Abbasi, S A

2001-10-12

As it is conventionally done, strategies for incorporating accident--prevention measures in any hazardous chemical process industry are developed on the basis of input from risk assessment. However, the two steps-- risk assessment and hazard reduction (or safety) measures--are not linked interactively in the existing methodologies. This prevents a quantitative assessment of the impacts of safety measures on risk control. We have made an attempt to develop a methodology in which risk assessment steps are interactively linked with implementation of safety measures. The resultant system tells us the extent of reduction of risk by each successive safety measure. It also tells based on sophisticated maximum credible accident analysis (MCAA) and probabilistic fault tree analysis (PFTA) whether a given unit can ever be made 'safe'. The application of the methodology has been illustrated with a case study.

System Risk Assessment and Allocation in Conceptual Design

NASA Technical Reports Server (NTRS)

Mahadevan, Sankaran; Smith, Natasha L.; Zang, Thomas A. (Technical Monitor)

2003-01-01

As aerospace systems continue to evolve in addressing newer challenges in air and space transportation, there exists a heightened priority for significant improvement in system performance, cost effectiveness, reliability, and safety. Tools, which synthesize multidisciplinary integration, probabilistic analysis, and optimization, are needed to facilitate design decisions allowing trade-offs between cost and reliability. This study investigates tools for probabilistic analysis and probabilistic optimization in the multidisciplinary design of aerospace systems. A probabilistic optimization methodology is demonstrated for the low-fidelity design of a reusable launch vehicle at two levels, a global geometry design and a local tank design. Probabilistic analysis is performed on a high fidelity analysis of a Navy missile system. Furthermore, decoupling strategies are introduced to reduce the computational effort required for multidisciplinary systems with feedback coupling.

Probabilistic self-organizing maps for continuous data.

PubMed

Lopez-Rubio, Ezequiel

2010-10-01

The original self-organizing feature map did not define any probability distribution on the input space. However, the advantages of introducing probabilistic methodologies into self-organizing map models were soon evident. This has led to a wide range of proposals which reflect the current emergence of probabilistic approaches to computational intelligence. The underlying estimation theories behind them derive from two main lines of thought: the expectation maximization methodology and stochastic approximation methods. Here, we present a comprehensive view of the state of the art, with a unifying perspective of the involved theoretical frameworks. In particular, we examine the most commonly used continuous probability distributions, self-organization mechanisms, and learning schemes. Special emphasis is given to the connections among them and their relative advantages depending on the characteristics of the problem at hand. Furthermore, we evaluate their performance in two typical applications of self-organizing maps: classification and visualization.

Probabilistic vs linear blending approaches to shared control for wheelchair driving.

PubMed

Ezeh, Chinemelu; Trautman, Pete; Devigne, Louise; Bureau, Valentin; Babel, Marie; Carlson, Tom

2017-07-01

Some people with severe mobility impairments are unable to operate powered wheelchairs reliably and effectively, using commercially available interfaces. This has sparked a body of research into "smart wheelchairs", which assist users to drive safely and create opportunities for them to use alternative interfaces. Various "shared control" techniques have been proposed to provide an appropriate level of assistance that is satisfactory and acceptable to the user. Most shared control techniques employ a traditional strategy called linear blending (LB), where the user's commands and wheelchair's autonomous commands are combined in some proportion. In this paper, however, we implement a more generalised form of shared control called probabilistic shared control (PSC). This probabilistic formulation improves the accuracy of modelling the interaction between the user and the wheelchair by taking into account uncertainty in the interaction. In this paper, we demonstrate the practical success of PSC over LB in terms of safety, particularly for novice users.

Probabilistic structural analysis to quantify uncertainties associated with turbopump blades

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.; Rubinstein, Robert; Chamis, Christos C.

1987-01-01

A probabilistic study of turbopump blades has been in progress at NASA Lewis Research Center for over the last two years. The objectives of this study are to evaluate the effects of uncertainties in geometry and material properties on the structural response of the turbopump blades to evaluate the tolerance limits on the design. A methodology based on probabilistic approach has been developed to quantify the effects of the random uncertainties. The results of this study indicate that only the variations in geometry have significant effects.

Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling

PubMed Central

Franke, Michael; Degen, Judith

2016-01-01

Recent advances in probabilistic pragmatics have achieved considerable success in modeling speakers’ and listeners’ pragmatic reasoning as probabilistic inference. However, these models are usually applied to population-level data, and so implicitly suggest a homogeneous population without individual differences. Here we investigate potential individual differences in Theory-of-Mind related depth of pragmatic reasoning in so-called reference games that require drawing ad hoc Quantity implicatures of varying complexity. We show by Bayesian model comparison that a model that assumes a heterogenous population is a better predictor of our data, especially for comprehension. We discuss the implications for the treatment of individual differences in probabilistic models of language use. PMID:27149675

Probabilistic Methodology for Estimation of Number and Economic Loss (Cost) of Future Landslides in the San Francisco Bay Region, California

USGS Publications Warehouse

Crovelli, Robert A.; Coe, Jeffrey A.

2008-01-01

The Probabilistic Landslide Assessment Cost Estimation System (PLACES) presented in this report estimates the number and economic loss (cost) of landslides during a specified future time in individual areas, and then calculates the sum of those estimates. The analytic probabilistic methodology is based upon conditional probability theory and laws of expectation and variance. The probabilistic methodology is expressed in the form of a Microsoft Excel computer spreadsheet program. Using historical records, the PLACES spreadsheet is used to estimate the number of future damaging landslides and total damage, as economic loss, from future landslides caused by rainstorms in 10 counties of the San Francisco Bay region in California. Estimates are made for any future 5-year period of time. The estimated total number of future damaging landslides for the entire 10-county region during any future 5-year period of time is about 330. Santa Cruz County has the highest estimated number of damaging landslides (about 90), whereas Napa, San Francisco, and Solano Counties have the lowest estimated number of damaging landslides (5?6 each). Estimated direct costs from future damaging landslides for the entire 10-county region for any future 5-year period are about US $76 million (year 2000 dollars). San Mateo County has the highest estimated costs ($16.62 million), and Solano County has the lowest estimated costs (about $0.90 million). Estimated direct costs are also subdivided into public and private costs.

Probabilistic evaluation of fuselage-type composite structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1992-01-01

A methodology is developed to computationally simulate the uncertain behavior of composite structures. The uncertain behavior includes buckling loads, natural frequencies, displacements, stress/strain etc., which are the consequences of the random variation (scatter) of the primitive (independent random) variables in the constituent, ply, laminate and structural levels. This methodology is implemented in the IPACS (Integrated Probabilistic Assessment of Composite Structures) computer code. A fuselage-type composite structure is analyzed to demonstrate the code's capability. The probability distribution functions of the buckling loads, natural frequency, displacement, strain and stress are computed. The sensitivity of each primitive (independent random) variable to a given structural response is also identified from the analyses.

Probabilistic assessment method of the non-monotonic dose-responses-Part I: Methodological approach.

PubMed

Chevillotte, Grégoire; Bernard, Audrey; Varret, Clémence; Ballet, Pascal; Bodin, Laurent; Roudot, Alain-Claude

2017-08-01

More and more studies aim to characterize non-monotonic dose response curves (NMDRCs). The greatest difficulty is to assess the statistical plausibility of NMDRCs from previously conducted dose response studies. This difficulty is linked to the fact that these studies present (i) few doses tested, (ii) a low sample size per dose, and (iii) the absence of any raw data. In this study, we propose a new methodological approach to probabilistically characterize NMDRCs. The methodology is composed of three main steps: (i) sampling from summary data to cover all the possibilities that may be presented by the responses measured by dose and to obtain a new raw database, (ii) statistical analysis of each sampled dose-response curve to characterize the slopes and their signs, and (iii) characterization of these dose-response curves according to the variation of the sign in the slope. This method allows characterizing all types of dose-response curves and can be applied both to continuous data and to discrete data. The aim of this study is to present the general principle of this probabilistic method which allows to assess the non-monotonic dose responses curves, and to present some results. Copyright © 2017 Elsevier Ltd. All rights reserved.

The European ASAMPSA_E project : towards guidance to model the impact of high amplitude natural hazards in the probabilistic safety assessment of nuclear power plants. Information on the project progress and needs from the geosciences.

NASA Astrophysics Data System (ADS)

Raimond, Emmanuel; Decker, Kurt; Guigueno, Yves; Klug, Joakim; Loeffler, Horst

2015-04-01

The Fukushima nuclear accident in Japan resulted from the combination of two correlated extreme external events (earthquake and tsunami). The consequences, in particular flooding, went beyond what was considered in the initial engineering design design of nuclear power plants (NPPs). Such situations can in theory be identified using probabilistic safety assessment (PSA) methodology. PSA results may then lead industry (system suppliers and utilities) or Safety Authorities to take appropriate decisions to reinforce the defence-in-depth of the NPP for low probability event but high amplitude consequences. In reality, the development of such PSA remains a challenging task. Definitions of the design basis of NPPs, for example, require data on events with occurrence probabilities not higher than 10-4 per year. Today, even lower probabilities, down to 10-8, are expected and typically used for probabilistic safety analyses (PSA) of NPPs and the examination of so-called design extension conditions. Modelling the combinations of natural or man-made hazards that can affect a NPP and affecting some meaningful probability of occurrence seems to be difficult. The European project ASAMPSAE (www.asampsa.eu) gathers more than 30 organizations (industry, research, safety control) from Europe, US and Japan and aims at identifying some meaningful practices to extend the scope and the quality of the existing probabilistic safety analysis developed for nuclear power plants. It offers a framework to discuss, at a technical level, how "extended PSA" can be developed efficiently and be used to verify if the robustness of Nuclear Power Plants (NPPs) in their environment is sufficient. The paper will present the objectives of this project, some first lessons and introduce which type of guidance is being developed. It will explain the need of expertise from geosciences to support the nuclear safety assessment in the different area (seismotectonic, hydrological, meteorological and biological hazards, …).

Modelling default and likelihood reasoning as probabilistic reasoning

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a qualitative default probabilistic (QDP) logic and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequent results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

The application of probabilistic fracture analysis to residual life evaluation of embrittled reactor vessels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickson, T.L.; Simonen, F.A.

1992-05-01

Probabilistic fracture mechanics analysis is a major element of comprehensive probabilistic methodology on which current NRC regulatory requirements for pressurized water reactor vessel integrity evaluation are based. Computer codes such as OCA-P and VISA-II perform probabilistic fracture analyses to estimate the increase in vessel failure probability that occurs as the vessel material accumulates radiation damage over the operating life of the vessel. The results of such analyses, when compared with limits of acceptable failure probabilities, provide an estimation of the residual life of a vessel. Such codes can be applied to evaluate the potential benefits of plant-specific mitigating actions designedmore » to reduce the probability of failure of a reactor vessel. 10 refs.« less

The application of probabilistic fracture analysis to residual life evaluation of embrittled reactor vessels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickson, T.L.; Simonen, F.A.

1992-01-01

Probabilistic fracture mechanics analysis is a major element of comprehensive probabilistic methodology on which current NRC regulatory requirements for pressurized water reactor vessel integrity evaluation are based. Computer codes such as OCA-P and VISA-II perform probabilistic fracture analyses to estimate the increase in vessel failure probability that occurs as the vessel material accumulates radiation damage over the operating life of the vessel. The results of such analyses, when compared with limits of acceptable failure probabilities, provide an estimation of the residual life of a vessel. Such codes can be applied to evaluate the potential benefits of plant-specific mitigating actions designedmore » to reduce the probability of failure of a reactor vessel. 10 refs.« less

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Phase transitions in coupled map lattices and in associated probabilistic cellular automata.

PubMed

Just, Wolfram

2006-10-01

Analytical tools are applied to investigate piecewise linear coupled map lattices in terms of probabilistic cellular automata. The so-called disorder condition of probabilistic cellular automata is closely related with attracting sets in coupled map lattices. The importance of this condition for the suppression of phase transitions is illustrated by spatially one-dimensional systems. Invariant densities and temporal correlations are calculated explicitly. Ising type phase transitions are found for one-dimensional coupled map lattices acting on repelling sets and for a spatially two-dimensional Miller-Huse-like system with stable long time dynamics. Critical exponents are calculated within a finite size scaling approach. The relevance of detailed balance of the resulting probabilistic cellular automaton for the critical behavior is pointed out.

A methodology for the extraction of quantitative risk indexes from medical injuries compensation claims.

PubMed

Dalle Carbonare, Simona; Folli, Fulvia; Patrini, Emanuele; Bellazzi, Riccardo

2009-01-01

The prevention of adverse events and medical injuries due to malpractice or suboptimal delivery of health care services is one of the major concerns of citizens and Health Care Organizations. One way to understand adverse events is to analyze the compensation requests for medical injuries that are claimed to hospital or health care services. In this paper we describe the results obtained by applying a probabilistic model, called the actuarial model, to analyze 317 cases of injuries with compensation requests collected from 1999 to the first semester of 2007 by the Azienda Ospedaliera (A.O.) of Lodi, in the Northern part of Italy. The approach, adapted from operational and financial risk management, proved to be useful to understand the risk structure in terms of frequency, severity, expected and unexpected loss related to adverse events.

Physically-Based Probabilistic Seismic Hazard Analysis Using Broad-Band Ground Motion Simulation: a Case Study for Prince Islands Fault, Marmara Sea

NASA Astrophysics Data System (ADS)

Mert, A.

2016-12-01

The main motivation of this study is the impending occurrence of a catastrophic earthquake along the Prince Island Fault (PIF) in Marmara Sea and the disaster risk around Marmara region, especially in İstanbul. This study provides the results of a physically-based Probabilistic Seismic Hazard Analysis (PSHA) methodology, using broad-band strong ground motion simulations, for sites within the Marmara region, Turkey, due to possible large earthquakes throughout the PIF segments in the Marmara Sea. The methodology is called physically-based because it depends on the physical processes of earthquake rupture and wave propagation to simulate earthquake ground motion time histories. We include the effects of all considerable magnitude earthquakes. To generate the high frequency (0.5-20 Hz) part of the broadband earthquake simulation, the real small magnitude earthquakes recorded by local seismic array are used as an Empirical Green's Functions (EGF). For the frequencies below 0.5 Hz the simulations are obtained using by Synthetic Green's Functions (SGF) which are synthetic seismograms calculated by an explicit 2D/3D elastic finite difference wave propagation routine. Using by a range of rupture scenarios for all considerable magnitude earthquakes throughout the PIF segments we provide a hazard calculation for frequencies 0.1-20 Hz. Physically based PSHA used here follows the same procedure of conventional PSHA except that conventional PSHA utilizes point sources or a series of point sources to represent earthquakes and this approach utilizes full rupture of earthquakes along faults. Further, conventional PSHA predicts ground-motion parameters using by empirical attenuation relationships, whereas this approach calculates synthetic seismograms for all magnitude earthquakes to obtain ground-motion parameters. PSHA results are produced for 2%, 10% and 50% hazards for all studied sites in Marmara Region.

Modern proposal of methodology for retrieval of characteristic synthetic rainfall hyetographs

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Burszta-Adamiak, Ewa; Łomotowski, Janusz; Stańczyk, Justyna

2017-11-01

Modern engineering workshop of designing and modelling complex drainage systems is based on hydrodynamic modelling and has a probabilistic character. Its practical application requires a change regarding rainfall models accepted at the input. Previously used artificial rainfall models of simplified form, e.g. block precipitation or Euler's type II model rainfall are no longer sufficient. It is noticeable that urgent clarification is needed as regards the methodology of standardized rainfall hyetographs that would take into consideration the specifics of local storm rainfall temporal dynamics. The aim of the paper is to present a proposal for innovative methodology for determining standardized rainfall hyetographs, based on statistical processing of the collection of actual local precipitation characteristics. Proposed methodology is based on the classification of standardized rainfall hyetographs with the use of cluster analysis. Its application is presented on the example of selected rain gauges localized in Poland. Synthetic rainfall hyetographs achieved as a final result may be used for hydrodynamic modelling of sewerage systems, including probabilistic detection of necessary capacity of retention reservoirs.

An approximate methods approach to probabilistic structural analysis

NASA Technical Reports Server (NTRS)

Mcclung, R. C.; Millwater, H. R.; Wu, Y.-T.; Thacker, B. H.; Burnside, O. H.

1989-01-01

A probabilistic structural analysis method (PSAM) is described which makes an approximate calculation of the structural response of a system, including the associated probabilistic distributions, with minimal computation time and cost, based on a simplified representation of the geometry, loads, and material. The method employs the fast probability integration (FPI) algorithm of Wu and Wirsching. Typical solution strategies are illustrated by formulations for a representative critical component chosen from the Space Shuttle Main Engine (SSME) as part of a major NASA-sponsored program on PSAM. Typical results are presented to demonstrate the role of the methodology in engineering design and analysis.

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Review of methods for developing regional probabilistic risk assessments, part 2: modeling invasive plant, insect, and pathogen species

Treesearch

P. B. Woodbury; D. A. Weinstein

2010-01-01

We reviewed probabilistic regional risk assessment methodologies to identify the methods that are currently in use and are capable of estimating threats to ecosystems from fire and fuels, invasive species, and their interactions with stressors. In a companion chapter, we highlight methods useful for evaluating risks from fire. In this chapter, we highlight methods...

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Terminal Model Of Newtonian Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail

1994-01-01

Paper presents study of theory of Newtonian dynamics of terminal attractors and repellers, focusing on issues of reversibility vs. irreversibility and deterministic evolution vs. probabilistic or chaotic evolution of dynamic systems. Theory developed called "terminal dynamics" emphasizes difference between it and classical Newtonian dynamics. Also holds promise for explaining irreversibility, unpredictability, probabilistic behavior, and chaos in turbulent flows, in thermodynamic phenomena, and in other dynamic phenomena and systems.

Stochastic model for fatigue crack size and cost effective design decisions. [for aerospace structures

NASA Technical Reports Server (NTRS)

Hanagud, S.; Uppaluri, B.

1975-01-01

This paper describes a methodology for making cost effective fatigue design decisions. The methodology is based on a probabilistic model for the stochastic process of fatigue crack growth with time. The development of a particular model for the stochastic process is also discussed in the paper. The model is based on the assumption of continuous time and discrete space of crack lengths. Statistical decision theory and the developed probabilistic model are used to develop the procedure for making fatigue design decisions on the basis of minimum expected cost or risk function and reliability bounds. Selections of initial flaw size distribution, NDT, repair threshold crack lengths, and inspection intervals are discussed.

Probabilistic analysis of structures involving random stress-strain behavior

NASA Technical Reports Server (NTRS)

Millwater, H. R.; Thacker, B. H.; Harren, S. V.

1991-01-01

The present methodology for analysis of structures with random stress strain behavior characterizes the uniaxial stress-strain curve in terms of (1) elastic modulus, (2) engineering stress at initial yield, (3) initial plastic-hardening slope, (4) engineering stress at point of ultimate load, and (5) engineering strain at point of ultimate load. The methodology is incorporated into the Numerical Evaluation of Stochastic Structures Under Stress code for probabilistic structural analysis. The illustrative problem of a thick cylinder under internal pressure, where both the internal pressure and the stress-strain curve are random, is addressed by means of the code. The response value is the cumulative distribution function of the equivalent plastic strain at the inner radius.

Probabilistic Sizing and Verification of Space Ceramic Structures

NASA Astrophysics Data System (ADS)

Denaux, David; Ballhause, Dirk; Logut, Daniel; Lucarelli, Stefano; Coe, Graham; Laine, Benoit

2012-07-01

Sizing of ceramic parts is best optimised using a probabilistic approach which takes into account the preexisting flaw distribution in the ceramic part to compute a probability of failure of the part depending on the applied load, instead of a maximum allowable load as for a metallic part. This requires extensive knowledge of the material itself but also an accurate control of the manufacturing process. In the end, risk reduction approaches such as proof testing may be used to lower the final probability of failure of the part. Sizing and verification of ceramic space structures have been performed by Astrium for more than 15 years, both with Zerodur and SiC: Silex telescope structure, Seviri primary mirror, Herschel telescope, Formosat-2 instrument, and other ceramic structures flying today. Throughout this period of time, Astrium has investigated and developed experimental ceramic analysis tools based on the Weibull probabilistic approach. In the scope of the ESA/ESTEC study: “Mechanical Design and Verification Methodologies for Ceramic Structures”, which is to be concluded in the beginning of 2012, existing theories, technical state-of-the-art from international experts, and Astrium experience with probabilistic analysis tools have been synthesized into a comprehensive sizing and verification method for ceramics. Both classical deterministic and more optimised probabilistic methods are available, depending on the criticality of the item and on optimisation needs. The methodology, based on proven theory, has been successfully applied to demonstration cases and has shown its practical feasibility.

Proposal of a method for evaluating tsunami risk using response-surface methodology

NASA Astrophysics Data System (ADS)

Fukutani, Y.

2017-12-01

Information on probabilistic tsunami inundation hazards is needed to define and evaluate tsunami risk. Several methods for calculating these hazards have been proposed (e.g. Løvholt et al. (2012), Thio (2012), Fukutani et al. (2014), Goda et al. (2015)). However, these methods are inefficient, and their calculation cost is high, since they require multiple tsunami numerical simulations, therefore lacking versatility. In this study, we proposed a simpler method for tsunami risk evaluation using response-surface methodology. Kotani et al. (2016) proposed an evaluation method for the probabilistic distribution of tsunami wave-height using a response-surface methodology. We expanded their study and developed a probabilistic distribution of tsunami inundation depth. We set the depth (x1) and the slip (x2) of an earthquake fault as explanatory variables and tsunami inundation depth (y) as an object variable. Subsequently, tsunami risk could be evaluated by conducting a Monte Carlo simulation, assuming that the generation probability of an earthquake follows a Poisson distribution, the probability distribution of tsunami inundation depth follows the distribution derived from a response-surface, and the damage probability of a target follows a log normal distribution. We applied the proposed method to a wood building located on the coast of Tokyo Bay. We implemented a regression analysis based on the results of 25 tsunami numerical calculations and developed a response-surface, which was defined as y=ax1+bx2+c (a:0.2615, b:3.1763, c=-1.1802). We assumed proper probabilistic distribution for earthquake generation, inundation height, and vulnerability. Based on these probabilistic distributions, we conducted Monte Carlo simulations of 1,000,000 years. We clarified that the expected damage probability of the studied wood building is 22.5%, assuming that an earthquake occurs. The proposed method is therefore a useful and simple way to evaluate tsunami risk using a response-surface and Monte Carlo simulation without conducting multiple tsunami numerical simulations.

Background for Joint Systems Aspects of AIR 6000

DTIC Science & Technology

2000-04-01

Checkland’s Soft Systems Methodology [7, 8,9]. The analytical techniques that are proposed for joint systems work are based on calculating probability...Supporting Global Interests 21 DSTO-CR-0155 SLMP Structural Life Management Plan SOW Stand-Off Weapon SSM Soft Systems Methodology UAV Uninhabited Aerial... Systems Methodology in Action, John Wiley & Sons, Chichester, 1990. [101 Pearl, Judea, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible

Probabilistic Structural Analysis Program

NASA Technical Reports Server (NTRS)

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.

PCEMCAN - Probabilistic Ceramic Matrix Composites Analyzer: User's Guide, Version 1.0

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Mital, Subodh K.; Murthy, Pappu L. N.

1998-01-01

PCEMCAN (Probabalistic CEramic Matrix Composites ANalyzer) is an integrated computer code developed at NASA Lewis Research Center that simulates uncertainties associated with the constituent properties, manufacturing process, and geometric parameters of fiber reinforced ceramic matrix composites and quantifies their random thermomechanical behavior. The PCEMCAN code can perform the deterministic as well as probabilistic analyses to predict thermomechanical properties. This User's guide details the step-by-step procedure to create input file and update/modify the material properties database required to run PCEMCAN computer code. An overview of the geometric conventions, micromechanical unit cell, nonlinear constitutive relationship and probabilistic simulation methodology is also provided in the manual. Fast probability integration as well as Monte-Carlo simulation methods are available for the uncertainty simulation. Various options available in the code to simulate probabilistic material properties and quantify sensitivity of the primitive random variables have been described. The description of deterministic as well as probabilistic results have been described using demonstration problems. For detailed theoretical description of deterministic and probabilistic analyses, the user is referred to the companion documents "Computational Simulation of Continuous Fiber-Reinforced Ceramic Matrix Composite Behavior," NASA TP-3602, 1996 and "Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites", NASA TM 4766, June 1997.

Development of a probabilistic analysis methodology for structural reliability estimation

NASA Technical Reports Server (NTRS)

Torng, T. Y.; Wu, Y.-T.

1991-01-01

The novel probabilistic analysis method for assessment of structural reliability presented, which combines fast-convolution with an efficient structural reliability analysis, can after identifying the most important point of a limit state proceed to establish a quadratic-performance function. It then transforms the quadratic function into a linear one, and applies fast convolution. The method is applicable to problems requiring computer-intensive structural analysis. Five illustrative examples of the method's application are given.

Probabilistic design of fibre concrete structures

NASA Astrophysics Data System (ADS)

Pukl, R.; Novák, D.; Sajdlová, T.; Lehký, D.; Červenka, J.; Červenka, V.

2017-09-01

Advanced computer simulation is recently well-established methodology for evaluation of resistance of concrete engineering structures. The nonlinear finite element analysis enables to realistically predict structural damage, peak load, failure, post-peak response, development of cracks in concrete, yielding of reinforcement, concrete crushing or shear failure. The nonlinear material models can cover various types of concrete and reinforced concrete: ordinary concrete, plain or reinforced, without or with prestressing, fibre concrete, (ultra) high performance concrete, lightweight concrete, etc. Advanced material models taking into account fibre concrete properties such as shape of tensile softening branch, high toughness and ductility are described in the paper. Since the variability of the fibre concrete material properties is rather high, the probabilistic analysis seems to be the most appropriate format for structural design and evaluation of structural performance, reliability and safety. The presented combination of the nonlinear analysis with advanced probabilistic methods allows evaluation of structural safety characterized by failure probability or by reliability index respectively. Authors offer a methodology and computer tools for realistic safety assessment of concrete structures; the utilized approach is based on randomization of the nonlinear finite element analysis of the structural model. Uncertainty of the material properties or their randomness obtained from material tests are accounted in the random distribution. Furthermore, degradation of the reinforced concrete materials such as carbonation of concrete, corrosion of reinforcement, etc. can be accounted in order to analyze life-cycle structural performance and to enable prediction of the structural reliability and safety in time development. The results can serve as a rational basis for design of fibre concrete engineering structures based on advanced nonlinear computer analysis. The presented methodology is illustrated on results from two probabilistic studies with different types of concrete structures related to practical applications and made from various materials (with the parameters obtained from real material tests).

Landslide hazard analysis for pipelines: The case of the Simonette river crossing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grivas, D.A.; Schultz, B.C.; O`Neil, G.

1995-12-31

The overall objective of this study is to develop a probabilistic methodology to analyze landslide hazards and their effects on the safety of buried pipelines. The methodology incorporates a range of models that can accommodate differences in the ground movement modes and the amount and type of information available at various site locations. Two movement modes are considered, namely (a) instantaneous (catastrophic) slides, and (b) gradual ground movement which may result in cumulative displacements over the pipeline design life (30--40 years) that are in excess of allowable values. Probabilistic analysis is applied in each case to address the uncertainties associatedmore » with important factors that control slope stability. Availability of information ranges from relatively well studied, instrumented installations to cases where data is limited to what can be derived from topographic and geologic maps. The methodology distinguishes between procedures applied where there is little information and those that can be used when relatively extensive data is available. important aspects of the methodology are illustrated in a case study involving a pipeline located in Northern Alberta, Canada, in the Simonette river valley.« less

Probabilistic population aging

PubMed Central

2017-01-01

We merge two methodologies, prospective measures of population aging and probabilistic population forecasts. We compare the speed of change and variability in forecasts of the old age dependency ratio and the prospective old age dependency ratio as well as the same comparison for the median age and the prospective median age. While conventional measures of population aging are computed on the basis of the number of years people have already lived, prospective measures are computed also taking account of the expected number of years they have left to live. Those remaining life expectancies change over time and differ from place to place. We compare the probabilistic distributions of the conventional and prospective measures using examples from China, Germany, Iran, and the United States. The changes over time and the variability of the prospective indicators are smaller than those that are observed in the conventional ones. A wide variety of new results emerge from the combination of methodologies. For example, for Germany, Iran, and the United States the likelihood that the prospective median age of the population in 2098 will be lower than it is today is close to 100 percent. PMID:28636675

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

NASA Astrophysics Data System (ADS)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Modelling default and likelihood reasoning as probabilistic

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

Fatigue crack growth model RANDOM2 user manual. Appendix 1: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN program RANDOM2 is presented in the form of a user's manual. RANDOM2 is based on fracture mechanics using a probabilistic fatigue crack growth model. It predicts the random lifetime of an engine component to reach a given crack size. Details of the theoretical background, input data instructions, and a sample problem illustrating the use of the program are included.

Fatigue strength reduction model: RANDOM3 and RANDOM4 user manual. Appendix 2: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN programs RANDOM3 and RANDOM4 are documented in the form of a user's manual. Both programs are based on fatigue strength reduction, using a probabilistic constitutive model. The programs predict the random lifetime of an engine component to reach a given fatigue strength. The theoretical backgrounds, input data instructions, and sample problems illustrating the use of the programs are included.

Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Mital, Subodh K.; Shah, Ashwin R.

1997-01-01

The properties of ceramic matrix composites (CMC's) are known to display a considerable amount of scatter due to variations in fiber/matrix properties, interphase properties, interphase bonding, amount of matrix voids, and many geometry- or fabrication-related parameters, such as ply thickness and ply orientation. This paper summarizes preliminary studies in which formal probabilistic descriptions of the material-behavior- and fabrication-related parameters were incorporated into micromechanics and macromechanics for CMC'S. In this process two existing methodologies, namely CMC micromechanics and macromechanics analysis and a fast probability integration (FPI) technique are synergistically coupled to obtain the probabilistic composite behavior or response. Preliminary results in the form of cumulative probability distributions and information on the probability sensitivities of the response to primitive variables for a unidirectional silicon carbide/reaction-bonded silicon nitride (SiC/RBSN) CMC are presented. The cumulative distribution functions are computed for composite moduli, thermal expansion coefficients, thermal conductivities, and longitudinal tensile strength at room temperature. The variations in the constituent properties that directly affect these composite properties are accounted for via assumed probabilistic distributions. Collectively, the results show that the present technique provides valuable information about the composite properties and sensitivity factors, which is useful to design or test engineers. Furthermore, the present methodology is computationally more efficient than a standard Monte-Carlo simulation technique; and the agreement between the two solutions is excellent, as shown via select examples.

General Purpose Probabilistic Programming Platform with Effective Stochastic Inference

DTIC Science & Technology

2018-04-01

2.2 Venture 10 2.3 BayesDB 12 2.4 Picture 17 2.5 MetaProb 20 3.0 METHODS , ASSUMPTIONS, AND PROCEDURES 22 4.0 RESULTS AND DISCUSSION 23 4.1...The methods section outlines the research approach. The results and discussion section gives representative quantitative and qualitative results...modeling via CrossCat, a probabilistic method that emulates many of the judgment calls ordinarily made by a human data analyst. This AI assistance

A probabilistic seismic risk assessment procedure for nuclear power plants: (I) Methodology

USGS Publications Warehouse

Huang, Y.-N.; Whittaker, A.S.; Luco, N.

2011-01-01

A new procedure for probabilistic seismic risk assessment of nuclear power plants (NPPs) is proposed. This procedure modifies the current procedures using tools developed recently for performance-based earthquake engineering of buildings. The proposed procedure uses (a) response-based fragility curves to represent the capacity of structural and nonstructural components of NPPs, (b) nonlinear response-history analysis to characterize the demands on those components, and (c) Monte Carlo simulations to determine the damage state of the components. The use of response-rather than ground-motion-based fragility curves enables the curves to be independent of seismic hazard and closely related to component capacity. The use of Monte Carlo procedure enables the correlation in the responses of components to be directly included in the risk assessment. An example of the methodology is presented in a companion paper to demonstrate its use and provide the technical basis for aspects of the methodology. ?? 2011 Published by Elsevier B.V.

Carbon dioxide fluid-flow modeling and injectivity calculations

USGS Publications Warehouse

Burke, Lauri

2011-01-01

These results were used to classify subsurface formations into three permeability classifications for the probabilistic calculations of storage efficiency and containment risk of the U.S. Geological Survey geologic carbon sequestration assessment methodology. This methodology is currently in use to determine the total carbon dioxide containment capacity of the onshore and State waters areas of the United States.

How to combine probabilistic and fuzzy uncertainties in fuzzy control

NASA Technical Reports Server (NTRS)

Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert

1991-01-01

Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

A probabilistic static stress analysis methodology has been developed to estimate the reliability of a composite structure. Closed form stress analysis methods are the primary analytical tools used in this methodology. These structural mechanics methods are used to identify independent variables whose variations significantly affect the performance of the structure. Once these variables are identified, scatter in their values is evaluated and statistically characterized. The scatter in applied loads and the structural parameters are then fitted to appropriate probabilistic distribution functions. Numerical integration techniques are applied to compute the structural reliability. The predicted reliability accounts for scatter due to variability in material strength, applied load, fabrication and assembly processes. The influence of structural geometry and mode of failure are also considerations in the evaluation. Example problems are given to illustrate various levels of analytical complexity.

Rocketdyne PSAM: In-house enhancement/application

NASA Technical Reports Server (NTRS)

Newell, J. F.; Rajagopal, K. R.; Ohara, K.

1991-01-01

The development was initiated of the Probabilistic Design Analysis (PDA) Process for rocket engines. This will enable engineers a quantitative assessment of calculated reliability during the design process. The PDA will help choose better designs, make them more robust, and help decide on critical tests to help demonstrate key reliability issues to aid in improving the confidence of the engine capabilities. Rockedyne's involvement with the Composite Loads Spectra (CLS) and Probabilistic Structural Analysis Methodology (PSAM) contracts started this effort and are key elements in the on-going developments. Internal development efforts and hardware applications complement and extend the CLS and PSAM efforts. The completion of the CLS option work and the follow-on PSAM developments will also be integral parts of this methodology. A brief summary of these efforts is presented.

Commercialization of NESSUS: Status

NASA Technical Reports Server (NTRS)

Thacker, Ben H.; Millwater, Harry R.

1991-01-01

A plan was initiated in 1988 to commercialize the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) probabilistic structural analysis software. The goal of the on-going commercialization effort is to begin the transfer of Probabilistic Structural Analysis Method (PSAM) developed technology into industry and to develop additional funding resources in the general area of structural reliability. The commercialization effort is summarized. The SwRI NESSUS Software System is a general purpose probabilistic finite element computer program using state of the art methods for predicting stochastic structural response due to random loads, material properties, part geometry, and boundary conditions. NESSUS can be used to assess structural reliability, to compute probability of failure, to rank the input random variables by importance, and to provide a more cost effective design than traditional methods. The goal is to develop a general probabilistic structural analysis methodology to assist in the certification of critical components in the next generation Space Shuttle Main Engine.

bayesPop: Probabilistic Population Projections

PubMed Central

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

A Comparison of Probabilistic and Deterministic Campaign Analysis for Human Space Exploration

NASA Technical Reports Server (NTRS)

Merrill, R. Gabe; Andraschko, Mark; Stromgren, Chel; Cirillo, Bill; Earle, Kevin; Goodliff, Kandyce

2008-01-01

Human space exploration is by its very nature an uncertain endeavor. Vehicle reliability, technology development risk, budgetary uncertainty, and launch uncertainty all contribute to stochasticity in an exploration scenario. However, traditional strategic analysis has been done in a deterministic manner, analyzing and optimizing the performance of a series of planned missions. History has shown that exploration scenarios rarely follow such a planned schedule. This paper describes a methodology to integrate deterministic and probabilistic analysis of scenarios in support of human space exploration. Probabilistic strategic analysis is used to simulate "possible" scenario outcomes, based upon the likelihood of occurrence of certain events and a set of pre-determined contingency rules. The results of the probabilistic analysis are compared to the nominal results from the deterministic analysis to evaluate the robustness of the scenario to adverse events and to test and optimize contingency planning.

Probabilistic numerics and uncertainty in computations

PubMed Central

Hennig, Philipp; Osborne, Michael A.; Girolami, Mark

2015-01-01

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations. PMID:26346321

Probabilistic numerics and uncertainty in computations.

PubMed

Hennig, Philipp; Osborne, Michael A; Girolami, Mark

2015-07-08

We deliver a call to arms for probabilistic numerical methods : algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

NASA Astrophysics Data System (ADS)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

Costing the satellite power system

NASA Technical Reports Server (NTRS)

Hazelrigg, G. A., Jr.

1978-01-01

The paper presents a methodology for satellite power system costing, places approximate limits on the accuracy possible in cost estimates made at this time, and outlines the use of probabilistic cost information in support of the decision-making process. Reasons for using probabilistic costing or risk analysis procedures instead of standard deterministic costing procedures are considered. Components of cost, costing estimating relationships, grass roots costing, and risk analysis are discussed. Risk analysis using a Monte Carlo simulation model is used to estimate future costs.

Probabilistic Modeling of Settlement Risk at Land Disposal Facilities - 12304

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foye, Kevin C.; Soong, Te-Yang

2012-07-01

The long-term reliability of land disposal facility final cover systems - and therefore the overall waste containment - depends on the distortions imposed on these systems by differential settlement/subsidence. The evaluation of differential settlement is challenging because of the heterogeneity of the waste mass (caused by inconsistent compaction, void space distribution, debris-soil mix ratio, waste material stiffness, time-dependent primary compression of the fine-grained soil matrix, long-term creep settlement of the soil matrix and the debris, etc.) at most land disposal facilities. Deterministic approaches to long-term final cover settlement prediction are not able to capture the spatial variability in the wastemore » mass and sub-grade properties which control differential settlement. An alternative, probabilistic solution is to use random fields to model the waste and sub-grade properties. The modeling effort informs the design, construction, operation, and maintenance of land disposal facilities. A probabilistic method to establish design criteria for waste placement and compaction is introduced using the model. Random fields are ideally suited to problems of differential settlement modeling of highly heterogeneous foundations, such as waste. Random fields model the seemingly random spatial distribution of a design parameter, such as compressibility. When used for design, the use of these models prompts the need for probabilistic design criteria. It also allows for a statistical approach to waste placement acceptance criteria. An example design evaluation was performed, illustrating the use of the probabilistic differential settlement simulation methodology to assemble a design guidance chart. The purpose of this design evaluation is to enable the designer to select optimal initial combinations of design slopes and quality control acceptance criteria that yield an acceptable proportion of post-settlement slopes meeting some design minimum. For this specific example, relative density, which can be determined through field measurements, was selected as the field quality control parameter for waste placement. This technique can be extended to include a rigorous performance-based methodology using other parameters (void space criteria, debris-soil mix ratio, pre-loading, etc.). As shown in this example, each parameter range, or sets of parameter ranges can be selected such that they can result in an acceptable, long-term differential settlement according to the probabilistic model. The methodology can also be used to re-evaluate the long-term differential settlement behavior at closed land disposal facilities to identify, if any, problematic facilities so that remedial action (e.g., reinforcement of upper and intermediate waste layers) can be implemented. Considering the inherent spatial variability in waste and earth materials and the need for engineers to apply sound quantitative practices to engineering analysis, it is important to apply the available probabilistic techniques to problems of differential settlement. One such method to implement probability-based differential settlement analyses for the design of landfill final covers has been presented. The design evaluation technique presented is one tool to bridge the gap from deterministic practice to probabilistic practice. (authors)« less

CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential

USGS Publications Warehouse

Moss, R.E.S.; Seed, R.B.; Kayen, R.E.; Stewart, J.P.; Der Kiureghian, A.; Cetin, K.O.

2006-01-01

This paper presents a complete methodology for both probabilistic and deterministic assessment of seismic soil liquefaction triggering potential based on the cone penetration test (CPT). A comprehensive worldwide set of CPT-based liquefaction field case histories were compiled and back analyzed, and the data then used to develop probabilistic triggering correlations. Issues investigated in this study include improved normalization of CPT resistance measurements for the influence of effective overburden stress, and adjustment to CPT tip resistance for the potential influence of "thin" liquefiable layers. The effects of soil type and soil character (i.e., "fines" adjustment) for the new correlations are based on a combination of CPT tip and sleeve resistance. To quantify probability for performancebased engineering applications, Bayesian "regression" methods were used, and the uncertainties of all variables comprising both the seismic demand and the liquefaction resistance were estimated and included in the analysis. The resulting correlations were developed using a Bayesian framework and are presented in both probabilistic and deterministic formats. The results are compared to previous probabilistic and deterministic correlations. ?? 2006 ASCE.

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

Probabilistic Tsunami Hazard Assessment: the Seaside, Oregon Pilot Study

NASA Astrophysics Data System (ADS)

Gonzalez, F. I.; Geist, E. L.; Synolakis, C.; Titov, V. V.

2004-12-01

A pilot study of Seaside, Oregon is underway, to develop methodologies for probabilistic tsunami hazard assessments that can be incorporated into Flood Insurance Rate Maps (FIRMs) developed by FEMA's National Flood Insurance Program (NFIP). Current NFIP guidelines for tsunami hazard assessment rely on the science, technology and methodologies developed in the 1970s; although generally regarded as groundbreaking and state-of-the-art for its time, this approach is now superseded by modern methods that reflect substantial advances in tsunami research achieved in the last two decades. In particular, post-1990 technical advances include: improvements in tsunami source specification; improved tsunami inundation models; better computational grids by virtue of improved bathymetric and topographic databases; a larger database of long-term paleoseismic and paleotsunami records and short-term, historical earthquake and tsunami records that can be exploited to develop improved probabilistic methodologies; better understanding of earthquake recurrence and probability models. The NOAA-led U.S. National Tsunami Hazard Mitigation Program (NTHMP), in partnership with FEMA, USGS, NSF and Emergency Management and Geotechnical agencies of the five Pacific States, incorporates these advances into site-specific tsunami hazard assessments for coastal communities in Alaska, California, Hawaii, Oregon and Washington. NTHMP hazard assessment efforts currently focus on developing deterministic, "credible worst-case" scenarios that provide valuable guidance for hazard mitigation and emergency management. The NFIP focus, on the other hand, is on actuarial needs that require probabilistic hazard assessments such as those that characterize 100- and 500-year flooding events. There are clearly overlaps in NFIP and NTHMP objectives. NTHMP worst-case scenario assessments that include an estimated probability of occurrence could benefit the NFIP; NFIP probabilistic assessments of 100- and 500-yr events could benefit the NTHMP. The joint NFIP/NTHMP pilot study at Seaside, Oregon is organized into three closely related components: Probabilistic, Modeling, and Impact studies. Probabilistic studies (Geist, et al., this session) are led by the USGS and include the specification of near- and far-field seismic tsunami sources and their associated probabilities. Modeling studies (Titov, et al., this session) are led by NOAA and include the development and testing of a Seaside tsunami inundation model and an associated database of computed wave height and flow velocity fields. Impact studies (Synolakis, et al., this session) are led by USC and include the computation and analyses of indices for the categorization of hazard zones. The results of each component study will be integrated to produce a Seaside tsunami hazard map. This presentation will provide a brief overview of the project and an update on progress, while the above-referenced companion presentations will provide details on the methods used and the preliminary results obtained by each project component.

A spatio-temporal model for probabilistic seismic hazard zonation of Tehran

NASA Astrophysics Data System (ADS)

Hashemi, Mahdi; Alesheikh, Ali Asghar; Zolfaghari, Mohammad Reza

2013-08-01

A precondition for all disaster management steps, building damage prediction, and construction code developments is a hazard assessment that shows the exceedance probabilities of different ground motion levels at a site considering different near- and far-field earthquake sources. The seismic sources are usually categorized as time-independent area sources and time-dependent fault sources. While the earlier incorporates the small and medium events, the later takes into account only the large characteristic earthquakes. In this article, a probabilistic approach is proposed to aggregate the effects of time-dependent and time-independent sources on seismic hazard. The methodology is then applied to generate three probabilistic seismic hazard maps of Tehran for 10%, 5%, and 2% exceedance probabilities in 50 years. The results indicate an increase in peak ground acceleration (PGA) values toward the southeastern part of the study area and the PGA variations are mostly controlled by the shear wave velocities across the city. In addition, the implementation of the methodology takes advantage of GIS capabilities especially raster-based analyses and representations. During the estimation of the PGA exceedance rates, the emphasis has been placed on incorporating the effects of different attenuation relationships and seismic source models by using a logic tree.

Risk assessment for construction projects of transport infrastructure objects

NASA Astrophysics Data System (ADS)

Titarenko, Boris

2017-10-01

The paper analyzes and compares different methods of risk assessment for construction projects of transport objects. The management of such type of projects demands application of special probabilistic methods due to large level of uncertainty of their implementation. Risk management in the projects requires the use of probabilistic and statistical methods. The aim of the work is to develop a methodology for using traditional methods in combination with robust methods that allow obtaining reliable risk assessments in projects. The robust approach is based on the principle of maximum likelihood and in assessing the risk allows the researcher to obtain reliable results in situations of great uncertainty. The application of robust procedures allows to carry out a quantitative assessment of the main risk indicators of projects when solving the tasks of managing innovation-investment projects. Calculation of damage from the onset of a risky event is possible by any competent specialist. And an assessment of the probability of occurrence of a risky event requires the involvement of special probabilistic methods based on the proposed robust approaches. Practice shows the effectiveness and reliability of results. The methodology developed in the article can be used to create information technologies and their application in automated control systems for complex projects.

Probabilistic Flood Maps to support decision-making: Mapping the Value of Information

NASA Astrophysics Data System (ADS)

Alfonso, L.; Mukolwe, M. M.; Di Baldassarre, G.

2016-02-01

Floods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision-making is required. In this paper, a novel methodology based on Decision-Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision-making in floodplain planning.

Physically based probabilistic seismic hazard analysis using broadband ground motion simulation: a case study for the Prince Islands Fault, Marmara Sea

NASA Astrophysics Data System (ADS)

Mert, Aydin; Fahjan, Yasin M.; Hutchings, Lawrence J.; Pınar, Ali

2016-08-01

The main motivation for this study was the impending occurrence of a catastrophic earthquake along the Prince Island Fault (PIF) in the Marmara Sea and the disaster risk around the Marmara region, especially in Istanbul. This study provides the results of a physically based probabilistic seismic hazard analysis (PSHA) methodology, using broadband strong ground motion simulations, for sites within the Marmara region, Turkey, that may be vulnerable to possible large earthquakes throughout the PIF segments in the Marmara Sea. The methodology is called physically based because it depends on the physical processes of earthquake rupture and wave propagation to simulate earthquake ground motion time histories. We included the effects of all considerable-magnitude earthquakes. To generate the high-frequency (0.5-20 Hz) part of the broadband earthquake simulation, real, small-magnitude earthquakes recorded by a local seismic array were used as empirical Green's functions. For the frequencies below 0.5 Hz, the simulations were obtained by using synthetic Green's functions, which are synthetic seismograms calculated by an explicit 2D /3D elastic finite difference wave propagation routine. By using a range of rupture scenarios for all considerable-magnitude earthquakes throughout the PIF segments, we produced a hazard calculation for frequencies of 0.1-20 Hz. The physically based PSHA used here followed the same procedure as conventional PSHA, except that conventional PSHA utilizes point sources or a series of point sources to represent earthquakes, and this approach utilizes the full rupture of earthquakes along faults. Furthermore, conventional PSHA predicts ground motion parameters by using empirical attenuation relationships, whereas this approach calculates synthetic seismograms for all magnitudes of earthquakes to obtain ground motion parameters. PSHA results were produced for 2, 10, and 50 % hazards for all sites studied in the Marmara region.

Decision making generalized by a cumulative probability weighting function

NASA Astrophysics Data System (ADS)

dos Santos, Lindomar Soares; Destefano, Natália; Martinez, Alexandre Souto

2018-01-01

Typical examples of intertemporal decision making involve situations in which individuals must choose between a smaller reward, but more immediate, and a larger one, delivered later. Analogously, probabilistic decision making involves choices between options whose consequences differ in relation to their probability of receiving. In Economics, the expected utility theory (EUT) and the discounted utility theory (DUT) are traditionally accepted normative models for describing, respectively, probabilistic and intertemporal decision making. A large number of experiments confirmed that the linearity assumed by the EUT does not explain some observed behaviors, as nonlinear preference, risk-seeking and loss aversion. That observation led to the development of new theoretical models, called non-expected utility theories (NEUT), which include a nonlinear transformation of the probability scale. An essential feature of the so-called preference function of these theories is that the probabilities are transformed by decision weights by means of a (cumulative) probability weighting function, w(p) . We obtain in this article a generalized function for the probabilistic discount process. This function has as particular cases mathematical forms already consecrated in the literature, including discount models that consider effects of psychophysical perception. We also propose a new generalized function for the functional form of w. The limiting cases of this function encompass some parametric forms already proposed in the literature. Far beyond a mere generalization, our function allows the interpretation of probabilistic decision making theories based on the assumption that individuals behave similarly in the face of probabilities and delays and is supported by phenomenological models.

Evaluation of a National Call Center and a Local Alerts System for Detection of New Cases of Ebola Virus Disease - Guinea, 2014-2015

DTIC Science & Technology

2016-03-11

Control and Prevention Evaluation of a National Call Center and a Local Alerts System for Detection of New Cases of Ebola Virus Disease — Guinea, 2014...principally through the use of a telephone alert system. Community members and health facilities report deaths and suspected Ebola cases to local alert ...sensitivity of the national call center with the local alerts system, the CDC country team performed probabilistic record linkage of the combined

USGS Methodology for Assessing Continuous Petroleum Resources

USGS Publications Warehouse

Charpentier, Ronald R.; Cook, Troy A.

2011-01-01

The U.S. Geological Survey (USGS) has developed a new quantitative methodology for assessing resources in continuous (unconventional) petroleum deposits. Continuous petroleum resources include shale gas, coalbed gas, and other oil and gas deposits in low-permeability ("tight") reservoirs. The methodology is based on an approach combining geologic understanding with well productivities. The methodology is probabilistic, with both input and output variables as probability distributions, and uses Monte Carlo simulation to calculate the estimates. The new methodology is an improvement of previous USGS methodologies in that it better accommodates the uncertainties in undrilled or minimally drilled deposits that must be assessed using analogs. The publication is a collection of PowerPoint slides with accompanying comments.

Development of Testing Methodologies for the Mechanical Properties of MEMS

NASA Technical Reports Server (NTRS)

Ekwaro-Osire, Stephen

2003-01-01

This effort is to investigate and design testing strategies to determine the mechanical properties of MicroElectroMechanical Systems (MEMS) as well as investigate the development of a MEMS Probabilistic Design Methodology (PDM). One item of potential interest is the design of a test for the Weibull size effect in pressure membranes. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. However, the primary area of investigation will most likely be analysis and modeling of material interfaces for strength as well as developing a strategy to handle stress singularities at sharp corners, filets, and material interfaces. This will be a continuation of the previous years work. The ultimate objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads.

Fracture mechanics analysis of cracked structures using weight function and neural network method

NASA Astrophysics Data System (ADS)

Chen, J. G.; Zang, F. G.; Yang, Y.; Shi, K. K.; Fu, X. L.

2018-06-01

Stress intensity factors(SIFs) due to thermal-mechanical load has been established by using weight function method. Two reference stress states sere used to determine the coefficients in the weight function. Results were evaluated by using data from literature and show a good agreement between them. So, the SIFs can be determined quickly using the weight function obtained when cracks subjected to arbitrary loads, and presented method can be used for probabilistic fracture mechanics analysis. A probabilistic methodology considering Monte-Carlo with neural network (MCNN) has been developed. The results indicate that an accurate probabilistic characteristic of the KI can be obtained by using the developed method. The probability of failure increases with the increasing of loads, and the relationship between is nonlinear.

An investigation into the probabilistic combination of quasi-static and random accelerations

NASA Technical Reports Server (NTRS)

Schock, R. W.; Tuell, L. P.

1984-01-01

The development of design load factors for aerospace and aircraft components and experiment support structures, which are subject to a simultaneous vehicle dynamic vibration (quasi-static) and acoustically generated random vibration, require the selection of a combination methodology. Typically, the procedure is to define the quasi-static and the random generated response separately, and arithmetically add or root sum square to get combined accelerations. Since the combination of a probabilistic and a deterministic function yield a probabilistic function, a viable alternate approach would be to determine the characteristics of the combined acceleration probability density function and select an appropriate percentile level for the combined acceleration. The following paper develops this mechanism and provides graphical data to select combined accelerations for most popular percentile levels.

Psychics, aliens, or experience? Using the Anomalistic Belief Scale to examine the relationship between type of belief and probabilistic reasoning.

PubMed

Prike, Toby; Arnold, Michelle M; Williamson, Paul

2017-08-01

A growing body of research has shown people who hold anomalistic (e.g., paranormal) beliefs may differ from nonbelievers in their propensity to make probabilistic reasoning errors. The current study explored the relationship between these beliefs and performance through the development of a new measure of anomalistic belief, called the Anomalistic Belief Scale (ABS). One key feature of the ABS is that it includes a balance of both experiential and theoretical belief items. Another aim of the study was to use the ABS to investigate the relationship between belief and probabilistic reasoning errors on conjunction fallacy tasks. As expected, results showed there was a relationship between anomalistic belief and propensity to commit the conjunction fallacy. Importantly, regression analyses on the factors that make up the ABS showed that the relationship between anomalistic belief and probabilistic reasoning occurred only for beliefs about having experienced anomalistic phenomena, and not for theoretical anomalistic beliefs. Copyright © 2017 Elsevier Inc. All rights reserved.

Probabilistic Modeling of High-Temperature Material Properties of a 5-Harness 0/90 Sylramic Fiber/ CVI-SiC/ MI-SiC Woven Composite

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.; Tong, Michael; Murthy, P. L. N.; Mital, Subodh

1998-01-01

An integrated probabilistic approach has been developed to assess composites for high temperature applications. This approach was used to determine thermal and mechanical properties and their probabilistic distributions of a 5-harness 0/90 Sylramic fiber/CVI-SiC/Mi-SiC woven Ceramic Matrix Composite (CMC) at high temperatures. The purpose of developing this approach was to generate quantitative probabilistic information on this CMC to help complete the evaluation for its potential application for HSCT combustor liner. This approach quantified the influences of uncertainties inherent in constituent properties called primitive variables on selected key response variables of the CMC at 2200 F. The quantitative information is presented in the form of Cumulative Density Functions (CDFs). Probability Density Functions (PDFS) and primitive variable sensitivities on response. Results indicate that the scatters in response variables were reduced by 30-50% when the uncertainties in the primitive variables, which showed the most influence, were reduced by 50%.

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

USGS Publications Warehouse

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.

76 FR 70768 - Biweekly Notice; Applications and Amendments to Facility Operating Licenses Involving No...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-15

... perform a probabilistic risk evaluation using the guidance contained in NRC approved NEI [Nuclear Energy... Issue Summary 2003-18, Supplement 2, ``Use of Nuclear Energy Institute (NEI) 99-01, Methodology for...

[Methodological design of the National Health and Nutrition Survey 2016].

PubMed

Romero-Martínez, Martín; Shamah-Levy, Teresa; Cuevas-Nasu, Lucía; Gómez-Humarán, Ignacio Méndez; Gaona-Pineda, Elsa Berenice; Gómez-Acosta, Luz María; Rivera-Dommarco, Juan Ángel; Hernández-Ávila, Mauricio

2017-01-01

Describe the design methodology of the halfway health and nutrition national survey (Ensanut-MC) 2016. The Ensanut-MC is a national probabilistic survey whose objective population are the inhabitants of private households in Mexico. The sample size was determined to make inferences on the urban and rural areas in four regions. Describes main design elements: target population, topics of study, sampling procedure, measurement procedure and logistics organization. A final sample of 9 479 completed household interviews, and a sample of 16 591 individual interviews. The response rate for households was 77.9%, and the response rate for individuals was 91.9%. The Ensanut-MC probabilistic design allows valid statistical inferences about interest parameters for Mexico´s public health and nutrition, specifically on overweight, obesity and diabetes mellitus. Updated information also supports the monitoring, updating and formulation of new policies and priority programs.

A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.

1989-05-01

A storm transposition approach is investigated as a possible tool of assessing the frequency of extreme precipitation depths, that is, depths of return period much greater than 100 years. This paper focuses on estimation of the annual exceedance probability of extreme average precipitation depths over a catchment. The probabilistic storm transposition methodology is presented, and the several conceptual and methodological difficulties arising in this approach are identified. The method is implemented and is partially evaluated by means of a semihypothetical example involving extreme midwestern storms and two hypothetical catchments (of 100 and 1000 mi2 (˜260 and 2600 km2)) located in central Iowa. The results point out the need for further research to fully explore the potential of this approach as a tool for assessing the probabilities of rare storms, and eventually floods, a necessary element of risk-based analysis and design of large hydraulic structures.

A Step Made Toward Designing Microelectromechanical System (MEMS) Structures With High Reliability

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.

2003-01-01

The mechanical design of microelectromechanical systems-particularly for micropower generation applications-requires the ability to predict the strength capacity of load-carrying components over the service life of the device. These microdevices, which typically are made of brittle materials such as polysilicon, show wide scatter (stochastic behavior) in strength as well as a different average strength for different sized structures (size effect). These behaviors necessitate either costly and time-consuming trial-and-error designs or, more efficiently, the development of a probabilistic design methodology for MEMS. Over the years, the NASA Glenn Research Center s Life Prediction Branch has developed the CARES/Life probabilistic design methodology to predict the reliability of advanced ceramic components. In this study, done in collaboration with Johns Hopkins University, the ability of the CARES/Life code to predict the reliability of polysilicon microsized structures with stress concentrations is successfully demonstrated.

Development of optimization-based probabilistic earthquake scenarios for the city of Tehran

NASA Astrophysics Data System (ADS)

Zolfaghari, M. R.; Peyghaleh, E.

2016-01-01

This paper presents the methodology and practical example for the application of optimization process to select earthquake scenarios which best represent probabilistic earthquake hazard in a given region. The method is based on simulation of a large dataset of potential earthquakes, representing the long-term seismotectonic characteristics in a given region. The simulation process uses Monte-Carlo simulation and regional seismogenic source parameters to generate a synthetic earthquake catalogue consisting of a large number of earthquakes, each characterized with magnitude, location, focal depth and fault characteristics. Such catalogue provides full distributions of events in time, space and size; however, demands large computation power when is used for risk assessment, particularly when other sources of uncertainties are involved in the process. To reduce the number of selected earthquake scenarios, a mixed-integer linear program formulation is developed in this study. This approach results in reduced set of optimization-based probabilistic earthquake scenario, while maintaining shape of hazard curves and full probabilistic picture by minimizing the error between hazard curves driven by full and reduced sets of synthetic earthquake scenarios. To test the model, the regional seismotectonic and seismogenic characteristics of northern Iran are used to simulate a set of 10,000-year worth of events consisting of some 84,000 earthquakes. The optimization model is then performed multiple times with various input data, taking into account probabilistic seismic hazard for Tehran city as the main constrains. The sensitivity of the selected scenarios to the user-specified site/return period error-weight is also assessed. The methodology could enhance run time process for full probabilistic earthquake studies like seismic hazard and risk assessment. The reduced set is the representative of the contributions of all possible earthquakes; however, it requires far less computation power. The authors have used this approach for risk assessment towards identification of effectiveness-profitability of risk mitigation measures, using optimization model for resource allocation. Based on the error-computation trade-off, 62-earthquake scenarios are chosen to be used for this purpose.

Development of a Probabilistic Tornado Wind Hazard Model for the Continental United States Volume I: Main Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boissonnade, A; Hossain, Q; Kimball, J

Since the mid-l980's, assessment of the wind and tornado risks at the Department of Energy (DOE) high and moderate hazard facilities has been based on the straight wind/tornado hazard curves given in UCRL-53526 (Coats, 1985). These curves were developed using a methodology that utilized a model, developed by McDonald, for severe winds at sub-tornado wind speeds and a separate model, developed by Fujita, for tornado wind speeds. For DOE sites not covered in UCRL-53526, wind and tornado hazard assessments are based on the criteria outlined in DOE-STD-1023-95 (DOE, 1996), utilizing the methodology in UCRL-53526; Subsequent to the publication of UCRL53526,more » in a study sponsored by the Nuclear Regulatory Commission (NRC), the Pacific Northwest Laboratory developed tornado wind hazard curves for the contiguous United States, NUREG/CR-4461 (Ramsdell, 1986). Because of the different modeling assumptions and underlying data used to develop the tornado wind information, the wind speeds at specified exceedance levels, at a given location, based on the methodology in UCRL-53526, are different than those based on the methodology in NUREG/CR-4461. In 1997, Lawrence Livermore National Laboratory (LLNL) was funded by the DOE to review the current methodologies for characterizing tornado wind hazards and to develop a state-of-the-art wind/tornado characterization methodology based on probabilistic hazard assessment techniques and current historical wind data. This report describes the process of developing the methodology and the database of relevant tornado information needed to implement the methodology. It also presents the tornado wind hazard curves obtained from the application of the method to DOE sites throughout the contiguous United States.« less

Simulation-Based Probabilistic Tsunami Hazard Analysis: Empirical and Robust Hazard Predictions

NASA Astrophysics Data System (ADS)

De Risi, Raffaele; Goda, Katsuichiro

2017-08-01

Probabilistic tsunami hazard analysis (PTHA) is the prerequisite for rigorous risk assessment and thus for decision-making regarding risk mitigation strategies. This paper proposes a new simulation-based methodology for tsunami hazard assessment for a specific site of an engineering project along the coast, or, more broadly, for a wider tsunami-prone region. The methodology incorporates numerous uncertain parameters that are related to geophysical processes by adopting new scaling relationships for tsunamigenic seismic regions. Through the proposed methodology it is possible to obtain either a tsunami hazard curve for a single location, that is the representation of a tsunami intensity measure (such as inundation depth) versus its mean annual rate of occurrence, or tsunami hazard maps, representing the expected tsunami intensity measures within a geographical area, for a specific probability of occurrence in a given time window. In addition to the conventional tsunami hazard curve that is based on an empirical statistical representation of the simulation-based PTHA results, this study presents a robust tsunami hazard curve, which is based on a Bayesian fitting methodology. The robust approach allows a significant reduction of the number of simulations and, therefore, a reduction of the computational effort. Both methods produce a central estimate of the hazard as well as a confidence interval, facilitating the rigorous quantification of the hazard uncertainties.

Evaluating the uncertainty of predicting future climate time series at the hourly time scale

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2011-12-01

A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

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

NASA Technical Reports Server (NTRS)

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.

Probabilistic fatigue life prediction of metallic and composite materials

NASA Astrophysics Data System (ADS)

Xiang, Yibing

Fatigue is one of the most common failure modes for engineering structures, such as aircrafts, rotorcrafts and aviation transports. Both metallic materials and composite materials are widely used and affected by fatigue damage. Huge uncertainties arise from material properties, measurement noise, imperfect models, future anticipated loads and environmental conditions. These uncertainties are critical issues for accurate remaining useful life (RUL) prediction for engineering structures in service. Probabilistic fatigue prognosis considering various uncertainties is of great importance for structural safety. The objective of this study is to develop probabilistic fatigue life prediction models for metallic materials and composite materials. A fatigue model based on crack growth analysis and equivalent initial flaw size concept is proposed for metallic materials. Following this, the developed model is extended to include structural geometry effects (notch effect), environmental effects (corroded specimens) and manufacturing effects (shot peening effects). Due to the inhomogeneity and anisotropy, the fatigue model suitable for metallic materials cannot be directly applied to composite materials. A composite fatigue model life prediction is proposed based on a mixed-mode delamination growth model and a stiffness degradation law. After the development of deterministic fatigue models of metallic and composite materials, a general probabilistic life prediction methodology is developed. The proposed methodology combines an efficient Inverse First-Order Reliability Method (IFORM) for the uncertainty propogation in fatigue life prediction. An equivalent stresstransformation has been developed to enhance the computational efficiency under realistic random amplitude loading. A systematical reliability-based maintenance optimization framework is proposed for fatigue risk management and mitigation of engineering structures.

How Can You Support RIDM/CRM/RM Through the Use of PRA

NASA Technical Reports Server (NTRS)

DoVemto. Tpmu

2011-01-01

Probabilistic Risk Assessment (PRA) is one of key Risk Informed Decision Making (RIDM) tools. It is a scenario-based methodology aimed at identifying and assessing Safety and Technical Performance risks in complex technological systems.

Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution.

PubMed

Zhang, Qin

2015-07-01

Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) is a newly presented model of PGMs, which can be applied to fault diagnosis of large and complex industrial systems, disease diagnosis, and so on. The basic methodology of DUCG has been previously presented, in which only the directed acyclic graph (DAG) was addressed. However, the mathematical meaning of DUCG was not discussed. In this paper, the DUCG with directed cyclic graphs (DCGs) is addressed. In contrast, BN does not allow DCGs, as otherwise the conditional independence will not be satisfied. The inference algorithm for the DUCG with DCGs is presented, which not only extends the capabilities of DUCG from DAGs to DCGs but also enables users to decompose a large and complex DUCG into a set of small, simple sub-DUCGs, so that a large and complex knowledge base can be easily constructed, understood, and maintained. The basic mathematical definition of a complete DUCG with or without DCGs is proved to be a joint probability distribution (JPD) over a set of random variables. The incomplete DUCG as a part of a complete DUCG may represent a part of JPD. Examples are provided to illustrate the methodology.

Toward Probabilistic Risk Analyses - Development of a Probabilistic Tsunami Hazard Assessment of Crescent City, CA

NASA Astrophysics Data System (ADS)

González, F. I.; Leveque, R. J.; Hatheway, D.; Metzger, N.

2011-12-01

Risk is defined in many ways, but most are consistent with Crichton's [1999] definition based on the ''risk triangle'' concept and the explicit identification of three risk elements: ''Risk is the probability of a loss, and this depends on three elements: hazard, vulnerability, and exposure. If any of these three elements in risk increases or decreases, then the risk increases or decreases respectively." The World Meteorological Organization, for example, cites Crichton [1999] and then defines risk as [WMO, 2008] Risk = function (Hazard x Vulnerability x Exposure) while the Asian Disaster Reduction Center adopts the more general expression [ADRC, 2005] Risk = function (Hazard, Vulnerability, Exposure) In practice, probabilistic concepts are invariably invoked, and at least one of the three factors are specified as probabilistic in nature. The Vulnerability and Exposure factors are defined in multiple ways in the relevant literature; but the Hazard factor, which is the focus of our presentation, is generally understood to deal only with the physical aspects of the phenomena and, in particular, the ability of the phenomena to inflict harm [Thywissen, 2006]. A Hazard factor can be estimated by a methodology known as Probabilistic Tsunami Hazard Assessment (PTHA) [González, et al., 2009]. We will describe the PTHA methodology and provide an example -- the results of a previous application to Seaside, OR. We will also present preliminary results for a PTHA of Crescent City, CA -- a pilot project and coastal modeling/mapping effort funded by the Federal Emergency Management Agency (FEMA) Region IX office as part of the new California Coastal Analysis and Mapping Project (CCAMP). CCAMP and the PTHA in Crescent City are being conducted under the nationwide FEMA Risk Mapping, Assessment, and Planning (Risk MAP) Program which focuses on providing communities with flood information and tools they can use to enhance their mitigation plans and better protect their citizens.

Probabilistic Seismic Risk Model for Western Balkans

NASA Astrophysics Data System (ADS)

Stejskal, Vladimir; Lorenzo, Francisco; Pousse, Guillaume; Radovanovic, Slavica; Pekevski, Lazo; Dojcinovski, Dragi; Lokin, Petar; Petronijevic, Mira; Sipka, Vesna

2010-05-01

A probabilistic seismic risk model for insurance and reinsurance purposes is presented for an area of Western Balkans, covering former Yugoslavia and Albania. This territory experienced many severe earthquakes during past centuries producing significant damage to many population centres in the region. The highest hazard is related to external Dinarides, namely to the collision zone of the Adriatic plate. The model is based on a unified catalogue for the region and a seismic source model consisting of more than 30 zones covering all the three main structural units - Southern Alps, Dinarides and the south-western margin of the Pannonian Basin. A probabilistic methodology using Monte Carlo simulation was applied to generate the hazard component of the model. Unique set of damage functions based on both loss experience and engineering assessments is used to convert the modelled ground motion severity into the monetary loss.

Programming Probabilistic Structural Analysis for Parallel Processing Computer

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Chen, Heh-Chyun; Twisdale, Lawrence A.; Chamis, Christos C.; Murthy, Pappu L. N.

1991-01-01

The ultimate goal of this research program is to make Probabilistic Structural Analysis (PSA) computationally efficient and hence practical for the design environment by achieving large scale parallelism. The paper identifies the multiple levels of parallelism in PSA, identifies methodologies for exploiting this parallelism, describes the development of a parallel stochastic finite element code, and presents results of two example applications. It is demonstrated that speeds within five percent of those theoretically possible can be achieved. A special-purpose numerical technique, the stochastic preconditioned conjugate gradient method, is also presented and demonstrated to be extremely efficient for certain classes of PSA problems.

Low Base-Substitution Mutation Rate in the Germline Genome of the Ciliate Tetrahymena thermophila

DTIC Science & Technology

2016-09-15

generations of mutation accumulation (MA). We applied an existing mutation-calling pipeline and developed a new probabilistic mutation detection approach...noise introduced by mismapped reads. We used both our new method and an existing mutation-calling pipeline (Sung, Tucker, et al. 2012) to analyse the...and larger MA experiments will be required to confidently estimate the mutational spectrum of a species with such a low mutation rate. Materials and

Development/Modernization of an Advanced Non-Light Water Reactor Probabilistic Risk Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henneke, Dennis W.; Robinson, James

In 2015, GE Hitachi Nuclear Energy (GEH) teamed with Argonne National Laboratory (Argonne) to perform Research and Development (R&D) of next-generation Probabilistic Risk Assessment (PRA) methodologies for the modernization of an advanced non-Light Water Reactor (non-LWR) PRA. This effort built upon a PRA developed in the early 1990s for GEH’s Power Reactor Inherently Safe Module (PRISM) Sodium Fast Reactor (SFR). The work had four main tasks: internal events development modeling the risk from the reactor for hazards occurring at-power internal to the plant; an all hazards scoping review to analyze the risk at a high level from external hazards suchmore » as earthquakes and high winds; an all modes scoping review to understand the risk at a high level from operating modes other than at-power; and risk insights to integrate the results from each of the three phases above. To achieve these objectives, GEH and Argonne used and adapted proven PRA methodologies and techniques to build a modern non-LWR all hazards/all modes PRA. The teams also advanced non-LWR PRA methodologies, which is an important outcome from this work. This report summarizes the project outcomes in two major phases. The first phase presents the methodologies developed for non-LWR PRAs. The methodologies are grouped by scope, from Internal Events At-Power (IEAP) to hazards analysis to modes analysis. The second phase presents details of the PRISM PRA model which was developed as a validation of the non-LWR methodologies. The PRISM PRA was performed in detail for IEAP, and at a broader level for hazards and modes. In addition to contributing methodologies, this project developed risk insights applicable to non-LWR PRA, including focus-areas for future R&D, and conclusions about the PRISM design.« less

A Probabilistic Performance Assessment Study of Potential Low-Level Radioactive Waste Disposal Sites in Taiwan

NASA Astrophysics Data System (ADS)

Knowlton, R. G.; Arnold, B. W.; Mattie, P. D.; Kuo, M.; Tien, N.

2006-12-01

For several years now, Taiwan has been engaged in a process to select a low-level radioactive waste (LLW) disposal site. Taiwan is generating LLW from operational and decommissioning wastes associated with nuclear power reactors, as well as research, industrial, and medical radioactive wastes. The preliminary selection process has narrowed the search to four potential candidate sites. These sites are to be evaluated in a performance assessment analysis to determine the likelihood of meeting the regulatory criteria for disposal. Sandia National Laboratories and Taiwan's Institute of Nuclear Energy Research have been working together to develop the necessary performance assessment methodology and associated computer models to perform these analyses. The methodology utilizes both deterministic (e.g., single run) and probabilistic (e.g., multiple statistical realizations) analyses to achieve the goals. The probabilistic approach provides a means of quantitatively evaluating uncertainty in the model predictions and a more robust basis for performing sensitivity analyses to better understand what is driving the dose predictions from the models. Two types of disposal configurations are under consideration: a shallow land burial concept and a cavern disposal concept. The shallow land burial option includes a protective cover to limit infiltration potential to the waste. Both conceptual designs call for the disposal of 55 gallon waste drums within concrete lined trenches or tunnels, and backfilled with grout. Waste emplaced in the drums may be solidified. Both types of sites are underlain or placed within saturated fractured bedrock material. These factors have influenced the conceptual model development of each site, as well as the selection of the models to employ for the performance assessment analyses. Several existing codes were integrated in order to facilitate a comprehensive performance assessment methodology to evaluate the potential disposal sites. First, a need existed to simulate the failure processes of the waste containers, with subsequent leaching of the waste form to the underlying host rock. The Breach, Leach, and Transport Multiple Species (BLT-MS) code was selected to meet these needs. BLT-MS also has a 2-D finite-element advective-dispersive transport module, with radionuclide in-growth and decay. BLT-MS does not solve the groundwater flow equation, but instead requires the input of Darcy flow velocity terms. These terms were abstracted from a groundwater flow model using the FEHM code. For the shallow land burial site, the HELP code was also used to evaluate the performance of the protective cover. The GoldSim code was used for two purposes: quantifying uncertainties in the predictions, and providing a platform to evaluate an alternative conceptual model involving matrix-diffusion transport. Results of the preliminary performance assessment analyses using examples to illustrate the computational framework will be presented. Sandia National Laboratories is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE AC04 94AL85000.

Use of Probabilistic Engineering Methods in the Detailed Design and Development Phases of the NASA Ares Launch Vehicle

NASA Technical Reports Server (NTRS)

Fayssal, Safie; Weldon, Danny

2008-01-01

The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program called Constellation to send crew and cargo to the international Space Station, to the moon, and beyond. As part of the Constellation program, a new launch vehicle, Ares I, is being developed by NASA Marshall Space Flight Center. Designing a launch vehicle with high reliability and increased safety requires a significant effort in understanding design variability and design uncertainty at the various levels of the design (system, element, subsystem, component, etc.) and throughout the various design phases (conceptual, preliminary design, etc.). In a previous paper [1] we discussed a probabilistic functional failure analysis approach intended mainly to support system requirements definition, system design, and element design during the early design phases. This paper provides an overview of the application of probabilistic engineering methods to support the detailed subsystem/component design and development as part of the "Design for Reliability and Safety" approach for the new Ares I Launch Vehicle. Specifically, the paper discusses probabilistic engineering design analysis cases that had major impact on the design and manufacturing of the Space Shuttle hardware. The cases represent important lessons learned from the Space Shuttle Program and clearly demonstrate the significance of probabilistic engineering analysis in better understanding design deficiencies and identifying potential design improvement for Ares I. The paper also discusses the probabilistic functional failure analysis approach applied during the early design phases of Ares I and the forward plans for probabilistic design analysis in the detailed design and development phases.

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty.

PubMed

Gregory, Jeremy R; Noshadravan, Arash; Olivetti, Elsa A; Kirchain, Randolph E

2016-06-21

We propose a methodology for conducting robust comparative life cycle assessments (LCA) by leveraging uncertainty. The method evaluates a broad range of the possible scenario space in a probabilistic fashion while simultaneously considering uncertainty in input data. The method is intended to ascertain which scenarios have a definitive environmentally preferable choice among the alternatives being compared and the significance of the differences given uncertainty in the parameters, which parameters have the most influence on this difference, and how we can identify the resolvable scenarios (where one alternative in the comparison has a clearly lower environmental impact). This is accomplished via an aggregated probabilistic scenario-aware analysis, followed by an assessment of which scenarios have resolvable alternatives. Decision-tree partitioning algorithms are used to isolate meaningful scenario groups. In instances where the alternatives cannot be resolved for scenarios of interest, influential parameters are identified using sensitivity analysis. If those parameters can be refined, the process can be iterated using the refined parameters. We also present definitions of uncertainty quantities that have not been applied in the field of LCA and approaches for characterizing uncertainty in those quantities. We then demonstrate the methodology through a case study of pavements.

PubMed related articles: a probabilistic topic-based model for content similarity

PubMed Central

Lin, Jimmy; Wilbur, W John

2007-01-01

Background We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ® in MEDLINE ®. Results The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of pmra over bm25 in terms of precision. Conclusion Our experiments suggest that the pmra model provides an effective ranking algorithm for related article search. PMID:17971238

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

The probabilistic convolution tree: efficient exact Bayesian inference for faster LC-MS/MS protein inference.

PubMed

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence"). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to O(k log(k)2) and the space to O(k log(k)) where k is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions.

The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

PubMed Central

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

Probabilistic simple sticker systems

NASA Astrophysics Data System (ADS)

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.

Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Draxl, Caroline; Hopson, Thomas

Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5 degrees x 0.67 degrees) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applicationsmore » (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.« less

Acoustic emission based damage localization in composites structures using Bayesian identification

NASA Astrophysics Data System (ADS)

Kundu, A.; Eaton, M. J.; Al-Jumali, S.; Sikdar, S.; Pullin, R.

2017-05-01

Acoustic emission based damage detection in composite structures is based on detection of ultra high frequency packets of acoustic waves emitted from damage sources (such as fibre breakage, fatigue fracture, amongst others) with a network of distributed sensors. This non-destructive monitoring scheme requires solving an inverse problem where the measured signals are linked back to the location of the source. This in turn enables rapid deployment of mitigative measures. The presence of significant amount of uncertainty associated with the operating conditions and measurements makes the problem of damage identification quite challenging. The uncertainties stem from the fact that the measured signals are affected by the irregular geometries, manufacturing imprecision, imperfect boundary conditions, existing damages/structural degradation, amongst others. This work aims to tackle these uncertainties within a framework of automated probabilistic damage detection. The method trains a probabilistic model of the parametrized input and output model of the acoustic emission system with experimental data to give probabilistic descriptors of damage locations. A response surface modelling the acoustic emission as a function of parametrized damage signals collected from sensors would be calibrated with a training dataset using Bayesian inference. This is used to deduce damage locations in the online monitoring phase. During online monitoring, the spatially correlated time data is utilized in conjunction with the calibrated acoustic emissions model to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a composite structure consisting of carbon fibre panel with stiffeners and damage source behaviour has been experimentally simulated using standard H-N sources. The methodology presented in this study would be applicable in the current form to structural damage detection under varying operational loads and would be investigated in future studies.

Probabilistic, Seismically-Induced Landslide Hazard Mapping of Western Oregon

NASA Astrophysics Data System (ADS)

Olsen, M. J.; Sharifi Mood, M.; Gillins, D. T.; Mahalingam, R.

2015-12-01

Earthquake-induced landslides can generate significant damage within urban communities by damaging structures, obstructing lifeline connection routes and utilities, generating various environmental impacts, and possibly resulting in loss of life. Reliable hazard and risk maps are important to assist agencies in efficiently allocating and managing limited resources to prepare for such events. This research presents a new methodology in order to communicate site-specific landslide hazard assessments in a large-scale, regional map. Implementation of the proposed methodology results in seismic-induced landslide hazard maps that depict the probabilities of exceeding landslide displacement thresholds (e.g. 0.1, 0.3, 1.0 and 10 meters). These maps integrate a variety of data sources including: recent landslide inventories, LIDAR and photogrammetric topographic data, geology map, mapped NEHRP site classifications based on available shear wave velocity data in each geologic unit, and USGS probabilistic seismic hazard curves. Soil strength estimates were obtained by evaluating slopes present along landslide scarps and deposits for major geologic units. Code was then developed to integrate these layers to perform a rigid, sliding block analysis to determine the amount and associated probabilities of displacement based on each bin of peak ground acceleration in the seismic hazard curve at each pixel. The methodology was applied to western Oregon, which contains weak, weathered, and often wet soils at steep slopes. Such conditions have a high landslide hazard even without seismic events. A series of landslide hazard maps highlighting the probabilities of exceeding the aforementioned thresholds were generated for the study area. These output maps were then utilized in a performance based design framework enabling them to be analyzed in conjunction with other hazards for fully probabilistic-based hazard evaluation and risk assessment. a) School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA

CARES/Life Used for Probabilistic Characterization of MEMS Pressure Sensor Membranes

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.

2002-01-01

Microelectromechanical systems (MEMS) devices are typically made from brittle materials such as silicon using traditional semiconductor manufacturing techniques. They can be etched (or micromachined) from larger structures or can be built up with material deposition processes. Maintaining dimensional control and consistent mechanical properties is considerably more difficult for MEMS because feature size is on the micrometer scale. Therefore, the application of probabilistic design methodology becomes necessary for MEMS. This was demonstrated at the NASA Glenn Research Center and Case Western Reserve University in an investigation that used the NASA-developed CARES/Life brittle material design program to study the probabilistic fracture strength behavior of single-crystal SiC, polycrystalline SiC, and amorphous Si3N4 pressurized 1-mm-square thin-film diaphragms. These materials are of interest because of their superior high-temperature characteristics, which are desirable for harsh environment applications such as turbine engine and rocket propulsion system hot sections.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components, part 2

NASA Technical Reports Server (NTRS)

1991-01-01

The technical effort and computer code enhancements performed during the sixth year of the Probabilistic Structural Analysis Methods program are summarized. Various capabilities are described to probabilistically combine structural response and structural resistance to compute component reliability. A library of structural resistance models is implemented in the Numerical Evaluations of Stochastic Structures Under Stress (NESSUS) code that included fatigue, fracture, creep, multi-factor interaction, and other important effects. In addition, a user interface was developed for user-defined resistance models. An accurate and efficient reliability method was developed and was successfully implemented in the NESSUS code to compute component reliability based on user-selected response and resistance models. A risk module was developed to compute component risk with respect to cost, performance, or user-defined criteria. The new component risk assessment capabilities were validated and demonstrated using several examples. Various supporting methodologies were also developed in support of component risk assessment.

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

NASA Astrophysics Data System (ADS)

Guan, Xuefei; Zhang, Jingdan; Zhou, S. Kevin; Rasselkorde, El Mahjoub; Abbasi, Waheed A.

2014-02-01

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

Space system operations and support cost analysis using Markov chains

NASA Technical Reports Server (NTRS)

Unal, Resit; Dean, Edwin B.; Moore, Arlene A.; Fairbairn, Robert E.

1990-01-01

This paper evaluates the use of Markov chain process in probabilistic life cycle cost analysis and suggests further uses of the process as a design aid tool. A methodology is developed for estimating operations and support cost and expected life for reusable space transportation systems. Application of the methodology is demonstrated for the case of a hypothetical space transportation vehicle. A sensitivity analysis is carried out to explore the effects of uncertainty in key model inputs.

Fracture mechanics methodology: Evaluation of structural components integrity

NASA Astrophysics Data System (ADS)

Sih, G. C.; de Oliveira Faria, L.

1984-09-01

The application of fracture mechanics to structural-design problems is discussed in lectures presented in the AGARD Fracture Mechanics Methodology course held in Lisbon, Portugal, in June 1981. The emphasis is on aeronautical design, and chapters are included on fatigue-life prediction for metals and composites, the fracture mechanics of engineering structural components, failure mechanics and damage evaluation of structural components, flaw-acceptance methods, and reliability in probabilistic design. Graphs, diagrams, drawings, and photographs are provided.

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilch, Martin M.

Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities andmore » uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.« less

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

Don't Fear Optimality: Sampling for Probabilistic-Logic Sequence Models

NASA Astrophysics Data System (ADS)

Thon, Ingo

One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, based on observations is called filtering. Standard probabilistic models such as Dynamic Bayesian Networks are able to solve this task efficiently using approximative methods such as particle filters. However, these models do not support logical or relational representations. The key contribution of this paper is the upgrade of a particle filter algorithm for use with a probabilistic logical representation through the definition of a proposal distribution. The performance of the algorithm depends largely on how well this distribution fits the target distribution. We adopt the idea of logical compilation into Binary Decision Diagrams for sampling. This allows us to use the optimal proposal distribution which is normally prohibitively slow.

Guided SAR image despeckling with probabilistic non local weights

NASA Astrophysics Data System (ADS)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

Methodology for the Incorporation of Passive Component Aging Modeling into the RAVEN/ RELAP-7 Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mandelli, Diego; Rabiti, Cristian; Cogliati, Joshua

2014-11-01

Passive system, structure and components (SSCs) will degrade over their operation life and this degradation may cause to reduction in the safety margins of a nuclear power plant. In traditional probabilistic risk assessment (PRA) using the event-tree/fault-tree methodology, passive SSC failure rates are generally based on generic plant failure data and the true state of a specific plant is not reflected realistically. To address aging effects of passive SSCs in the traditional PRA methodology [1] does consider physics based models that account for the operating conditions in the plant, however, [1] does not include effects of surveillance/inspection. This paper representsmore » an overall methodology for the incorporation of aging modeling of passive components into the RAVEN/RELAP-7 environment which provides a framework for performing dynamic PRA. Dynamic PRA allows consideration of both epistemic and aleatory uncertainties (including those associated with maintenance activities) in a consistent phenomenological and probabilistic framework and is often needed when there is complex process/hardware/software/firmware/ human interaction [2]. Dynamic PRA has gained attention recently due to difficulties in the traditional PRA modeling of aging effects of passive components using physics based models and also in the modeling of digital instrumentation and control systems. RAVEN (Reactor Analysis and Virtual control Environment) [3] is a software package under development at the Idaho National Laboratory (INL) as an online control logic driver and post-processing tool. It is coupled to the plant transient code RELAP-7 (Reactor Excursion and Leak Analysis Program) also currently under development at INL [3], as well as RELAP 5 [4]. The overall methodology aims to: • Address multiple aging mechanisms involving large number of components in a computational feasible manner where sequencing of events is conditioned on the physical conditions predicted in a simulation environment such as RELAP-7. • Identify the risk-significant passive components, their failure modes and anticipated rates of degradation • Incorporate surveillance and maintenance activities and their effects into the plant state and into component aging progress. • Asses aging affects in a dynamic simulation environment 1. C. L. SMITH, V. N. SHAH, T. KAO, G. APOSTOLAKIS, “Incorporating Ageing Effects into Probabilistic Risk Assessment –A Feasibility Study Utilizing Reliability Physics Models,” NUREG/CR-5632, USNRC, (2001). 2. T. ALDEMIR, “A Survey of Dynamic Methodologies for Probabilistic Safety Assessment of Nuclear Power Plants, Annals of Nuclear Energy, 52, 113-124, (2013). 3. C. RABITI, A. ALFONSI, J. COGLIATI, D. MANDELLI and R. KINOSHITA “Reactor Analysis and Virtual Control Environment (RAVEN) FY12 Report,” INL/EXT-12-27351, (2012). 4. D. ANDERS et.al, "RELAP-7 Level 2 Milestone Report: Demonstration of a Steady State Single Phase PWR Simulation with RELAP-7," INL/EXT-12-25924, (2012).« less

Implementation of efficient trajectories for an ultrasonic scanner using chaotic maps

NASA Astrophysics Data System (ADS)

Almeda, A.; Baltazar, A.; Treesatayapun, C.; Mijarez, R.

2012-05-01

Typical ultrasonic methodology for nondestructive scanning evaluation uses systematic scanning paths. In many cases, this approach is time inefficient and also energy and computational power consuming. Here, a methodology for the scanning of defects using an ultrasonic echo-pulse scanning technique combined with chaotic trajectory generation is proposed. This is implemented in a Cartesian coordinate robotic system developed in our lab. To cover the entire search area, a chaotic function and a proposed mirror mapping were incorporated. To improve detection probability, our proposed scanning methodology is complemented with a probabilistic approach of discontinuity detection. The developed methodology was found to be more efficient than traditional ones used to localize and characterize hidden flaws.

Ecosystem services provided by agroecosystems: a qualitative and quantitative assessment of this relationship in the Pampa region, Argentina.

PubMed

Rositano, Florencia; Ferraro, Diego Omar

2014-03-01

The development of an analytical framework relating agricultural conditions and ecosystem services (ES) provision could be very useful for developing land-use systems which sustain natural resources for future use. According to this, a conceptual network was developed, based on literature review and expert knowledge, about the functional relationships between agricultural management and ES provision in the Pampa region (Argentina). We selected eight ES to develop this conceptual network: (1) carbon (C) balance, (2) nitrogen (N) balance, (3) groundwater contamination control, (4) soil water balance, (5) soil structural maintenance, (6) N2O emission control, (7) regulation of biotic adversities, and (8) biodiversity maintenance. This conceptual network revealed a high degree of interdependence among ES provided by Pampean agroecosystems, finding two trade-offs, and two synergies among them. Then, we analyzed the conceptual network structure, and found that both environmental and management variables influenced ES provision. Finally, we selected four ES to parameterize and quantify along 10 growing seasons (2000/2001-2009/2010) through a probabilistic methodology called Bayesian Networks. Only N balance was negatively impacted by agricultural management; while C balance, groundwater contamination control, and N2O emission control were not. Outcomes of our work emphasize the idea that qualitative and quantitative methodologies should be implemented together to assess ES provision in Pampean agroecosystems, as well as in other agricultural systems.

Exploratory and spatial data analysis (EDA-SDA) for determining regional background levels and anomalies of potentially toxic elements in soils from Catorce-Matehuala, Mexico

USGS Publications Warehouse

Chiprés, J.A.; Castro-Larragoitia, J.; Monroy, M.G.

2009-01-01

The threshold between geochemical background and anomalies can be influenced by the methodology selected for its estimation. Environmental evaluations, particularly those conducted in mineralized areas, must consider this when trying to determinate the natural geochemical status of a study area, quantifying human impacts, or establishing soil restoration values for contaminated sites. Some methods in environmental geochemistry incorporate the premise that anomalies (natural or anthropogenic) and background data are characterized by their own probabilistic distributions. One of these methods uses exploratory data analysis (EDA) on regional geochemical data sets coupled with a geographic information system (GIS) to spatially understand the processes that influence the geochemical landscape in a technique that can be called a spatial data analysis (SDA). This EDA-SDA methodology was used to establish the regional background range from the area of Catorce-Matehuala in north-central Mexico. Probability plots of the data, particularly for those areas affected by human activities, show that the regional geochemical background population is composed of smaller subpopulations associated with factors such as soil type and parent material. This paper demonstrates that the EDA-SDA method offers more certainty in defining thresholds between geochemical background and anomaly than a numeric technique, making it a useful tool for regional geochemical landscape analysis and environmental geochemistry studies.

A PROBABILISTIC METHOD FOR ESTIMATING MONITORING POINT DENSITY FOR CONTAINMENT SYSTEM LEAK DETECTION

EPA Science Inventory

The use of physical and hydraulic containment systems for the isolation of contaminated ground water and aquifer materials ssociated with hazardous waste sites has increased during the last decade. The existing methodologies for monitoring and evaluating leakage from hazardous w...

USEPA SHEDS MODEL: METHODOLOGY FOR EXPOSURE ASSESSMENT FOR WOOD PRESERVATIVES

EPA Science Inventory

A physically-based, Monte Carlo probabilistic model (SHEDS-Wood: Stochastic Human Exposure and Dose Simulation model for wood preservatives) has been applied to assess the exposure and dose of children to arsenic (As) and chromium (Cr) from contact with chromated copper arsenat...

Augmenting Probabilistic Risk Assesment with Malevolent Initiators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curtis Smith; David Schwieder

2011-11-01

As commonly practiced, the use of probabilistic risk assessment (PRA) in nuclear power plants only considers accident initiators such as natural hazards, equipment failures, and human error. Malevolent initiators are ignored in PRA, but are considered the domain of physical security, which uses vulnerability assessment based on an officially specified threat (design basis threat). This paper explores the implications of augmenting and extending existing PRA models by considering new and modified scenarios resulting from malevolent initiators. Teaming the augmented PRA models with conventional vulnerability assessments can cost-effectively enhance security of a nuclear power plant. This methodology is useful for operatingmore » plants, as well as in the design of new plants. For the methodology, we have proposed an approach that builds on and extends the practice of PRA for nuclear power plants for security-related issues. Rather than only considering 'random' failures, we demonstrated a framework that is able to represent and model malevolent initiating events and associated plant impacts.« less

Developing and Implementing the Data Mining Algorithms in RAVEN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantificationmore » analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.« less

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Reliability-Based Control Design for Uncertain Systems

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a robust control design methodology for systems with probabilistic parametric uncertainty. Control design is carried out by solving a reliability-based multi-objective optimization problem where the probability of violating design requirements is minimized. Simultaneously, failure domains are optimally enlarged to enable global improvements in the closed-loop performance. To enable an efficient numerical implementation, a hybrid approach for estimating reliability metrics is developed. This approach, which integrates deterministic sampling and asymptotic approximations, greatly reduces the numerical burden associated with complex probabilistic computations without compromising the accuracy of the results. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Probabilistic risk assessment of the Space Shuttle. Phase 3: A study of the potential of losing the vehicle during nominal operation. Volume 2: Integrated loss of vehicle model

NASA Technical Reports Server (NTRS)

Fragola, Joseph R.; Maggio, Gaspare; Frank, Michael V.; Gerez, Luis; Mcfadden, Richard H.; Collins, Erin P.; Ballesio, Jorge; Appignani, Peter L.; Karns, James J.

1995-01-01

The application of the probabilistic risk assessment methodology to a Space Shuttle environment, particularly to the potential of losing the Shuttle during nominal operation is addressed. The different related concerns are identified and combined to determine overall program risks. A fault tree model is used to allocate system probabilities to the subsystem level. The loss of the vehicle due to failure to contain energetic gas and debris, to maintain proper propulsion and configuration is analyzed, along with the loss due to Orbiter, external tank failure, and landing failure or error.

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Language acquisition and use: learning and applying probabilistic constraints.

PubMed

Seidenberg, M S

1997-03-14

What kinds of knowledge underlie the use of language and how is this knowledge acquired? Linguists equate knowing a language with knowing a grammar. Classic "poverty of the stimulus" arguments suggest that grammar identification is an intractable inductive problem and that acquisition is possible only because children possess innate knowledge of grammatical structure. An alternative view is emerging from studies of statistical and probabilistic aspects of language, connectionist models, and the learning capacities of infants. This approach emphasizes continuity between how language is acquired and how it is used. It retains the idea that innate capacities constrain language learning, but calls into question whether they include knowledge of grammatical structure.

Probabilistic Priority Message Checking Modeling Based on Controller Area Networks

NASA Astrophysics Data System (ADS)

Lin, Cheng-Min

Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.

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

PubMed Central

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. PMID:23970868

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

PubMed

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.

Development of a Probabilistic Assessment Methodology for Evaluation of Carbon Dioxide Storage

USGS Publications Warehouse

Burruss, Robert A.; Brennan, Sean T.; Freeman, P.A.; Merrill, Matthew D.; Ruppert, Leslie F.; Becker, Mark F.; Herkelrath, William N.; Kharaka, Yousif K.; Neuzil, Christopher E.; Swanson, Sharon M.; Cook, Troy A.; Klett, Timothy R.; Nelson, Philip H.; Schenk, Christopher J.

2009-01-01

This report describes a probabilistic assessment methodology developed by the U.S. Geological Survey (USGS) for evaluation of the resource potential for storage of carbon dioxide (CO2) in the subsurface of the United States as authorized by the Energy Independence and Security Act (Public Law 110-140, 2007). The methodology is based on USGS assessment methodologies for oil and gas resources created and refined over the last 30 years. The resource that is evaluated is the volume of pore space in the subsurface in the depth range of 3,000 to 13,000 feet that can be described within a geologically defined storage assessment unit consisting of a storage formation and an enclosing seal formation. Storage assessment units are divided into physical traps (PTs), which in most cases are oil and gas reservoirs, and the surrounding saline formation (SF), which encompasses the remainder of the storage formation. The storage resource is determined separately for these two types of storage. Monte Carlo simulation methods are used to calculate a distribution of the potential storage size for individual PTs and the SF. To estimate the aggregate storage resource of all PTs, a second Monte Carlo simulation step is used to sample the size and number of PTs. The probability of successful storage for individual PTs or the entire SF, defined in this methodology by the likelihood that the amount of CO2 stored will be greater than a prescribed minimum, is based on an estimate of the probability of containment using present-day geologic knowledge. The report concludes with a brief discussion of needed research data that could be used to refine assessment methodologies for CO2 sequestration.

A Methodology for the Integration of a Mechanistic Source Term Analysis in a Probabilistic Framework for Advanced Reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grabaskas, Dave; Brunett, Acacia J.; Bucknor, Matthew

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory are currently engaged in a joint effort to modernize and develop probabilistic risk assessment (PRA) techniques for advanced non-light water reactors. At a high level, the primary outcome of this project will be the development of next-generation PRA methodologies that will enable risk-informed prioritization of safety- and reliability-focused research and development, while also identifying gaps that may be resolved through additional research. A subset of this effort is the development of PRA methodologies to conduct a mechanistic source term (MST) analysis for event sequences that could result in the release ofmore » radionuclides. The MST analysis seeks to realistically model and assess the transport, retention, and release of radionuclides from the reactor to the environment. The MST methods developed during this project seek to satisfy the requirements of the Mechanistic Source Term element of the ASME/ANS Non-LWR PRA standard. The MST methodology consists of separate analysis approaches for risk-significant and non-risk significant event sequences that may result in the release of radionuclides from the reactor. For risk-significant event sequences, the methodology focuses on a detailed assessment, using mechanistic models, of radionuclide release from the fuel, transport through and release from the primary system, transport in the containment, and finally release to the environment. The analysis approach for non-risk significant event sequences examines the possibility of large radionuclide releases due to events such as re-criticality or the complete loss of radionuclide barriers. This paper provides details on the MST methodology, including the interface between the MST analysis and other elements of the PRA, and provides a simplified example MST calculation for a sodium fast reactor.« less

Research Analysis on MOOC Course Dropout and Retention Rates

ERIC Educational Resources Information Center

Gomez-Zermeno, Marcela Gerogina; Aleman de La Garza, Lorena

2016-01-01

This research's objective was to identify the terminal efficiency of the Massive Online Open Course "Educational Innovation with Open Resources" offered by a Mexican private university. A quantitative methodology was used, combining descriptive statistics and probabilistic models to analyze the levels of retention, completion, and…

Modeling Spanish Mood Choice in Belief Statements

ERIC Educational Resources Information Center

Robinson, Jason R.

2013-01-01

This work develops a computational methodology new to linguistics that empirically evaluates competing linguistic theories on Spanish verbal mood choice through the use of computational techniques to learn mood and other hidden linguistic features from Spanish belief statements found in corpora. The machine learned probabilistic linguistic models…

Methodology to identify risk-significant components for inservice inspection and testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, M.T.; Hartley, R.S.; Jones, J.L. Jr.

1992-08-01

Periodic inspection and testing of vital system components should be performed to ensure the safe and reliable operation of Department of Energy (DOE) nuclear processing facilities. Probabilistic techniques may be used to help identify and rank components by their relative risk. A risk-based ranking would allow varied DOE sites to implement inspection and testing programs in an effective and cost-efficient manner. This report describes a methodology that can be used to rank components, while addressing multiple risk issues.

A Probabilistic Approach to Crosslingual Information Retrieval

DTIC Science & Technology

2001-06-01

language expansion step can be performed before the translation process. Implemented as a call to the INQUERY function get_modified_query with one of the...database consists of American English while the dictionary is British English. Therefore, e.g. the Spanish word basura is translated to rubbish and

Ant system: optimization by a colony of cooperating agents.

PubMed

Dorigo, M; Maniezzo, V; Colorni, A

1996-01-01

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

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

PubMed Central

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. PMID:27303323

A Probabilistic Design Method Applied to Smart Composite Structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1995-01-01

A probabilistic design method is described and demonstrated using a smart composite wing. Probabilistic structural design incorporates naturally occurring uncertainties including those in constituent (fiber/matrix) material properties, fabrication variables, structure geometry and control-related parameters. Probabilistic sensitivity factors are computed to identify those parameters that have a great influence on a specific structural reliability. Two performance criteria are used to demonstrate this design methodology. The first criterion requires that the actuated angle at the wing tip be bounded by upper and lower limits at a specified reliability. The second criterion requires that the probability of ply damage due to random impact load be smaller than an assigned value. When the relationship between reliability improvement and the sensitivity factors is assessed, the results show that a reduction in the scatter of the random variable with the largest sensitivity factor (absolute value) provides the lowest failure probability. An increase in the mean of the random variable with a negative sensitivity factor will reduce the failure probability. Therefore, the design can be improved by controlling or selecting distribution parameters associated with random variables. This can be implemented during the manufacturing process to obtain maximum benefit with minimum alterations.

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

Review of methods for developing probabilistic risk assessments

Treesearch

D. A. Weinstein; P.B. Woodbury

2010-01-01

We describe methodologies currently in use or those under development containing features for estimating fire occurrence risk assessment. We describe two major categories of fire risk assessment tools: those that predict fire under current conditions, assuming that vegetation, climate, and the interactions between them and fire remain relatively similar to their...

Schedule Risks Due to Delays in Advanced Technology Development

NASA Technical Reports Server (NTRS)

Reeves, John D. Jr.; Kayat, Kamal A.; Lim, Evan

2008-01-01

This paper discusses a methodology and modeling capability that probabilistically evaluates the likelihood and impacts of delays in advanced technology development prior to the start of design, development, test, and evaluation (DDT&E) of complex space systems. The challenges of understanding and modeling advanced technology development considerations are first outlined, followed by a discussion of the problem in the context of lunar surface architecture analysis. The current and planned methodologies to address the problem are then presented along with sample analyses and results. The methodology discussed herein provides decision-makers a thorough understanding of the schedule impacts resulting from the inclusion of various enabling advanced technology assumptions within system design.

In search of a statistical probability model for petroleum-resource assessment : a critique of the probabilistic significance of certain concepts and methods used in petroleum-resource assessment : to that end, a probabilistic model is sketched

USGS Publications Warehouse

Grossling, Bernardo F.

1975-01-01

Exploratory drilling is still in incipient or youthful stages in those areas of the world where the bulk of the potential petroleum resources is yet to be discovered. Methods of assessing resources from projections based on historical production and reserve data are limited to mature areas. For most of the world's petroleum-prospective areas, a more speculative situation calls for a critical review of resource-assessment methodology. The language of mathematical statistics is required to define more rigorously the appraisal of petroleum resources. Basically, two approaches have been used to appraise the amounts of undiscovered mineral resources in a geologic province: (1) projection models, which use statistical data on the past outcome of exploration and development in the province; and (2) estimation models of the overall resources of the province, which use certain known parameters of the province together with the outcome of exploration and development in analogous provinces. These two approaches often lead to widely different estimates. Some of the controversy that arises results from a confusion of the probabilistic significance of the quantities yielded by each of the two approaches. Also, inherent limitations of analytic projection models-such as those using the logistic and Gomperts functions --have often been ignored. The resource-assessment problem should be recast in terms that provide for consideration of the probability of existence of the resource and of the probability of discovery of a deposit. Then the two above-mentioned models occupy the two ends of the probability range. The new approach accounts for (1) what can be expected with reasonably high certainty by mere projections of what has been accomplished in the past; (2) the inherent biases of decision-makers and resource estimators; (3) upper bounds that can be set up as goals for exploration; and (4) the uncertainties in geologic conditions in a search for minerals. Actual outcomes can then be viewed as phenomena subject to statistical uncertainty and responsive to changes in economic and technologic factors.

Development of a Probabilistic Dynamic Synthesis Method for the Analysis of Nondeterministic Structures

NASA Technical Reports Server (NTRS)

Brown, A. M.

1998-01-01

Accounting for the statistical geometric and material variability of structures in analysis has been a topic of considerable research for the last 30 years. The determination of quantifiable measures of statistical probability of a desired response variable, such as natural frequency, maximum displacement, or stress, to replace experience-based "safety factors" has been a primary goal of these studies. There are, however, several problems associated with their satisfactory application to realistic structures, such as bladed disks in turbomachinery. These include the accurate definition of the input random variables (rv's), the large size of the finite element models frequently used to simulate these structures, which makes even a single deterministic analysis expensive, and accurate generation of the cumulative distribution function (CDF) necessary to obtain the probability of the desired response variables. The research presented here applies a methodology called probabilistic dynamic synthesis (PDS) to solve these problems. The PDS method uses dynamic characteristics of substructures measured from modal test as the input rv's, rather than "primitive" rv's such as material or geometric uncertainties. These dynamic characteristics, which are the free-free eigenvalues, eigenvectors, and residual flexibility (RF), are readily measured and for many substructures, a reasonable sample set of these measurements can be obtained. The statistics for these rv's accurately account for the entire random character of the substructure. Using the RF method of component mode synthesis, these dynamic characteristics are used to generate reduced-size sample models of the substructures, which are then coupled to form system models. These sample models are used to obtain the CDF of the response variable by either applying Monte Carlo simulation or by generating data points for use in the response surface reliability method, which can perform the probabilistic analysis with an order of magnitude less computational effort. Both free- and forced-response analyses have been performed, and the results indicate that, while there is considerable room for improvement, the method produces usable and more representative solutions for the design of realistic structures with a substantial savings in computer time.

Community-based early warning systems for flood risk mitigation in Nepal

NASA Astrophysics Data System (ADS)

Smith, Paul J.; Brown, Sarah; Dugar, Sumit

2017-03-01

This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2-3 to 7-8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

A database and probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention in the United States

USGS Publications Warehouse

Warwick, Peter D.; Verma, Mahendra K.; Attanasi, Emil; Olea, Ricardo A.; Blondes, Madalyn S.; Freeman, Philip; Brennan, Sean T.; Merrill, Matthew; Jahediesfanjani, Hossein; Roueche, Jacqueline; Lohr, Celeste D.

2017-01-01

The U.S. Geological Survey (USGS) has developed an assessment methodology for estimating the potential incremental technically recoverable oil resources resulting from carbon dioxide-enhanced oil recovery (CO2-EOR) in reservoirs with appropriate depth, pressure, and oil composition. The methodology also includes a procedure for estimating the CO2 that remains in the reservoir after the CO2-EOR process is complete. The methodology relies on a reservoir-level database that incorporates commercially available geologic and engineering data. The mathematical calculations of this assessment methodology were tested and produced realistic results for the Permian Basin Horseshoe Atoll, Upper Pennsylvanian-Wolfcampian Play (Texas, USA). The USGS plans to use the new methodology to conduct an assessment of technically recoverable hydrocarbons and associated CO2 sequestration resulting from CO2-EOR in the United States.

A Probabilistic and Observation Based Methodology to Estimate Small Craft Harbor Vulnerability to Tsunami Events

NASA Astrophysics Data System (ADS)

Keen, A. S.; Lynett, P. J.; Ayca, A.

2016-12-01

Because of the damage resulting from the 2010 Chile and 2011 Japanese tele-tsunamis, the tsunami risk to the small craft marinas in California has become an important concern. The talk will outline an assessment tool which can be used to assess the tsunami hazard to small craft harbors. The methodology is based on the demand and structural capacity of the floating dock system, composed of floating docks/fingers and moored vessels. The structural demand is determined using a Monte Carlo methodology. Monte Carlo methodology is a probabilistic computational tool where the governing might be well known, but the independent variables of the input (demand) as well as the resisting structural components (capacity) may not be completely known. The Monte Carlo approach uses a distribution of each variable, and then uses that random variable within the described parameters, to generate a single computation. The process then repeats hundreds or thousands of times. The numerical model "Method of Splitting Tsunamis" (MOST) has been used to determine the inputs for the small craft harbors within California. Hydrodynamic model results of current speed, direction and surface elevation were incorporated via the drag equations to provide the bases of the demand term. To determine the capacities, an inspection program was developed to identify common features of structural components. A total of six harbors have been inspected ranging from Crescent City in Northern California to Oceanside Harbor in Southern California. Results from the inspection program were used to develop component capacity tables which incorporated the basic specifications of each component (e.g. bolt size and configuration) and a reduction factor (which accounts for the component reduction in capacity with age) to estimate in situ capacities. Like the demand term, these capacities are added probabilistically into the model. To date the model has been applied to Santa Cruz Harbor as well as Noyo River. Once calibrated, the model was able to hindcast the damage produced in Santa Cruz Harbor during the 2010 Chile and 2011 Japan events. Results of the Santa Cruz analysis will be presented and discussed.

Probabilistic analysis of the torsional effects on the tall building resistance due to earthquake even

NASA Astrophysics Data System (ADS)

Králik, Juraj; Králik, Juraj

2017-07-01

The paper presents the results from the deterministic and probabilistic analysis of the accidental torsional effect of reinforced concrete tall buildings due to earthquake even. The core-column structural system was considered with various configurations in plane. The methodology of the seismic analysis of the building structures in Eurocode 8 and JCSS 2000 is discussed. The possibilities of the utilization the LHS method to analyze the extensive and robust tasks in FEM is presented. The influence of the various input parameters (material, geometry, soil, masses and others) is considered. The deterministic and probability analysis of the seismic resistance of the structure was calculated in the ANSYS program.

Developing an Event-Tree Probabilistic Tsunami Inundation Model for NE Atlantic Coasts: Application to a Case Study

NASA Astrophysics Data System (ADS)

Omira, R.; Matias, L.; Baptista, M. A.

2016-12-01

This study constitutes a preliminary assessment of probabilistic tsunami inundation in the NE Atlantic region. We developed an event-tree approach to calculate the likelihood of tsunami flood occurrence and exceedance of a specific near-shore wave height for a given exposure time. Only tsunamis of tectonic origin are considered here, taking into account local, regional, and far-field sources. The approach used here consists of an event-tree method that gathers probability models for seismic sources, tsunami numerical modeling, and statistical methods. It also includes a treatment of aleatoric uncertainties related to source location and tidal stage. Epistemic uncertainties are not addressed in this study. The methodology is applied to the coastal test-site of Sines located in the NE Atlantic coast of Portugal. We derive probabilistic high-resolution maximum wave amplitudes and flood distributions for the study test-site considering 100- and 500-year exposure times. We find that the probability that maximum wave amplitude exceeds 1 m somewhere along the Sines coasts reaches about 60 % for an exposure time of 100 years and is up to 97 % for an exposure time of 500 years. The probability of inundation occurrence (flow depth >0 m) varies between 10 % and 57 %, and from 20 % up to 95 % for 100- and 500-year exposure times, respectively. No validation has been performed here with historical tsunamis. This paper illustrates a methodology through a case study, which is not an operational assessment.

Probabilistic analysis for fatigue strength degradation of materials

NASA Technical Reports Server (NTRS)

Royce, Lola

1989-01-01

This report presents the results of the first year of a research program conducted for NASA-LeRC by the University of Texas at San Antonio. The research included development of methodology that provides a probabilistic treatment of lifetime prediction of structural components of aerospace propulsion systems subjected to fatigue. Material strength degradation models, based on primitive variables, include both a fatigue strength reduction model and a fatigue crack growth model. Linear elastic fracture mechanics is utilized in the latter model. Probabilistic analysis is based on simulation, and both maximum entropy and maximum penalized likelihood methods are used for the generation of probability density functions. The resulting constitutive relationships are included in several computer programs, RANDOM2, RANDOM3, and RANDOM4. These programs determine the random lifetime of an engine component, in mechanical load cycles, to reach a critical fatigue strength or crack size. The material considered was a cast nickel base superalloy, one typical of those used in the Space Shuttle Main Engine.

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

NASA Technical Reports Server (NTRS)

Bast, Callie C.; Boyce, Lola

1995-01-01

The development of methodology for a probabilistic material strength degradation is described. 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 five effects that typically reduce lifetime strength: high temperature, high-cycle mechanical fatigue, low-cycle mechanical fatigue, creep and thermal fatigue. 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 predictions of high-cycle mechanical fatigue and high temperature effects with experiments are presented. Results from this limited verification study strongly supported that material degradation can be represented by randomized multifactor interaction models.

Spatial planning using probabilistic flood maps

NASA Astrophysics Data System (ADS)

Alfonso, Leonardo; Mukolwe, Micah; Di Baldassarre, Giuliano

2015-04-01

Probabilistic flood maps account for uncertainty in flood inundation modelling and convey a degree of certainty in the outputs. Major sources of uncertainty include input data, topographic data, model structure, observation data and parametric uncertainty. Decision makers prefer less ambiguous information from modellers; this implies that uncertainty is suppressed to yield binary flood maps. Though, suppressing information may potentially lead to either surprise or misleading decisions. Inclusion of uncertain information in the decision making process is therefore desirable and transparent. To this end, we utilise the Prospect theory and information from a probabilistic flood map to evaluate potential decisions. Consequences related to the decisions were evaluated using flood risk analysis. Prospect theory explains how choices are made given options for which probabilities of occurrence are known and accounts for decision makers' characteristics such as loss aversion and risk seeking. Our results show that decision making is pronounced when there are high gains and loss, implying higher payoffs and penalties, therefore a higher gamble. Thus the methodology may be appropriately considered when making decisions based on uncertain information.

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

PubMed

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.

Grade Inflation and Law School Admissions

ERIC Educational Resources Information Center

Wongsurawat, Winai

2008-01-01

Purpose: The purpose of this paper is to evaluate the evidence on whether grade inflation has led to an increasing emphasis on standardized test scores as a criterion for law school admissions. Design/methodology/approach: Fit probabilistic models to admissions data for American law schools during the mid to late 1990s, a period during which…

Probabilistic inspection strategies for minimizing service failures

NASA Technical Reports Server (NTRS)

Brot, Abraham

1994-01-01

The INSIM computer program is described which simulates the 'limited fatigue life' environment in which aircraft structures generally operate. The use of INSIM to develop inspection strategies which aim to minimize service failures is demonstrated. Damage-tolerance methodology, inspection thresholds and customized inspections are simulated using the probability of failure as the driving parameter.

On Information Retrieval (IR) Systems: Revisiting Their Development, Evaluation Methodologies, and Assumptions (SIGs LAN, ED).

ERIC Educational Resources Information Center

Stirling, Keith

2000-01-01

Describes a session on information retrieval systems that planned to discuss relevance measures with Web-based information retrieval; retrieval system performance and evaluation; probabilistic independence of index terms; vector-based models; metalanguages and digital objects; how users assess the reliability, timeliness and bias of information;…

Automated Test Case Generator for Phishing Prevention Using Generative Grammars and Discriminative Methods

ERIC Educational Resources Information Center

Palka, Sean

2015-01-01

This research details a methodology designed for creating content in support of various phishing prevention tasks including live exercises and detection algorithm research. Our system uses probabilistic context-free grammars (PCFG) and variable interpolation as part of a multi-pass method to create diverse and consistent phishing email content on…

Monitoring Human Development Goals: A Straightforward (Bayesian) Methodology for Cross-National Indices

ERIC Educational Resources Information Center

Abayomi, Kobi; Pizarro, Gonzalo

2013-01-01

We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country…

A Comprehensive Validation Approach Using The RAVEN Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua J

2015-06-01

The RAVEN computer code , developed at the Idaho National Laboratory, is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is a multi-purpose probabilistic and uncertainty quantification platform, capable to communicate with any system code. A natural extension of the RAVEN capabilities is the imple- mentation of an integrated validation methodology, involving several different metrics, that represent an evolution of the methods currently used in the field. The state-of-art vali- dation approaches use neither exploration of the input space through sampling strategies, nor a comprehensive variety of metrics neededmore » to interpret the code responses, with respect experimental data. The RAVEN code allows to address both these lacks. In the following sections, the employed methodology, and its application to the newer developed thermal-hydraulic code RELAP-7, is reported.The validation approach has been applied on an integral effect experiment, representing natu- ral circulation, based on the activities performed by EG&G Idaho. Four different experiment configurations have been considered and nodalized.« less

Integration of fuzzy analytic hierarchy process and probabilistic dynamic programming in formulating an optimal fleet management model

NASA Astrophysics Data System (ADS)

Teoh, Lay Eng; Khoo, Hooi Ling

2013-09-01

This study deals with two major aspects of airlines, i.e. supply and demand management. The aspect of supply focuses on the mathematical formulation of an optimal fleet management model to maximize operational profit of the airlines while the aspect of demand focuses on the incorporation of mode choice modeling as parts of the developed model. The proposed methodology is outlined in two-stage, i.e. Fuzzy Analytic Hierarchy Process is first adopted to capture mode choice modeling in order to quantify the probability of probable phenomena (for aircraft acquisition/leasing decision). Then, an optimization model is developed as a probabilistic dynamic programming model to determine the optimal number and types of aircraft to be acquired and/or leased in order to meet stochastic demand during the planning horizon. The findings of an illustrative case study show that the proposed methodology is viable. The results demonstrate that the incorporation of mode choice modeling could affect the operational profit and fleet management decision of the airlines at varying degrees.

National assessment of geologic carbon dioxide storage resources: methodology implementation

USGS Publications Warehouse

Blondes, Madalyn S.; Brennan, Sean T.; Merrill, Matthew D.; Buursink, Marc L.; Warwick, Peter D.; Cahan, Steven M.; Corum, Margo D.; Cook, Troy A.; Craddock, William H.; DeVera, Christina A.; Drake II, Ronald M.; Drew, Lawrence J.; Freeman, P.A.; Lohr, Celeste D.; Olea, Ricardo A.; Roberts-Ashby, Tina L.; Slucher, Ernie R.; Varela, Brian A.

2013-01-01

In response to the 2007 Energy Independence and Security Act, the U.S. Geological Survey (USGS) conducted a national assessment of potential geologic storage resources for carbon dioxide (CO2). Storage of CO2 in subsurface saline formations is one important method to reduce greenhouse gas emissions and curb global climate change. This report provides updates and implementation details of the assessment methodology of Brennan and others (2010, http://pubs.usgs.gov/of/2010/1127/) and describes the probabilistic model used to calculate potential storage resources in subsurface saline formations.

Test Methodologies for Personal Protective Equipment Against Anti-Personnel Mine Blast (Methodologies d’essais pour le materiel de protection prsonnel contre le souffle produit par les mines antipersonnel)

DTIC Science & Technology

2004-03-01

probabilistic by design. Finally, as the fragments disperse, fragment density decreases, and the probability of a fragment strike drops rapidly. Given the...Any PPE subjected to such testing needs to be exposed repeatedly to several mines in order to obtain a sufficient number of strikes . This will allow...velocity of each fragment, and the location of fragment strikes cannot be controlled precisely. This means that the same test must be repeated a

Design for Reliability and Safety Approach for the NASA New Launch Vehicle

NASA Technical Reports Server (NTRS)

Safie, Fayssal, M.; Weldon, Danny M.

2007-01-01

The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program intended for sending crew and cargo to the international Space Station (ISS), to the moon, and beyond. This program is called Constellation. As part of the Constellation program, NASA is developing new launch vehicles aimed at significantly increase safety and reliability, reduce the cost of accessing space, and provide a growth path for manned space exploration. Achieving these goals requires a rigorous process that addresses reliability, safety, and cost upfront and throughout all the phases of the life cycle of the program. This paper discusses the "Design for Reliability and Safety" approach for the NASA new crew launch vehicle called ARES I. The ARES I is being developed by NASA Marshall Space Flight Center (MSFC) in support of the Constellation program. The ARES I consists of three major Elements: A solid First Stage (FS), an Upper Stage (US), and liquid Upper Stage Engine (USE). Stacked on top of the ARES I is the Crew exploration vehicle (CEV). The CEV consists of a Launch Abort System (LAS), Crew Module (CM), Service Module (SM), and a Spacecraft Adapter (SA). The CEV development is being led by NASA Johnson Space Center (JSC). Designing for high reliability and safety require a good integrated working environment and a sound technical design approach. The "Design for Reliability and Safety" approach addressed in this paper discusses both the environment and the technical process put in place to support the ARES I design. To address the integrated working environment, the ARES I project office has established a risk based design group called "Operability Design and Analysis" (OD&A) group. This group is an integrated group intended to bring together the engineering, design, and safety organizations together to optimize the system design for safety, reliability, and cost. On the technical side, the ARES I project has, through the OD&A environment, implemented a probabilistic approach to analyze and evaluate design uncertainties and understand their impact on safety, reliability, and cost. This paper focuses on the use of the various probabilistic approaches that have been pursued by the ARES I project. Specifically, the paper discusses an integrated functional probabilistic analysis approach that addresses upffont some key areas to support the ARES I Design Analysis Cycle (DAC) pre Preliminary Design (PD) Phase. This functional approach is a probabilistic physics based approach that combines failure probabilities with system dynamics and engineering failure impact models to identify key system risk drivers and potential system design requirements. The paper also discusses other probabilistic risk assessment approaches planned by the ARES I project to support the PD phase and beyond.

SHEDS-PM: A POPULATION EXPOSURE MODEL FOR PREDICTING DISTRIBUTIONS OF PM EXPOSURE AND DOSE FROM BOTH OUTDOOR AND INDOOR SOURCES

EPA Science Inventory

The US EPA National Exposure Research Laboratory (NERL) has developed a population exposure and dose model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS) model. SHEDS-PM uses a probabilistic approach that incorporates both variabi...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farmer, J.D.; Ott, E.; Yorke, J.A.

Dimension is perhaps the most basic property of an attractor. In this paper we discuss a variety of different definitions of dimension, compute their values for a typical example, and review previous work on the dimension of chaotic attractors. The relevant definitions of dimension are of two general types, those that depend only on metric properties, and those that depend on probabilistic properties (that is, they depend on the frequency with which a typical trajectory visits different regions of the attractor). Both our example and the previous work that we review support the conclusion that all of the probabilistic dimensionsmore » take on the same value, which we call the dimension of the natural measure, and all of the metric dimensions take on a common value, which we call the fractal dimension. Furthermore, the dimension of the natural measure is typically equal to the Lyapunov dimension, which is defined in terms of Lyapunov numbers, and thus is usually far easier to calculate than any other definition. Because it is computable and more physically relevant, we feel that the dimension of the natural measure is more important than the fractal dimension.« less

Probabilistic atlas and geometric variability estimation to drive tissue segmentation.

PubMed

Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie

2014-09-10

Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd.

Exploration of Advanced Probabilistic and Stochastic Design Methods

NASA Technical Reports Server (NTRS)

Mavris, Dimitri N.

2003-01-01

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Three-Dimensional Finite Element Ablative Thermal Response and Thermostructural Design of Thermal Protection Systems

NASA Technical Reports Server (NTRS)

Dec, John A.; Braun, Robert D.

2011-01-01

A finite element ablation and thermal response program is presented for simulation of three-dimensional transient thermostructural analysis. The three-dimensional governing differential equations and finite element formulation are summarized. A novel probabilistic design methodology for thermal protection systems is presented. The design methodology is an eight step process beginning with a parameter sensitivity study and is followed by a deterministic analysis whereby an optimum design can determined. The design process concludes with a Monte Carlo simulation where the probabilities of exceeding design specifications are estimated. The design methodology is demonstrated by applying the methodology to the carbon phenolic compression pads of the Crew Exploration Vehicle. The maximum allowed values of bondline temperature and tensile stress are used as the design specifications in this study.

Reduced activation in ventral striatum and ventral tegmental area during probabilistic decision-making in schizophrenia.

PubMed

Rausch, Franziska; Mier, Daniela; Eifler, Sarah; Esslinger, Christine; Schilling, Claudia; Schirmbeck, Frederike; Englisch, Susanne; Meyer-Lindenberg, Andreas; Kirsch, Peter; Zink, Mathias

2014-07-01

Patients with schizophrenia suffer from deficits in monitoring and controlling their own thoughts. Within these so-called metacognitive impairments, alterations in probabilistic reasoning might be one cognitive phenomenon disposing to delusions. However, so far little is known about alterations in associated brain functionality. A previously established task for functional magnetic resonance imaging (fMRI), which requires a probabilistic decision after a variable amount of stimuli, was applied to 23 schizophrenia patients and 28 healthy controls matched for age, gender and educational levels. We compared activation patterns during decision-making under conditions of certainty versus uncertainty and evaluated the process of final decision-making in ventral striatum (VS) and ventral tegmental area (VTA). We replicated a pre-described extended cortical activation pattern during probabilistic reasoning. During final decision-making, activations in several fronto- and parietocortical areas, as well as in VS and VTA became apparent. In both of these regions schizophrenia patients showed a significantly reduced activation. These results further define the network underlying probabilistic decision-making. The observed hypo-activation in regions commonly associated with dopaminergic neurotransmission fits into current concepts of disrupted prediction error signaling in schizophrenia and suggests functional links to reward anticipation. Forthcoming studies with patients at risk for psychosis and drug-naive first episode patients are necessary to elucidate the development of these findings over time and the interplay with associated clinical symptoms. Copyright © 2014 Elsevier B.V. All rights reserved.

INTEGRATION OF RELIABILITY WITH MECHANISTIC THERMALHYDRAULICS: REPORT ON APPROACH AND TEST PROBLEM RESULTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

J. S. Schroeder; R. W. Youngblood

The Risk-Informed Safety Margin Characterization (RISMC) pathway of the Light Water Reactor Sustainability Program is developing simulation-based methods and tools for analyzing safety margin from a modern perspective. [1] There are multiple definitions of 'margin.' One class of definitions defines margin in terms of the distance between a point estimate of a given performance parameter (such as peak clad temperature), and a point-value acceptance criterion defined for that parameter (such as 2200 F). The present perspective on margin is that it relates to the probability of failure, and not just the distance between a nominal operating point and a criterion.more » In this work, margin is characterized through a probabilistic analysis of the 'loads' imposed on systems, structures, and components, and their 'capacity' to resist those loads without failing. Given the probabilistic load and capacity spectra, one can assess the probability that load exceeds capacity, leading to component failure. Within the project, we refer to a plot of these probabilistic spectra as 'the logo.' Refer to Figure 1 for a notional illustration. The implications of referring to 'the logo' are (1) RISMC is focused on being able to analyze loads and spectra probabilistically, and (2) calling it 'the logo' tacitly acknowledges that it is a highly simplified picture: meaningful analysis of a given component failure mode may require development of probabilistic spectra for multiple physical parameters, and in many practical cases, 'load' and 'capacity' will not vary independently.« less

A Probabilistic Framework for the Validation and Certification of Computer Simulations

NASA Technical Reports Server (NTRS)

Ghanem, Roger; Knio, Omar

2000-01-01

The paper presents a methodology for quantifying, propagating, and managing the uncertainty in the data required to initialize computer simulations of complex phenomena. The purpose of the methodology is to permit the quantitative assessment of a certification level to be associated with the predictions from the simulations, as well as the design of a data acquisition strategy to achieve a target level of certification. The value of a methodology that can address the above issues is obvious, specially in light of the trend in the availability of computational resources, as well as the trend in sensor technology. These two trends make it possible to probe physical phenomena both with physical sensors, as well as with complex models, at previously inconceivable levels. With these new abilities arises the need to develop the knowledge to integrate the information from sensors and computer simulations. This is achieved in the present work by tracing both activities back to a level of abstraction that highlights their commonalities, thus allowing them to be manipulated in a mathematically consistent fashion. In particular, the mathematical theory underlying computer simulations has long been associated with partial differential equations and functional analysis concepts such as Hilbert spares and orthogonal projections. By relying on a probabilistic framework for the modeling of data, a Hilbert space framework emerges that permits the modeling of coefficients in the governing equations as random variables, or equivalently, as elements in a Hilbert space. This permits the development of an approximation theory for probabilistic problems that parallels that of deterministic approximation theory. According to this formalism, the solution of the problem is identified by its projection on a basis in the Hilbert space of random variables, as opposed to more traditional techniques where the solution is approximated by its first or second-order statistics. The present representation, in addition to capturing significantly more information than the traditional approach, facilitates the linkage between different interacting stochastic systems as is typically observed in real-life situations.

Impact of refining the assessment of dietary exposure to cadmium in the European adult population.

PubMed

Ferrari, Pietro; Arcella, Davide; Heraud, Fanny; Cappé, Stefano; Fabiansson, Stefan

2013-01-01

Exposure assessment constitutes an important step in any risk assessment of potentially harmful substances present in food. The European Food Safety Authority (EFSA) first assessed dietary exposure to cadmium in Europe using a deterministic framework, resulting in mean values of exposure in the range of health-based guidance values. Since then, the characterisation of foods has been refined to better match occurrence and consumption data, and a new strategy to handle left-censoring in occurrence data was devised. A probabilistic assessment was performed and compared with deterministic estimates, using occurrence values at the European level and consumption data from 14 national dietary surveys. Mean estimates in the probabilistic assessment ranged from 1.38 (95% CI = 1.35-1.44) to 2.08 (1.99-2.23) µg kg⁻¹ bodyweight (bw) week⁻¹ across the different surveys, which were less than 10% lower than deterministic (middle bound) mean values that ranged from 1.50 to 2.20 µg kg⁻¹ bw week⁻¹. Probabilistic 95th percentile estimates of dietary exposure ranged from 2.65 (2.57-2.72) to 4.99 (4.62-5.38) µg kg⁻¹ bw week⁻¹, which were, with the exception of one survey, between 3% and 17% higher than middle-bound deterministic estimates. Overall, the proportion of subjects exceeding the tolerable weekly intake of 2.5 µg kg⁻¹ bw ranged from 14.8% (13.6-16.0%) to 31.2% (29.7-32.5%) according to the probabilistic assessment. The results of this work indicate that mean values of dietary exposure to cadmium in the European population were of similar magnitude using determinist or probabilistic assessments. For higher exposure levels, probabilistic estimates were almost consistently larger than deterministic counterparts, thus reflecting the impact of using the full distribution of occurrence values to determine exposure levels. It is considered prudent to use probabilistic methodology should exposure estimates be close to or exceeding health-based guidance values.

A Probabilistic Approach to Predict Thermal Fatigue Life for Ball Grid Array Solder Joints

NASA Astrophysics Data System (ADS)

Wei, Helin; Wang, Kuisheng

2011-11-01

Numerous studies of the reliability of solder joints have been performed. Most life prediction models are limited to a deterministic approach. However, manufacturing induces uncertainty in the geometry parameters of solder joints, and the environmental temperature varies widely due to end-user diversity, creating uncertainties in the reliability of solder joints. In this study, a methodology for accounting for variation in the lifetime prediction for lead-free solder joints of ball grid array packages (PBGA) is demonstrated. The key aspects of the solder joint parameters and the cyclic temperature range related to reliability are involved. Probabilistic solutions of the inelastic strain range and thermal fatigue life based on the Engelmaier model are developed to determine the probability of solder joint failure. The results indicate that the standard deviation increases significantly when more random variations are involved. Using the probabilistic method, the influence of each variable on the thermal fatigue life is quantified. This information can be used to optimize product design and process validation acceptance criteria. The probabilistic approach creates the opportunity to identify the root causes of failed samples from product fatigue tests and field returns. The method can be applied to better understand how variation affects parameters of interest in an electronic package design with area array interconnections.

Probability in reasoning: a developmental test on conditionals.

PubMed

Barrouillet, Pierre; Gauffroy, Caroline

2015-04-01

Probabilistic theories have been claimed to constitute a new paradigm for the psychology of reasoning. A key assumption of these theories is captured by what they call the Equation, the hypothesis that the meaning of the conditional is probabilistic in nature and that the probability of If p then q is the conditional probability, in such a way that P(if p then q)=P(q|p). Using the probabilistic truth-table task in which participants are required to evaluate the probability of If p then q sentences, the present study explored the pervasiveness of the Equation through ages (from early adolescence to adulthood), types of conditionals (basic, causal, and inducements) and contents. The results reveal that the Equation is a late developmental achievement only endorsed by a narrow majority of educated adults for certain types of conditionals depending on the content they involve. Age-related changes in evaluating the probability of all the conditionals studied closely mirror the development of truth-value judgements observed in previous studies with traditional truth-table tasks. We argue that our modified mental model theory can account for this development, and hence for the findings related with the probability task, which do not consequently support the probabilistic approach of human reasoning over alternative theories. Copyright © 2014 Elsevier B.V. All rights reserved.

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

Wind/tornado design criteria, development to achieve required probabilistic performance goals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, D.S.

1991-06-01

This paper describes the strategy for developing new design criteria for a critical facility to withstand loading induced by the wind/tornado hazard. The proposed design requirements for resisting wind/tornado loads are based on probabilistic performance goals. The proposed design criteria were prepared by a Working Group consisting of six experts in wind/tornado engineering and meteorology. Utilizing their best technical knowledge and judgment in the wind/tornado field, they met and discussed the methodologies and reviewed available data. A review of the available wind/tornado hazard model for the site, structural response evaluation methods, and conservative acceptance criteria lead to proposed design criteriamore » that has a high probability of achieving the required performance goals.« less

Chapter 8: US geological survey Circum-Arctic Resource Appraisal (CARA): Introduction and summary of organization and methods

USGS Publications Warehouse

Charpentier, R.R.; Gautier, D.L.

2011-01-01

The USGS has assessed undiscovered petroleum resources in the Arctic through geological mapping, basin analysis and quantitative assessment. The new map compilation provided the base from which geologists subdivided the Arctic for burial history modelling and quantitative assessment. The CARA was a probabilistic, geologically based study that used existing USGS methodology, modified somewhat for the circumstances of the Arctic. The assessment relied heavily on analogue modelling, with numerical input as lognormal distributions of sizes and numbers of undiscovered accumulations. Probabilistic results for individual assessment units were statistically aggregated taking geological dependencies into account. Fourteen papers in this Geological Society volume present summaries of various aspects of the CARA. ?? 2011 The Geological Society of London.

Wind/tornado design criteria, development to achieve required probabilistic performance goals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, D.S.

This paper describes the strategy for developing new design criteria for a critical facility to withstand loading induced by the wind/tornado hazard. The proposed design requirements for resisting wind/tornado loads are based on probabilistic performance goals. The proposed design criteria were prepared by a Working Group consisting of six experts in wind/tornado engineering and meteorology. Utilizing their best technical knowledge and judgment in the wind/tornado field, they met and discussed the methodologies and reviewed available data. A review of the available wind/tornado hazard model for the site, structural response evaluation methods, and conservative acceptance criteria lead to proposed design criteriamore » that has a high probability of achieving the required performance goals.« less

A Methodology for the Development of a Reliability Database for an Advanced Reactor Probabilistic Risk Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grabaskas, Dave; Brunett, Acacia J.; Bucknor, Matthew

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory are currently engaged in a joint effort to modernize and develop probabilistic risk assessment (PRA) techniques for advanced non-light water reactors. At a high level the primary outcome of this project will be the development of next-generation PRA methodologies that will enable risk-informed prioritization of safety- and reliability-focused research and development, while also identifying gaps that may be resolved through additional research. A subset of this effort is the development of a reliability database (RDB) methodology to determine applicable reliability data for inclusion in the quantification of the PRA. The RDBmore » method developed during this project seeks to satisfy the requirements of the Data Analysis element of the ASME/ANS Non-LWR PRA standard. The RDB methodology utilizes a relevancy test to examine reliability data and determine whether it is appropriate to include as part of the reliability database for the PRA. The relevancy test compares three component properties to establish the level of similarity to components examined as part of the PRA. These properties include the component function, the component failure modes, and the environment/boundary conditions of the component. The relevancy test is used to gauge the quality of data found in a variety of sources, such as advanced reactor-specific databases, non-advanced reactor nuclear databases, and non-nuclear databases. The RDB also establishes the integration of expert judgment or separate reliability analysis with past reliability data. This paper provides details on the RDB methodology, and includes an example application of the RDB methodology for determining the reliability of the intermediate heat exchanger of a sodium fast reactor. The example explores a variety of reliability data sources, and assesses their applicability for the PRA of interest through the use of the relevancy test.« less

Prediction of road accidents: A Bayesian hierarchical approach.

PubMed

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Probabilistic Ontology Development Methodology

DTIC Science & Technology

2014-06-01

Test, and Evaluation; Acquisition; and Planning and Marketing ," in Handbook of Systems Engineering and Management .: John Wiley & Sons, 2009, pp...Intelligence and knowledge management . However, many real world problems in these disciplines are burdened by incomplete information and other sources...knowledge engineering, Artificial Intelligence and knowledge management . However, many real world problems in these disciplines are burdened by

A PROBABILISTIC ARSENIC EXPOSURE ASSESSMENT FOR CHILDREN WHO CONTACT CAA - TREATED PLAYSETS AND DECKS: PART 1. MODEL METHODOLOGY, VARIABILITY RESULTS, AND MODEL EVALUATION

EPA Science Inventory

Concerns have been raised regarding the safety of young children who may contact arsenic residues while playing on and around chromated copper arsenate (CCA)-treated wood playsets and decks. Although CCA registrants voluntarily canceled the production of treated wood for residen...

Building Scalable Knowledge Graphs for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Maskey, Manil; Gatlin, Patrick; Zhang, Jia; Duan, Xiaoyi; Miller, J. J.; Bugbee, Kaylin; Christopher, Sundar; Freitag, Brian

2017-01-01

Knowledge Graphs link key entities in a specific domain with other entities via relationships. From these relationships, researchers can query knowledge graphs for probabilistic recommendations to infer new knowledge. Scientific papers are an untapped resource which knowledge graphs could leverage to accelerate research discovery. Goal: Develop an end-to-end (semi) automated methodology for constructing Knowledge Graphs for Earth Science.

Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

ERIC Educational Resources Information Center

Gelman, Andrew; Lee, Daniel; Guo, Jiqiang

2015-01-01

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: methodology, model constraints and joint inversion with electrical resistivity data

NASA Astrophysics Data System (ADS)

Rosas-Carbajal, Marina; Linde, Niklas; Kalscheuer, Thomas; Vrugt, Jasper A.

2014-03-01

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 is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Overview of the SAE G-11 RMSL (Reliability, Maintainability, Supportability, and Logistics) Division Activities and Technical Projects

NASA Technical Reports Server (NTRS)

Singhal, Surendra N.

2003-01-01

The SAE G-11 RMSL (Reliability, Maintainability, Supportability, and Logistics) Division activities include identification and fulfillment of joint industry, government, and academia needs for development and implementation of RMSL technologies. Four Projects in the Probabilistic Methods area and two in the area of RMSL have been identified. These are: (1) Evaluation of Probabilistic Technology - progress has been made toward the selection of probabilistic application cases. Future effort will focus on assessment of multiple probabilistic softwares in solving selected engineering problems using probabilistic methods. Relevance to Industry & Government - Case studies of typical problems encountering uncertainties, results of solutions to these problems run by different codes, and recommendations on which code is applicable for what problems; (2) Probabilistic Input Preparation - progress has been made in identifying problem cases such as those with no data, little data and sufficient data. Future effort will focus on developing guidelines for preparing input for probabilistic analysis, especially with no or little data. Relevance to Industry & Government - Too often, we get bogged down thinking we need a lot of data before we can quantify uncertainties. Not True. There are ways to do credible probabilistic analysis with little data; (3) Probabilistic Reliability - probabilistic reliability literature search has been completed along with what differentiates it from statistical reliability. Work on computation of reliability based on quantification of uncertainties in primitive variables is in progress. Relevance to Industry & Government - Correct reliability computations both at the component and system level are needed so one can design an item based on its expected usage and life span; (4) Real World Applications of Probabilistic Methods (PM) - A draft of volume 1 comprising aerospace applications has been released. Volume 2, a compilation of real world applications of probabilistic methods with essential information demonstrating application type and timehost savings by the use of probabilistic methods for generic applications is in progress. Relevance to Industry & Government - Too often, we say, 'The Proof is in the Pudding'. With help from many contributors, we hope to produce such a document. Problem is - not too many people are coming forward due to proprietary nature. So, we are asking to document only minimum information including problem description, what method used, did it result in any savings, and how much?; (5) Software Reliability - software reliability concept, program, implementation, guidelines, and standards are being documented. Relevance to Industry & Government - software reliability is a complex issue that must be understood & addressed in all facets of business in industry, government, and other institutions. We address issues, concepts, ways to implement solutions, and guidelines for maximizing software reliability; (6) Maintainability Standards - maintainability/serviceability industry standard/guidelines and industry best practices and methodologies used in performing maintainability/ serviceability tasks are being documented. Relevance to Industry & Government - Any industry or government process, project, and/or tool must be maintained and serviced to realize the life and performance it was designed for. We address issues and develop guidelines for optimum performance & life.

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Probabilistic risk assessment of the Space Shuttle. Phase 3: A study of the potential of losing the vehicle during nominal operation, volume 1

NASA Technical Reports Server (NTRS)

Fragola, Joseph R.; Maggio, Gaspare; Frank, Michael V.; Gerez, Luis; Mcfadden, Richard H.; Collins, Erin P.; Ballesio, Jorge; Appignani, Peter L.; Karns, James J.

1995-01-01

This document is the Executive Summary of a technical report on a probabilistic risk assessment (PRA) of the Space Shuttle vehicle performed under the sponsorship of the Office of Space Flight of the US National Aeronautics and Space Administration. It briefly summarizes the methodology and results of the Shuttle PRA. The primary objective of this project was to support management and engineering decision-making with respect to the Shuttle program by producing (1) a quantitative probabilistic risk model of the Space Shuttle during flight, (2) a quantitative assessment of in-flight safety risk, (3) an identification and prioritization of the design and operations that principally contribute to in-flight safety risk, and (4) a mechanism for risk-based evaluation proposed modifications to the Shuttle System. Secondary objectives were to provide a vehicle for introducing and transferring PRA technology to the NASA community, and to demonstrate the value of PRA by applying it beneficially to a real program of great international importance.

Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding.

PubMed

Rios, Anthony; Kavuluru, Ramakanth

2013-09-01

Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.

Constructor theory of probability

PubMed Central

2016-01-01

Unitary quantum theory, having no Born Rule, is non-probabilistic. Hence the notorious problem of reconciling it with the unpredictability and appearance of stochasticity in quantum measurements. Generalizing and improving upon the so-called ‘decision-theoretic approach’, I shall recast that problem in the recently proposed constructor theory of information—where quantum theory is represented as one of a class of superinformation theories, which are local, non-probabilistic theories conforming to certain constructor-theoretic conditions. I prove that the unpredictability of measurement outcomes (to which constructor theory gives an exact meaning) necessarily arises in superinformation theories. Then I explain how the appearance of stochasticity in (finitely many) repeated measurements can arise under superinformation theories. And I establish sufficient conditions for a superinformation theory to inform decisions (made under it) as if it were probabilistic, via a Deutsch–Wallace-type argument—thus defining a class of decision-supporting superinformation theories. This broadens the domain of applicability of that argument to cover constructor-theory compliant theories. In addition, in this version some of the argument's assumptions, previously construed as merely decision-theoretic, follow from physical properties expressed by constructor-theoretic principles. PMID:27616914

Probabilistic Cellular Automata

PubMed Central

Agapie, Alexandru; Giuclea, Marius

2014-01-01

Abstract 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. PMID:24999557

Probabilistic cellular automata.

PubMed

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.

On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy.

PubMed

Amigó, José M; Hirata, Yoshito; Aihara, Kazuyuki

2017-08-01

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.

Different Types of Sensation Seeking: A Person-Oriented Approach in Sensation-Seeking Research

ERIC Educational Resources Information Center

Suranyi, Zsuzsanna; Hitchcock, David B.; Hittner, James B.; Vargha, Andras; Urban, Robert

2013-01-01

Previous research on sensation seeking (SS) was dominated by a variable-oriented approach indicating that SS level has a linear relation with a host of problem behaviors. Our aim was to provide a person-oriented methodology--a probabilistic clustering--that enables examination of both inter- and intra-individual differences in not only the level,…

Probabilistic-Based Modeling and Simulation Assessment

DTIC Science & Technology

2010-06-01

developed to determine the relative importance of structural components of the vehicle under differnet crash and blast scenarios. With the integration of...the vehicle under different crash and blast scenarios. With the integration of the high fidelity neck and head model, a methodology to calculate the...parameter variability, correlation, and multiple (often competing) failure metrics. Important scenarios include vehicular collisions, blast /fragment

Development of a Physically-Based Methodology for Predicting Material Variability in Fatigue Crack Initiation and Growth Response

DTIC Science & Technology

2004-12-01

64, (2000), Federal Aviation Administration, Washington, DC. 14. Y.T. Wu, M.P. Enright, and H.R. Millwater , "Probabilistic Methods for Design...Assessment of Reliability with Inspection," AIAA Journal, AIAA, 40 (5), (2002), 937-946. 15. M.P. Enright, L. Huyse, R.C. McClung, and H.R. Millwater

Convergence Properties of a Class of Probabilistic Adaptive Schemes Called Sequential Reproductive Plans. Psychology and Education Series, Technical Report No. 210.

ERIC Educational Resources Information Center

Martin, Nancy

Presented is a technical report concerning the use of a mathematical model describing certain aspects of the duplication and selection processes in natural genetic adaptation. This reproductive plan/model occurs in artificial genetics (the use of ideas from genetics to develop general problem solving techniques for computers). The reproductive…

Optimally Stopped Optimization

NASA Astrophysics Data System (ADS)

Vinci, Walter; Lidar, Daniel

We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known, and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time, optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark the performance of a D-Wave 2X quantum annealer and the HFS solver, a specialized classical heuristic algorithm designed for low tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N = 1098 variables, the D-Wave device is between one to two orders of magnitude faster than the HFS solver.

Bias Characterization in Probabilistic Genotype Data and Improved Signal Detection with Multiple Imputation

PubMed Central

Palmer, Cameron; Pe’er, Itsik

2016-01-01

Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data. PMID:27310603

Probabilistic Estimates of Global Mean Sea Level and its Underlying Processes

NASA Astrophysics Data System (ADS)

Hay, C.; Morrow, E.; Kopp, R. E.; Mitrovica, J. X.

2015-12-01

Local sea level can vary significantly from the global mean value due to a suite of processes that includes ongoing sea-level changes due to the last ice age, land water storage, ocean circulation changes, and non-uniform sea-level changes that arise when modern-day land ice rapidly melts. Understanding these sources of spatial and temporal variability is critical to estimating past and present sea-level change and projecting future sea-level rise. Using two probabilistic techniques, a multi-model Kalman smoother and Gaussian process regression, we have reanalyzed 20th century tide gauge observations to produce a new estimate of global mean sea level (GMSL). Our methods allow us to extract global information from the sparse tide gauge field by taking advantage of the physics-based and model-derived geometry of the contributing processes. Both methods provide constraints on the sea-level contribution of glacial isostatic adjustment (GIA). The Kalman smoother tests multiple discrete models of glacial isostatic adjustment (GIA), probabilistically computing the most likely GIA model given the observations, while the Gaussian process regression characterizes the prior covariance structure of a suite of GIA models and then uses this structure to estimate the posterior distribution of local rates of GIA-induced sea-level change. We present the two methodologies, the model-derived geometries of the underlying processes, and our new probabilistic estimates of GMSL and GIA.

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

PubMed Central

Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

2007-01-01

Background Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org. PMID:17912365

Transactional problem content in cost discounting: parallel effects for probability and delay.

PubMed

Jones, Stephen; Oaksford, Mike

2011-05-01

Four experiments investigated the effects of transactional content on temporal and probabilistic discounting of costs. Kusev, van Schaik, Ayton, Dent, and Chater (2009) have shown that content other than gambles can alter decision-making behavior even when associated value and probabilities are held constant. Transactions were hypothesized to lead to similar effects because the cost to a purchaser always has a linked gain, the purchased commodity. Gain amount has opposite effects on delay and probabilistic discounting (e.g., Benzion, Rapoport, & Yagil, 1989; Green, Myerson, & Ostaszewski, 1999), a finding that is not consistent with descriptive decision theory (Kahneman & Tversky, 1979; Loewenstein & Prelec, 1992). However, little or no effect on discounting has been observed for losses or costs. Experiment 1, using transactions, showed parallel effects for temporal and probabilistic discounting: Smaller amounts were discounted more than large amounts. As the cost rises, people value the commodity more, and they consequently discount less. Experiment 2 ruled out a possible methodological cause for this effect. Experiment 3 replicated Experiment 1. Experiment 4, using gambles, showed no effect for temporal discounting, because of the absence of the linked gain, but the same effect for probabilistic discounting, because prospects implicitly introduce a linked gain (Green et al., 1999; Prelec & Loewenstein, 1991). As found by Kusev et al. (2009), these findings are not consistent with decision theory and suggest closer attention should be paid to the effects of content on decision making.

Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra.

PubMed

Claxton, Karl; Sculpher, Mark; McCabe, Chris; Briggs, Andrew; Akehurst, Ron; Buxton, Martin; Brazier, John; O'Hagan, Tony

2005-04-01

Recently the National Institute for Clinical Excellence (NICE) updated its methods guidance for technology assessment. One aspect of the new guidance is to require the use of probabilistic sensitivity analysis with all cost-effectiveness models submitted to the Institute. The purpose of this paper is to place the NICE guidance on dealing with uncertainty into a broader context of the requirements for decision making; to explain the general approach that was taken in its development; and to address each of the issues which have been raised in the debate about the role of probabilistic sensitivity analysis in general. The most appropriate starting point for developing guidance is to establish what is required for decision making. On the basis of these requirements, the methods and framework of analysis which can best meet these needs can then be identified. It will be argued that the guidance on dealing with uncertainty and, in particular, the requirement for probabilistic sensitivity analysis, is justified by the requirements of the type of decisions that NICE is asked to make. Given this foundation, the main issues and criticisms raised during and after the consultation process are reviewed. Finally, some of the methodological challenges posed by the need fully to characterise decision uncertainty and to inform the research agenda will be identified and discussed. Copyright (c) 2005 John Wiley & Sons, Ltd.

ZERO: probabilistic routing for deploy and forget Wireless Sensor Networks.

PubMed

Vilajosana, Xavier; Llosa, Jordi; Pacho, Jose Carlos; Vilajosana, Ignasi; Juan, Angel A; Vicario, Jose Lopez; Morell, Antoni

2010-01-01

As Wireless Sensor Networks are being adopted by industry and agriculture for large-scale and unattended deployments, the need for reliable and energy-conservative protocols become critical. Physical and Link layer efforts for energy conservation are not mostly considered by routing protocols that put their efforts on maintaining reliability and throughput. Gradient-based routing protocols route data through most reliable links aiming to ensure 99% packet delivery. However, they suffer from the so-called "hot spot" problem. Most reliable routes waste their energy fast, thus partitioning the network and reducing the area monitored. To cope with this "hot spot" problem we propose ZERO a combined approach at Network and Link layers to increase network lifespan while conserving reliability levels by means of probabilistic load balancing techniques.

Advanced probabilistic methods for quantifying the effects of various uncertainties in structural response

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.

1988-01-01

The effects of actual variations, also called uncertainties, in geometry and material properties on the structural response of a space shuttle main engine turbopump blade are evaluated. A normal distribution was assumed to represent the uncertainties statistically. Uncertainties were assumed to be totally random, partially correlated, and fully correlated. The magnitude of these uncertainties were represented in terms of mean and variance. Blade responses, recorded in terms of displacements, natural frequencies, and maximum stress, was evaluated and plotted in the form of probabilistic distributions under combined uncertainties. These distributions provide an estimate of the range of magnitudes of the response and probability of occurrence of a given response. Most importantly, these distributions provide the information needed to estimate quantitatively the risk in a structural design.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

NASA Astrophysics Data System (ADS)

Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian

2016-08-01

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Methodology for prediction and estimation of consequences of possible atmospheric releases of hazardous matter: "Kursk" submarine study

NASA Astrophysics Data System (ADS)

Baklanov, A.; Mahura, A.; Sørensen, J. H.

2003-06-01

There are objects with some periods of higher than normal levels of risk of accidental atmospheric releases (nuclear, chemical, biological, etc.). Such accidents or events may occur due to natural hazards, human errors, terror acts, and during transportation of waste or various operations at high risk. A methodology for risk assessment is suggested and it includes two approaches: 1) probabilistic analysis of possible atmospheric transport patterns using long-term trajectory and dispersion modelling, and 2) forecast and evaluation of possible contamination and consequences for the environment and population using operational dispersion modelling. The first approach could be applied during the preparation stage, and the second - during the operation stage. The suggested methodology is applied on an example of the most important phases (lifting, transportation, and decommissioning) of the ``Kursk" nuclear submarine operation. It is found that the temporal variability of several probabilistic indicators (fast transport probability fields, maximum reaching distance, maximum possible impact zone, and average integral concentration of 137Cs) showed that the fall of 2001 was the most appropriate time for the beginning of the operation. These indicators allowed to identify the hypothetically impacted geographical regions and territories. In cases of atmospheric transport toward the most populated areas, the forecasts of possible consequences during phases of the high and medium potential risk levels based on a unit hypothetical release (e.g. 1 Bq) are performed. The analysis showed that the possible deposition fractions of 10-11 (Bq/m2) over the Kola Peninsula, and 10-12 - 10-13 (Bq/m2) for the remote areas of the Scandinavia and Northwest Russia could be observed. The suggested methodology may be used successfully for any potentially dangerous object involving risk of atmospheric release of hazardous materials of nuclear, chemical or biological nature.

Methodology for prediction and estimation of consequences of possible atmospheric releases of hazardous matter: "Kursk"? submarine study

NASA Astrophysics Data System (ADS)

Baklanov, A.; Mahura, A.; Sørensen, J. H.

2003-03-01

There are objects with some periods of higher than normal levels of risk of accidental atmospheric releases (nuclear, chemical, biological, etc.). Such accidents or events may occur due to natural hazards, human errors, terror acts, and during transportation of waste or various operations at high risk. A methodology for risk assessment is suggested and it includes two approaches: 1) probabilistic analysis of possible atmospheric transport patterns using long-term trajectory and dispersion modelling, and 2) forecast and evaluation of possible contamination and consequences for the environment and population using operational dispersion modelling. The first approach could be applied during the preparation stage, and the second - during the operation stage. The suggested methodology is applied on an example of the most important phases (lifting, transportation, and decommissioning) of the "Kursk" nuclear submarine operation. It is found that the temporal variability of several probabilistic indicators (fast transport probability fields, maximum reaching distance, maximum possible impact zone, and average integral concentration of 137Cs) showed that the fall of 2001 was the most appropriate time for the beginning of the operation. These indicators allowed to identify the hypothetically impacted geographical regions and territories. In cases of atmospheric transport toward the most populated areas, the forecasts of possible consequences during phases of the high and medium potential risk levels based on a unit hypothetical release are performed. The analysis showed that the possible deposition fractions of 1011 over the Kola Peninsula, and 10-12 - 10-13 for the remote areas of the Scandinavia and Northwest Russia could be observed. The suggested methodology may be used successfully for any potentially dangerous object involving risk of atmospheric release of hazardous materials of nuclear, chemical or biological nature.

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

NASA Technical Reports Server (NTRS)

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.

PRA (Probabilistic Risk Assessments) Participation versus Validation

NASA Technical Reports Server (NTRS)

DeMott, Diana; Banke, Richard

2013-01-01

Probabilistic Risk Assessments (PRAs) are performed for projects or programs where the consequences of failure are highly undesirable. PRAs primarily address the level of risk those projects or programs posed during operations. PRAs are often developed after the design has been completed. Design and operational details used to develop models include approved and accepted design information regarding equipment, components, systems and failure data. This methodology basically validates the risk parameters of the project or system design. For high risk or high dollar projects, using PRA methodologies during the design process provides new opportunities to influence the design early in the project life cycle to identify, eliminate or mitigate potential risks. Identifying risk drivers before the design has been set allows the design engineers to understand the inherent risk of their current design and consider potential risk mitigation changes. This can become an iterative process where the PRA model can be used to determine if the mitigation technique is effective in reducing risk. This can result in more efficient and cost effective design changes. PRA methodology can be used to assess the risk of design alternatives and can demonstrate how major design changes or program modifications impact the overall program or project risk. PRA has been used for the last two decades to validate risk predictions and acceptability. Providing risk information which can positively influence final system and equipment design the PRA tool can also participate in design development, providing a safe and cost effective product.

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

USGS Publications Warehouse

Barbraud, C.; Nichols, J.D.; Hines, J.E.; Hafner, H.

2003-01-01

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence-absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence-absence data arising from Pollock's robust capture-recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence-absence data on two species of herons (Purple Heron, Ardea purpurea and Grey Heron, Ardea cinerea). Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi

PubMed Central

Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni

2016-01-01

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a “specialized” domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the “community structure” of the ToH and their difficulties in executing so-called “counterintuitive” movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand—a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits. PMID:27074140

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi.

PubMed

Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni

2016-04-01

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.

Probabilistic material strength degradation model for Inconel 718 components subjected to high temperature, high-cycle and low-cycle mechanical fatigue, creep and thermal fatigue effects

NASA Technical Reports Server (NTRS)

Bast, Callie C.; Boyce, Lola

1995-01-01

This report presents the results of both the fifth and sixth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA). The research included 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 five effects that typically reduce lifetime strength: high temperature, high-cycle mechanical fatigue, low-cycle 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 five variables, namely, high temperature, high-cycle and low-cycle 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 an updated version of PROMISS, entitled PROMISS93, a sensitivity study for the combined effects of high-cycle mechanical fatigue, creep and thermal fatigue was performed. Then using the current version of PROMISS, entitled PROMISS94, a second sensitivity study including the effect of low-cycle mechanical fatigue, as well as, the three previous effects 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 high-cycle 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.

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

2017-06-01

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

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

PubMed Central

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. PMID:29742139

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

PubMed

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.

Methodological framework for the probabilistic risk assessment of multi-hazards at a municipal scale: a case study in the Fella river valley, Eastern Italian Alps

NASA Astrophysics Data System (ADS)

Hussin, Haydar; van Westen, Cees; Reichenbach, Paola

2013-04-01

Local and regional authorities in mountainous areas that deal with hydro-meteorological hazards like landslides and floods try to set aside budgets for emergencies and risk mitigation. However, future losses are often not calculated in a probabilistic manner when allocating budgets or determining how much risk is acceptable. The absence of probabilistic risk estimates can create a lack of preparedness for reconstruction and risk reduction costs and a deficiency in promoting risk mitigation and prevention in an effective way. The probabilistic risk of natural hazards at local scale is usually ignored all together due to the difficulty in acknowledging, processing and incorporating uncertainties in the estimation of losses (e.g. physical damage, fatalities and monetary loss). This study attempts to set up a working framework for a probabilistic risk assessment (PRA) of landslides and floods at a municipal scale using the Fella river valley (Eastern Italian Alps) as a multi-hazard case study area. The emphasis is on the evaluation and determination of the uncertainty in the estimation of losses from multi-hazards. To carry out this framework some steps are needed: (1) by using physically based stochastic landslide and flood models we aim to calculate the probability of the physical impact on individual elements at risk, (2) this is then combined with a statistical analysis of the vulnerability and monetary value of the elements at risk in order to include their uncertainty in the risk assessment, (3) finally the uncertainty from each risk component is propagated into the loss estimation. The combined effect of landslides and floods on the direct risk to communities in narrow alpine valleys is also one of important aspects that needs to be studied.

Joint Probabilistic Projection of Female and Male Life Expectancy

PubMed Central

Raftery, Adrian E.; Lalic, Nevena; Gerland, Patrick

2014-01-01

BACKGROUND The United Nations (UN) produces population projections for all countries every two years. These are used by international organizations, governments, the private sector and researchers for policy planning, for monitoring development goals, as inputs to economic and environmental models, and for social and health research. The UN is considering producing fully probabilistic population projections, for which joint probabilistic projections of future female and male life expectancy at birth are needed. OBJECTIVE We propose a methodology for obtaining joint probabilistic projections of female and male life expectancy at birth. METHODS We first project female life expectancy using a one-sex method for probabilistic projection of life expectancy. We then project the gap between female and male life expectancy. We propose an autoregressive model for the gap in a future time period for a particular country, which is a function of female life expectancy and a t-distributed random perturbation. This method takes into account mortality data limitations, is comparable across countries, and accounts for shocks. We estimate all parameters based on life expectancy estimates for 1950–2010. The methods are implemented in the bayesLife and bayesPop R packages. RESULTS We evaluated our model using out-of-sample projections for the period 1995–2010, and found that our method performed better than several possible alternatives. CONCLUSIONS We find that the average gap between female and male life expectancy has been increasing for female life expectancy below 75, and decreasing for female life expectancy above 75. Our projections of the gap are lower than the UN’s 2008 projections for most countries and so lead to higher projections of male life expectancy. PMID:25580082

Fast, Nonlinear, Fully Probabilistic Inversion of Large Geophysical Problems

NASA Astrophysics Data System (ADS)

Curtis, A.; Shahraeeni, M.; Trampert, J.; Meier, U.; Cho, G.

2010-12-01

Almost all Geophysical inverse problems are in reality nonlinear. Fully nonlinear inversion including non-approximated physics, and solving for probability distribution functions (pdf’s) that describe the solution uncertainty, generally requires sampling-based Monte-Carlo style methods that are computationally intractable in most large problems. In order to solve such problems, physical relationships are usually linearized leading to efficiently-solved, (possibly iterated) linear inverse problems. However, it is well known that linearization can lead to erroneous solutions, and in particular to overly optimistic uncertainty estimates. What is needed across many Geophysical disciplines is a method to invert large inverse problems (or potentially tens of thousands of small inverse problems) fully probabilistically and without linearization. This talk shows how very large nonlinear inverse problems can be solved fully probabilistically and incorporating any available prior information using mixture density networks (driven by neural network banks), provided the problem can be decomposed into many small inverse problems. In this talk I will explain the methodology, compare multi-dimensional pdf inversion results to full Monte Carlo solutions, and illustrate the method with two applications: first, inverting surface wave group and phase velocities for a fully-probabilistic global tomography model of the Earth’s crust and mantle, and second inverting industrial 3D seismic data for petrophysical properties throughout and around a subsurface hydrocarbon reservoir. The latter problem is typically decomposed into 104 to 105 individual inverse problems, each solved fully probabilistically and without linearization. The results in both cases are sufficiently close to the Monte Carlo solution to exhibit realistic uncertainty, multimodality and bias. This provides far greater confidence in the results, and in decisions made on their basis.

A Preliminary Bayesian Analysis of Incomplete Longitudinal Data from a Small Sample: Methodological Advances in an International Comparative Study of Educational Inequality

ERIC Educational Resources Information Center

Hsieh, Chueh-An; Maier, Kimberly S.

2009-01-01

The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection.…

Fast probabilistic file fingerprinting for big data

PubMed Central

2013-01-01

Background Biological data acquisition is raising new challenges, both in data analysis and handling. Not only is it proving hard to analyze the data at the rate it is generated today, but simply reading and transferring data files can be prohibitively slow due to their size. This primarily concerns logistics within and between data centers, but is also important for workstation users in the analysis phase. Common usage patterns, such as comparing and transferring files, are proving computationally expensive and are tying down shared resources. Results We present an efficient method for calculating file uniqueness for large scientific data files, that takes less computational effort than existing techniques. This method, called Probabilistic Fast File Fingerprinting (PFFF), exploits the variation present in biological data and computes file fingerprints by sampling randomly from the file instead of reading it in full. Consequently, it has a flat performance characteristic, correlated with data variation rather than file size. We demonstrate that probabilistic fingerprinting can be as reliable as existing hashing techniques, with provably negligible risk of collisions. We measure the performance of the algorithm on a number of data storage and access technologies, identifying its strengths as well as limitations. Conclusions Probabilistic fingerprinting may significantly reduce the use of computational resources when comparing very large files. Utilisation of probabilistic fingerprinting techniques can increase the speed of common file-related workflows, both in the data center and for workbench analysis. The implementation of the algorithm is available as an open-source tool named pfff, as a command-line tool as well as a C library. The tool can be downloaded from http://biit.cs.ut.ee/pfff. PMID:23445565

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, B.G.J.; Grindrod, P.

Her Majesty`s Inspectorate of Polution (HMIP) of the United Kingdom has developed a procedure for the post closure assessment of the underground disposal of radioactive waste. In this paper the method of using theory and ideas from the mathematical sciences for assessment is described. The system simulation methodology seeks to discover key combinations of processes or effects which may yield behaviour of interest by sampling across functional and parametric uncertainties, and treating the systems within a probabilistic framework. This paper also discusses how HMIP assessment methodology has been presented, independent of any current application, for review by leading scientists whomore » are independent of the performance assessment field.« less

FASP, an analytic resource appraisal program for petroleum play analysis

USGS Publications Warehouse

Crovelli, R.A.; Balay, R.H.

1986-01-01

An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis is presented in a FORTRAN program termed FASP. This play-analysis methodology is a geostochastic system for petroleum resource appraisal in explored as well as frontier areas. An established geologic model considers both the uncertainty of the presence of the assessed hydrocarbon and its amount if present. The program FASP produces resource estimates of crude oil, nonassociated gas, dissolved gas, and gas for a geologic play in terms of probability distributions. The analytic method is based upon conditional probability theory and many laws of expectation and variance. ?? 1986.

Unifying Model-Based and Reactive Programming within a Model-Based Executive

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)

1999-01-01

Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

Integration of RAMS in LCC analysis for linear transport infrastructures. A case study for railways.

NASA Astrophysics Data System (ADS)

Calle-Cordón, Álvaro; Jiménez-Redondo, Noemi; Morales-Gámiz, F. J.; García-Villena, F. A.; Garmabaki, Amir H. S.; Odelius, Johan

2017-09-01

Life-cycle cost (LCC) analysis is an economic technique used to assess the total costs associated with the lifetime of a system in order to support decision making in long term strategic planning. For complex systems, such as railway and road infrastructures, the cost of maintenance plays an important role in the LCC analysis. Costs associated with maintenance interventions can be more reliably estimated by integrating the probabilistic nature of the failures associated to these interventions in the LCC models. Reliability, Maintainability, Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in a quantitative way by using probabilistic information extracted from registered maintenance activities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliable predictions of system maintenance costs and the dependencies of these costs with specific cost drivers through sensitivity analyses. This paper presents an innovative approach for a combined RAMS & LCC methodology for railway and road transport infrastructures being developed under the on-going H2020 project INFRALERT. Such RAMS & LCC analysis provides relevant probabilistic information to be used for condition and risk-based planning of maintenance activities as well as for decision support in long term strategic investment planning.

Probabilistic description of probable maximum precipitation

NASA Astrophysics Data System (ADS)

Ben Alaya, Mohamed Ali; Zwiers, Francis W.; Zhang, Xuebin

2017-04-01

Probable Maximum Precipitation (PMP) is the key parameter used to estimate probable Maximum Flood (PMF). PMP and PMF are important for dam safety and civil engineering purposes. Even if the current knowledge of storm mechanisms remains insufficient to properly evaluate limiting values of extreme precipitation, PMP estimation methods are still based on deterministic consideration, and give only single values. This study aims to provide a probabilistic description of the PMP based on the commonly used method, the so-called moisture maximization. To this end, a probabilistic bivariate extreme values model is proposed to address the limitations of traditional PMP estimates via moisture maximization namely: (i) the inability to evaluate uncertainty and to provide a range PMP values, (ii) the interpretation that a maximum of a data series as a physical upper limit (iii) and the assumption that a PMP event has maximum moisture availability. Results from simulation outputs of the Canadian Regional Climate Model CanRCM4 over North America reveal the high uncertainties inherent in PMP estimates and the non-validity of the assumption that PMP events have maximum moisture availability. This later assumption leads to overestimation of the PMP by an average of about 15% over North America, which may have serious implications for engineering design.

Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem

NASA Astrophysics Data System (ADS)

Man, J.; Li, W.; Zeng, L.; Wu, L.

2015-12-01

The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the Polynomial Chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-01-01

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm. PMID:28587084

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors.

PubMed

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-05-25

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ -connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

Life prediction of different commercial dental implants as influence by uncertainties in their fatigue material properties and loading conditions.

PubMed

Pérez, M A

2012-12-01

Probabilistic analyses allow the effect of uncertainty in system parameters to be determined. In the literature, many researchers have investigated static loading effects on dental implants. However, the intrinsic variability and uncertainty of most of the main problem parameters are not accounted for. The objective of this research was to apply a probabilistic computational approach to predict the fatigue life of three different commercial dental implants considering the variability and uncertainty in their fatigue material properties and loading conditions. For one of the commercial dental implants, the influence of its diameter in the fatigue life performance was also studied. This stochastic technique was based on the combination of a probabilistic finite element method (PFEM) and a cumulative damage approach known as B-model. After 6 million of loading cycles, local failure probabilities of 0.3, 0.4 and 0.91 were predicted for the Lifecore, Avinent and GMI implants, respectively (diameter of 3.75mm). The influence of the diameter for the GMI implant was studied and the results predicted a local failure probability of 0.91 and 0.1 for the 3.75mm and 5mm, respectively. In all cases the highest failure probability was located at the upper screw-threads. Therefore, the probabilistic methodology proposed herein may be a useful tool for performing a qualitative comparison between different commercial dental implants. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Fifth Annual Workshop on the Application of Probabilistic Methods for Gas Turbine Engines

NASA Technical Reports Server (NTRS)

Briscoe, Victoria (Compiler)

2002-01-01

These are the proceedings of the 5th Annual FAA/Air Force/NASA/Navy Workshop on the Probabilistic Methods for Gas Turbine Engines hosted by NASA Glenn Research Center and held at the Holiday Inn Cleveland West. The history of this series of workshops stems from the recognition that both military and commercial aircraft engines are inevitably subjected to similar design and manufacturing principles. As such, it was eminently logical to combine knowledge bases on how some of these overlapping principles and methodologies are being applied. We have started the process by creating synergy and cooperation between the FAA, Air Force, Navy, and NASA in these workshops. The recent 3-day workshop was specifically designed to benefit the development of probabilistic methods for gas turbine engines by addressing recent technical accomplishments and forging new ideas. We accomplished our goals of minimizing duplication, maximizing the dissemination of information, and improving program planning to all concerned. This proceeding includes the final agenda, abstracts, presentations, and panel notes, plus the valuable contact information from our presenters and attendees. We hope that this proceeding will be a tool to enhance understanding of the developers and users of probabilistic methods. The fifth workshop doubled its attendance and had the success of collaboration with the many diverse groups represented including government, industry, academia, and our international partners. So, "Start your engines!" and utilize these proceedings towards creating safer and more reliable gas turbine engines for our commercial and military partners.

Verification of recursive probabilistic integration (RPI) method for fatigue life management using non-destructive inspections

NASA Astrophysics Data System (ADS)

Chen, Tzikang J.; Shiao, Michael

2016-04-01

This paper verified a generic and efficient assessment concept for probabilistic fatigue life management. The concept is developed based on an integration of damage tolerance methodology, simulations methods1, 2, and a probabilistic algorithm RPI (recursive probability integration)3-9 considering maintenance for damage tolerance and risk-based fatigue life management. RPI is an efficient semi-analytical probabilistic method for risk assessment subjected to various uncertainties such as the variability in material properties including crack growth rate, initial flaw size, repair quality, random process modeling of flight loads for failure analysis, and inspection reliability represented by probability of detection (POD). In addition, unlike traditional Monte Carlo simulations (MCS) which requires a rerun of MCS when maintenance plan is changed, RPI can repeatedly use a small set of baseline random crack growth histories excluding maintenance related parameters from a single MCS for various maintenance plans. In order to fully appreciate the RPI method, a verification procedure was performed. In this study, MC simulations in the orders of several hundred billions were conducted for various flight conditions, material properties, and inspection scheduling, POD and repair/replacement strategies. Since the MC simulations are time-consuming methods, the simulations were conducted parallelly on DoD High Performance Computers (HPC) using a specialized random number generator for parallel computing. The study has shown that RPI method is several orders of magnitude more efficient than traditional Monte Carlo simulations.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

A Probabilistic Tsunami Hazard Assessment Methodology and Its Application to Crescent City, CA

NASA Astrophysics Data System (ADS)

Gonzalez, F. I.; Leveque, R. J.; Waagan, K.; Adams, L.; Lin, G.

2012-12-01

A PTHA methodology, based in large part on Probabilistic Seismic Hazard Assessment methods (e.g., Cornell, 1968; SSHAC, 1997; Geist and Parsons, 2005), was previously applied to Seaside, OR (Gonzalez, et al., 2009). This initial version of the method has been updated to include: a revised method to estimate tidal uncertainty; an improved method for generating stochastic realizations to estimate slip distribution uncertainty (Mai and Beroza, 2002; Blair, et al., 2011); additional near-field sources in the Cascadia Subduction Zone, based on the work of Goldfinger, et al. (2012); far-field sources in Japan, based on information updated since the 3 March 2011 Tohoku tsunami (Japan Earthquake Research Committee, 2011). The GeoClaw tsunami model (Berger, et. al, 2011) is used to simulate generation, propagation and inundation. We will discuss this revised PTHA methodology and the results of its application to Crescent City, CA. Berger, M.J., D. L. George, R. J. LeVeque, and K. T. Mandli, The GeoClaw software for depth-averaged flows with adaptive refinement, Adv. Water Res. 34 (2011), pp. 1195-1206. Blair, J.L., McCrory, P.A., Oppenheimer, D.H., and Waldhauser, F. (2011): A Geo-referenced 3D model of the Juan de Fuca Slab and associated seismicity: U.S. Geological Survey Data Series 633, v.1.0, available at http://pubs.usgs.gov/ds/633/. Cornell, C. A. (1968): Engineering seismic risk analysis, Bull. Seismol. Soc. Am., 58, 1583-1606. Geist, E. L., and T. Parsons (2005): Probabilistic Analysis of Tsunami Hazards, Nat. Hazards, 37 (3), 277-314. Goldfinger, C., Nelson, C.H., Morey, A.E., Johnson, J.E., Patton, J.R., Karabanov, E., Gutiérrez-Pastor, J., Eriksson, A.T., Gràcia, E., Dunhill, G., Enkin, R.J., Dallimore, A., and Vallier, T. (2012): Turbidite event history—Methods and implications for Holocene paleoseismicity of the Cascadia subduction zone: U.S. Geological Survey Professional Paper 1661-F, 170 p. (Available at http://pubs.usgs.gov/pp/pp1661f/). González, F.I., E.L. Geist, B. Jaffe, U. Kânoglu, H. Mofjeld, C.E. Synolakis, V.V Titov, D. Arcas, D. Bellomo, D. Carlton, T. Horning, J. Johnson, J. Newman, T. Parsons, R. Peters, C. Peterson, G .Priest, A. Venturato, J. Weber, F. Wong, and A. Yalciner (2009): Probabilistic Tsunami Hazard Assessment at Seaside, Oregon, for Near- and Far-Field Seismic Sources, J. Geophys. Res., 114, C11023, doi:10.1029/2008JC005132. Japan Earthquake Research Committee, (2011): http://www.jishin.go.jp/main/p_hyoka02.htm Mai, P. M., and G. C. Beroza (2002): A spatial random field model to characterize complexity in earthquake slip, J. Geophys. Res., 107(B11), 2308, doi:10.1029/2001JB000588. SSHAC (Senior Seismic Hazard Analysis Committee) (1997): Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of Experts, Main Report Rep. NUREG/CR-6372 UCRL-ID-122160 Vol. 1, 256 pp, U.S. Nuclear Regulatory Commission.

2018 Ground Robotics Capabilities Conference and Exhibiton

DTIC Science & Technology

2018-04-11

Transportable Robot System (MTRS) Inc 1 Non -standard Equipment (approved) Explosive Ordnance Disposal Common Robotic System-Heavy (CRS-H) Inc 1 AROC: 3-Star...and engineering • AI risk mitigation methodologies and techniques are at best immature – E.g., V&V; Probabilistic software analytics; code level...controller to minimize potential UxS mishaps and unauthorized Command and Control (C2). • PSP-10 – Ensure that software systems which exhibit non

Model-Free Stochastic Localization of CBRN Releases

DTIC Science & Technology

2013-01-01

Ioannis Ch. Paschalidis,‡ Senior Member, IEEE Abstract—We present a novel two-stage methodology for locating a Chemical, Biological, Radiological, or...Nuclear (CBRN) source in an urban area using a network of sensors. In contrast to earlier work, our approach does not solve an inverse dispersion problem...but relies on data obtained from a simulation of the CBRN dispersion to obtain probabilistic descriptors of sensor measurements under a variety of CBRN

FAVOR: A new fracture mechanics code for reactor pressure vessels subjected to pressurized thermal shock

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickson, T.L.

1993-01-01

This report discusses probabilistic fracture mechanics (PFM) analysis which is a major element of the comprehensive probabilistic methodology endorsed by the NRC for evaluation of the integrity of Pressurized Water Reactor (PWR) pressure vessels subjected to pressurized-thermal-shock (PTS) transients. It is anticipated that there will be an increasing need for an improved and validated PTS PFM code which is accepted by the NRC and utilities, as more plants approach the PTS screening criteria and are required to perform plant-specific analyses. The NRC funded Heavy Section Steel Technology (HSST) Program at Oak Ridge National Laboratories is currently developing the FAVOR (Fracturemore » Analysis of Vessels: Oak Ridge) PTS PFM code, which is intended to meet this need. The FAVOR code incorporates the most important features of both OCA-P and VISA-II and contains some new capabilities such as PFM global modeling methodology, the capability to approximate the effects of thermal streaming on circumferential flaws located inside a plume region created by fluid and thermal stratification, a library of stress intensity factor influence coefficients, generated by the NQA-1 certified ABAQUS computer code, for an adequate range of two and three dimensional inside surface flaws, the flexibility to generate a variety of output reports, and user friendliness.« less

FAVOR: A new fracture mechanics code for reactor pressure vessels subjected to pressurized thermal shock

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickson, T.L.

1993-04-01

This report discusses probabilistic fracture mechanics (PFM) analysis which is a major element of the comprehensive probabilistic methodology endorsed by the NRC for evaluation of the integrity of Pressurized Water Reactor (PWR) pressure vessels subjected to pressurized-thermal-shock (PTS) transients. It is anticipated that there will be an increasing need for an improved and validated PTS PFM code which is accepted by the NRC and utilities, as more plants approach the PTS screening criteria and are required to perform plant-specific analyses. The NRC funded Heavy Section Steel Technology (HSST) Program at Oak Ridge National Laboratories is currently developing the FAVOR (Fracturemore » Analysis of Vessels: Oak Ridge) PTS PFM code, which is intended to meet this need. The FAVOR code incorporates the most important features of both OCA-P and VISA-II and contains some new capabilities such as PFM global modeling methodology, the capability to approximate the effects of thermal streaming on circumferential flaws located inside a plume region created by fluid and thermal stratification, a library of stress intensity factor influence coefficients, generated by the NQA-1 certified ABAQUS computer code, for an adequate range of two and three dimensional inside surface flaws, the flexibility to generate a variety of output reports, and user friendliness.« less

Accounting for pH heterogeneity and variability in modelling human health risks from cadmium in contaminated land.

PubMed

Gay, J Rebecca; Korre, Anna

2009-07-01

The authors have previously published a methodology which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment, incorporating uncertainty, of risks to human health from exposure to contaminated land. The model assumes a constant soil to plant concentration factor (CF(veg)) when calculating intake of contaminants. This model is modified here to enhance its use in a situation where CF(veg) varies according to soil pH, as is the case for cadmium. The original methodology uses sequential indicator simulation (SIS) to map soil concentration estimates for one contaminant across a site. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. The proposed improvement involves not only the geostatistical estimation of the contaminant concentration, but also that of soil pH, which in turn leads to a variable CF(veg) estimate which influences the human intake results. The results presented demonstrate that taking pH into account can influence the outcome of the risk assessment greatly. It is proposed that a similar adaptation could be used for other combinations of soil variables which influence CF(veg).

Probabilistic evaluation of uncertainties and risks in aerospace components

NASA Technical Reports Server (NTRS)

Shah, A. R.; Shiao, M. C.; Nagpal, V. K.; Chamis, C. C.

1992-01-01

This paper summarizes a methodology developed at NASA Lewis Research Center which computationally simulates the structural, material, and load uncertainties associated with Space Shuttle Main Engine (SSME) components. The methodology was applied to evaluate the scatter in static, buckling, dynamic, fatigue, and damage behavior of the SSME turbo pump blade. Also calculated are the probability densities of typical critical blade responses, such as effective stress, natural frequency, damage initiation, most probable damage path, etc. Risk assessments were performed for different failure modes, and the effect of material degradation on the fatigue and damage behaviors of a blade were calculated using a multi-factor interaction equation. Failure probabilities for different fatigue cycles were computed and the uncertainties associated with damage initiation and damage propagation due to different load cycle were quantified. Evaluations on the effects of mistuned blades on a rotor were made; uncertainties in the excitation frequency were found to significantly amplify the blade responses of a mistuned rotor. The effects of the number of blades on a rotor were studied. The autocorrelation function of displacements and the probability density function of the first passage time for deterministic and random barriers for structures subjected to random processes also were computed. A brief discussion was included on the future direction of probabilistic structural analysis.

PROBABILISTIC PROGRAMMING FOR ADVANCED MACHINE LEARNING (PPAML) DISCRIMINATIVE LEARNING FOR GENERATIVE TASKS (DILIGENT)

DTIC Science & Technology

2017-11-29

Structural connections of the frames (fragments) in the knowledge. We call the fundamental elements of the knowledge a limited number of elements...the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance with SAF/AQR memorandum dated...AVAILABILITY STATEMENT Approved for Public Release; Distribution Unlimited. This report is the result of contracted fundamental research deemed exempt from

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.

2018-04-01

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

SSHAC Level 1 Probabilistic Seismic Hazard Analysis for the Idaho National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Payne, Suzette Jackson; Coppersmith, Ryan; Coppersmith, Kevin

A Probabilistic Seismic Hazard Analysis (PSHA) was completed for the Materials and Fuels Complex (MFC), Advanced Test Reactor (ATR), and Naval Reactors Facility (NRF) at the Idaho National Laboratory (INL). The PSHA followed the approaches and procedures for Senior Seismic Hazard Analysis Committee (SSHAC) Level 1 study and included a Participatory Peer Review Panel (PPRP) to provide the confident technical basis and mean-centered estimates of the ground motions. A new risk-informed methodology for evaluating the need for an update of an existing PSHA was developed as part of the Seismic Risk Assessment (SRA) project. To develop and implement the newmore » methodology, the SRA project elected to perform two SSHAC Level 1 PSHAs. The first was for the Fuel Manufacturing Facility (FMF), which is classified as a Seismic Design Category (SDC) 3 nuclear facility. The second was for the ATR Complex, which has facilities classified as SDC-4. The new methodology requires defensible estimates of ground motion levels (mean and full distribution of uncertainty) for its criteria and evaluation process. The INL SSHAC Level 1 PSHA demonstrates the use of the PPRP, evaluation and integration through utilization of a small team with multiple roles and responsibilities (four team members and one specialty contractor), and the feasibility of a short duration schedule (10 months). Additionally, a SSHAC Level 1 PSHA was conducted for NRF to provide guidance on the potential use of a design margin above rock hazard levels for the Spent Fuel Handling Recapitalization Project (SFHP) process facility.« less

An evolving-requirements technology assessment process for advanced propulsion concepts

NASA Astrophysics Data System (ADS)

McClure, Erin Kathleen

The following dissertation investigates the development of a methodology suitable for the evaluation of advanced propulsion concepts. At early stages of development, both the future performance of these concepts and their requirements are highly uncertain, making it difficult to forecast their future value. Developing advanced propulsion concepts requires a huge investment of resources. The methodology was developed to enhance the decision-makers understanding of the concepts, so that they could mitigate the risks associated with developing such concepts. A systematic methodology to identify potential advanced propulsion concepts and assess their robustness is necessary to reduce the risk of developing advanced propulsion concepts. Existing advanced design methodologies have evaluated the robustness of technologies or concepts to variations in requirements, but they are not suitable to evaluate a large number of dissimilar concepts. Variations in requirements have been shown to impact the development of advanced propulsion concepts, and any method designed to evaluate these concepts must incorporate the possible variations of the requirements into the assessment. In order to do so, a methodology was formulated to be capable of accounting for two aspects of the problem. First, it had to systemically identify a probabilistic distribution for the future requirements. Such a distribution would allow decision-makers to quantify the uncertainty introduced by variations in requirements. Second, the methodology must be able to assess the robustness of the propulsion concepts as a function of that distribution. This dissertation describes in depth these enabling elements and proceeds to synthesize them into a new method, the Evolving Requirements Technology Assessment (ERTA). As a proof of concept, the ERTA method was used to evaluate and compare advanced propulsion systems that will be capable of powering a hurricane tracking, High Altitude, Long Endurance (HALE) unmanned aerial vehicle (UAV). The use of the ERTA methodology to assess HALE UAV propulsion concepts demonstrated that potential variations in requirements do significantly impact the assessment and selection of propulsion concepts. The proof of concept also demonstrated that traditional forecasting techniques, such as the cross impact analysis, could be used to forecast the requirements for advanced propulsion concepts probabilistically. "Fitness", a measure of relative goodness, was used to evaluate the concepts. Finally, stochastic optimizations were used to evaluate the propulsion concepts across the range of requirement sets that were considered.

A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release

NASA Astrophysics Data System (ADS)

Rajaona, Harizo; Septier, François; Armand, Patrick; Delignon, Yves; Olry, Christophe; Albergel, Armand; Moussafir, Jacques

2015-12-01

In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.

A probabilistic analysis of silicon cost

NASA Technical Reports Server (NTRS)

Reiter, L. J.

1983-01-01

Silicon materials costs represent both a cost driver and an area where improvement can be made in the manufacture of photovoltaic modules. The cost from three processes for the production of low-cost silicon being developed under the U.S. Department of Energy's (DOE) National Photovoltaic Program is analyzed. The approach is based on probabilistic inputs and makes use of two models developed at the Jet Propulsion Laboratory: SIMRAND (SIMulation of Research ANd Development) and IPEG (Improved Price Estimating Guidelines). The approach, assumptions, and limitations are detailed along with a verification of the cost analyses methodology. Results, presented in the form of cumulative probability distributions for silicon cost, indicate that there is a 55% chance of reaching the DOE target of $16/kg for silicon material. This is a technically achievable cost based on expert forecasts of the results of ongoing research and development and do not imply any market prices for a given year.

Probabilistic structural mechanics research for parallel processing computers

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Chen, Heh-Chyun; Twisdale, Lawrence A.; Martin, William R.

1991-01-01

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

A Comprehensive Probabilistic Tsunami Hazard Assessment: Multiple Sources and Short-Term Interactions

NASA Astrophysics Data System (ADS)

Anita, G.; Selva, J.; Laura, S.

2011-12-01

We develop a comprehensive and total probabilistic tsunami hazard assessment (TotPTHA), in which many different possible source types concur to the definition of the total tsunami hazard at given target sites. In a multi-hazard and multi-risk perspective, such an innovative approach allows, in principle, to consider all possible tsunamigenic sources, from seismic events, to slides, asteroids, volcanic eruptions, etc. In this respect, we also formally introduce and discuss the treatment of interaction/cascade effects in the TotPTHA analysis. We demonstrate how external triggering events may induce significant temporary variations in the tsunami hazard. Because of this, such effects should always be considered, at least in short-term applications, to obtain unbiased analyses. Finally, we prove the feasibility of the TotPTHA and of the treatment of interaction/cascade effects by applying this methodology to an ideal region with realistic characteristics (Neverland).

Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aldemir, Tunc; Denning, Richard; Catalyurek, Umit

Reduction in safety margin can be expected as passive structures and components undergo degradation with time. Limitations in the traditional probabilistic risk assessment (PRA) methodology constrain its value as an effective tool to address the impact of aging effects on risk and for quantifying the impact of aging management strategies in maintaining safety margins. A methodology has been developed to address multiple aging mechanisms involving large numbers of components (with possibly statistically dependent failures) within the PRA framework in a computationally feasible manner when the sequencing of events is conditioned on the physical conditions predicted in a simulation environment, suchmore » as the New Generation System Code (NGSC) concept. Both epistemic and aleatory uncertainties can be accounted for within the same phenomenological framework and maintenance can be accounted for in a coherent fashion. The framework accommodates the prospective impacts of various intervention strategies such as testing, maintenance, and refurbishment. The methodology is illustrated with several examples.« less

A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG).

PubMed

Amezquita-Sanchez, Juan P; Adeli, Anahita; Adeli, Hojjat

2016-05-15

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, greater than expected by age. A new methodology is presented to identify MCI patients during a working memory task using MEG signals. The methodology consists of four steps: In step 1, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose the MEG signal into a set of adaptive sub-bands according to its contained frequency information. In step 2, a nonlinear dynamics measure based on permutation entropy (PE) analysis is employed to analyze the sub-bands and detect features to be used for MCI detection. In step 3, an analysis of variation (ANOVA) is used for feature selection. In step 4, the enhanced probabilistic neural network (EPNN) classifier is applied to the selected features to distinguish between MCI and healthy patients. The usefulness and effectiveness of the proposed methodology are validated using the sensed MEG data obtained experimentally from 18 MCI and 19 control patients. Copyright © 2016 Elsevier B.V. All rights reserved.

Design for Reliability and Safety Approach for the New NASA Launch Vehicle

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.; Weldon, Danny M.

2007-01-01

The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program intended for sending crew and cargo to the international Space Station (ISS), to the moon, and beyond. This program is called Constellation. As part of the Constellation program, NASA is developing new launch vehicles aimed at significantly increase safety and reliability, reduce the cost of accessing space, and provide a growth path for manned space exploration. Achieving these goals requires a rigorous process that addresses reliability, safety, and cost upfront and throughout all the phases of the life cycle of the program. This paper discusses the "Design for Reliability and Safety" approach for the NASA new launch vehicles, the ARES I and ARES V. Specifically, the paper addresses the use of an integrated probabilistic functional analysis to support the design analysis cycle and a probabilistic risk assessment (PRA) to support the preliminary design and beyond.

A partially reflecting random walk on spheres algorithm for electrical impedance tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maire, Sylvain, E-mail: maire@univ-tln.fr; Simon, Martin, E-mail: simon@math.uni-mainz.de

2015-12-15

In this work, we develop a probabilistic estimator for the voltage-to-current map arising in electrical impedance tomography. This novel so-called partially reflecting random walk on spheres estimator enables Monte Carlo methods to compute the voltage-to-current map in an embarrassingly parallel manner, which is an important issue with regard to the corresponding inverse problem. Our method uses the well-known random walk on spheres algorithm inside subdomains where the diffusion coefficient is constant and employs replacement techniques motivated by finite difference discretization to deal with both mixed boundary conditions and interface transmission conditions. We analyze the global bias and the variance ofmore » the new estimator both theoretically and experimentally. Subsequently, the variance of the new estimator is considerably reduced via a novel control variate conditional sampling technique which yields a highly efficient hybrid forward solver coupling probabilistic and deterministic algorithms.« less

Communities of Practice or Communities of Coping?: Employee Compliance among CSRs in Israeli Call Centres

ERIC Educational Resources Information Center

Raz, Aviad E.

2007-01-01

Purpose: The purpose of this paper is to describe and analyse the formation of CoPs (communities of practice) in three call centres of cellular communication operating companies in Israel. Design/methodology/approach: This study is based on a qualitative methodology including observations, interviews and textual analysis. Findings: In all three…

Improved Methodology for Developing Cost Uncertainty Models for Naval Vessels

DTIC Science & Technology

2009-04-22

Deegan , 2007). Risk cannot be assessed with a point estimate, as it represents a single value that serves as a best guess for the parameter to be...or stakeholders ( Deegan & Fields, 2007). This paper analyzes the current NAVSEA 05C Cruiser (CG(X)) probabilistic cost model including data...provided by Mr. Chris Deegan and his CG(X) analysts. The CG(X) model encompasses all factors considered for cost of the entire program, including

A probabilistic approach to aircraft design emphasizing stability and control uncertainties

NASA Astrophysics Data System (ADS)

Delaurentis, Daniel Andrew

In order to address identified deficiencies in current approaches to aerospace systems design, a new method has been developed. This new method for design is based on the premise that design is a decision making activity, and that deterministic analysis and synthesis can lead to poor, or misguided decision making. This is due to a lack of disciplinary knowledge of sufficient fidelity about the product, to the presence of uncertainty at multiple levels of the aircraft design hierarchy, and to a failure to focus on overall affordability metrics as measures of goodness. Design solutions are desired which are robust to uncertainty and are based on the maximum knowledge possible. The new method represents advances in the two following general areas. 1. Design models and uncertainty. The research performed completes a transition from a deterministic design representation to a probabilistic one through a modeling of design uncertainty at multiple levels of the aircraft design hierarchy, including: (1) Consistent, traceable uncertainty classification and representation; (2) Concise mathematical statement of the Probabilistic Robust Design problem; (3) Variants of the Cumulative Distribution Functions (CDFs) as decision functions for Robust Design; (4) Probabilistic Sensitivities which identify the most influential sources of variability. 2. Multidisciplinary analysis and design. Imbedded in the probabilistic methodology is a new approach for multidisciplinary design analysis and optimization (MDA/O), employing disciplinary analysis approximations formed through statistical experimentation and regression. These approximation models are a function of design variables common to the system level as well as other disciplines. For aircraft, it is proposed that synthesis/sizing is the proper avenue for integrating multiple disciplines. Research hypotheses are translated into a structured method, which is subsequently tested for validity. Specifically, the implementation involves the study of the relaxed static stability technology for a supersonic commercial transport aircraft. The probabilistic robust design method is exercised resulting in a series of robust design solutions based on different interpretations of "robustness". Insightful results are obtained and the ability of the method to expose trends in the design space are noted as a key advantage.

Robot Path Planning in Uncertain Environments: A Language Measure-theoretic Approach

DTIC Science & Technology

2014-01-01

Paper DS-14-1028 to appear in the Special Issue on Stochastic Models, Control and Algorithms in Robotics, ASME Journal of Dynamic Systems...Measurement and Control Robot Path Planning in Uncertain Environments: A Language Measure-theoretic Approach⋆ Devesh K. Jha† Yue Li† Thomas A. Wettergren‡† Asok...algorithm, called ν⋆, that was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control -theoretic

Novel methodology for pharmaceutical expenditure forecast.

PubMed

Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the 'EU Pharmaceutical expenditure forecast'; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time to reach peak sales for new drugs; 2) estimating the statistical distribution of the budget impact; and 3) testing different pricing and reimbursement policy decisions on health expenditures. This methodology was independent of historical data and appeared to be highly flexible and adapted to test robustness and provide probabilistic analysis to support policy decision making.

Economic Analysis of a Multi-Site Prevention Program: Assessment of Program Costs and Characterizing Site-level Variability

PubMed Central

Corso, Phaedra S.; Ingels, Justin B.; Kogan, Steven M.; Foster, E. Michael; Chen, Yi-Fu; Brody, Gene H.

2013-01-01

Programmatic cost analyses of preventive interventions commonly have a number of methodological difficulties. To determine the mean total costs and properly characterize variability, one often has to deal with small sample sizes, skewed distributions, and especially missing data. Standard approaches for dealing with missing data such as multiple imputation may suffer from a small sample size, a lack of appropriate covariates, or too few details around the method used to handle the missing data. In this study, we estimate total programmatic costs for a prevention trial evaluating the Strong African American Families-Teen program. This intervention focuses on the prevention of substance abuse and risky sexual behavior. To account for missing data in the assessment of programmatic costs we compare multiple imputation to probabilistic sensitivity analysis. The latter approach uses collected cost data to create a distribution around each input parameter. We found that with the multiple imputation approach, the mean (95% confidence interval) incremental difference was $2149 ($397, $3901). With the probabilistic sensitivity analysis approach, the incremental difference was $2583 ($778, $4346). Although the true cost of the program is unknown, probabilistic sensitivity analysis may be a more viable alternative for capturing variability in estimates of programmatic costs when dealing with missing data, particularly with small sample sizes and the lack of strong predictor variables. Further, the larger standard errors produced by the probabilistic sensitivity analysis method may signal its ability to capture more of the variability in the data, thus better informing policymakers on the potentially true cost of the intervention. PMID:23299559

Economic analysis of a multi-site prevention program: assessment of program costs and characterizing site-level variability.

PubMed

Corso, Phaedra S; Ingels, Justin B; Kogan, Steven M; Foster, E Michael; Chen, Yi-Fu; Brody, Gene H

2013-10-01

Programmatic cost analyses of preventive interventions commonly have a number of methodological difficulties. To determine the mean total costs and properly characterize variability, one often has to deal with small sample sizes, skewed distributions, and especially missing data. Standard approaches for dealing with missing data such as multiple imputation may suffer from a small sample size, a lack of appropriate covariates, or too few details around the method used to handle the missing data. In this study, we estimate total programmatic costs for a prevention trial evaluating the Strong African American Families-Teen program. This intervention focuses on the prevention of substance abuse and risky sexual behavior. To account for missing data in the assessment of programmatic costs we compare multiple imputation to probabilistic sensitivity analysis. The latter approach uses collected cost data to create a distribution around each input parameter. We found that with the multiple imputation approach, the mean (95 % confidence interval) incremental difference was $2,149 ($397, $3,901). With the probabilistic sensitivity analysis approach, the incremental difference was $2,583 ($778, $4,346). Although the true cost of the program is unknown, probabilistic sensitivity analysis may be a more viable alternative for capturing variability in estimates of programmatic costs when dealing with missing data, particularly with small sample sizes and the lack of strong predictor variables. Further, the larger standard errors produced by the probabilistic sensitivity analysis method may signal its ability to capture more of the variability in the data, thus better informing policymakers on the potentially true cost of the intervention.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

2012-12-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

Testing for ontological errors in probabilistic forecasting models of natural systems

PubMed Central

Marzocchi, Warner; Jordan, Thomas H.

2014-01-01

Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

The Path to English Literacy: Analyzing Elementary Sight Word Procurement Using Computer Assisted Language Learning (CALL) in Contrast to Traditional Methodologies

ERIC Educational Resources Information Center

Madill, Michael T. R.

2014-01-01

Didactical approaches related to teaching English as a Foreign Language (EFL) have developed into a complex array of instructional methodologies, each having potential benefits attributed to elementary reading development. One such effective practice is Computer Assisted Language Learning (CALL), which uses various forms of technology such as…

A Probabilistic Analysis of Surface Water Flood Risk in London.

PubMed

Jenkins, Katie; Hall, Jim; Glenis, Vassilis; Kilsby, Chris

2018-06-01

Flooding in urban areas during heavy rainfall, often characterized by short duration and high-intensity events, is known as "surface water flooding." Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively. © 2017 Society for Risk Analysis.

Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty

NASA Astrophysics Data System (ADS)

Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

2014-11-01

This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.

A Probabilistic Tsunami Hazard Study of the Auckland Region, Part I: Propagation Modelling and Tsunami Hazard Assessment at the Shoreline

NASA Astrophysics Data System (ADS)

Power, William; Wang, Xiaoming; Lane, Emily; Gillibrand, Philip

2013-09-01

Regional source tsunamis represent a potentially devastating threat to coastal communities in New Zealand, yet are infrequent events for which little historical information is available. It is therefore essential to develop robust methods for quantitatively estimating the hazards posed, so that effective mitigation measures can be implemented. We develop a probabilistic model for the tsunami hazard posed to the Auckland region of New Zealand from the Kermadec Trench and the southern New Hebrides Trench subduction zones. An innovative feature of our model is the systematic analysis of uncertainty regarding the magnitude-frequency distribution of earthquakes in the source regions. The methodology is first used to estimate the tsunami hazard at the coastline, and then used to produce a set of scenarios that can be applied to produce probabilistic maps of tsunami inundation for the study region; the production of these maps is described in part II. We find that the 2,500 year return period regional source tsunami hazard for the densely populated east coast of Auckland is dominated by events originating in the Kermadec Trench, while the equivalent hazard to the sparsely populated west coast is approximately equally due to events on the Kermadec Trench and the southern New Hebrides Trench.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Quantum probabilistic logic programming

NASA Astrophysics Data System (ADS)

Balu, Radhakrishnan

2015-05-01

We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful feature to capture relationship between distributions that are not independent. The expressive power of the language is based on a mechanism to implement statistical ensembles and to solve the underlying SAT instances using quantum mechanical machinery. We exploit the fact that classical random variables have quantum decompositions to build the Horn clauses. We establish the semantics of the language in a rigorous fashion by considering an existing probabilistic logic language called PRISM with classical probability measures defined on the Herbrand base and extending it to the quantum context. In the classical case H-interpretations form the sample space and probability measures defined on them lead to consistent definition of probabilities for well formed formulae. In the quantum counterpart, we define probability amplitudes on Hinterpretations facilitating the model generations and verifications via quantum mechanical superpositions and entanglements. We cast the well formed formulae of the language as quantum mechanical observables thus providing an elegant interpretation for their probabilities. We discuss several examples to combine statistical ensembles and predicates of first order logic to reason with situations involving uncertainty.

A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors

PubMed Central

Dezhdar, Tara; Moshourab, Rabih A.; Fründ, Ingo; Lewin, Gary R.; Schmuker, Michael

2015-01-01

The subjective experience of thermal pain follows the detection and encoding of noxious stimuli by primary afferent neurons called nociceptors. However, nociceptor morphology has been hard to access and the mechanisms of signal transduction remain unresolved. In order to understand how heat transducers in nociceptors are activated in vivo, it is important to estimate the temperatures that directly activate the skin-embedded nociceptor membrane. Hence, the nociceptor’s temperature threshold must be estimated, which in turn will depend on the depth at which transduction happens in the skin. Since the temperature at the receptor cannot be accessed experimentally, such an estimation can currently only be achieved through modeling. However, the current state-of-the-art model to estimate temperature at the receptor suffers from the fact that it cannot account for the natural stochastic variability of neuronal responses. We improve this model using a probabilistic approach which accounts for uncertainties and potential noise in system. Using a data set of 24 C-fibers recorded in vitro, we show that, even without detailed knowledge of the bio-thermal properties of the system, the probabilistic model that we propose here is capable of providing estimates of threshold and depth in cases where the classical method fails. PMID:26638830

Probabilistic seasonal Forecasts to deterministic Farm Leve Decisions: Innovative Approach

NASA Astrophysics Data System (ADS)

Mwangi, M. W.

2015-12-01

Climate change and vulnerability are major challenges in ensuring household food security. Climate information services have the potential to cushion rural households from extreme climate risks. However, most the probabilistic nature of climate information products is not easily understood by majority of smallholder farmers. Despite the probabilistic nature, climate information have proved to be a valuable climate risk adaptation strategy at the farm level. This calls for innovative ways to help farmers understand and apply climate information services to inform their farm level decisions. The study endeavored to co-design and test appropriate innovation systems for climate information services uptake and scale up necessary for achieving climate risk development. In addition it also determined the conditions necessary to support the effective performance of the proposed innovation system. Data and information sources included systematic literature review, secondary sources, government statistics, focused group discussions, household surveys and semi-structured interviews. Data wasanalyzed using both quantitative and qualitative data analysis techniques. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) software. Qualitative data was analyzed using qualitative techniques, which involved establishing the categories and themes, relationships/patterns and conclusions in line with the study objectives. Sustainable livelihood, reduced household poverty and climate change resilience were the impact that resulted from the study.

A tesselated probabilistic representation for spatial robot perception and navigation

NASA Technical Reports Server (NTRS)

Elfes, Alberto

1989-01-01

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

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

PubMed

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.

Feature aided Monte Carlo probabilistic data association filter for ballistic missile tracking

NASA Astrophysics Data System (ADS)

Ozdemir, Onur; Niu, Ruixin; Varshney, Pramod K.; Drozd, Andrew L.; Loe, Richard

2011-05-01

The problem of ballistic missile tracking in the presence of clutter is investigated. Probabilistic data association filter (PDAF) is utilized as the basic filtering algorithm. We propose to use sequential Monte Carlo methods, i.e., particle filters, aided with amplitude information (AI) in order to improve the tracking performance of a single target in clutter when severe nonlinearities exist in the system. We call this approach "Monte Carlo probabilistic data association filter with amplitude information (MCPDAF-AI)." Furthermore, we formulate a realistic problem in the sense that we use simulated radar cross section (RCS) data for a missile warhead and a cylinder chaff using Lucernhammer1, a state of the art electromagnetic signature prediction software, to model target and clutter amplitude returns as additional amplitude features which help to improve data association and tracking performance. A performance comparison is carried out between the extended Kalman filter (EKF) and the particle filter under various scenarios using single and multiple sensors. The results show that, when only one sensor is used, the MCPDAF performs significantly better than the EKF in terms of tracking accuracy under severe nonlinear conditions for ballistic missile tracking applications. However, when the number of sensors is increased, even under severe nonlinear conditions, the EKF performs as well as the MCPDAF.

Data assimilation for unsaturated flow models with restart adaptive probabilistic collocation based Kalman filter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Man, Jun; Li, Weixuan; Zeng, Lingzao

2016-06-01

The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we proposemore » a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.« less

Initial Probabilistic Evaluation of Reactor Pressure Vessel Fracture with Grizzly and Raven

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spencer, Benjamin; Hoffman, William; Sen, Sonat

2015-10-01

The Grizzly code is being developed with the goal of creating a general tool that can be applied to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled reactor pressure vessels (RPVs). Grizzly can be used to model the thermal/mechanical response of an RPV under transient conditions that would be observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtainmore » stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. These capabilities have been demonstrated previously. A typical RPV is likely to contain a large population of pre-existing flaws introduced during the manufacturing process. This flaw population is characterized stastistically through probability density functions of the flaw distributions. The use of probabilistic techniques is necessary to assess the likelihood of crack initiation during a transient event. This report documents initial work to perform probabilistic analysis of RPV fracture during a PTS event using a combination of the RAVEN risk analysis code and Grizzly. This work is limited in scope, considering only a single flaw with deterministic geometry, but with uncertainty introduced in the parameters that influence fracture toughness. These results are benchmarked against equivalent models run in the FAVOR code. When fully developed, the RAVEN/Grizzly methodology for modeling probabilistic fracture in RPVs will provide a general capability that can be used to consider a wider variety of vessel and flaw conditions that are difficult to consider with current tools. In addition, this will provide access to advanced probabilistic techniques provided by RAVEN, including adaptive sampling and parallelism, which can dramatically decrease run times.« less

Probabilistic Scenario-based Seismic Risk Analysis for Critical Infrastructures Method and Application for a Nuclear Power Plant

NASA Astrophysics Data System (ADS)

Klügel, J.

2006-12-01

Deterministic scenario-based seismic hazard analysis has a long tradition in earthquake engineering for developing the design basis of critical infrastructures like dams, transport infrastructures, chemical plants and nuclear power plants. For many applications besides of the design of infrastructures it is of interest to assess the efficiency of the design measures taken. These applications require a method allowing to perform a meaningful quantitative risk analysis. A new method for a probabilistic scenario-based seismic risk analysis has been developed based on a probabilistic extension of proven deterministic methods like the MCE- methodology. The input data required for the method are entirely based on the information which is necessary to perform any meaningful seismic hazard analysis. The method is based on the probabilistic risk analysis approach common for applications in nuclear technology developed originally by Kaplan & Garrick (1981). It is based (1) on a classification of earthquake events into different size classes (by magnitude), (2) the evaluation of the frequency of occurrence of events, assigned to the different classes (frequency of initiating events, (3) the development of bounding critical scenarios assigned to each class based on the solution of an optimization problem and (4) in the evaluation of the conditional probability of exceedance of critical design parameters (vulnerability analysis). The advantage of the method in comparison with traditional PSHA consists in (1) its flexibility, allowing to use different probabilistic models for earthquake occurrence as well as to incorporate advanced physical models into the analysis, (2) in the mathematically consistent treatment of uncertainties, and (3) in the explicit consideration of the lifetime of the critical structure as a criterion to formulate different risk goals. The method was applied for the evaluation of the risk of production interruption losses of a nuclear power plant during its residual lifetime.

Probabilistic seismic history matching using binary images

NASA Astrophysics Data System (ADS)

Davolio, Alessandra; Schiozer, Denis Jose

2018-02-01

Currently, the goal of history-matching procedures is not only to provide a model matching any observed data but also to generate multiple matched models to properly handle uncertainties. One such approach is a probabilistic history-matching methodology based on the discrete Latin Hypercube sampling algorithm, proposed in previous works, which was particularly efficient for matching well data (production rates and pressure). 4D seismic (4DS) data have been increasingly included into history-matching procedures. A key issue in seismic history matching (SHM) is to transfer data into a common domain: impedance, amplitude or pressure, and saturation. In any case, seismic inversions and/or modeling are required, which can be time consuming. An alternative to avoid these procedures is using binary images in SHM as they allow the shape, rather than the physical values, of observed anomalies to be matched. This work presents the incorporation of binary images in SHM within the aforementioned probabilistic history matching. The application was performed with real data from a segment of the Norne benchmark case that presents strong 4D anomalies, including softening signals due to pressure build up. The binary images are used to match the pressurized zones observed in time-lapse data. Three history matchings were conducted using: only well data, well and 4DS data, and only 4DS. The methodology is very flexible and successfully utilized the addition of binary images for seismic objective functions. Results proved the good convergence of the method in few iterations for all three cases. The matched models of the first two cases provided the best results, with similar well matching quality. The second case provided models presenting pore pressure changes according to the expected dynamic behavior (pressurized zones) observed on 4DS data. The use of binary images in SHM is relatively new with few examples in the literature. This work enriches this discussion by presenting a new application to match pressure in a reservoir segment with complex pressure behavior.

Assessing the need for an update of a probabilistic seismic hazard analysis using a SSHAC Level 1 study and the Seismic Hazard Periodic Reevaluation Methodology

DOE PAGES

Payne, Suzette J.; Coppersmith, Kevin J.; Coppersmith, Ryan; ...

2017-08-23

A key decision for nuclear facilities is evaluating the need for an update of an existing seismic hazard analysis in light of new data and information that has become available since the time that the analysis was completed. We introduce the newly developed risk-informed Seismic Hazard Periodic Review Methodology (referred to as the SHPRM) and present how a Senior Seismic Hazard Analysis Committee (SSHAC) Level 1 probabilistic seismic hazard analysis (PSHA) was performed in an implementation of this new methodology. The SHPRM offers a defensible and documented approach that considers both the changes in seismic hazard and engineering-based risk informationmore » of an existing nuclear facility to assess the need for an update of an existing PSHA. The SHPRM has seven evaluation criteria that are employed at specific analysis, decision, and comparison points which are applied to seismic design categories established for nuclear facilities in United States. The SHPRM is implemented using a SSHAC Level 1 study performed for the Idaho National Laboratory, USA. The implementation focuses on the first six of the seven evaluation criteria of the SHPRM which are all provided from the SSHAC Level 1 PSHA. Finally, to illustrate outcomes of the SHPRM that do not lead to the need for an update and those that do, the example implementations of the SHPRM are performed for nuclear facilities that have target performance goals expressed as the mean annual frequency of unacceptable performance at 1x10 -4, 4x10 -5 and 1x10 -5.« less

Assessing the need for an update of a probabilistic seismic hazard analysis using a SSHAC Level 1 study and the Seismic Hazard Periodic Reevaluation Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Payne, Suzette J.; Coppersmith, Kevin J.; Coppersmith, Ryan

A key decision for nuclear facilities is evaluating the need for an update of an existing seismic hazard analysis in light of new data and information that has become available since the time that the analysis was completed. We introduce the newly developed risk-informed Seismic Hazard Periodic Review Methodology (referred to as the SHPRM) and present how a Senior Seismic Hazard Analysis Committee (SSHAC) Level 1 probabilistic seismic hazard analysis (PSHA) was performed in an implementation of this new methodology. The SHPRM offers a defensible and documented approach that considers both the changes in seismic hazard and engineering-based risk informationmore » of an existing nuclear facility to assess the need for an update of an existing PSHA. The SHPRM has seven evaluation criteria that are employed at specific analysis, decision, and comparison points which are applied to seismic design categories established for nuclear facilities in United States. The SHPRM is implemented using a SSHAC Level 1 study performed for the Idaho National Laboratory, USA. The implementation focuses on the first six of the seven evaluation criteria of the SHPRM which are all provided from the SSHAC Level 1 PSHA. Finally, to illustrate outcomes of the SHPRM that do not lead to the need for an update and those that do, the example implementations of the SHPRM are performed for nuclear facilities that have target performance goals expressed as the mean annual frequency of unacceptable performance at 1x10 -4, 4x10 -5 and 1x10 -5.« less

Use of raster-based data layers to model spatial variation of seismotectonic data in probabilistic seismic hazard assessment

NASA Astrophysics Data System (ADS)

Zolfaghari, Mohammad R.

2009-07-01

Recent achievements in computer and information technology have provided the necessary tools to extend the application of probabilistic seismic hazard mapping from its traditional engineering use to many other applications. Examples for such applications are risk mitigation, disaster management, post disaster recovery planning and catastrophe loss estimation and risk management. Due to the lack of proper knowledge with regard to factors controlling seismic hazards, there are always uncertainties associated with all steps involved in developing and using seismic hazard models. While some of these uncertainties can be controlled by more accurate and reliable input data, the majority of the data and assumptions used in seismic hazard studies remain with high uncertainties that contribute to the uncertainty of the final results. In this paper a new methodology for the assessment of seismic hazard is described. The proposed approach provides practical facility for better capture of spatial variations of seismological and tectonic characteristics, which allows better treatment of their uncertainties. In the proposed approach, GIS raster-based data models are used in order to model geographical features in a cell-based system. The cell-based source model proposed in this paper provides a framework for implementing many geographically referenced seismotectonic factors into seismic hazard modelling. Examples for such components are seismic source boundaries, rupture geometry, seismic activity rate, focal depth and the choice of attenuation functions. The proposed methodology provides improvements in several aspects of the standard analytical tools currently being used for assessment and mapping of regional seismic hazard. The proposed methodology makes the best use of the recent advancements in computer technology in both software and hardware. The proposed approach is well structured to be implemented using conventional GIS tools.

Damage prognosis of adhesively-bonded joints in laminated composite structural components of unmanned aerial vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrar, Charles R; Gobbato, Maurizio; Conte, Joel

2009-01-01

The extensive use of lightweight advanced composite materials in unmanned aerial vehicles (UAVs) drastically increases the sensitivity to both fatigue- and impact-induced damage of their critical structural components (e.g., wings and tail stabilizers) during service life. The spar-to-skin adhesive joints are considered one of the most fatigue sensitive subcomponents of a lightweight UAV composite wing with damage progressively evolving from the wing root. This paper presents a comprehensive probabilistic methodology for predicting the remaining service life of adhesively-bonded joints in laminated composite structural components of UAVs. Non-destructive evaluation techniques and Bayesian inference are used to (i) assess the current statemore » of damage of the system and, (ii) update the probability distribution of the damage extent at various locations. A probabilistic model for future loads and a mechanics-based damage model are then used to stochastically propagate damage through the joint. Combined local (e.g., exceedance of a critical damage size) and global (e.g.. flutter instability) failure criteria are finally used to compute the probability of component failure at future times. The applicability and the partial validation of the proposed methodology are then briefly discussed by analyzing the debonding propagation, along a pre-defined adhesive interface, in a simply supported laminated composite beam with solid rectangular cross section, subjected to a concentrated load applied at mid-span. A specially developed Eliler-Bernoulli beam finite element with interlaminar slip along the damageable interface is used in combination with a cohesive zone model to study the fatigue-induced degradation in the adhesive material. The preliminary numerical results presented are promising for the future validation of the methodology.« less

Lunar Exploration Architecture Level Key Drivers and Sensitivities

NASA Technical Reports Server (NTRS)

Goodliff, Kandyce; Cirillo, William; Earle, Kevin; Reeves, J. D.; Shyface, Hilary; Andraschko, Mark; Merrill, R. Gabe; Stromgren, Chel; Cirillo, Christopher

2009-01-01

Strategic level analysis of the integrated behavior of lunar transportation and lunar surface systems architecture options is performed to assess the benefit, viability, affordability, and robustness of system design choices. This analysis employs both deterministic and probabilistic modeling techniques so that the extent of potential future uncertainties associated with each option are properly characterized. The results of these analyses are summarized in a predefined set of high-level Figures of Merit (FOMs) so as to provide senior NASA Constellation Program (CxP) and Exploration Systems Mission Directorate (ESMD) management with pertinent information to better inform strategic level decision making. The strategic level exploration architecture model is designed to perform analysis at as high a level as possible but still capture those details that have major impacts on system performance. The strategic analysis methodology focuses on integrated performance, affordability, and risk analysis, and captures the linkages and feedbacks between these three areas. Each of these results leads into the determination of the high-level FOMs. This strategic level analysis methodology has been previously applied to Space Shuttle and International Space Station assessments and is now being applied to the development of the Constellation Program point-of-departure lunar architecture. This paper provides an overview of the strategic analysis methodology and the lunar exploration architecture analyses to date. In studying these analysis results, the strategic analysis team has identified and characterized key drivers affecting the integrated architecture behavior. These key drivers include inclusion of a cargo lander, mission rate, mission location, fixed-versus- variable costs/return on investment, and the requirement for probabilistic analysis. Results of sensitivity analysis performed on lunar exploration architecture scenarios are also presented.

Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

NASA Astrophysics Data System (ADS)

Han, E.; Ines, A.

2015-12-01

Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on actual seasonal climate forecasts for: rice cropping in the Philippines and maize cropping in India and Kenya.

Multimodel Ensemble Methods for Prediction of Wake-Vortex Transport and Decay Originating NASA

NASA Technical Reports Server (NTRS)

Korner, Stephan; Ahmad, Nashat N.; Holzapfel, Frank; VanValkenburg, Randal L.

2017-01-01

Several multimodel ensemble methods are selected and further developed to improve the deterministic and probabilistic prediction skills of individual wake-vortex transport and decay models. The different multimodel ensemble methods are introduced, and their suitability for wake applications is demonstrated. The selected methods include direct ensemble averaging, Bayesian model averaging, and Monte Carlo simulation. The different methodologies are evaluated employing data from wake-vortex field measurement campaigns conducted in the United States and Germany.

What weight should be assigned to future environmental impacts? A probabilistic cost benefit analysis using recent advances on discounting.

PubMed

Almansa, Carmen; Martínez-Paz, José M

2011-03-01

Cost-benefit analysis is a standard methodological platform for public investment evaluation. In high environmental impact projects, with a long-term effect on future generations, the choice of discount rate and time horizon is of particular relevance, because it can lead to very different profitability assessments. This paper describes some recent approaches to environmental discounting and applies them, together with a number of classical procedures, to the economic evaluation of a plant for the desalination of irrigation return water from intensive farming, aimed at halting the degradation of an area of great ecological value, the Mar Menor, in South Eastern Spain. A Monte Carlo procedure is used in four CBA approaches and three time horizons to carry out a probabilistic sensitivity analysis designed to integrate the views of an international panel of experts in environmental discounting with the uncertainty affecting the market price of the project's main output, i.e., irrigation water for a water-deprived area. The results show which discounting scenarios most accurately estimate the socio-environmental profitability of the project while also considering the risk associated with these two key parameters. The analysis also provides some methodological findings regarding ways of assessing financial and environmental profitability in decisions concerning public investment in the environment. Copyright © 2010 Elsevier B.V. All rights reserved.

Subsea release of oil from a riser: an ecological risk assessment.

PubMed

Nazir, Muddassir; Khan, Faisal; Amyotte, Paul; Sadiq, Rehan

2008-10-01

This study illustrates a newly developed methodology, as a part of the U.S. EPA ecological risk assessment (ERA) framework, to predict exposure concentrations in a marine environment due to underwater release of oil and gas. It combines the hydrodynamics of underwater blowout, weathering algorithms, and multimedia fate and transport to measure the exposure concentration. Naphthalene and methane are used as surrogate compounds for oil and gas, respectively. Uncertainties are accounted for in multimedia input parameters in the analysis. The 95th percentile of the exposure concentration (EC(95%)) is taken as the representative exposure concentration for the risk estimation. A bootstrapping method is utilized to characterize EC(95%) and associated uncertainty. The toxicity data of 19 species available in the literature are used to calculate the 5th percentile of the predicted no observed effect concentration (PNEC(5%)) by employing the bootstrapping method. The risk is characterized by transforming the risk quotient (RQ), which is the ratio of EC(95%) to PNEC(5%), into a cumulative risk distribution. This article describes a probabilistic basis for the ERA, which is essential from risk management and decision-making viewpoints. Two case studies of underwater oil and gas mixture release, and oil release with no gaseous mixture are used to show the systematic implementation of the methodology, elements of ERA, and the probabilistic method in assessing and characterizing the risk.

Automation on the generation of genome-scale metabolic models.

PubMed

Reyes, R; Gamermann, D; Montagud, A; Fuente, D; Triana, J; Urchueguía, J F; de Córdoba, P Fernández

2012-12-01

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

Revision of Time-Independent Probabilistic Seismic Hazard Maps for Alaska

USGS Publications Warehouse

Wesson, Robert L.; Boyd, Oliver S.; Mueller, Charles S.; Bufe, Charles G.; Frankel, Arthur D.; Petersen, Mark D.

2007-01-01

We present here time-independent probabilistic seismic hazard maps of Alaska and the Aleutians for peak ground acceleration (PGA) and 0.1, 0.2, 0.3, 0.5, 1.0 and 2.0 second spectral acceleration at probability levels of 2 percent in 50 years (annual probability of 0.000404), 5 percent in 50 years (annual probability of 0.001026) and 10 percent in 50 years (annual probability of 0.0021). These maps represent a revision of existing maps based on newly obtained data and assumptions reflecting best current judgments about methodology and approach. These maps have been prepared following the procedures and assumptions made in the preparation of the 2002 National Seismic Hazard Maps for the lower 48 States. A significant improvement relative to the 2002 methodology is the ability to include variable slip rate along a fault where appropriate. These maps incorporate new data, the responses to comments received at workshops held in Fairbanks and Anchorage, Alaska, in May, 2005, and comments received after draft maps were posted on the National Seismic Hazard Mapping Web Site. These maps will be proposed for adoption in future revisions to the International Building Code. In this documentation we describe the maps and in particular explain and justify changes that have been made relative to the 1999 maps. We are also preparing a series of experimental maps of time-dependent hazard that will be described in future documents.

A probabilistic approach to interpreting verbal autopsies: methodology and preliminary validation in Vietnam.

PubMed

Byass, Peter; Huong, Dao Lan; Minh, Hoang Van

2003-01-01

Verbal autopsy (VA) has become an important tool in the past 20 years for determining cause of death in communities where there is no routine registration. In many cases, expert physicians have been used to interpret the VA findings and so assign individual causes of death. However, this is time consuming and not always repeatable. Other approaches such as algorithms and neural networks have been developed in some settings. This paper aims to develop a method that is simple, reliable and consistent, which could represent an advance in VA interpretation. This paper describes the development of a Bayesian probability model for VA interpretation as an attempt to find a better approach. This methodology and a preliminary implementation are described, with an evaluation based on VA material from rural Vietnam. The new model was tested against a series of 189 VA interviews from a rural community in Vietnam. Using this very basic model, over 70% of individual causes of death corresponded with those determined by two physicians increasing to over 80% if those cases ascribed to old age or as being indeterminate by the physicians were excluded. Although there is a clear need to improve the preliminary model and to test more extensively with larger and more varied datasets, these preliminary results suggest that there may be good potential in this probabilistic approach.

Comparison and validation of shallow landslides susceptibility maps generated by bi-variate and multi-variate linear probabilistic GIS-based techniques. A case study from Ribeira Quente Valley (S. Miguel Island, Azores)

NASA Astrophysics Data System (ADS)

Marques, R.; Amaral, P.; Zêzere, J. L.; Queiroz, G.; Goulart, C.

2009-04-01

Slope instability research and susceptibility mapping is a fundamental component of hazard assessment and is of extreme importance for risk mitigation, land-use management and emergency planning. Landslide susceptibility zonation has been actively pursued during the last two decades and several methodologies are still being improved. Among all the methods presented in the literature, indirect quantitative probabilistic methods have been extensively used. In this work different linear probabilistic methods, both bi-variate and multi-variate (Informative Value, Fuzzy Logic, Weights of Evidence and Logistic Regression), were used for the computation of the spatial probability of landslide occurrence, using the pixel as mapping unit. The methods used are based on linear relationships between landslides and 9 considered conditioning factors (altimetry, slope angle, exposition, curvature, distance to streams, wetness index, contribution area, lithology and land-use). It was assumed that future landslides will be conditioned by the same factors as past landslides in the study area. The presented work was developed for Ribeira Quente Valley (S. Miguel Island, Azores), a study area of 9,5 km2, mainly composed of volcanic deposits (ash and pumice lapilli) produced by explosive eruptions in Furnas Volcano. This materials associated to the steepness of the slopes (38,9% of the area has slope angles higher than 35°, reaching a maximum of 87,5°), make the area very prone to landslide activity. A total of 1.495 shallow landslides were mapped (at 1:5.000 scale) and included in a GIS database. The total affected area is 401.744 m2 (4,5% of the study area). Most slope movements are translational slides frequently evolving into debris-flows. The landslides are elongated, with maximum length generally equivalent to the slope extent, and their width normally does not exceed 25 m. The failure depth rarely exceeds 1,5 m and the volume is usually smaller than 700 m3. For modelling purposes, the landslides were randomly divided in two sub-datasets: a modelling dataset with 748 events (2,2% of the study area) and a validation dataset with 747 events (2,3% of the study area). The susceptibility algorithms achieved with the different probabilistic techniques, were rated individually using success rate and prediction rate curves. The best model performance was obtained with the logistic regression, although the results from the different methods do not show significant differences neither in success nor in prediction rate curves. These evidences revealed that: (1) the modelling landslide dataset is representative of the entire landslide population characteristics; and (2) the increase of complexity and robustness in the probabilistic methodology did not produce a significant increase in success or prediction rates. Therefore, it was concluded that the resolution and quality of the input variables are much more important than the probabilistic model chosen to assess landslide susceptibility. This work was developed on the behalf of VOLCSOILRISK project (Volcanic Soils Geotechnical Characterization for Landslide Risk Mitigation), supported by Direcção Regional da Ciência e Tecnologia - Governo Regional dos Açores.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Hirt, Evelyn H.; Veeramany, Arun

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipmentmore » condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.« less

Mechanical system reliability for long life space systems

NASA Technical Reports Server (NTRS)

Kowal, Michael T.

1994-01-01

The creation of a compendium of mechanical limit states was undertaken in order to provide a reference base for the application of first-order reliability methods to mechanical systems in the context of the development of a system level design methodology. The compendium was conceived as a reference source specific to the problem of developing the noted design methodology, and not an exhaustive or exclusive compilation of mechanical limit states. The compendium is not intended to be a handbook of mechanical limit states for general use. The compendium provides a diverse set of limit-state relationships for use in demonstrating the application of probabilistic reliability methods to mechanical systems. The compendium is to be used in the reliability analysis of moderately complex mechanical systems.

Holographic multiverse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garriga, J.; Vilenkin, A., E-mail: jaume.garriga@ub.edu, E-mail: vilenkin@cosmos.phy.tufts.edu

2009-01-15

We explore the idea that the dynamics of the inflationary multiverse is encoded in its future boundary, where it is described by a lower dimensional theory which is conformally invariant in the UV. We propose that a measure for the multiverse, which is needed in order to extract quantitative probabilistic predictions, can be derived in terms of the boundary theory by imposing a UV cutoff. In the inflationary bulk, this is closely related (though not identical) to the so-called scale factor cutoff measure.

Optimally Stopped Optimization

NASA Astrophysics Data System (ADS)

Vinci, Walter; Lidar, Daniel A.

2016-11-01

We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark simulated annealing on a class of maximum-2-satisfiability (MAX2SAT) problems. We also compare the performance of a D-Wave 2X quantum annealer to the Hamze-Freitas-Selby (HFS) solver, a specialized classical heuristic algorithm designed for low-tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N =1098 variables, the D-Wave device is 2 orders of magnitude faster than the HFS solver, and, modulo known caveats related to suboptimal annealing times, exhibits identical scaling with problem size.

On splice site prediction using weight array models: a comparison of smoothing techniques

NASA Astrophysics Data System (ADS)

Taher, Leila; Meinicke, Peter; Morgenstern, Burkhard

2007-11-01

In most eukaryotic genes, protein-coding exons are separated by non-coding introns which are removed from the primary transcript by a process called "splicing". The positions where introns are cut and exons are spliced together are called "splice sites". Thus, computational prediction of splice sites is crucial for gene finding in eukaryotes. Weight array models are a powerful probabilistic approach to splice site detection. Parameters for these models are usually derived from m-tuple frequencies in trusted training data and subsequently smoothed to avoid zero probabilities. In this study we compare three different ways of parameter estimation for m-tuple frequencies, namely (a) non-smoothed probability estimation, (b) standard pseudo counts and (c) a Gaussian smoothing procedure that we recently developed.

Canceled to Be Called Back: A Retrospective Cohort Study of Canceled Helicopter Emergency Medical Service Scene Calls That Are Later Transferred to a Trauma Center.

PubMed

Nolan, Brodie; Ackery, Alun; Nathens, Avery; Sawadsky, Bruce; Tien, Homer

In our trauma system, helicopter emergency medical services (HEMS) can be requested to attend a scene call for an injured patient before arrival by land paramedics. Land paramedics can cancel this response if they deem it unnecessary. The purpose of this study is to describe the frequency of canceled HEMS scene calls that were subsequently transferred to 2 trauma centers and to assess for any impact on morbidity and mortality. Probabilistic matching was used to identify canceled HEMS scene call patients who were later transported to 2 trauma centers over a 48-month period. Registry data were used to compare canceled scene call patients with direct from scene patients. There were 290 requests for HEMS scene calls, of which 35.2% were canceled. Of those canceled, 24.5% were later transported to our trauma centers. Canceled scene call patients were more likely to be older and to be discharged home from the trauma center without being admitted. There is a significant amount of undertriage of patients for whom an HEMS response was canceled and later transported to a trauma center. These patients face similar morbidity and mortality as patients who are brought directly from scene to a trauma center. Copyright © 2018 Air Medical Journal Associates. Published by Elsevier Inc. All rights reserved.

Systemic Operational Design: Improving Operational Planning for the Netherlands Armed Forces

DTIC Science & Technology

2006-05-25

This methodology is called Soft Systems Methodology . His methodology is a structured way of thinking in which not only a perceived problematic...Many similarities exist between Systemic Operational Design and Soft Systems Methodology , their epistemology is related. Furthermore, they both have...Systems Thinking: Managing Chaos and Complexity. Boston: Butterworth Heinemann, 1999. Checkland, Peter, and Jim Scholes. Soft Systems Methodology in

U.S. EPA'S ACUTE REFERENCE EXPOSURE METHODOLOGY FOR ACUTE INHALATION EXPOSURES

EPA Science Inventory

The US EPA National Center for Environmental Assessment has developed a methodology to derive acute inhalation toxicity benchmarks, called acute reference exposures (AREs), for noncancer effects. The methodology provides guidance for the derivation of chemical-specific benchmark...

Probabilistic Analysis and Density Parameter Estimation Within Nessus

NASA Astrophysics Data System (ADS)

Godines, Cody R.; Manteufel, Randall D.

2002-12-01

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can be obtained by using NESSUS' LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS. These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.

Probabilistic Analysis and Density Parameter Estimation Within Nessus

NASA Technical Reports Server (NTRS)

Godines, Cody R.; Manteufel, Randall D.; Chamis, Christos C. (Technical Monitor)

2002-01-01

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can be obtained by using NESSUS' LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS. These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Thio, H. K.; Løvholt, F.; Harbitz, C. B.; Polet, J.; Lorito, S.; Basili, R.; Volpe, M.; Romano, F.; Selva, J.; Piatanesi, A.; Davies, G.; Griffin, J.; Baptista, M. A.; Omira, R.; Babeyko, A. Y.; Power, W. L.; Salgado Gálvez, M.; Behrens, J.; Yalciner, A. C.; Kanoglu, U.; Pekcan, O.; Ross, S.; Parsons, T.; LeVeque, R. J.; Gonzalez, F. I.; Paris, R.; Shäfer, A.; Canals, M.; Fraser, S. A.; Wei, Y.; Weiss, R.; Zaniboni, F.; Papadopoulos, G. A.; Didenkulova, I.; Necmioglu, O.; Suppasri, A.; Lynett, P. J.; Mokhtari, M.; Sørensen, M.; von Hillebrandt-Andrade, C.; Aguirre Ayerbe, I.; Aniel-Quiroga, Í.; Guillas, S.; Macias, J.

2016-12-01

The large tsunami disasters of the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Lorito, S.; Basili, R.; Harbitz, C. B.; Løvholt, F.; Polet, J.; Thio, H. K.

2017-12-01

The tsunamis occurred worldwide in the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but often disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

A probabilistic tsunami hazard assessment for Indonesia

NASA Astrophysics Data System (ADS)

Horspool, N.; Pranantyo, I.; Griffin, J.; Latief, H.; Natawidjaja, D. H.; Kongko, W.; Cipta, A.; Bustaman, B.; Anugrah, S. D.; Thio, H. K.

2014-11-01

Probabilistic hazard assessments are a fundamental tool for assessing the threats posed by hazards to communities and are important for underpinning evidence-based decision-making regarding risk mitigation activities. Indonesia has been the focus of intense tsunami risk mitigation efforts following the 2004 Indian Ocean tsunami, but this has been largely concentrated on the Sunda Arc with little attention to other tsunami prone areas of the country such as eastern Indonesia. We present the first nationally consistent probabilistic tsunami hazard assessment (PTHA) for Indonesia. This assessment produces time-independent forecasts of tsunami hazards at the coast using data from tsunami generated by local, regional and distant earthquake sources. The methodology is based on the established monte carlo approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. We account for sources of epistemic and aleatory uncertainty in the analysis through the use of logic trees and sampling probability density functions. For short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, south coast of Java and the north coast of Papua. For longer return periods (500-2500 years), the tsunami hazard is highest along the Sunda Arc, reflecting the larger maximum magnitudes. The annual probability of experiencing a tsunami with a height of > 0.5 m at the coast is greater than 10% for Sumatra, Java, the Sunda islands (Bali, Lombok, Flores, Sumba) and north Papua. The annual probability of experiencing a tsunami with a height of > 3.0 m, which would cause significant inundation and fatalities, is 1-10% in Sumatra, Java, Bali, Lombok and north Papua, and 0.1-1% for north Sulawesi, Seram and Flores. The results of this national-scale hazard assessment provide evidence for disaster managers to prioritise regions for risk mitigation activities and/or more detailed hazard or risk assessment.

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Løvholt, Finn

2017-04-01

The large tsunami disasters of the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

The Use of Probabilistic Methods to Evaluate the Systems Impact of Component Design Improvements on Large Turbofan Engines

NASA Technical Reports Server (NTRS)

Packard, Michael H.

2002-01-01

Probabilistic Structural Analysis (PSA) is now commonly used for predicting the distribution of time/cycles to failure of turbine blades and other engine components. These distributions are typically based on fatigue/fracture and creep failure modes of these components. Additionally, reliability analysis is used for taking test data related to particular failure modes and calculating failure rate distributions of electronic and electromechanical components. How can these individual failure time distributions of structural, electronic and electromechanical component failure modes be effectively combined into a top level model for overall system evaluation of component upgrades, changes in maintenance intervals, or line replaceable unit (LRU) redesign? This paper shows an example of how various probabilistic failure predictions for turbine engine components can be evaluated and combined to show their effect on overall engine performance. A generic model of a turbofan engine was modeled using various Probabilistic Risk Assessment (PRA) tools (Quantitative Risk Assessment Software (QRAS) etc.). Hypothetical PSA results for a number of structural components along with mitigation factors that would restrict the failure mode from propagating to a Loss of Mission (LOM) failure were used in the models. The output of this program includes an overall failure distribution for LOM of the system. The rank and contribution to the overall Mission Success (MS) is also given for each failure mode and each subsystem. This application methodology demonstrates the effectiveness of PRA for assessing the performance of large turbine engines. Additionally, the effects of system changes and upgrades, the application of different maintenance intervals, inclusion of new sensor detection of faults and other upgrades were evaluated in determining overall turbine engine reliability.

Design of Composite Structures for Reliability and Damage Tolerance

NASA Technical Reports Server (NTRS)

Rais-Rohani, Masoud

1999-01-01

A summary of research conducted during the first year is presented. The research objectives were sought by conducting two tasks: (1) investigation of probabilistic design techniques for reliability-based design of composite sandwich panels, and (2) examination of strain energy density failure criterion in conjunction with response surface methodology for global-local design of damage tolerant helicopter fuselage structures. This report primarily discusses the efforts surrounding the first task and provides a discussion of some preliminary work involving the second task.

[Prevalence of coca paste use and social risk].

PubMed

Míguez, Hugo A

2008-01-01

The results of a probabilistic study performed in an extremely poor area where an ethnographic methodology was applied for the identification of cocaine paste consumption are analyzed. The studied community's general population's life prevalence was of 13,2 %. Prevalence was 51,1 % within the male population between 14 and 30 years old. Within the same poverty situation, greater consumption was associated with greater deficiencies. Data shows how cocaine paste consumption compulsion accentuates the displacement of the most vulnerable groups towards the limits of social survival.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Probabilistic topic modeling for the analysis and classification of genomic sequences

PubMed Central

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

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madankan, R.; Pouget, S.; Singla, P., E-mail: psingla@buffalo.edu

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This papermore » presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.« less

78 FR 4369 - Rates for Interstate Inmate Calling Services

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-22

.... Marginal Location Methodology. In 2008, ICS providers submitted the ICS Provider Proposal for ICS rates. The ICS Provider Proposal uses the ``marginal location'' methodology, previously adopted by the... ``marginal location'' methodology provides a ``basis for rates that represent `fair compensation' as set...

High redshift galaxies in the ALHAMBRA survey . I. Selection method and number counts based on redshift PDFs

NASA Astrophysics Data System (ADS)

Viironen, K.; Marín-Franch, A.; López-Sanjuan, C.; Varela, J.; Chaves-Montero, J.; Cristóbal-Hornillos, D.; Molino, A.; Fernández-Soto, A.; Vilella-Rojo, G.; Ascaso, B.; Cenarro, A. J.; Cerviño, M.; Cepa, J.; Ederoclite, A.; Márquez, I.; Masegosa, J.; Moles, M.; Oteo, I.; Pović, M.; Aguerri, J. A. L.; Alfaro, E.; Aparicio-Villegas, T.; Benítez, N.; Broadhurst, T.; Cabrera-Caño, J.; Castander, J. F.; Del Olmo, A.; González Delgado, R. M.; Husillos, C.; Infante, L.; Martínez, V. J.; Perea, J.; Prada, F.; Quintana, J. M.

2015-04-01

Context. Most observational results on the high redshift restframe UV-bright galaxies are based on samples pinpointed using the so-called dropout technique or Ly-α selection. However, the availability of multifilter data now allows the dropout selections to be replaced by direct methods based on photometric redshifts. In this paper we present the methodology to select and study the population of high redshift galaxies in the ALHAMBRA survey data. Aims: Our aim is to develop a less biased methodology than the traditional dropout technique to study the high redshift galaxies in ALHAMBRA and other multifilter data. Thanks to the wide area ALHAMBRA covers, we especially aim at contributing to the study of the brightest, least frequent, high redshift galaxies. Methods: The methodology is based on redshift probability distribution functions (zPDFs). It is shown how a clean galaxy sample can be obtained by selecting the galaxies with high integrated probability of being within a given redshift interval. However, reaching both a complete and clean sample with this method is challenging. Hence, a method to derive statistical properties by summing the zPDFs of all the galaxies in the redshift bin of interest is introduced. Results: Using this methodology we derive the galaxy rest frame UV number counts in five redshift bins centred at z = 2.5,3.0,3.5,4.0, and 4.5, being complete up to the limiting magnitude at mUV(AB) = 24, where mUV refers to the first ALHAMBRA filter redwards of the Ly-α line. With the wide field ALHAMBRA data we especially contribute to the study of the brightest ends of these counts, accurately sampling the surface densities down to mUV(AB) = 21-22. Conclusions: We show that using the zPDFs it is easy to select a very clean sample of high redshift galaxies. We also show that it is better to do statistical analysis of the properties of galaxies using a probabilistic approach, which takes into account both the incompleteness and contamination issues in a natural way. Based on observations collected at the German-Spanish Astronomical Center, Calar Alto, jointly operated by the Max-Planck-Institut für Astronomie (MPIA) at Heidelberg and the Instituto de Astrofísica de Andalucía (CSIC).

Shale Gas Exploration and Exploitation Induced Risks - SHEER

NASA Astrophysics Data System (ADS)

Capuano, Paolo; Orlecka-Sikora, Beata; Lasocki, Stanislaw; Cesca, Simone; Gunning, Andrew; jaroslawsky, Janusz; Garcia-Aristizabal, Alexander; Westwood, Rachel; Gasparini, Paolo

2017-04-01

Shale gas operations may affect the quality of air, water and landscapes; furthermore, it can induce seismic activity, with the possible impacts on the surrounding infrastructure. The SHEER project aims at setting up a probabilistic methodology to assess and mitigate the short and the long term environmental risks connected to the exploration and exploitation of shale gas. In particular we are investigating risks associated with groundwater contamination, air pollution and induced seismicity. A shale gas test site located in Poland (Wysin) has been monitored before, during and after the fracking operations with the aim of assessing environmental risks connected with groundwater contamination, air pollution and earthquakes induced by fracking and injection of waste water. The severity of each of these hazards depends strongly on the unexpected enhanced permeability pattern, which may develop as an unwanted by-product of the fracking processes and may become pathway for gas and fluid migration towards underground water reservoirs or the surface. The project is devoted to monitor and understand how far this enhanced permeability pattern develops both in space and time. The considered hazards may be at least partially inter-related as they all depend on this enhanced permeability pattern. Therefore they are being approached from a multi-hazard, multi parameter perspective. We expect to develop methodologies and procedures to track and model fracture evolution around shale gas exploitation sites and a robust statistically based, multi-parameter methodology to assess environmental impacts and risks across the operational lifecycle of shale gas. The developed methodologies are going to be applied and tested on a comprehensive database consisting of seismicity, changes of the quality of ground-waters and air, ground deformations, and operational data collected from the ongoing monitoring episode (Wysin) and past episodes: Lubocino (Poland), Preese Hall (UK), Oklahoma (USA), Groningen Field (Netherlands), Gross Schönebeck (Germany), The Geysers (USA), Cooper Basin(Australia). Best practices to be applied in Europe to monitor and minimize any environmental impacts will be worked out with the involvement of governmental decisional bodies, private industries and experts This work was supported under SHEER: "Shale Gas Exploration and Exploitation Induced Risks" project n.640896, funded from Horizon 2020 - R&I Framework Programme, call H2020-LCE-2014-1

2009 Space Shuttle Probabilistic Risk Assessment Overview

NASA Technical Reports Server (NTRS)

Hamlin, Teri L.; Canga, Michael A.; Boyer, Roger L.; Thigpen, Eric B.

2010-01-01

Loss of a Space Shuttle during flight has severe consequences, including loss of a significant national asset; loss of national confidence and pride; and, most importantly, loss of human life. The Shuttle Probabilistic Risk Assessment (SPRA) is used to identify risk contributors and their significance; thus, assisting management in determining how to reduce risk. In 2006, an overview of the SPRA Iteration 2.1 was presented at PSAM 8 [1]. Like all successful PRAs, the SPRA is a living PRA and has undergone revisions since PSAM 8. The latest revision to the SPRA is Iteration 3. 1, and it will not be the last as the Shuttle program progresses and more is learned. This paper discusses the SPRA scope, overall methodology, and results, as well as provides risk insights. The scope, assumptions, uncertainties, and limitations of this assessment provide risk-informed perspective to aid management s decision-making process. In addition, this paper compares the Iteration 3.1 analysis and results to the Iteration 2.1 analysis and results presented at PSAM 8.

Data Assimilation - Advances and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Brian J.

2014-07-30

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batchmore » sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.« less

A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

Probabilistic Thermal Analysis During Mars Reconnaissance Orbiter Aerobraking

NASA Technical Reports Server (NTRS)

Dec, John A.

2007-01-01

A method for performing a probabilistic thermal analysis during aerobraking has been developed. The analysis is performed on the Mars Reconnaissance Orbiter solar array during aerobraking. The methodology makes use of a response surface model derived from a more complex finite element thermal model of the solar array. The response surface is a quadratic equation which calculates the peak temperature for a given orbit drag pass at a specific location on the solar panel. Five different response surface equations are used, one of which predicts the overall maximum solar panel temperature, and the remaining four predict the temperatures of the solar panel thermal sensors. The variables used to define the response surface can be characterized as either environmental, material property, or modeling variables. Response surface variables are statistically varied in a Monte Carlo simulation. The Monte Carlo simulation produces mean temperatures and 3 sigma bounds as well as the probability of exceeding the designated flight allowable temperature for a given orbit. Response surface temperature predictions are compared with the Mars Reconnaissance Orbiter flight temperature data.

Probabilistic risk analysis and terrorism risk.

PubMed

Ezell, Barry Charles; Bennett, Steven P; von Winterfeldt, Detlof; Sokolowski, John; Collins, Andrew J

2010-04-01

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.

Evolution of Fairness in the Not Quite Ultimatum Game

NASA Astrophysics Data System (ADS)

Ichinose, Genki; Sayama, Hiroki

2014-05-01

The Ultimatum Game (UG) is an economic game where two players (proposer and responder) decide how to split a certain amount of money. While traditional economic theories based on rational decision making predict that the proposer should make a minimal offer and the responder should accept it, human subjects tend to behave more fairly in UG. Previous studies suggested that extra information such as reputation, empathy, or spatial structure is needed for fairness to evolve in UG. Here we show that fairness can evolve without additional information if players make decisions probabilistically and may continue interactions when the offer is rejected, which we call the Not Quite Ultimatum Game (NQUG). Evolutionary simulations of NQUG showed that the probabilistic decision making contributes to the increase of proposers' offer amounts to avoid rejection, while the repetition of the game works to responders' advantage because they can wait until a good offer comes. These simple extensions greatly promote evolution of fairness in both proposers' offers and responders' acceptance thresholds.

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Analytical resource assessment method for continuous (unconventional) oil and gas accumulations - The "ACCESS" Method

USGS Publications Warehouse

Crovelli, Robert A.; revised by Charpentier, Ronald R.

2012-01-01

The U.S. Geological Survey (USGS) periodically assesses petroleum resources of areas within the United States and the world. The purpose of this report is to explain the development of an analytic probabilistic method and spreadsheet software system called Analytic Cell-Based Continuous Energy Spreadsheet System (ACCESS). The ACCESS method is based upon mathematical equations derived from probability theory. The ACCESS spreadsheet can be used to calculate estimates of the undeveloped oil, gas, and NGL (natural gas liquids) resources in a continuous-type assessment unit. An assessment unit is a mappable volume of rock in a total petroleum system. In this report, the geologic assessment model is defined first, the analytic probabilistic method is described second, and the spreadsheet ACCESS is described third. In this revised version of Open-File Report 00-044 , the text has been updated to reflect modifications that were made to the ACCESS program. Two versions of the program are added as appendixes.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bucknor, Matthew; Grabaskas, David; Brunett, Acacia J.

We report that many advanced reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended because of deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize within a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has beenmore » examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper provides an overview of a passive system reliability demonstration analysis for an external event. Considering an earthquake with the possibility of site flooding, the analysis focuses on the behavior of the passive Reactor Cavity Cooling System following potential physical damage and system flooding. The assessment approach seeks to combine mechanistic and simulation-based methods to leverage the benefits of the simulation-based approach without the need to substantially deviate from conventional probabilistic risk assessment techniques. Lastly, although this study is presented as only an example analysis, the results appear to demonstrate a high level of reliability of the Reactor Cavity Cooling System (and the reactor system in general) for the postulated transient event.« less

Probabilistic Simulation of Combined Thermo-Mechanical Cyclic Fatigue in Composites

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2011-01-01

A methodology to compute probabilistically-combined thermo-mechanical fatigue life of polymer matrix laminated composites has been developed and is demonstrated. Matrix degradation effects caused by long-term environmental exposure and mechanical/thermal cyclic loads are accounted for in the simulation process. A unified time-temperature-stress-dependent multifactor-interaction relationship developed at NASA Glenn Research Center has been used to model the degradation/aging of material properties due to cyclic loads. The fast probability-integration method is used to compute probabilistic distribution of response. Sensitivities of fatigue life reliability to uncertainties in the primitive random variables (e.g., constituent properties, fiber volume ratio, void volume ratio, ply thickness, etc.) computed and their significance in the reliability-based design for maximum life is discussed. The effect of variation in the thermal cyclic loads on the fatigue reliability for a (0/+/-45/90)s graphite/epoxy laminate with a ply thickness of 0.127 mm, with respect to impending failure modes has been studied. The results show that, at low mechanical-cyclic loads and low thermal-cyclic amplitudes, fatigue life for 0.999 reliability is most sensitive to matrix compressive strength, matrix modulus, thermal expansion coefficient, and ply thickness. Whereas at high mechanical-cyclic loads and high thermal-cyclic amplitudes, fatigue life at 0.999 reliability is more sensitive to the shear strength of matrix, longitudinal fiber modulus, matrix modulus, and ply thickness.

Probabilistic Simulation for Combined Cycle Fatigue in Composites

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A methodology to compute probabilistic fatigue life of polymer matrix laminated composites has been developed and demonstrated. Matrix degradation effects caused by long term environmental exposure and mechanical/thermal cyclic loads are accounted for in the simulation process. A unified time-temperature-stress dependent multifactor interaction relationship developed at NASA Glenn Research Center has been used to model the degradation/aging of material properties due to cyclic loads. The fast probability integration method is used to compute probabilistic distribution of response. Sensitivities of fatigue life reliability to uncertainties in the primitive random variables (e.g., constituent properties, fiber volume ratio, void volume ratio, ply thickness, etc.) computed and their significance in the reliability-based design for maximum life is discussed. The effect of variation in the thermal cyclic loads on the fatigue reliability for a (0/+/- 45/90)s graphite/epoxy laminate with a ply thickness of 0.127 mm, with respect to impending failure modes has been studied. The results show that, at low mechanical cyclic loads and low thermal cyclic amplitudes, fatigue life for 0.999 reliability is most sensitive to matrix compressive strength, matrix modulus, thermal expansion coefficient, and ply thickness. Whereas at high mechanical cyclic loads and high thermal cyclic amplitudes, fatigue life at 0.999 reliability is more sensitive to the shear strength of matrix, longitudinal fiber modulus, matrix modulus, and ply thickness.

Probabilistic Simulation of Combined Thermo-Mechanical Cyclic Fatigue in Composites

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A methodology to compute probabilistically-combined thermo-mechanical fatigue life of polymer matrix laminated composites has been developed and is demonstrated. Matrix degradation effects caused by long-term environmental exposure and mechanical/thermal cyclic loads are accounted for in the simulation process. A unified time-temperature-stress-dependent multifactor-interaction relationship developed at NASA Glenn Research Center has been used to model the degradation/aging of material properties due to cyclic loads. The fast probability-integration method is used to compute probabilistic distribution of response. Sensitivities of fatigue life reliability to uncertainties in the primitive random variables (e.g., constituent properties, fiber volume ratio, void volume ratio, ply thickness, etc.) computed and their significance in the reliability-based design for maximum life is discussed. The effect of variation in the thermal cyclic loads on the fatigue reliability for a (0/+/-45/90)s graphite/epoxy laminate with a ply thickness of 0.127 mm, with respect to impending failure modes has been studied. The results show that, at low mechanical-cyclic loads and low thermal-cyclic amplitudes, fatigue life for 0.999 reliability is most sensitive to matrix compressive strength, matrix modulus, thermal expansion coefficient, and ply thickness. Whereas at high mechanical-cyclic loads and high thermal-cyclic amplitudes, fatigue life at 0.999 reliability is more sensitive to the shear strength of matrix, longitudinal fiber modulus, matrix modulus, and ply thickness.

Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars

PubMed Central

Poon, Art F. Y.; Brouwer, Kimberly C.; Strathdee, Steffanie A.; Firestone-Cruz, Michelle; Lozada, Remedios M.; Kosakovsky Pond, Sergei L.; Heckathorn, Douglas D.; Frost, Simon D. W.

2009-01-01

Background Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Methodology/Principal Findings Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. Conclusions SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics. PMID:19738904

Reliability approach to rotating-component design. [fatigue life and stress concentration

NASA Technical Reports Server (NTRS)

Kececioglu, D. B.; Lalli, V. R.

1975-01-01

A probabilistic methodology for designing rotating mechanical components using reliability to relate stress to strength is explained. The experimental test machines and data obtained for steel to verify this methodology are described. A sample mechanical rotating component design problem is solved by comparing a deterministic design method with the new design-by reliability approach. The new method shows that a smaller size and weight can be obtained for specified rotating shaft life and reliability, and uses the statistical distortion-energy theory with statistical fatigue diagrams for optimum shaft design. Statistical methods are presented for (1) determining strength distributions for steel experimentally, (2) determining a failure theory for stress variations in a rotating shaft subjected to reversed bending and steady torque, and (3) relating strength to stress by reliability.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

PubMed

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

Probabilistic models of genetic variation in structured populations applied to global human studies.

PubMed

Hao, Wei; Song, Minsun; Storey, John D

2016-03-01

Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation. We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions. A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html jstorey@princeton.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

PubMed Central

Alomari, Yazan M.; MdZin, Reena Rahayu

2015-01-01

Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved. PMID:25793010

Sarma-based key-group method for rock slope reliability analyses

NASA Astrophysics Data System (ADS)

Yarahmadi Bafghi, A. R.; Verdel, T.

2005-08-01

The methods used in conducting static stability analyses have remained pertinent to this day for reasons of both simplicity and speed of execution. The most well-known of these methods for purposes of stability analysis of fractured rock masses is the key-block method (KBM).This paper proposes an extension to the KBM, called the key-group method (KGM), which combines not only individual key-blocks but also groups of collapsable blocks into an iterative and progressive analysis of the stability of discontinuous rock slopes. To take intra-group forces into account, the Sarma method has been implemented within the KGM in order to generate a Sarma-based KGM, abbreviated SKGM. We will discuss herein the hypothesis behind this new method, details regarding its implementation, and validation through comparison with results obtained from the distinct element method.Furthermore, as an alternative to deterministic methods, reliability analyses or probabilistic analyses have been proposed to take account of the uncertainty in analytical parameters and models. The FOSM and ASM probabilistic methods could be implemented within the KGM and SKGM framework in order to take account of the uncertainty due to physical and mechanical data (density, cohesion and angle of friction). We will then show how such reliability analyses can be introduced into SKGM to give rise to the probabilistic SKGM (PSKGM) and how it can be used for rock slope reliability analyses. Copyright

Ramifications of increased training in quantitative methodology.

PubMed

Zimiles, Herbert

2009-01-01

Comments on the article "Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America" by Aiken, West, and Millsap. The current author asks three questions that are provoked by the comprehensive identification of gaps and deficiencies in the training of quantitative methodology that led Aiken, West, and Millsap to call for expanded graduate instruction resources and programs. This comment calls for greater attention to how advances and expansion in the training of quantitative analysis are influencing who chooses to study psychology and how and what will be studied. PsycINFO Database Record 2009 APA.

Probabilistic framework for the estimation of the adult and child toxicokinetic intraspecies uncertainty factors.

PubMed

Pelekis, Michael; Nicolich, Mark J; Gauthier, Joseph S

2003-12-01

Human health risk assessments use point values to develop risk estimates and thus impart a deterministic character to risk, which, by definition, is a probability phenomenon. The risk estimates are calculated based on individuals and then, using uncertainty factors (UFs), are extrapolated to the population that is characterized by variability. Regulatory agencies have recommended the quantification of the impact of variability in risk assessments through the application of probabilistic methods. In the present study, a framework that deals with the quantitative analysis of uncertainty (U) and variability (V) in target tissue dose in the population was developed by applying probabilistic analysis to physiologically-based toxicokinetic models. The mechanistic parameters that determine kinetics were described with probability density functions (PDFs). Since each PDF depicts the frequency of occurrence of all expected values of each parameter in the population, the combined effects of multiple sources of U/V were accounted for in the estimated distribution of tissue dose in the population, and a unified (adult and child) intraspecies toxicokinetic uncertainty factor UFH-TK was determined. The results show that the proposed framework accounts effectively for U/V in population toxicokinetics. The ratio of the 95th percentile to the 50th percentile of the annual average concentration of the chemical at the target tissue organ (i.e., the UFH-TK) varies with age. The ratio is equivalent to a unified intraspecies toxicokinetic UF, and it is one of the UFs by which the NOAEL can be divided to obtain the RfC/RfD. The 10-fold intraspecies UF is intended to account for uncertainty and variability in toxicokinetics (3.2x) and toxicodynamics (3.2x). This article deals exclusively with toxicokinetic component of UF. The framework provides an alternative to the default methodology and is advantageous in that the evaluation of toxicokinetic variability is based on the distribution of the effective target tissue dose, rather than applied dose. It allows for the replacement of the default adult and children intraspecies UF with toxicokinetic data-derived values and provides accurate chemical-specific estimates for their magnitude. It shows that proper application of probability and toxicokinetic theories can reduce uncertainties when establishing exposure limits for specific compounds and provide better assurance that established limits are adequately protective. It contributes to the development of a probabilistic noncancer risk assessment framework and will ultimately lead to the unification of cancer and noncancer risk assessment methodologies.

The weather roulette: assessing the economic value of seasonal wind speed predictions

NASA Astrophysics Data System (ADS)

Christel, Isadora; Cortesi, Nicola; Torralba-Fernandez, Veronica; Soret, Albert; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco

2016-04-01

Climate prediction is an emerging and highly innovative research area. For the wind energy sector, predicting the future variability of wind resources over the coming weeks or seasons is especially relevant to quantify operation and maintenance logistic costs or to inform energy trading decision with potential cost savings and/or economic benefits. Recent advances in climate predictions have already shown that probabilistic forecasting can improve the current prediction practices, which are based in the use of retrospective climatology and the assumption that what happened in the past is the best estimation of future conditions. Energy decision makers now have this new set of climate services but, are they willing to use them? Our aim is to properly explain the potential economic benefits of adopting probabilistic predictions, compared with the current practice, by using the weather roulette methodology (Hagedorn & Smith, 2009). This methodology is a diagnostic tool created to inform in a more intuitive and relevant way about the skill and usefulness of a forecast in the decision making process, by providing an economic and financial oriented assessment of the benefits of using a particular forecast system. We have selected a region relevant to the energy stakeholders where the predictions of the EUPORIAS climate service prototype for the energy sector (RESILIENCE) are skillful. In this region, we have applied the weather roulette to compare the overall prediction success of RESILIENCE's predictions and climatology illustrating it as an effective interest rate, an economic term that is easier to understand for energy stakeholders.

Fuzzy probabilistic design of water distribution networks

NASA Astrophysics Data System (ADS)

Fu, Guangtao; Kapelan, Zoran

2011-05-01

The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two-objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster-Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost-effective but also have higher reliability to cope with severe future uncertainties.

Cross hole GPR traveltime inversion using a fast and accurate neural network as a forward model

NASA Astrophysics Data System (ADS)

Mejer Hansen, Thomas

2017-04-01

Probabilistic formulated inverse problems can be solved using Monte Carlo based sampling methods. In principle both advanced prior information, such as based on geostatistics, and complex non-linear forward physical models can be considered. However, in practice these methods can be associated with huge computational costs that in practice limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error, that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival travel time inversion of cross hole ground-penetrating radar (GPR) data. An accurate forward model, based on 2D full-waveform modeling followed by automatic travel time picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the full forward model, and considerably faster, and more accurate, than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of the types of inverse problems that can be solved using non-linear Monte Carlo sampling techniques.

Robust Design Optimization via Failure Domain Bounding

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2007-01-01

This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.

Probabilistic models and uncertainty quantification for the ionization reaction rate of atomic Nitrogen

NASA Astrophysics Data System (ADS)

Miki, K.; Panesi, M.; Prudencio, E. E.; Prudhomme, S.

2012-05-01

The objective in this paper is to analyze some stochastic models for estimating the ionization reaction rate constant of atomic Nitrogen (N + e- → N+ + 2e-). Parameters of the models are identified by means of Bayesian inference using spatially resolved absolute radiance data obtained from the Electric Arc Shock Tube (EAST) wind-tunnel. The proposed methodology accounts for uncertainties in the model parameters as well as physical model inadequacies, providing estimates of the rate constant that reflect both types of uncertainties. We present four different probabilistic models by varying the error structure (either additive or multiplicative) and by choosing different descriptions of the statistical correlation among data points. In order to assess the validity of our methodology, we first present some calibration results obtained with manufactured data and then proceed by using experimental data collected at EAST experimental facility. In order to simulate the radiative signature emitted in the shock-heated air plasma, we use a one-dimensional flow solver with Park's two-temperature model that simulates non-equilibrium effects. We also discuss the implications of the choice of the stochastic model on the estimation of the reaction rate and its uncertainties. Our analysis shows that the stochastic models based on correlated multiplicative errors are the most plausible models among the four models proposed in this study. The rate of the atomic Nitrogen ionization is found to be (6.2 ± 3.3) × 1011 cm3 mol-1 s-1 at 10,000 K.

VALFAST: Secure Probabilistic Validation of Hundreds of Kepler Planet Candidates

NASA Astrophysics Data System (ADS)

Morton, Tim; Petigura, E.; Johnson, J. A.; Howard, A.; Marcy, G. W.; Baranec, C.; Law, N. M.; Riddle, R. L.; Ciardi, D. R.; Robo-AO Team

2014-01-01

The scope, scale, and tremendous success of the Kepler mission has necessitated the rapid development of probabilistic validation as a new conceptual framework for analyzing transiting planet candidate signals. While several planet validation methods have been independently developed and presented in the literature, none has yet come close to addressing the entire Kepler survey. I present the results of applying VALFAST---a planet validation code based on the methodology described in Morton (2012)---to every Kepler Object of Interest. VALFAST is unique in its combination of detail, completeness, and speed. Using the transit light curve shape, realistic population simulations, and (optionally) diverse follow-up observations, it calculates the probability that a transit candidate signal is the result of a true transiting planet or any of a number of astrophysical false positive scenarios, all in just a few minutes on a laptop computer. In addition to efficiently validating the planetary nature of hundreds of new KOIs, this broad application of VALFAST also demonstrates its ability to reliably identify likely false positives. This extensive validation effort is also the first to incorporate data from all of the largest Kepler follow-up observing efforts: the CKS survey of ~1000 KOIs with Keck/HIRES, the Robo-AO survey of >1700 KOIs, and high-resolution images obtained through the Kepler Follow-up Observing Program. In addition to enabling the core science that the Kepler mission was designed for, this methodology will be critical to obtain statistical results from future surveys such as TESS and PLATO.

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

PubMed

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 incorporating risk scoring knowledge as prior information can improve the performance in risk stratification. Experimental results reveal that our models achieve competitive performance in risk stratification in comparison with existing supervised approaches. In addition, the unsupervised nature of our models makes them highly portable to the risk stratification tasks of various diseases. Moreover, patient sub-profiles and sub-profile-specific risk tiers generated by our models are coherent and informative, and provide significant potential to be explored for the further tasks, such as patient cohort analysis. We hypothesize that the proposed framework can readily meet the demand for risk stratification from a large volume of EHRs in an open-ended fashion. Copyright © 2015 Elsevier Inc. All rights reserved.

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

PubMed Central

Do, Hongdo; Molania, Ramyar

2017-01-01

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis. PMID:29097403

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

Statistical analysis of radiation dose derived from ingestion of foods

NASA Astrophysics Data System (ADS)

Dougherty, Ward L.

2001-09-01

This analysis undertook the task of designing and implementing a methodology to determine an individual's probabilistic radiation dose from ingestion of foods utilizing Crystal Ball. A dietary intake model was determined by comparing previous existing models. Two principal radionuclides were considered-Lead210 (Pb-210) and Radium 226 (Ra-226). Samples from three different local grocery stores-Publix, Winn Dixie, and Albertsons-were counted on a gamma spectroscopy system with a GeLi detector. The same food samples were considered as those in the original FIPR database. A statistical analysis, utilizing the Crystal Ball program, was performed on the data to assess the most accurate distribution to use for these data. This allowed a determination of a radiation dose to an individual based on the above-information collected. Based on the analyses performed, radiation dose for grocery store samples was lower for Radium-226 than FIPR debris analyses, 2.7 vs. 5.91 mrem/yr. Lead-210 had a higher dose in the grocery store sample than the FIPR debris analyses, 21.4 vs. 518 mrem/yr. The output radiation dose was higher for all evaluations when an accurate estimation of distributions for each value was considered. Radium-226 radiation dose for FIPR and grocery rose to 9.56 and 4.38 mrem/yr. Radiation dose from ingestion of Pb-210 rose to 34.7 and 854 mrem/yr for FIPR and grocery data, respectively. Lead-210 was higher than initial doses for many reasons: Different peak examined, lower edge of detection limit, and minimum detectable concentration was considered. FIPR did not utilize grocery samples as a control because they calculated radiation dose that appeared unreasonably high. Consideration of distributions with the initial values allowed reevaluation of radiation does and showed a significant difference to original deterministic values. This work shows the value and importance of considering distributions to ensure that a person's radiation dose is accurately calculated. Probabilistic dose methodology was proved to be a more accurate and realistic method of radiation dose determination. This type of methodology provides a visual presentation of dose distribution that can be a vital aid in risk methodology.

Bayesian probabilistic approach for inverse source determination from limited and noisy chemical or biological sensor concentration measurements

NASA Astrophysics Data System (ADS)

Yee, Eugene

2007-04-01

Although a great deal of research effort has been focused on the forward prediction of the dispersion of contaminants (e.g., chemical and biological warfare agents) released into the turbulent atmosphere, much less work has been directed toward the inverse prediction of agent source location and strength from the measured concentration, even though the importance of this problem for a number of practical applications is obvious. In general, the inverse problem of source reconstruction is ill-posed and unsolvable without additional information. It is demonstrated that a Bayesian probabilistic inferential framework provides a natural and logically consistent method for source reconstruction from a limited number of noisy concentration data. In particular, the Bayesian approach permits one to incorporate prior knowledge about the source as well as additional information regarding both model and data errors. The latter enables a rigorous determination of the uncertainty in the inference of the source parameters (e.g., spatial location, emission rate, release time, etc.), hence extending the potential of the methodology as a tool for quantitative source reconstruction. A model (or, source-receptor relationship) that relates the source distribution to the concentration data measured by a number of sensors is formulated, and Bayesian probability theory is used to derive the posterior probability density function of the source parameters. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source-receptor relationship, is described. Furthermore, we describe the application of efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) for sampling from the posterior distribution of the source parameters, the latter of which is required to undertake the Bayesian computation. The Bayesian inferential methodology for source reconstruction is validated against real dispersion data for two cases involving contaminant dispersion in highly disturbed flows over urban and complex environments where the idealizations of horizontal homogeneity and/or temporal stationarity in the flow cannot be applied to simplify the problem. Furthermore, the methodology is applied to the case of reconstruction of multiple sources.

Novel methodology for pharmaceutical expenditure forecast

PubMed Central

Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

Background and objective The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the ‘EU Pharmaceutical expenditure forecast’; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. Results This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time to reach peak sales for new drugs; 2) estimating the statistical distribution of the budget impact; and 3) testing different pricing and reimbursement policy decisions on health expenditures. Conclusions This methodology was independent of historical data and appeared to be highly flexible and adapted to test robustness and provide probabilistic analysis to support policy decision making. PMID:27226843

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

Evaluation of Lithofacies Up-Scaling Methods for Probabilistic Prediction of Carbon Dioxide Behavior

NASA Astrophysics Data System (ADS)

Park, J. Y.; Lee, S.; Lee, Y. I.; Kihm, J. H.; Kim, J. M.

2017-12-01

Behavior of carbon dioxide injected into target reservoir (storage) formations is highly dependent on heterogeneities of geologic lithofacies and properties. These heterogeneous lithofacies and properties basically have probabilistic characteristics. Thus, their probabilistic evaluation has to be implemented properly into predicting behavior of injected carbon dioxide in heterogeneous storage formations. In this study, a series of three-dimensional geologic modeling is performed first using SKUA-GOCAD (ASGA and Paradigm) to establish lithofacies models of the Janggi Conglomerate in the Janggi Basin, Korea within a modeling domain. The Janggi Conglomerate is composed of mudstone, sandstone, and conglomerate, and it has been identified as a potential reservoir rock (clastic saline formation) for geologic carbon dioxide storage. Its lithofacies information are obtained from four boreholes and used in lithofacies modeling. Three different up-scaling methods (i.e., nearest to cell center, largest proportion, and random) are applied, and lithofacies modeling is performed 100 times for each up-scaling method. The lithofacies models are then compared and analyzed with the borehole data to evaluate the relative suitability of the three up-scaling methods. Finally, the lithofacies models are converted into coarser lithofacies models within the same modeling domain with larger grid blocks using the three up-scaling methods, and a series of multiphase thermo-hydrological numerical simulation is performed using TOUGH2-MP (Zhang et al., 2008) to predict probabilistically behavior of injected carbon dioxide. The coarser lithofacies models are also compared and analyzed with the borehole data and finer lithofacies models to evaluate the relative suitability of the three up-scaling methods. Three-dimensional geologic modeling, up-scaling, and multiphase thermo-hydrological numerical simulation as linked methodologies presented in this study can be utilized as a practical probabilistic evaluation tool to predict behavior of injected carbon dioxide and even to analyze its leakage risk. This work was supported by the Korea CCS 2020 Project of the Korea Carbon Capture and Sequestration R&D Center (KCRC) funded by the National Research Foundation (NRF), Ministry of Science and ICT (MSIT), Korea.

Effective normalization for copy number variation detection from whole genome sequencing.

PubMed

Janevski, Angel; Varadan, Vinay; Kamalakaran, Sitharthan; Banerjee, Nilanjana; Dimitrova, Nevenka

2012-01-01

Whole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations. We apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions. The GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls. Choice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Probabilistic Risk Analysis of Run-up and Inundation in Hawaii due to Distant Tsunamis

NASA Astrophysics Data System (ADS)

Gica, E.; Teng, M. H.; Liu, P. L.

2004-12-01

Risk assessment of natural hazards usually includes two aspects, namely, the probability of the natural hazard occurrence and the degree of damage caused by the natural hazard. Our current study is focused on the first aspect, i.e., the development and evaluation of a methodology that can predict the probability of coastal inundation due to distant tsunamis in the Pacific Basin. The calculation of the probability of tsunami inundation could be a simple statistical problem if a sufficiently long record of field data on inundation was available. Unfortunately, such field data are very limited in the Pacific Basin due to the reason that field measurement of inundation requires the physical presence of surveyors on site. In some areas, no field measurements were ever conducted in the past. Fortunately, there are more complete and reliable historical data on earthquakes in the Pacific Basin partly because earthquakes can be measured remotely. There are also numerical simulation models such as the Cornell COMCOT model that can predict tsunami generation by an earthquake, propagation in the open ocean, and inundation onto a coastal land. Our objective is to develop a methodology that can link the probability of earthquakes in the Pacific Basin with the inundation probability in a coastal area. The probabilistic methodology applied here involves the following steps: first, the Pacific Rim is divided into blocks of potential earthquake sources based on the past earthquake record and fault information. Then the COMCOT model is used to predict the inundation at a distant coastal area due to a tsunami generated by an earthquake of a particular magnitude in each source block. This simulation generates a response relationship between the coastal inundation and an earthquake of a particular magnitude and location. Since the earthquake statistics is known for each block, by summing the probability of all earthquakes in the Pacific Rim, the probability of the inundation in a coastal area can be determined through the response relationship. Although the idea of the statistical methodology applied here is not new, this study is the first to apply it to study the probability of inundation caused by earthquake-generated distant tsunamis in the Pacific Basin. As a case study, the methodology is applied to predict the tsunami inundation risk in Hilo Bay in Hawaii. Since relatively more field data on tsunami inundation are available for Hilo Bay, this case study can help to evaluate the applicability of the methodology for predicting tsunami inundation risk in the Pacific Basin. Detailed results will be presented at the AGU meeting.

Comparative risk assessments for the city of Pointe-à-Pitre (French West Indies): earthquakes and storm surge

NASA Astrophysics Data System (ADS)

Reveillere, A. R.; Bertil, D. B.; Douglas, J. D.; Grisanti, L. G.; Lecacheux, S. L.; Monfort, D. M.; Modaressi, H. M.; Müller, H. M.; Rohmer, J. R.; Sedan, O. S.

2012-04-01

In France, risk assessments for natural hazards are usually carried out separately and decision makers lack comprehensive information. Moreover, since the cause of the hazard (e.g. meteorological, geological) and the physical phenomenon that causes damage (e.g. inundation, ground shaking) may be fundamentally different, the quantitative comparison of single risk assessments that were not conducted in a compatible framework is not straightforward. Comprehensive comparative risk assessments exist in a few other countries. For instance, the Risk Map Germany project has developed and applied a methodology for quantitatively comparing the risk of relevant natural hazards at various scales (city, state) in Germany. The present on-going work applies a similar methodology to the Pointe-à-Pitre urban area, which represents more than half of the population of Guadeloupe, an overseas region in the French West Indies. Relevant hazards as well as hazard intensity levels differ from continental Europe, which will lead to different conclusions. French West Indies are prone to a large number of hazards, among which hurricanes, volcanic eruptions and earthquakes dominate. Hurricanes cause damage through three phenomena: wind, heavy rainfall and storm surge, the latter having had a preeminent role during the largest historical event in 1928. Seismic risk is characterized by many induced phenomena, among which earthquake shocks dominate. This study proposes a comparison of earthquake and cyclonic storm surge risks. Losses corresponding to hazard intensities having the same probability of occurrence are calculated. They are quantified in a common loss unit, chosen to be the direct economic losses. Intangible or indirect losses are not considered. The methodology therefore relies on (i) a probabilistic hazard assessment, (ii) a loss ratio estimation for the exposed elements and (iii) an economic estimation of these assets. Storm surge hazard assessment is based on the selection of relevant historical cyclones and on the simulation of the associated wave and cyclonic surge. The combined local sea elevations, called "set-up", are then fitted with a statistical distribution in order to obtain its time return characteristics. Several run-ups are then extracted, the inundation areas are calculated and the relative losses of the affected assets are deduced. The Probabilistic Seismic Hazard Assessment and the exposed elements location and seismic vulnerability result from past public risk assessment studies. The loss estimations are computed for several return time periods, measured in percentage of buildings being in a given EMS-98 damage state per grid block, which are then converted into loss ratio. In parallel, an asset estimation is conducted. It is mainly focused on private housing, but it considers some major public infrastructures as well. The final outcome of this work is a direct economic loss-frequency plot for earthquake and storm surge. The Probable Maximum Loss and the Average Annual Loss derivate from this risk curve. In addition, different sources of uncertainty are identified through the loss estimation process. The full propagation of these uncertainties can provide an interval of confidence, which can be assigned to the risk-curve and we show how such additional information can be useful for risk comparison.

Development and application of the dynamic system doctor to nuclear reactor probabilistic risk assessments.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kunsman, David Marvin; Aldemir, Tunc; Rutt, Benjamin

2008-05-01

This LDRD project has produced a tool that makes probabilistic risk assessments (PRAs) of nuclear reactors - analyses which are very resource intensive - more efficient. PRAs of nuclear reactors are being increasingly relied on by the United States Nuclear Regulatory Commission (U.S.N.R.C.) for licensing decisions for current and advanced reactors. Yet, PRAs are produced much as they were 20 years ago. The work here applied a modern systems analysis technique to the accident progression analysis portion of the PRA; the technique was a system-independent multi-task computer driver routine. Initially, the objective of the work was to fuse the accidentmore » progression event tree (APET) portion of a PRA to the dynamic system doctor (DSD) created by Ohio State University. Instead, during the initial efforts, it was found that the DSD could be linked directly to a detailed accident progression phenomenological simulation code - the type on which APET construction and analysis relies, albeit indirectly - and thereby directly create and analyze the APET. The expanded DSD computational architecture and infrastructure that was created during this effort is called ADAPT (Analysis of Dynamic Accident Progression Trees). ADAPT is a system software infrastructure that supports execution and analysis of multiple dynamic event-tree simulations on distributed environments. A simulator abstraction layer was developed, and a generic driver was implemented for executing simulators on a distributed environment. As a demonstration of the use of the methodological tool, ADAPT was applied to quantify the likelihood of competing accident progression pathways occurring for a particular accident scenario in a particular reactor type using MELCOR, an integrated severe accident analysis code developed at Sandia. (ADAPT was intentionally created with flexibility, however, and is not limited to interacting with only one code. With minor coding changes to input files, ADAPT can be linked to other such codes.) The results of this demonstration indicate that the approach can significantly reduce the resources required for Level 2 PRAs. From the phenomenological viewpoint, ADAPT can also treat the associated epistemic and aleatory uncertainties. This methodology can also be used for analyses of other complex systems. Any complex system can be analyzed using ADAPT if the workings of that system can be displayed as an event tree, there is a computer code that simulates how those events could progress, and that simulator code has switches to turn on and off system events, phenomena, etc. Using and applying ADAPT to particular problems is not human independent. While the human resources for the creation and analysis of the accident progression are significantly decreased, knowledgeable analysts are still necessary for a given project to apply ADAPT successfully. This research and development effort has met its original goals and then exceeded them.« less

Accouting for Greenhouse Gas Emissions from Reservoirs

NASA Astrophysics Data System (ADS)

Beaulieu, J. J.; Deemer, B. R.; Harrison, J. A.; Nietch, C. T.; Waldo, S.

2016-12-01

Nearly three decades of research has demonstrated that the impoundment of rivers and the flooding of terrestrial ecosystems behind dams can increase rates of greenhouse gas emission, particularly methane. The 2006 IPCC Guidelines for National Greenhouse Gas Inventories includes a methodology for estimating methane emissions from flooded lands, but the methodology was published as an appendix to be used as a `basis for future methodological development' due to a lack of data. Since the 2006 Guidelines were published there has been a 6-fold increase in the number of peer reviewed papers published on the topic including reports from reservoirs in India, China, Africa, and Russia. Furthermore, several countries, including Iceland, Switzerland, and Finland, have developed country specific methodologies for including flooded lands methane emissions in their National Greenhouse Gas Inventories. This presentation will include a review of the literature on flooded land methane emissions and approaches that have been used to upscale emissions for national inventories. We will also present ongoing research in the United States to develop a country specific methodology. In the U.S., research approaches include: 1) an effort to develop predictive relationships between methane emissions and reservoir characteristics that are available in national databases, such as reservoir size and drainage area, and 2) a national-scale probabilistic survey of reservoir methane emissions linked to the National Lakes Assessment.

Accounting For Greenhouse Gas Emissions From Flooded ...

EPA Pesticide Factsheets

Nearly three decades of research has demonstrated that the inundation of rivers and terrestrial ecosystems behind dams can lead to enhanced rates of greenhouse gas emissions, particularly methane. The 2006 IPCC Guidelines for National Greenhouse Gas Inventories includes a methodology for estimating methane emissions from flooded lands, but the methodology was published as an appendix to be used a ‘basis for future methodological development’ due to a lack of data. Since the 2006 Guidelines were published there has been a 6-fold increase in the number of peer reviewed papers published on the topic including reports from reservoirs in India, China, Africa, and Russia. Furthermore, several countries, including Iceland, Switzerland, and Finland, have developed country specific methodologies for including flooded lands methane emissions in their National Greenhouse Gas Inventories. This presentation will include a review of the literature on flooded land methane emissions and approaches that have been used to upscale emissions for national inventories. We will also present ongoing research in the United States to develop a country specific methodology. The research approaches include 1) an effort to develop predictive relationships between methane emissions and reservoir characteristics that are available in national databases, such as reservoir size and drainage area, and 2) a national-scale probabilistic survey of reservoir methane emissions. To inform th

Accounting for Greenhouse Gas Emissions from Reservoirs ...

EPA Pesticide Factsheets

Nearly three decades of research has demonstrated that the impoundment of rivers and the flooding of terrestrial ecosystems behind dams can increase rates of greenhouse gas emission, particularly methane. The 2006 IPCC Guidelines for National Greenhouse Gas Inventories includes a methodology for estimating methane emissions from flooded lands, but the methodology was published as an appendix to be used as a ‘basis for future methodological development’ due to a lack of data. Since the 2006 Guidelines were published there has been a 6-fold increase in the number of peer reviewed papers published on the topic including reports from reservoirs in India, China, Africa, and Russia. Furthermore, several countries, including Iceland, Switzerland, and Finland, have developed country specific methodologies for including flooded lands methane emissions in their National Greenhouse Gas Inventories. This presentation will include a review of the literature on flooded land methane emissions and approaches that have been used to upscale emissions for national inventories. We will also present ongoing research in the United States to develop a country specific methodology. In the U.S., research approaches include: 1) an effort to develop predictive relationships between methane emissions and reservoir characteristics that are available in national databases, such as reservoir size and drainage area, and 2) a national-scale probabilistic survey of reservoir methane em

A Call for a New National Norming Methodology.

ERIC Educational Resources Information Center

Ligon, Glynn; Mangino, Evangelina

Issues related to achieving adequate national norms are reviewed, and a new methodology is proposed that would work to provide a true measure of national achievement levels on an annual basis and would enable reporting results in current-year norms. Statistical methodology and technology could combine to create a national norming process that…

Reliability-based econometrics of aerospace structural systems: Design criteria and test options. Ph.D. Thesis - Georgia Inst. of Tech.

NASA Technical Reports Server (NTRS)

Thomas, J. M.; Hanagud, S.

1974-01-01

The design criteria and test options for aerospace structural reliability were investigated. A decision methodology was developed for selecting a combination of structural tests and structural design factors. The decision method involves the use of Bayesian statistics and statistical decision theory. Procedures are discussed for obtaining and updating data-based probabilistic strength distributions for aerospace structures when test information is available and for obtaining subjective distributions when data are not available. The techniques used in developing the distributions are explained.

SRB attrition rate study of the aft skirt due to water impact cavity collapse loading

NASA Technical Reports Server (NTRS)

Crockett, C. D.

1976-01-01

A methodology was presented so that realistic attrition prediction could aid in selecting an optimum design option for minimizing the effects of updated loads on the Space Shuttle Solid Rocket Booster (SRB) aft skirt. The updated loads resulted in water impact attrition rates greater than 10 percent for the aft skirt structure. Adding weight to reinforce the aft skirt was undesirable. The refined method treats the occurrences of the load distribution probabilistically, radially and longitudinally, with respect to the critical structural response.

EVALUATING THE SUSTAINABILITY OF GREEN CHEMISTRIES

EPA Science Inventory

The U.S. EPA's National Risk Management Research Laboratory is developing a methodology for the evaluation of reaction chemistries. This methodology, called GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Proc...

Toward Theory-Based Research in Political Communication.

ERIC Educational Resources Information Center

Simon, Adam F.; Iyengar, Shanto

1996-01-01

Praises the theoretical and methodological potential of the field of political communication. Calls for greater interaction and cross fertilization among the fields of political science, sociology, economics, and psychology. Briefly discusses relevant research methodologies. (MJP)

Chapter 43: Assessment of NE Greenland: Prototype for development of Circum-ArcticResource Appraisal methodology

USGS Publications Warehouse

Gautier, D.L.; Stemmerik, L.; Christiansen, F.G.; Sorensen, K.; Bidstrup, T.; Bojesen-Koefoed, J. A.; Bird, K.J.; Charpentier, R.R.; Houseknecht, D.W.; Klett, T.R.; Schenk, C.J.; Tennyson, Marilyn E.

2011-01-01

Geological features of NE Greenland suggest large petroleum potential, as well as high uncertainty and risk. The area was the prototype for development of methodology used in the US Geological Survey (USGS) Circum-Arctic Resource Appraisal (CARA), and was the first area evaluated. In collaboration with the Geological Survey of Denmark and Greenland (GEUS), eight "assessment units" (AU) were defined, six of which were probabilistically assessed. The most prospective areas are offshore in the Danmarkshavn Basin. This study supersedes a previous USGS assessment, from which it differs in several important respects: oil estimates are reduced and natural gas estimates are increased to reflect revised understanding of offshore geology. Despite the reduced estimates, the CARA indicates that NE Greenland may be an important future petroleum province. ?? 2011 The Geological Society of London.

PROBABILISTIC CROSS-IDENTIFICATION IN CROWDED FIELDS AS AN ASSIGNMENT PROBLEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Budavári, Tamás; Basu, Amitabh, E-mail: budavari@jhu.edu, E-mail: basu.amitabh@jhu.edu

2016-10-01

One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning to the fundamental assignment problem of discrete mathematics and efficiently solve the two-way catalog-level matching in the realm of combinatorial optimization using the so-called Hungarian algorithm. We introduce the method, demonstrate its performance in a mock universe where the true associations are known, and discuss the applicability of the new procedure to large surveys.

Probabilistic Cross-identification in Crowded Fields as an Assignment Problem

NASA Astrophysics Data System (ADS)

Budavári, Tamás; Basu, Amitabh

2016-10-01

One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning to the fundamental assignment problem of discrete mathematics and efficiently solve the two-way catalog-level matching in the realm of combinatorial optimization using the so-called Hungarian algorithm. We introduce the method, demonstrate its performance in a mock universe where the true associations are known, and discuss the applicability of the new procedure to large surveys.

ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries.

PubMed

Delvosalle, Christian; Fievez, Cécile; Pipart, Aurore; Debray, Bruno

2006-03-31

In the frame of the Accidental Risk Assessment Methodology for Industries (ARAMIS) project, this paper aims at presenting the work carried out in the part of the project devoted to the definition of accident scenarios. This topic is a key-point in risk assessment and serves as basis for the whole risk quantification. The first result of the work is the building of a methodology for the identification of major accident hazards (MIMAH), which is carried out with the development of generic fault and event trees based on a typology of equipment and substances. The term "major accidents" must be understood as the worst accidents likely to occur on the equipment, assuming that no safety systems are installed. A second methodology, called methodology for the identification of reference accident scenarios (MIRAS) takes into account the influence of safety systems on both the frequencies and possible consequences of accidents. This methodology leads to identify more realistic accident scenarios. The reference accident scenarios are chosen with the help of a tool called "risk matrix", crossing the frequency and the consequences of accidents. This paper presents both methodologies and an application on an ethylene oxide storage.

A Probabilistic Assessment Methodology for the Evaluation of Geologic Carbon Dioxide Storage

USGS Publications Warehouse

Brennan, Sean T.; Burruss, Robert A.; Merrill, Matthew D.; Freeman, P.A.; Ruppert, Leslie F.

2010-01-01

In 2007, the Energy Independence and Security Act (Public Law 110-140) authorized the U.S. Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for carbon dioxide (CO2) in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Energy. The first year of that activity was specified for development of a methodology to estimate storage potential that could be applied uniformly to geologic formations across the United States. After its release, the methodology was to receive public comment and external expert review. An initial methodology was developed and published in March 2009 (Burruss and others, 2009), and public comments were received. The report was then sent to a panel of experts for external review. The external review report was received by the USGS in December 2009. This report is in response to those external comments and reviews and describes how the previous assessment methodology (Burruss and others, 2009) was revised. The resource that is assessed is the technically accessible storage resource, which is defined as the mass of CO2 that can be stored in the pore volume of a storage formation. The methodology that is presented in this report is intended to be used for assessments at scales ranging from regional to subbasinal in which storage assessment units are defined on the basis of common geologic and hydrologic characteristics. The methodology does not apply to site-specific evaluation of storage resources or capacity.

SU-F-J-187: The Statistical NTCP and TCP Models in the Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jang, S; Frometa, T; Pyakuryal, A

2016-06-15

Purpose: The statistical models (SM) are typically used as a subjective description of a population for which there is only limited sample data, and especially in cases where the relationship between variables is known. The normal tissue complications and tumor control are frequently stochastic effects in the Radiotherapy (RT). Based on probabilistic treatments, it recently has been formulated new NTCP and TCP models for the RT. Investigating the particular requirements for their clinical use in the proton therapy (PT) is the goal of this work. Methods: The SM can be used as phenomenological or mechanistic models. The former way allowsmore » fitting real data and getting theirparameters. In the latter one, we should do efforts for determining the parameters through the acceptable estimations, measurements, and/or simulation experiments. Experimental methodologies for determination of the parameters have been developed from the fraction cells surviving the proton irradiation curves in tumor and OAR, and precise RBE models are used for calculating the variable of effective dose. As the executions of these methodologies have a high costs, so we have developed computer tools enable to perform simulation experiments as complement to limitations of the real ones. Results: The requirements for the use of the SM in the PT, such as validation and improvement of the elaborated and existent methodologies for determining the SM parameters and effective dose respectively, were determined. Conclusion: The SM realistically simulates the main processes in the PT, and for this reason these can be implemented in this therapy, which are simples, computable and they have other advantages over some current models. It has been determined some negative aspects for some currently used probabilistic models in the RT, like the LKB NTCP and others derived from logistic functions; which can be improved with the proposed methods in this study.« less

HIV Risks, Testing, and Treatment in the Former Soviet Union: Challenges and Future Directions in Research and Methodology.

PubMed

Saadat, Victoria M

2015-01-01

The dissolution of the USSR resulted in independence for constituent republics but left them battling an unstable economic environment and healthcare. Increases in injection drug use, prostitution, and migration were all widespread responses to this transition and have contributed to the emergence of an HIV epidemic in the countries of former Soviet Union. Researchers have begun to identify the risks of HIV infection as well as the barriers to HIV testing and treatment in the former Soviet Union. Significant methodological challenges have arisen and need to be addressed. The objective of this review is to determine common threads in HIV research in the former Soviet Union and provide useful recommendations for future research studies. In this systematic review of the literature, Pubmed was searched for English-language studies using the key search terms "HIV", "AIDS", "human immunodeficiency virus", "acquired immune deficiency syndrome", "Central Asia", "Kazakhstan", "Kyrgyzstan", "Uzbekistan", "Tajikistan", "Turkmenistan", "Russia", "Ukraine", "Armenia", "Azerbaijan", and "Georgia". Studies were evaluated against eligibility criteria for inclusion. Thirty-nine studies were identified across the two main topic areas of HIV risk and barriers to testing and treatment, themes subsequently referred to as "risk" and "barriers". Study design was predominantly cross-sectional. The most frequently used sampling methods were peer-to-peer and non-probabilistic sampling. The most frequently reported risks were condom misuse, risky intercourse, and unsafe practices among injection drug users. Common barriers to testing included that testing was inconvenient, and that results would not remain confidential. Frequent barriers to treatment were based on a distrust in the treatment system. The findings of this review reveal methodological limitations that span the existing studies. Small sample size, cross-sectional design, and non-probabilistic sampling methods were frequently reported limitations. Future work is needed to examine barriers to testing and treatment as well as longitudinal studies on HIV risk over time in most-at-risk populations.

Development and Validation of a New Air Carrier Block Time Prediction Model and Methodology

NASA Astrophysics Data System (ADS)

Litvay, Robyn Olson

Commercial airline operations rely on predicted block times as the foundation for critical, successive decisions that include fuel purchasing, crew scheduling, and airport facility usage planning. Small inaccuracies in the predicted block times have the potential to result in huge financial losses, and, with profit margins for airline operations currently almost nonexistent, potentially negate any possible profit. Although optimization techniques have resulted in many models targeting airline operations, the challenge of accurately predicting and quantifying variables months in advance remains elusive. The objective of this work is the development of an airline block time prediction model and methodology that is practical, easily implemented, and easily updated. Research was accomplished, and actual U.S., domestic, flight data from a major airline was utilized, to develop a model to predict airline block times with increased accuracy and smaller variance in the actual times from the predicted times. This reduction in variance represents tens of millions of dollars (U.S.) per year in operational cost savings for an individual airline. A new methodology for block time prediction is constructed using a regression model as the base, as it has both deterministic and probabilistic components, and historic block time distributions. The estimation of the block times for commercial, domestic, airline operations requires a probabilistic, general model that can be easily customized for a specific airline’s network. As individual block times vary by season, by day, and by time of day, the challenge is to make general, long-term estimations representing the average, actual block times while minimizing the variation. Predictions of block times for the third quarter months of July and August of 2011 were calculated using this new model. The resulting, actual block times were obtained from the Research and Innovative Technology Administration, Bureau of Transportation Statistics (Airline On-time Performance Data, 2008-2011) for comparison and analysis. Future block times are shown to be predicted with greater accuracy, without exception and network-wide, for a major, U.S., domestic airline.

SSHAC Level 1 Probabilistic Seismic Hazard Analysis for the Idaho National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Payne, Suzette; Coppersmith, Ryan; Coppersmith, Kevin

A Probabilistic Seismic Hazard Analysis (PSHA) was completed for the Materials and Fuels Complex (MFC), Naval Reactors Facility (NRF), and the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) (Figure 1-1). The PSHA followed the approaches and procedures appropriate for a Study Level 1 provided in the guidance advanced by the Senior Seismic Hazard Analysis Committee (SSHAC) in U.S. Nuclear Regulatory Commission (NRC) NUREG/CR-6372 and NUREG-2117 (NRC, 1997; 2012a). The SSHAC Level 1 PSHAs for MFC and ATR were conducted as part of the Seismic Risk Assessment (SRA) project (INL Project number 31287) to develop and apply a new-riskmore » informed methodology, respectively. The SSHAC Level 1 PSHA was conducted for NRF to provide guidance on the potential use of a design margin above rock hazard levels. The SRA project is developing a new risk-informed methodology that will provide a systematic approach for evaluating the need for an update of an existing PSHA. The new methodology proposes criteria to be employed at specific analysis, decision, or comparison points in its evaluation process. The first four of seven criteria address changes in inputs and results of the PSHA and are given in U.S. Department of Energy (DOE) Standard, DOE-STD-1020-2012 (DOE, 2012a) and American National Standards Institute/American Nuclear Society (ANSI/ANS) 2.29 (ANS, 2008a). The last three criteria address evaluation of quantitative hazard and risk-focused information of an existing nuclear facility. The seven criteria and decision points are applied to Seismic Design Category (SDC) 3, 4, and 5, which are defined in American Society of Civil Engineers/Structural Engineers Institute (ASCE/SEI) 43-05 (ASCE, 2005). The application of the criteria and decision points could lead to an update or could determine that such update is not necessary.« less

Global Infrasound Association Based on Probabilistic Clutter Categorization

NASA Astrophysics Data System (ADS)

Arora, Nimar; Mialle, Pierrick

2016-04-01

The IDC advances its methods and continuously improves its automatic system for the infrasound technology. The IDC focuses on enhancing the automatic system for the identification of valid signals and the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria. An objective of this study is to reduce the number of associated infrasound arrivals that are rejected from the automatic bulletins when generating the reviewed event bulletins. Indeed, a considerable number of signal detections are due to local clutter sources such as microbaroms, waterfalls, dams, gas flares, surf (ocean breaking waves) etc. These sources are either too diffuse or too local to form events. Worse still, the repetitive nature of this clutter leads to a large number of false event hypotheses due to the random matching of clutter at multiple stations. Previous studies, for example [1], have worked on categorization of clutter using long term trends on detection azimuth, frequency, and amplitude at each station. In this work we continue the same line of reasoning to build a probabilistic model of clutter that is used as part of NETVISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydroacoustic and infrasound processing built on a unified probabilistic framework. References: [1] Infrasound categorization Towards a statistics based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NETVISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013

Advanced Reactor Passive System Reliability Demonstration Analysis for an External Event

DOE PAGES

Bucknor, Matthew; Grabaskas, David; Brunett, Acacia J.; ...

2017-01-24

We report that many advanced reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended because of deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize within a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has beenmore » examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper provides an overview of a passive system reliability demonstration analysis for an external event. Considering an earthquake with the possibility of site flooding, the analysis focuses on the behavior of the passive Reactor Cavity Cooling System following potential physical damage and system flooding. The assessment approach seeks to combine mechanistic and simulation-based methods to leverage the benefits of the simulation-based approach without the need to substantially deviate from conventional probabilistic risk assessment techniques. Lastly, although this study is presented as only an example analysis, the results appear to demonstrate a high level of reliability of the Reactor Cavity Cooling System (and the reactor system in general) for the postulated transient event.« less

Methodological considerations of acoustic playbacks to test the behavioral significance of call directionality in male northern elephant seals (Mirounga angustirostris)

NASA Astrophysics Data System (ADS)

Holt, Marla M.; Insley, Stephen J.; Southall, Brandon L.; Schusterman, Ronald J.

2005-09-01

While attempting to gain access to receptive females, male northern elephant seals form dominance hierarchies through multiple dyadic interactions involving visual and acoustic signals. These signals are both highly stereotyped and directional. Previous behavioral observations suggested that males attend to the directional cues of these signals. We used in situ vocal playbacks to test whether males attend to directional cues of the acoustic components of a competitors calls (i.e., variation in call spectra and source levels). Here, we will focus on playback methodology. Playback calls were multiple exemplars of a marked dominant male from an isolated area, recorded with a directional microphone and DAT recorder and edited into a natural sequence that controlled call amplitude. Control calls were recordings of ambient rookery sounds with the male calls removed. Subjects were 20 marked males (10 adults and 10 subadults) all located at An~o Nuevo, CA. Playback presentations, calibrated for sound-pressure level, were broadcast at a distance of 7 m from each subject. Most responses were classified into the following categories: visual orientation, postural change, calling, movement toward or away from the loudspeaker, and re-directed aggression. We also investigated developmental, hierarchical, and ambient noise variables that were thought to influence male behavior.

Using Approximate Bayesian Computation to infer sex ratios from acoustic data.

PubMed

Lehnen, Lisa; Schorcht, Wigbert; Karst, Inken; Biedermann, Martin; Kerth, Gerald; Puechmaille, Sebastien J

2018-01-01

Population sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex. Acoustic sexing is an option if vocalizations differ between sexes, but is precluded by overlapping distributions of the values of male and female vocalizations in many species. A method allowing the inference of sex ratios despite such an overlap will therefore greatly increase the information extractable from acoustic data. To meet this demand, we developed a novel approach using Approximate Bayesian Computation (ABC) to infer the sex ratio of populations from acoustic data. Additionally, parameters characterizing the male and female distribution of acoustic values (mean and standard deviation) are inferred. This information is then used to probabilistically assign a sex to a single acoustic signal. We furthermore develop a simpler means of sex ratio estimation based on the exclusion of calls from the overlap zone. Applying our methods to simulated data demonstrates that sex ratio and acoustic parameter characteristics of males and females are reliably inferred by the ABC approach. Applying both the ABC and the exclusion method to empirical datasets (echolocation calls recorded in colonies of lesser horseshoe bats, Rhinolophus hipposideros) provides similar sex ratios as molecular sexing. Our methods aim to facilitate evidence-based conservation, and to benefit scientists investigating ecological or conservation questions related to sex- or group specific behaviour across a wide range of organisms emitting acoustic signals. The developed methodology is non-invasive, low-cost and time-efficient, thus allowing the study of many sites and individuals. We provide an R-script for the easy application of the method and discuss potential future extensions and fields of applications. The script can be easily adapted to account for numerous biological systems by adjusting the type and number of groups to be distinguished (e.g. age, social rank, cryptic species) and the acoustic parameters investigated.

Designing a Strategic Plan through an Emerging Knowledge Generation Process: The ATM Experience

ERIC Educational Resources Information Center

Zanotti, Francesco

2012-01-01

Purpose: The aim of this contribution is to describe a new methodology for designing strategic plans and how it was implemented by ATM, a public transportation agency based in Milan, Italy. Design/methodology/approach: This methodology is founded on a new system theory, called "quantum systemics". It is based on models and metaphors both…

Improving Self Service the Six Sigma Way at Newcastle University Library

ERIC Educational Resources Information Center

Kumi, Susan; Morrow, John

2006-01-01

Purpose: To report on the collaborative project between Newcastle University Library and 3M which aimed to increase self-issue levels using six sigma methodology. Design/methodology/approach: The six-month long project is outlined and gives an insight into the process improvement methodology called six sigma. An explanation of why we ran the…

A Methodology for Modeling Nuclear Power Plant Passive Component Aging in Probabilistic Risk Assessment under the Impact of Operating Conditions, Surveillance and Maintenance Activities

NASA Astrophysics Data System (ADS)

Guler Yigitoglu, Askin

In the context of long operation of nuclear power plants (NPPs) (i.e., 60-80 years, and beyond), investigation of the aging of passive systems, structures and components (SSCs) is important to assess safety margins and to decide on reactor life extension as indicated within the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. In the traditional probabilistic risk assessment (PRA) methodology, evaluating the potential significance of aging of passive SSCs on plant risk is challenging. Although passive SSC failure rates can be added as initiating event frequencies or basic event failure rates in the traditional event-tree/fault-tree methodology, these failure rates are generally based on generic plant failure data which means that the true state of a specific plant is not reflected in a realistic manner on aging effects. Dynamic PRA methodologies have gained attention recently due to their capability to account for the plant state and thus address the difficulties in the traditional PRA modeling of aging effects of passive components using physics-based models (and also in the modeling of digital instrumentation and control systems). Physics-based models can capture the impact of complex aging processes (e.g., fatigue, stress corrosion cracking, flow-accelerated corrosion, etc.) on SSCs and can be utilized to estimate passive SSC failure rates using realistic NPP data from reactor simulation, as well as considering effects of surveillance and maintenance activities. The objectives of this dissertation are twofold: The development of a methodology for the incorporation of aging modeling of passive SSC into a reactor simulation environment to provide a framework for evaluation of their risk contribution in both the dynamic and traditional PRA; and the demonstration of the methodology through its application to pressurizer surge line pipe weld and steam generator tubes in commercial nuclear power plants. In the proposed methodology, a multi-state physics based model is selected to represent the aging process. The model is modified via sojourn time approach to reflect the operational and maintenance history dependence of the transition rates. Thermal-hydraulic parameters of the model are calculated via the reactor simulation environment and uncertainties associated with both parameters and the models are assessed via a two-loop Monte Carlo approach (Latin hypercube sampling) to propagate input probability distributions through the physical model. The effort documented in this thesis towards this overall objective consists of : i) defining a process for selecting critical passive components and related aging mechanisms, ii) aging model selection, iii) calculating the probability that aging would cause the component to fail, iv) uncertainty/sensitivity analyses, v) procedure development for modifying an existing PRA to accommodate consideration of passive component failures, and, vi) including the calculated failure probability in the modified PRA. The proposed methodology is applied to pressurizer surge line pipe weld aging and steam generator tube degradation in pressurized water reactors.

Interdisciplinary Research at the Intersection of CALL, NLP, and SLA: Methodological Implications from an Input Enhancement Project

ERIC Educational Resources Information Center

Ziegler, Nicole; Meurers, Detmar; Rebuschat, Patrick; Ruiz, Simón; Moreno-Vega, José L.; Chinkina, Maria; Li, Wenjing; Grey, Sarah

2017-01-01

Despite the promise of research conducted at the intersection of computer-assisted language learning (CALL), natural language processing, and second language acquisition, few studies have explored the potential benefits of using intelligent CALL systems to deepen our understanding of the process and products of second language (L2) learning. The…

Probabilistic seismic loss estimation via endurance time method

NASA Astrophysics Data System (ADS)

Tafakori, Ehsan; Pourzeynali, Saeid; Estekanchi, Homayoon E.

2017-01-01

Probabilistic Seismic Loss Estimation is a methodology used as a quantitative and explicit expression of the performance of buildings using terms that address the interests of both owners and insurance companies. Applying the ATC 58 approach for seismic loss assessment of buildings requires using Incremental Dynamic Analysis (IDA), which needs hundreds of time-consuming analyses, which in turn hinders its wide application. The Endurance Time Method (ETM) is proposed herein as part of a demand propagation prediction procedure and is shown to be an economical alternative to IDA. Various scenarios were considered to achieve this purpose and their appropriateness has been evaluated using statistical methods. The most precise and efficient scenario was validated through comparison against IDA driven response predictions of 34 code conforming benchmark structures and was proven to be sufficiently precise while offering a great deal of efficiency. The loss values were estimated by replacing IDA with the proposed ETM-based procedure in the ATC 58 procedure and it was found that these values suffer from varying inaccuracies, which were attributed to the discretized nature of damage and loss prediction functions provided by ATC 58.

Source processes for the probabilistic assessment of tsunami hazards

USGS Publications Warehouse

Geist, Eric L.; Lynett, Patrick J.

2014-01-01

The importance of tsunami hazard assessment has increased in recent years as a result of catastrophic consequences from events such as the 2004 Indian Ocean and 2011 Japan tsunamis. In particular, probabilistic tsunami hazard assessment (PTHA) methods have been emphasized to include all possible ways a tsunami could be generated. Owing to the scarcity of tsunami observations, a computational approach is used to define the hazard. This approach includes all relevant sources that may cause a tsunami to impact a site and all quantifiable uncertainty. Although only earthquakes were initially considered for PTHA, recent efforts have also attempted to include landslide tsunami sources. Including these sources into PTHA is considerably more difficult because of a general lack of information on relating landslide area and volume to mean return period. The large variety of failure types and rheologies associated with submarine landslides translates to considerable uncertainty in determining the efficiency of tsunami generation. Resolution of these and several other outstanding problems are described that will further advance PTHA methodologies leading to a more accurate understanding of tsunami hazard.

The ARGO Project: assessing NA-TECH risks on off-shore oil platforms

NASA Astrophysics Data System (ADS)

Capuano, Paolo; Basco, Anna; Di Ruocco, Angela; Esposito, Simona; Fusco, Giannetta; Garcia-Aristizabal, Alexander; Mercogliano, Paola; Salzano, Ernesto; Solaro, Giuseppe; Teofilo, Gianvito; Scandone, Paolo; Gasparini, Paolo

2017-04-01

ARGO (Analysis of natural and anthropogenic risks on off-shore oil platforms) is a 2 years project, funded by the DGS-UNMIG (Directorate General for Safety of Mining and Energy Activities - National Mining Office for Hydrocarbons and Georesources) of Italian Ministry of Economic Development. The project, coordinated by AMRA (Center for the Analysis and Monitoring of Environmental Risk), aims at providing technical support for the analysis of natural and anthropogenic risks on offshore oil platforms. In order to achieve this challenging objective, ARGO brings together climate experts, risk management experts, seismologists, geologists, chemical engineers, earth and coastal observation experts. ARGO has developed methodologies for the probabilistic analysis of industrial accidents triggered by natural events (NA-TECH) on offshore oil platforms in the Italian seas, including extreme events related to climate changes. Furthermore the environmental effect of offshore activities has been investigated, including: changes on seismicity and on the evolution of coastal areas close to offshore platforms. Then a probabilistic multi-risk framework has been developed for the analysis of NA-TECH events on offshore installations for hydrocarbon extraction.

A probabilistic sizing tool and Monte Carlo analysis for entry vehicle ablative thermal protection systems

NASA Astrophysics Data System (ADS)

Mazzaracchio, Antonio; Marchetti, Mario

2010-03-01

Implicit ablation and thermal response software was developed to analyse and size charring ablative thermal protection systems for entry vehicles. A statistical monitor integrated into the tool, which uses the Monte Carlo technique, allows a simulation to run over stochastic series. This performs an uncertainty and sensitivity analysis, which estimates the probability of maintaining the temperature of the underlying material within specified requirements. This approach and the associated software are primarily helpful during the preliminary design phases of spacecraft thermal protection systems. They are proposed as an alternative to traditional approaches, such as the Root-Sum-Square method. The developed tool was verified by comparing the results with those from previous work on thermal protection system probabilistic sizing methodologies, which are based on an industry standard high-fidelity ablation and thermal response program. New case studies were analysed to establish thickness margins on sizing heat shields that are currently proposed for vehicles using rigid aeroshells for future aerocapture missions at Neptune, and identifying the major sources of uncertainty in the material response.

The Spatial Assessment of the Current Seismic Hazard State for Hard Rock Underground Mines

NASA Astrophysics Data System (ADS)

Wesseloo, Johan

2018-06-01

Mining-induced seismic hazard assessment is an important component in the management of safety and financial risk in mines. As the seismic hazard is a response to the mining activity, it is non-stationary and variable both in space and time. This paper presents an approach for implementing a probabilistic seismic hazard assessment to assess the current hazard state of a mine. Each of the components of the probabilistic seismic hazard assessment is considered within the context of hard rock underground mines. The focus of this paper is the assessment of the in-mine hazard distribution and does not consider the hazard to nearby public or structures. A rating system and methodologies to present hazard maps, for the purpose of communicating to different stakeholders in the mine, i.e. mine managers, technical personnel and the work force, are developed. The approach allows one to update the assessment with relative ease and within short time periods as new data become available, enabling the monitoring of the spatial and temporal change in the seismic hazard.

On some methods for assessing earthquake predictions

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.; Peresan, A.

2017-09-01

A regional approach to the problem of assessing earthquake predictions inevitably faces a deficit of data. We point out some basic limits of assessment methods reported in the literature, considering the practical case of the performance of the CN pattern recognition method in the prediction of large Italian earthquakes. Along with the classical hypothesis testing, a new game approach, the so-called parimutuel gambling (PG) method, is examined. The PG, originally proposed for the evaluation of the probabilistic earthquake forecast, has been recently adapted for the case of 'alarm-based' CN prediction. The PG approach is a non-standard method; therefore it deserves careful examination and theoretical analysis. We show that the PG alarm-based version leads to an almost complete loss of information about predicted earthquakes (even for a large sample). As a result, any conclusions based on the alarm-based PG approach are not to be trusted. We also show that the original probabilistic PG approach does not necessarily identifies the genuine forecast correctly among competing seismicity rate models, even when applied to extensive data.

Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.

PubMed

Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W C; Cao, Jinde

2015-08-28

In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results.

Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy

PubMed Central

Yendiki, Anastasia; Panneck, Patricia; Srinivasan, Priti; Stevens, Allison; Zöllei, Lilla; Augustinack, Jean; Wang, Ruopeng; Salat, David; Ehrlich, Stefan; Behrens, Tim; Jbabdi, Saad; Gollub, Randy; Fischl, Bruce

2011-01-01

We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls. PMID:22016733

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

Synchronization Analysis of Master-Slave Probabilistic Boolean Networks

PubMed Central

Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W. C.; Cao, Jinde

2015-01-01

In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results. PMID:26315380

A Probabilistic Approach to Mitigate Composition Attacks on Privacy in Non-Coordinated Environments

PubMed Central

Sarowar Sattar, A.H.M.; Li, Jiuyong; Liu, Jixue; Heatherly, Raymond; Malin, Bradley

2014-01-01

Organizations share data about individuals to drive business and comply with law and regulation. However, an adversary may expose confidential information by tracking an individual across disparate data publications using quasi-identifying attributes (e.g., age, geocode and sex) associated with the records. Various studies have shown that well-established privacy protection models (e.g., k-anonymity and its extensions) fail to protect an individual’s privacy against this “composition attack”. This type of attack can be thwarted when organizations coordinate prior to data publication, but such a practice is not always feasible. In this paper, we introduce a probabilistic model called (d, α)-linkable, which mitigates composition attack without coordination. The model ensures that d confidential values are associated with a quasi-identifying group with a likelihood of α. We realize this model through an efficient extension to k-anonymization and use extensive experiments to show our strategy significantly reduces the likelihood of a successful composition attack and can preserve more utility than alternative privacy models, such as differential privacy. PMID:25598581

EVALUATING METRICS FOR GREEN CHEMISTRIES: INFORMATION AND CALCULATION NEEDS

EPA Science Inventory

Research within the U.S. EPA's National Risk Management Research Laboratory is developing a methodology for the evaluation of green chemistries. This methodology called GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Ob...

78 FR 9987 - Call for Expert Reviewers to the U.S. Government Review of the 2013 Supplement to the 2006...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-12

... . Following an invitation from the UNFCCC to ``undertake further methodological work on wetlands, focusing on... developing additional national-level inventory methodological guidance on wetlands, including default...

The tomato genome

USDA-ARS?s Scientific Manuscript database

The tomato genome sequence was undertaken at a time when state-of-the-art sequencing methodologies were undergoing a transition to co-called next generation methodologies. The result was an international consortium undertaking a strategy merging both old and new approaches. Because biologists were...

Researcher / Researched: Repositioning Research Paradigms

ERIC Educational Resources Information Center

Meerwald, Agnes May Lin

2013-01-01

"Researcher / Researched" calls for a complementary research methodology by proposing autoethnography as both a method and text that crosses the boundaries of conventional and alternative methodologies in higher education. Autoethnography rearticulates the researcher / researched positions by blurring the boundary between them. This…

Identifying Key Words in 9-1-1 Calls for Stroke: A Mixed Methods Approach.

PubMed

Richards, Christopher T; Wang, Baiyang; Markul, Eddie; Albarran, Frank; Rottman, Doreen; Aggarwal, Neelum T; Lindeman, Patricia; Stein-Spencer, Leslee; Weber, Joseph M; Pearlman, Kenneth S; Tataris, Katie L; Holl, Jane L; Klabjan, Diego; Prabhakaran, Shyam

2017-01-01

Identifying stroke during a 9-1-1 call is critical to timely prehospital care. However, emergency medical dispatchers (EMDs) recognize stroke in less than half of 9-1-1 calls, potentially due to the words used by callers to communicate stroke signs and symptoms. We hypothesized that callers do not typically use words and phrases considered to be classical descriptors of stroke, such as focal neurologic deficits, but that a mixed-methods approach can identify words and phrases commonly used by 9-1-1 callers to describe acute stroke victims. We performed a mixed-method, retrospective study of 9-1-1 call audio recordings for adult patients with confirmed stroke who were transported by ambulance in a large urban city. Content analysis, a qualitative methodology, and computational linguistics, a quantitative methodology, were used to identify key words and phrases used by 9-1-1 callers to describe acute stroke victims. Because a caller's level of emotional distress contributes to the communication during a 9-1-1 call, the Emotional Content and Cooperation Score was scored by a multidisciplinary team. A total of 110 9-1-1 calls, received between June and September 2013, were analyzed. EMDs recognized stroke in 48% of calls, and the emotional state of most callers (95%) was calm. In 77% of calls in which EMDs recognized stroke, callers specifically used the word "stroke"; however, the word "stroke" was used in only 38% of calls. Vague, non-specific words and phrases were used to describe stroke victims' symptoms in 55% of calls, and 45% of callers used distractor words and phrases suggestive of non-stroke emergencies. Focal neurologic symptoms were described in 39% of calls. Computational linguistics identified 9 key words that were more commonly used in calls where the EMD identified stroke. These words were concordant with terms identified through qualitative content analysis. Most 9-1-1 callers used vague, non-specific, or distractor words and phrases and infrequently provide classic stroke descriptions during 9-1-1 calls for stroke. Both qualitative and quantitative methodologies identified similar key words and phrases associated with accurate EMD stroke recognition. This study suggests that tools incorporating commonly used words and phrases could potentially improve EMD stroke recognition.

Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion

NASA Astrophysics Data System (ADS)

Hansen, T. M.; Cordua, K. S.

2017-12-01

Probabilistically formulated inverse problems can be solved using Monte Carlo-based sampling methods. In principle, both advanced prior information, based on for example, complex geostatistical models and non-linear forward models can be considered using such methods. However, Monte Carlo methods may be associated with huge computational costs that, in practice, limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical forward response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival traveltime inversion of crosshole ground penetrating radar data. An accurate forward model, based on 2-D full-waveform modeling followed by automatic traveltime picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the accurate and computationally expensive forward model, and also considerably faster and more accurate (i.e. with better resolution), than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of non-linear and non-Gaussian inverse problems that have to be solved using Monte Carlo sampling techniques.

Probabilistic Design Methodology and its Application to the Design of an Umbilical Retract Mechanism

NASA Technical Reports Server (NTRS)

Onyebueke, Landon; Ameye, Olusesan

2002-01-01

A lot has been learned from past experience with structural and machine element failures. The understanding of failure modes and the application of an appropriate design analysis method can lead to improved structural and machine element safety as well as serviceability. To apply Probabilistic Design Methodology (PDM), all uncertainties are modeled as random variables with selected distribution types, means, and standard deviations. It is quite difficult to achieve a robust design without considering the randomness of the design parameters which is the case in the use of the Deterministic Design Approach. The US Navy has a fleet of submarine-launched ballistic missiles. An umbilical plug joins the missile to the submarine in order to provide electrical and cooling water connections. As the missile leaves the submarine, an umbilical retract mechanism retracts the umbilical plug clear of the advancing missile after disengagement during launch and retrains the plug in the retracted position. The design of the current retract mechanism in use was based on the deterministic approach which puts emphasis on factor of safety. A new umbilical retract mechanism that is simpler in design, lighter in weight, more reliable, easier to adjust, and more cost effective has become desirable since this will increase the performance and efficiency of the system. This paper reports on a recent project performed at Tennessee State University for the US Navy that involved the application of PDM to the design of an umbilical retract mechanism. This paper demonstrates how the use of PDM lead to the minimization of weight and cost, and the maximization of reliability and performance.

Methodology for assessing the safety of Hydrogen Systems: HyRAM 1.1 technical reference manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina; Hecht, Ethan; Reynolds, John Thomas

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a stan- dardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. De- partment of Energy to increase access to technical data about hydrogen safety andmore » to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.1. HyRAM 1.1 includes generic probabilities for hydrogen equipment fail- ures, probabilistic models for the impact of heat flux on humans and structures, and computa- tionally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.1 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.« less

Quantifying effectiveness of failure prediction and response in HPC systems : methodology and example.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Chen, Frank Xiaoxiao; Pebay, Philippe Pierre

2010-06-01

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe context-relevant methodologies for determining themore » accuracy and cost-benefit of predictors. While many research studies have quantified the expected impact of growing system size, and the associated shortened mean time to failure (MTTF), on application performance in large-scale high-performance computing (HPC) platforms, there has been little if any work to quantify the possible gains from predicting system resource failures with significant but imperfect accuracy. This possibly stems from HPC system complexity and the fact that, to date, no one has established any good predictors of failure in these systems. Our work in the OVIS project aims to discover these predictors via a variety of data collection techniques and statistical analysis methods that yield probabilistic predictions. The question then is, 'How good or useful are these predictions?' We investigate methods for answering this question in a general setting, and illustrate them using a specific failure predictor discovered on a production system at Sandia.« less

Aircraft Conflict Analysis and Real-Time Conflict Probing Using Probabilistic Trajectory Modeling

NASA Technical Reports Server (NTRS)

Yang, Lee C.; Kuchar, James K.

2000-01-01

Methods for maintaining separation between aircraft in the current airspace system have been built from a foundation of structured routes and evolved procedures. However, as the airspace becomes more congested and the chance of failures or operational error become more problematic, automated conflict alerting systems have been proposed to help provide decision support and to serve as traffic monitoring aids. The problem of conflict detection and resolution has been tackled from a number of different ways, but in this thesis, it is recast as a problem of prediction in the presence of uncertainties. Much of the focus is concentrated on the errors and uncertainties from the working trajectory model used to estimate future aircraft positions. The more accurate the prediction, the more likely an ideal (no false alarms, no missed detections) alerting system can be designed. Additional insights into the problem were brought forth by a review of current operational and developmental approaches found in the literature. An iterative, trial and error approach to threshold design was identified. When examined from a probabilistic perspective, the threshold parameters were found to be a surrogate to probabilistic performance measures. To overcome the limitations in the current iterative design method, a new direct approach is presented where the performance measures are directly computed and used to perform the alerting decisions. The methodology is shown to handle complex encounter situations (3-D, multi-aircraft, multi-intent, with uncertainties) with relative ease. Utilizing a Monte Carlo approach, a method was devised to perform the probabilistic computations in near realtime. Not only does this greatly increase the method's potential as an analytical tool, but it also opens up the possibility for use as a real-time conflict alerting probe. A prototype alerting logic was developed and has been utilized in several NASA Ames Research Center experimental studies.

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.

A~probabilistic tsunami hazard assessment for Indonesia

NASA Astrophysics Data System (ADS)

Horspool, N.; Pranantyo, I.; Griffin, J.; Latief, H.; Natawidjaja, D. H.; Kongko, W.; Cipta, A.; Bustaman, B.; Anugrah, S. D.; Thio, H. K.

2014-05-01

Probabilistic hazard assessments are a fundamental tool for assessing the threats posed by hazards to communities and are important for underpinning evidence based decision making on risk mitigation activities. Indonesia has been the focus of intense tsunami risk mitigation efforts following the 2004 Indian Ocean Tsunami, but this has been largely concentrated on the Sunda Arc, with little attention to other tsunami prone areas of the country such as eastern Indonesia. We present the first nationally consistent Probabilistic Tsunami Hazard Assessment (PTHA) for Indonesia. This assessment produces time independent forecasts of tsunami hazard at the coast from tsunami generated by local, regional and distant earthquake sources. The methodology is based on the established monte-carlo approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. We account for sources of epistemic and aleatory uncertainty in the analysis through the use of logic trees and through sampling probability density functions. For short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, south coast of Java and the north coast of Papua. For longer return periods (500-2500 years), the tsunami hazard is highest along the Sunda Arc, reflecting larger maximum magnitudes along the Sunda Arc. The annual probability of experiencing a tsunami with a height at the coast of > 0.5 m is greater than 10% for Sumatra, Java, the Sunda Islands (Bali, Lombok, Flores, Sumba) and north Papua. The annual probability of experiencing a tsunami with a height of >3.0 m, which would cause significant inundation and fatalities, is 1-10% in Sumatra, Java, Bali, Lombok and north Papua, and 0.1-1% for north Sulawesi, Seram and Flores. The results of this national scale hazard assessment provide evidence for disaster managers to prioritise regions for risk mitigation activities and/or more detailed hazard or risk assessment.

Risk assessment for furan contamination through the food chain in Belgian children.

PubMed

Scholl, Georges; Huybrechts, Inge; Humblet, Marie-France; Scippo, Marie-Louise; De Pauw, Edwin; Eppe, Gauthier; Saegerman, Claude

2012-08-01

Young, old, pregnant and immuno-compromised persons are of great concern for risk assessors as they represent the sub-populations most at risk. The present paper focuses on risk assessment linked to furan exposure in children. Only the Belgian population was considered because individual contamination and consumption data that are required for accurate risk assessment were available for Belgian children only. Two risk assessment approaches, the so-called deterministic and probabilistic, were applied and the results were compared for the estimation of daily intake. A significant difference between the average Estimated Daily Intake (EDI) was underlined between the deterministic (419 ng kg⁻¹ body weight (bw) day⁻¹) and the probabilistic (583 ng kg⁻¹ bw day⁻¹) approaches, which results from the mathematical treatment of the null consumption and contamination data. The risk was characterised by two ways: (1) the classical approach by comparison of the EDI to a reference dose (RfD(chronic-oral)) and (2) the most recent approach, namely the Margin of Exposure (MoE) approach. Both reached similar conclusions: the risk level is not of a major concern, but is neither negligible. In the first approach, only 2.7 or 6.6% (respectively in the deterministic and in the probabilistic way) of the studied population presented an EDI above the RfD(chronic-oral). In the second approach, the percentage of children displaying a MoE above 10,000 and below 100 is 3-0% and 20-0.01% in the deterministic and probabilistic modes, respectively. In addition, children were compared to adults and significant differences between the contamination patterns were highlighted. While major contamination was linked to coffee consumption in adults (55%), no item predominantly contributed to the contamination in children. The most important were soups (19%), dairy products (17%), pasta and rice (11%), fruit and potatoes (9% each).

Probabilistic Learning in Junior High School: Investigation of Student Probabilistic Thinking Levels

NASA Astrophysics Data System (ADS)

Kurniasih, R.; Sujadi, I.

2017-09-01

This paper was to investigate level on students’ probabilistic thinking. Probabilistic thinking level is level of probabilistic thinking. Probabilistic thinking is thinking about probabilistic or uncertainty matter in probability material. The research’s subject was students in grade 8th Junior High School students. The main instrument is a researcher and a supporting instrument is probabilistic thinking skills test and interview guidelines. Data was analyzed using triangulation method. The results showed that the level of students probabilistic thinking before obtaining a teaching opportunity at the level of subjective and transitional. After the students’ learning level probabilistic thinking is changing. Based on the results of research there are some students who have in 8th grade level probabilistic thinking numerically highest of levels. Level of students’ probabilistic thinking can be used as a reference to make a learning material and strategy.

Towards health impact assessment of drinking-water privatization--the example of waterborne carcinogens in North Rhine-Westphalia (Germany).

PubMed Central

Fehr, Rainer; Mekel, Odile; Lacombe, Martin; Wolf, Ulrike

2003-01-01

Worldwide there is a tendency towards deregulation in many policy sectors - this, for example, includes liberalization and privatization of drinking-water management. However, concerns about the negative impacts this might have on human health call for prospective health impact assessment (HIA) on the management of drinking-water. On the basis of an established generic 10-step HIA procedure and on risk assessment methodology, this paper aims to produce quantitative estimates concerning health effects from increased exposure to carcinogens in drinking-water. Using data from North Rhine-Westphalia in Germany, probabilistic estimates of excess lifetime cancer risk, as well as estimates of additional cases of cancer from increased carcinogen exposure levels are presented. The results show how exposure to contaminants that are strictly within current limits could increase cancer risks and case-loads substantially. On the basis of the current analysis, we suggest that with uniform increases in pollutant levels, a single chemical (arsenic) is responsible for a large fraction of expected additional risk. The study also illustrates the uncertainty involved in predicting the health impacts of changes in water quality. Future analysis should include additional carcinogens, non-cancer risks including those due to microbial contamination, and the impacts of system failures and of illegal action, which may be increasingly likely to occur under changed management arrangements. If, in spite of concerns, water is privatized, it is particularly important to provide adequate surveillance of water quality. PMID:12894324

Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghil, M.; Kravtsov, S.; Robertson, A. W.

2008-10-14

This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influencemore » large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.« less

Modeling socio-cultural processes in network-centric environments

NASA Astrophysics Data System (ADS)

Santos, Eunice E.; Santos, Eugene, Jr.; Korah, John; George, Riya; Gu, Qi; Kim, Keumjoo; Li, Deqing; Russell, Jacob; Subramanian, Suresh

2012-05-01

The major focus in the field of modeling & simulation for network centric environments has been on the physical layer while making simplifications for the human-in-the-loop. However, the human element has a big impact on the capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model and analyze various socio-cultural processes within network centric environments. The key innovation in our methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional mappings between them. We represent socio-cultural information such as friendships, professional relationships and temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We will leverage previous work on a network performance modeling framework called Network-Centric Operations Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating relevant factors and providing analyses of the results.

Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

PubMed

López-Cruz, Pedro L; Bielza, Concha; Larrañaga, Pedro; Benavides-Piccione, Ruth; DeFelipe, Javier

2011-12-01

Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.

Seven Performance Drivers.

ERIC Educational Resources Information Center

Ross, Linda

2003-01-01

Recent work with automotive e-commerce clients led to the development of a performance analysis methodology called the Seven Performance Drivers, including: standards, incentives, capacity, knowledge and skill, measurement, feedback, and analysis. This methodology has been highly effective in introducing and implementing performance improvement.…

SIMPLIFYING EVALUATIONS OF GREEN CHEMISTRIES: HOW MUCH INFORMATION DO WE NEED?

EPA Science Inventory

Research within the U.S. EPA's National Risk Management Research Laboratory is developing a methodology for the evaluation of green chemistries. This methodology called GREENSCOPE (Gauging Reaction Effectiveness for the Environmental Sustainability of Chemistries with a multi-Ob...

Probability-Based Design Criteria of the ASCE 7 Tsunami Loads and Effects Provisions (Invited)

NASA Astrophysics Data System (ADS)

Chock, G.

2013-12-01

Mitigation of tsunami risk requires a combination of emergency preparedness for evacuation in addition to providing structural resilience of critical facilities, infrastructure, and key resources necessary for immediate response and economic and social recovery. Critical facilities would include emergency response, medical, tsunami refuges and shelters, ports and harbors, lifelines, transportation, telecommunications, power, financial institutions, and major industrial/commercial facilities. The Tsunami Loads and Effects Subcommittee of the ASCE/SEI 7 Standards Committee is developing a proposed new Chapter 6 - Tsunami Loads and Effects for the 2016 edition of the ASCE 7 Standard. ASCE 7 provides the minimum design loads and requirements for structures subject to building codes such as the International Building Code utilized in the USA. In this paper we will provide a review emphasizing the intent of these new code provisions and explain the design methodology. The ASCE 7 provisions for Tsunami Loads and Effects enables a set of analysis and design methodologies that are consistent with performance-based engineering based on probabilistic criteria. . The ASCE 7 Tsunami Loads and Effects chapter will be initially applicable only to the states of Alaska, Washington, Oregon, California, and Hawaii. Ground shaking effects and subsidence from a preceding local offshore Maximum Considered Earthquake will also be considered prior to tsunami arrival for Alaska and states in the Pacific Northwest regions governed by nearby offshore subduction earthquakes. For national tsunami design provisions to achieve a consistent reliability standard of structural performance for community resilience, a new generation of tsunami inundation hazard maps for design is required. The lesson of recent tsunami is that historical records alone do not provide a sufficient measure of the potential heights of future tsunamis. Engineering design must consider the occurrence of events greater than scenarios in the historical record, and should properly be based on the underlying seismicity of subduction zones. Therefore, Probabilistic Tsunami Hazard Analysis (PTHA) consistent with source seismicity must be performed in addition to consideration of historical event scenarios. A method of Probabilistic Tsunami Hazard Analysis has been established that is generally consistent with Probabilistic Seismic Hazard Analysis in the treatment of uncertainty. These new tsunami design zone maps will define the coastal zones where structures of greater importance would be designed for tsunami resistance and community resilience. Structural member acceptability criteria will be based on performance objectives for a 2,500-year Maximum Considered Tsunami. The approach developed by the ASCE Tsunami Loads and Effects Subcommittee of the ASCE 7 Standard would result in the first national unification of tsunami hazard criteria for design codes reflecting the modern approach of Performance-Based Engineering.

The analysis of probability task completion; Taxonomy of probabilistic thinking-based across gender in elementary school students

NASA Astrophysics Data System (ADS)

Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

2017-08-01

Formulation of mathematical learning goals now is not only oriented on cognitive product, but also leads to cognitive process, which is probabilistic thinking. Probabilistic thinking is needed by students to make a decision. Elementary school students are required to develop probabilistic thinking as foundation to learn probability at higher level. A framework of probabilistic thinking of students had been developed by using SOLO taxonomy, which consists of prestructural probabilistic thinking, unistructural probabilistic thinking, multistructural probabilistic thinking and relational probabilistic thinking. This study aimed to analyze of probability task completion based on taxonomy of probabilistic thinking. The subjects were two students of fifth grade; boy and girl. Subjects were selected by giving test of mathematical ability and then based on high math ability. Subjects were given probability tasks consisting of sample space, probability of an event and probability comparison. The data analysis consisted of categorization, reduction, interpretation and conclusion. Credibility of data used time triangulation. The results was level of boy's probabilistic thinking in completing probability tasks indicated multistructural probabilistic thinking, while level of girl's probabilistic thinking in completing probability tasks indicated unistructural probabilistic thinking. The results indicated that level of boy's probabilistic thinking was higher than level of girl's probabilistic thinking. The results could contribute to curriculum developer in developing probability learning goals for elementary school students. Indeed, teachers could teach probability with regarding gender difference.

Probability theory versus simulation of petroleum potential in play analysis

USGS Publications Warehouse

Crovelli, R.A.

1987-01-01

An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis is presented. This play-analysis methodology is a geostochastic system for petroleum resource appraisal in explored as well as frontier areas. An objective was to replace an existing Monte Carlo simulation method in order to increase the efficiency of the appraisal process. Underlying the two methods is a single geologic model which considers both the uncertainty of the presence of the assessed hydrocarbon and its amount if present. The results of the model are resource estimates of crude oil, nonassociated gas, dissolved gas, and gas for a geologic play in terms of probability distributions. The analytic method is based upon conditional probability theory and a closed form solution of all means and standard deviations, along with the probabilities of occurrence. ?? 1987 J.C. Baltzer A.G., Scientific Publishing Company.

Using information Theory in Optimal Test Point Selection for Health Management in NASA's Exploration Vehicles

NASA Technical Reports Server (NTRS)

Mehr, Ali Farhang; Tumer, Irem

2005-01-01

In this paper, we will present a new methodology that measures the "worth" of deploying an additional testing instrument (sensor) in terms of the amount of information that can be retrieved from such measurement. This quantity is obtained using a probabilistic model of RLV's that has been partially developed in the NASA Ames Research Center. A number of correlated attributes are identified and used to obtain the worth of deploying a sensor in a given test point from an information-theoretic viewpoint. Once the information-theoretic worth of sensors is formulated and incorporated into our general model for IHM performance, the problem can be formulated as a constrained optimization problem where reliability and operational safety of the system as a whole is considered. Although this research is conducted specifically for RLV's, the proposed methodology in its generic form can be easily extended to other domains of systems health monitoring.

Strategic Analysis Overview

NASA Technical Reports Server (NTRS)

Cirillo, William M.; Earle, Kevin D.; Goodliff, Kandyce E.; Reeves, J. D.; Stromgren, Chel; Andraschko, Mark R.; Merrill, R. Gabe

2008-01-01

NASA s Constellation Program employs a strategic analysis methodology in providing an integrated analysis capability of Lunar exploration scenarios and to support strategic decision-making regarding those scenarios. The strategic analysis methodology integrates the assessment of the major contributors to strategic objective satisfaction performance, affordability, and risk and captures the linkages and feedbacks between all three components. Strategic analysis supports strategic decision making by senior management through comparable analysis of alternative strategies, provision of a consistent set of high level value metrics, and the enabling of cost-benefit analysis. The tools developed to implement the strategic analysis methodology are not element design and sizing tools. Rather, these models evaluate strategic performance using predefined elements, imported into a library from expert-driven design/sizing tools or expert analysis. Specific components of the strategic analysis tool set include scenario definition, requirements generation, mission manifesting, scenario lifecycle costing, crew time analysis, objective satisfaction benefit, risk analysis, and probabilistic evaluation. Results from all components of strategic analysis are evaluated a set of pre-defined figures of merit (FOMs). These FOMs capture the high-level strategic characteristics of all scenarios and facilitate direct comparison of options. The strategic analysis methodology that is described in this paper has previously been applied to the Space Shuttle and International Space Station Programs and is now being used to support the development of the baseline Constellation Program lunar architecture. This paper will present an overview of the strategic analysis methodology and will present sample results from the application of the strategic analysis methodology to the Constellation Program lunar architecture.

Quantum epistemology from subquantum ontology: Quantum mechanics from theory of classical random fields

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

2017-02-01

The scientific methodology based on two descriptive levels, ontic (reality as it is) and epistemic (observational), is briefly presented. Following Schrödinger, we point to the possible gap between these two descriptions. Our main aim is to show that, although ontic entities may be unaccessible for observations, they can be useful for clarification of the physical nature of operational epistemic entities. We illustrate this thesis by the concrete example: starting with the concrete ontic model preceding quantum mechanics (the latter is treated as an epistemic model), namely, prequantum classical statistical field theory (PCSFT), we propose the natural physical interpretation for the basic quantum mechanical entity-the quantum state ("wave function"). The correspondence PCSFT ↦ QM is not straightforward, it couples the covariance operators of classical (prequantum) random fields with the quantum density operators. We use this correspondence to clarify the physical meaning of the pure quantum state and the superposition principle-by using the formalism of classical field correlations. In classical mechanics the phase space description can be considered as the ontic description, here states are given by points λ =(x , p) of phase space. The dynamics of the ontic state is given by the system of Hamiltonian equations.We can also consider probability distributions on the phase space (or equivalently random variables valued in it). We call them probabilistic ontic states. Dynamics of probabilistic ontic states is given by the Liouville equation.In classical physics we can (at least in principle) measure both the coordinate and momentum and hence ontic states can be treated as epistemic states as well (or it is better to say that here epistemic states can be treated as ontic states). Probabilistic ontic states represent probabilities for outcomes of joint measurement of position and momentum.However, this was a very special, although very important, example of description of physical phenomena. In general there are no reasons to expect that properties of ontic states are approachable through our measurements. There is a gap between ontic and epistemic descriptions, cf. also with 't Hooft [49,50] and G G. Groessing et al. [51]. In general the presence of such a gap also implies unapproachability of the probabilistic ontic states, i.e., probability distributions on the space of ontic states. De Broglie [28] called such probability distributions hidden probabilities and distinguished them sharply from probability distributions of measurements outcomes, see also Lochak [29]. (The latter distributions are described by the quantum formalism.)This ontic-epistemic approach based on the combination of two descriptive levels for natural phenomena is closely related to the old Bild conception which was originated in the works of Hertz. Later it was heavily explored by Schrödinger in the quantum domain, see, e.g., [8,11] for detailed analysis. According to Hertz one cannot expect to construct a complete theoretical model based explicitly on observable quantities. The complete theoretical model can contain quantities which are unapproachable for external measurement inspection. For example, Hertz by trying to create a mechanical model for Maxwell's electromagnetism invented hidden masses. The main distinguishing property of a theoretical model (in contrast to an observational model) is the continuity of description, i.e., the absence of gaps in description. From this viewpoint, the quantum mechanical description is not continuous: there is a gap between premeasurement dynamics and the measurement outcome. QM cannot say anything what happens in the process of measurement, this is the well known measurement problem of QM [32], cf. [52,53]. Continuity of description is closely related to causality. However, here we cannot go in more detail, see [8,11].The important question is about interrelation between two levels of description, ontic-epistemic (or theoretical-observational). In the introduction we have already cited Schrödinger who emphasized the possible complexity of this interrelation. In particular, in general there is no reason to expect a straightforward coupling of the form, cf. [9,10]:

A comparison of macroinvertebrate and habitat methods of data collection in the Little Colorado River Watershed, Arizona 2007

USGS Publications Warehouse

Spindler, Patrice; Paretti, Nick V.

2007-01-01

The Arizona Department of Environmental Quality (ADEQ) and the U.S. Environmental Protection Agency (USEPA) Ecological Monitoring and Assessment Program (EMAP), use different field methods for collecting macroinvertebrate samples and habitat data for bioassessment purposes. Arizona’s Biocriteria index was developed using a riffle habitat sampling methodology, whereas the EMAP method employs a multi-habitat sampling protocol. There was a need to demonstrate comparability of these different bioassessment methodologies to allow use of the EMAP multi-habitat protocol for both statewide probabilistic assessments for integration of the EMAP data into the national (305b) assessment and for targeted in-state bioassessments for 303d determinations of standards violations and impaired aquatic life conditions. The purpose of this study was to evaluate whether the two methods yield similar bioassessment results, such that the data could be used interchangeably in water quality assessments. In this Regional EMAP grant funded project, a probabilistic survey of 30 sites in the Little Colorado River basin was conducted in the spring of 2007. Macroinvertebrate and habitat data were collected using both ADEQ and EMAP sampling methods, from adjacent reaches within these stream channels.

All analyses indicated that the two macroinvertebrate sampling methods were significantly correlated. ADEQ and EMAP samples were classified into the same scoring categories (meeting, inconclusive, violating the biocriteria standard) 82% of the time. When the ADEQ-IBI was applied to both the ADEQ and EMAP taxa lists, the resulting IBI scores were significantly correlated (r=0.91), even though only 4 of the 7 metrics in the IBI were significantly correlated. The IBI scores from both methods were significantly correlated to the percent of riffle habitat, even though the average percent riffle habitat was only 30% of the stream reach. Multivariate analyses found that the percent riffle was an important attribute for both datasets in classifying IBI scores into assessment categories.

Habitat measurements generated from EMAP and ADEQ methods were also significantly correlated; 13 of 16 habitat measures were significantly correlated (p<0.01). The visual-based percentage estimates of percent riffle and pool habitats, vegetative cover and percent canopy cover, and substrate measurements of percent fine substrate and embeddedness were all remarkably similar, given the different field methods used. A multivariate analysis identified substrate and flow conditions, as well as canopy cover as important combinations of habitat attributes affecting both IBI scores. These results indicate that similar habitat measures can be obtained using two different field sampling protocols. In addition, similar combinations of these habitat parameters were important to macroinvertebrate community condition in multivariate analyses of both ADEQ and EMAP datasets.

These results indicate the two sampling methods for macroinvertebrates and habitat data were very similar in terms of bioassessment results and stressors. While the bioassessment category was not identical for all sites, overall the assessments were significantly correlated, providing similar bioassessment results for the cold water streams used in this study. The findings of this study indicate that ADEQ can utilize either a riffle-based sampling methodology or a multi-habitat sampling approach in cold water streams as both yield similar results relative to the macroinvertebrate assemblage. These results will allow for use of either macroinvertebrate dataset to determine water quality standards compliance with the ADEQ Indexes of Biological Integrity, for which threshold values were just recently placed into the Arizona Surface Water Quality Standards. While this survey did not include warm water desert streams of Arizona, we would predict that EMAP and ADEQ sampling methodologies would provide similar bioassessment results and would not be significantly different, as we have found that the percent riffle habitat in cold and warm water perennial, wadeable streams is not significantly different. However, a comparison study of sampling methodologies in warm water streams should be conducted to confirm the predicted similarity of bioassessment results. ADEQ will continue to implement a monitoring strategy that includes probabilistic monitoring for a statewide ecological assessment of stream conditions. Conclusions from this study will guide decisions regarding the most appropriate sampling methods for future probabilistic monitoring sample plans.

Harvesting model uncertainty for the simulation of interannual variability

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2009-08-01

An innovative modeling strategy is introduced to account for uncertainty in the convective parameterization (CP) scheme of a coupled ocean-atmosphere model. The methodology involves calling the CP scheme several times at every given time step of the model integration to pick the most probable convective state. Each call of the CP scheme is unique in that one of its critical parameter values (which is unobserved but required by the scheme) is chosen randomly over a given range. This methodology is tested with the relaxed Arakawa-Schubert CP scheme in the Center for Ocean-Land-Atmosphere Studies (COLA) coupled general circulation model (CGCM). Relative to the control COLA CGCM, this methodology shows improvement in the El Niño-Southern Oscillation simulation and the Indian summer monsoon precipitation variability.

Behavioral networks as a model for intelligent agents

NASA Technical Reports Server (NTRS)

Sliwa, Nancy E.

1990-01-01

On-going work at NASA Langley Research Center in the development and demonstration of a paradigm called behavioral networks as an architecture for intelligent agents is described. This work focuses on the need to identify a methodology for smoothly integrating the characteristics of low-level robotic behavior, including actuation and sensing, with intelligent activities such as planning, scheduling, and learning. This work assumes that all these needs can be met within a single methodology, and attempts to formalize this methodology in a connectionist architecture called behavioral networks. Behavioral networks are networks of task processes arranged in a task decomposition hierarchy. These processes are connected by both command/feedback data flow, and by the forward and reverse propagation of weights which measure the dynamic utility of actions and beliefs.

Predicting the Reliability of Brittle Material Structures Subjected to Transient Proof Test and Service Loading

NASA Astrophysics Data System (ADS)

Nemeth, Noel N.; Jadaan, Osama M.; Palfi, Tamas; Baker, Eric H.

Brittle materials today are being used, or considered, for a wide variety of high tech applications that operate in harsh environments, including static and rotating turbine parts, thermal protection systems, dental prosthetics, fuel cells, oxygen transport membranes, radomes, and MEMS. Designing brittle material components to sustain repeated load without fracturing while using the minimum amount of material requires the use of a probabilistic design methodology. The NASA CARES/Life 1 (Ceramic Analysis and Reliability Evaluation of Structure/Life) code provides a general-purpose analysis tool that predicts the probability of failure of a ceramic component as a function of its time in service. This capability includes predicting the time-dependent failure probability of ceramic components against catastrophic rupture when subjected to transient thermomechanical loads (including cyclic loads). The developed methodology allows for changes in material response that can occur with temperature or time (i.e. changing fatigue and Weibull parameters with temperature or time). For this article an overview of the transient reliability methodology and how this methodology is extended to account for proof testing is described. The CARES/Life code has been modified to have the ability to interface with commercially available finite element analysis (FEA) codes executed for transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

Sustainable Odds: Towards Quantitative Decision Support when Relevant Probabilities are not Available

NASA Astrophysics Data System (ADS)

Smith, L. A.

2012-04-01

There is, at present, no attractive foundation for quantitative probabilistic decision support in the face of model inadequacy, or given ambiguity (deep uncertainty) regarding the relative likelihood of various outcomes, known or unknown. True model error arguably precludes the extraction of objective probabilities from an ensemble of model runs drawn from an available (inadequate) model class, while the acknowledgement of incomplete understanding precludes the justified use of (if not the very formation of) an individual's subjective probabilities. An alternative approach based on Sustainable Odds is proposed and investigated. Sustainable Odds differ from "fair odds" (and are easily distinguished any claim which implying well defined probabilities) as the probabilities implied by sustainable odds summed over all outcomes is expected to exceed one. Traditionally, a person's fair odds are found by identifying the probability level at which one would happily accept either side of a bet, thus the probabilities implied by fair odds always sum to one. Knowing that one has incomplete information and perhaps even erroneous beliefs, there is no compelling reason a rational agent should accept the constraint implied by "fair odds" in any bet. Rather, a rational agent might insist on longer odds both on the event and against the event in order to account for acknowledged ignorance. Let probabilistic odds imply any set of odds for which the implied probabilities sum to one; once model error is acknowledged can one rationally demand non-probabilistic odds? The danger of using fair odds (or probabilities) in decision making is illustrated by considering the risk of ruin a cooperative insurance scheme using probabilistic odds is exposed to. Cases where knowing merely that the insurer's model is imperfect, and nothing else, is sufficient to place bets which drive the insurer to an unexpectedly early ruin are presented. Methodologies which allow the insurer to avoid this early ruin are explored; those which prevent early ruin are said to provide "sustainable odds", and it is suggested that these must be non-probabilistic. The aim here is not for the insurance cooperative to make a profit in the long run (or to form a book in any one round) but rather to increase the chance that the cooperative will not go bust, merely breaking even in the long run and thereby continuing to provide a service. In the perfect model scenario, with complete knowledge of all uncertainties and unlimited computational resources, fair odds may prove to be sustainable. The implications these results hold in the case of games against nature, which is perhaps a more relevant context for decision makers concerned with geophysical systems, are discussed. The claim that acknowledged model error makes fair (probabilistic) odds an irrational aim is considered, as are the challenges of working within the framework of sustainable (but non-probabilistic) odds.

Deliverology

ERIC Educational Resources Information Center

Nordstrum, Lee E.; LeMahieu, Paul G.; Dodd, Karen

2017-01-01

Purpose: This paper is one of seven in this volume elaborating different approaches to quality improvement in education. This paper aims to delineate a methodology called Deliverology. Design/methodology/approach: The paper presents the origins, theoretical foundations, core principles and a case study showing an application of Deliverology in the…

Markov Chain Model with Catastrophe to Determine Mean Time to Default of Credit Risky Assets

NASA Astrophysics Data System (ADS)

Dharmaraja, Selvamuthu; Pasricha, Puneet; Tardelli, Paola

2017-11-01

This article deals with the problem of probabilistic prediction of the time distance to default for a firm. To model the credit risk, the dynamics of an asset is described as a function of a homogeneous discrete time Markov chain subject to a catastrophe, the default. The behaviour of the Markov chain is investigated and the mean time to the default is expressed in a closed form. The methodology to estimate the parameters is given. Numerical results are provided to illustrate the applicability of the proposed model on real data and their analysis is discussed.

Seismic Evaluation of A Historical Structure In Kastamonu - Turkey

NASA Astrophysics Data System (ADS)

Pınar, USTA; Işıl ÇARHOĞLU, Asuman; EVCİ, Ahmet

2018-01-01

The Kastomonu province is a seismically active zone. the city has many historical buildings made of stone-masonry. In case of any probable future earthquakes, existing buildings may suffer substantial or heavy damages. In the present study, one of the historical traditional house located in Kastamonu were structurally investigated through probabilistic seismic risk assessment methodology. In the study, the building was modeled by using the Finite Element Modeling (FEM) software, SAP2000. Time history analyses were carried out using 10 different ground motion data on the FEM models. Displacements were interpreted, and the results were displayed graphically and discussed.

Fundamentals of clinical methodology: 2. Etiology.

PubMed

Sadegh-Zadeh, K

1998-03-01

The concept of etiology is analyzed and the possibilities and limitations of deterministic, probabilistic, and fuzzy etiology are explored. Different kinds of formal structures for the relation of causation are introduced which enable us to explicate the notion of cause on qualitative, comparative, and quantitative levels. The conceptual framework developed is an approach to a theory of causality that may be useful in etiologic research, in building nosological systems, and in differential diagnosis, therapeutic decision-making, and controlled clinical trials. The bearings of the theory are exemplified by examining the current Chlamydia pneumoniae hypothesis on the incidence of myocardial infarction.

Elemental Abundance Distributions in Basalt Clays and Meteorites: Is It a Biosignature?

NASA Technical Reports Server (NTRS)

Fisk, M. R.; Storrie-Lombardi, M. C.; Joseph, J.

2005-01-01

Volcanic glass altered by microorganisms exhibits distinctive textures differing significantly from abiotic alteration [1-4]. We have previously presented morphological evidence of bioweathering in sub-oceanic basalt glass [5] and olivine [6], and noted similar alterations in Nakhla [7]. We have also introduced an autonomous Bayesian probabilistic classification methodology to identify biotic and abiotic alteration in sub-oceanic basalts using elemental abundance data [8]. We now present data from multiple sub-oceanic sites addressing the more general question of utilizing elemental abundance distribution in clays as a valid biosignature for the exploration of putative clay alteration products in meteorites.

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere

NASA Astrophysics Data System (ADS)

Armand, P.; Brocheton, F.; Poulet, D.; Vendel, F.; Dubourg, V.; Yalamas, T.

2014-10-01

This paper is an original contribution to uncertainty quantification in atmospheric transport & dispersion (AT&D) at the local scale (1-10 km). It is proposed to account for the imprecise knowledge of the meteorological and release conditions in the case of an accidental hazardous atmospheric emission. The aim is to produce probabilistic risk maps instead of a deterministic toxic load map in order to help the stakeholders making their decisions. Due to the urge attached to such situations, the proposed methodology is able to produce such maps in a limited amount of time. It resorts to a Lagrangian particle dispersion model (LPDM) using wind fields interpolated from a pre-established database that collects the results from a computational fluid dynamics (CFD) model. This enables a decoupling of the CFD simulations from the dispersion analysis, thus a considerable saving of computational time. In order to make the Monte-Carlo-sampling-based estimation of the probability field even faster, it is also proposed to recourse to the use of a vector Gaussian process surrogate model together with high performance computing (HPC) resources. The Gaussian process (GP) surrogate modelling technique is coupled with a probabilistic principal component analysis (PCA) for reducing the number of GP predictors to fit, store and predict. The design of experiments (DOE) from which the surrogate model is built, is run over a cluster of PCs for making the total production time as short as possible. The use of GP predictors is validated by comparing the results produced by this technique with those obtained by crude Monte Carlo sampling.

A preliminary probabilistic analysis of tsunami sources of seismic and non-seismic origin applied to the city of Naples, Italy

NASA Astrophysics Data System (ADS)

Tonini, R.; Anita, G.

2011-12-01

In both worldwide and regional historical catalogues, most of the tsunamis are caused by earthquakes and a minor percentage is represented by all the other non-seismic sources. On the other hand, tsunami hazard and risk studies are often applied to very specific areas, where this global trend can be different or even inverted, depending on the kind of potential tsunamigenic sources which characterize the case study. So far, few probabilistic approaches consider the contribution of landslides and/or phenomena derived by volcanic activity, i.e. pyroclastic flows and flank collapses, as predominant in the PTHA, also because of the difficulties to estimate the correspondent recurrence time. These considerations are valid, for example, for the city of Naples, Italy, which is surrounded by a complex active volcanic system (Vesuvio, Campi Flegrei, Ischia) that presents a significant number of potential tsunami sources of non-seismic origin compared to the seismic ones. In this work we present the preliminary results of a probabilistic multi-source tsunami hazard assessment applied to Naples. The method to estimate the uncertainties will be based on Bayesian inference. This is the first step towards a more comprehensive task which will provide a tsunami risk quantification for this town in the frame of the Italian national project ByMuR (http://bymur.bo.ingv.it). This three years long ongoing project has the final objective of developing a Bayesian multi-risk methodology to quantify the risk related to different natural hazards (volcanoes, earthquakes and tsunamis) applied to the city of Naples.

A comparison of metrics for assessing state-of-the-art climate models and implications for probabilistic projections of climate change

NASA Astrophysics Data System (ADS)

Ring, Christoph; Pollinger, Felix; Kaspar-Ott, Irena; Hertig, Elke; Jacobeit, Jucundus; Paeth, Heiko

2018-03-01

A major task of climate science are reliable projections of climate change for the future. To enable more solid statements and to decrease the range of uncertainty, global general circulation models and regional climate models are evaluated based on a 2 × 2 contingency table approach to generate model weights. These weights are compared among different methodologies and their impact on probabilistic projections of temperature and precipitation changes is investigated. Simulated seasonal precipitation and temperature for both 50-year trends and climatological means are assessed at two spatial scales: in seven study regions around the globe and in eight sub-regions of the Mediterranean area. Overall, 24 models of phase 3 and 38 models of phase 5 of the Coupled Model Intercomparison Project altogether 159 transient simulations of precipitation and 119 of temperature from four emissions scenarios are evaluated against the ERA-20C reanalysis over the 20th century. The results show high conformity with previous model evaluation studies. The metrics reveal that mean of precipitation and both temperature mean and trend agree well with the reference dataset and indicate improvement for the more recent ensemble mean, especially for temperature. The method is highly transferrable to a variety of further applications in climate science. Overall, there are regional differences of simulation quality, however, these are less pronounced than those between the results for 50-year mean and trend. The trend results are suitable for assigning weighting factors to climate models. Yet, the implications for probabilistic climate projections is strictly dependent on the region and season.

Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition.

PubMed

Alemani, Davide; Pappalardo, Francesco; Pennisi, Marzio; Motta, Santo; Brusic, Vladimir

2012-02-28

In the last decades the Lattice Boltzmann method (LB) has been successfully used to simulate a variety of processes. The LB model describes the microscopic processes occurring at the cellular level and the macroscopic processes occurring at the continuum level with a unique function, the probability distribution function. Recently, it has been tried to couple deterministic approaches with probabilistic cellular automata (probabilistic CA) methods with the aim to model temporal evolution of tumor growths and three dimensional spatial evolution, obtaining hybrid methodologies. Despite the good results attained by CA-PDE methods, there is one important issue which has not been completely solved: the intrinsic stochastic nature of the interactions at the interface between cellular (microscopic) and continuum (macroscopic) level. CA methods are able to cope with the stochastic phenomena because of their probabilistic nature, while PDE methods are fully deterministic. Even if the coupling is mathematically correct, there could be important statistical effects that could be missed by the PDE approach. For such a reason, to be able to develop and manage a model that takes into account all these three level of complexity (cellular, molecular and continuum), we believe that PDE should be replaced with a statistic and stochastic model based on the numerical discretization of the Boltzmann equation: The Lattice Boltzmann (LB) method. In this work we introduce a new hybrid method to simulate tumor growth and immune system, by applying Cellular Automata Lattice Boltzmann (CA-LB) approach. Copyright © 2011 Elsevier B.V. All rights reserved.

Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

NASA Astrophysics Data System (ADS)

D'Isanto, A.; Polsterer, K. L.

2018-01-01

Context. The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. Aims: We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. Methods: A modified version of a deep convolutional network was combined with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) were applied as performance criteria. We have adopted a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS (DR9). Results: We show that the proposed method is able to predict redshift PDFs independently from the type of source, for example galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature. Conclusions: The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

Will current probabilistic climate change information, as such, improve adaptation?

NASA Astrophysics Data System (ADS)

Lopez, A.; Smith, L. A.

2012-04-01

Probabilistic climate scenarios are currently being provided to end users, to employ as probabilities in adaptation decision making, with the explicit suggestion that they quantify the impacts of climate change relevant to a variety of sectors. These "probabilities" are, however, rather sensitive to the assumptions in, and the structure of the modelling approaches used to generate them. It is often argued that stakeholders require probabilistic climate change information to adequately evaluate and plan adaptation pathways. On the other hand, some circumstantial evidence suggests that on the ground decision making rarely uses well defined probability distributions of climate change as inputs. Nevertheless it is within this context of probability distributions of climate change that we discuss possible drawbacks of supplying information that, while presented as robust and decision relevant, , is in fact unlikely to be so due to known flaws both in the underlying models and in the methodology used to "account for" those known flaws. How might one use a probability forecast that is expected to change in the future, not due to a refinement in our information but due to fundamental flaws in its construction? What then are the alternatives? While the answer will depend on the context of the problem at hand, a good approach will be strongly informed by the timescale of the given planning decision, and the consideration of all the non-climatic factors that have to be taken into account in the corresponding risk assessment. Using a water resources system as an example, we illustrate an alternative approach to deal with these challenges and make robust adaptation decisions today.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

System Synthesis in Preliminary Aircraft Design using Statistical Methods

NASA Technical Reports Server (NTRS)

DeLaurentis, Daniel; Mavris, Dimitri N.; Schrage, Daniel P.

1996-01-01

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).