92 research outputs found

    A comparison between probabilistic and Dempster-Shafer Theory approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories

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    Model uncertainty is a primary source of uncertainty in the assessment of the performance of repositories for the disposal of nuclear wastes, due to the complexity of the system and the large spatial and temporal scales involved. This work considers multiple assumptions on the system behavior and corresponding alternative plausible modeling hypotheses. To characterize the uncertainty in the correctness of the different hypotheses, the opinions of different experts are treated probabilistically or, in alternative, by the belief and plausibility functions of the Dempster-Shafer theory. A comparison is made with reference to a flow model for the evaluation of the hydraulic head distributions present at a radioactive waste repository site. Three experts are assumed available for the evaluation of the uncertainties associated with the hydrogeological properties of the repository and the groundwater flow mechanisms

    Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools.

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    International audienceThe health assessment of composite structures from acoustic emission data is generally tackled by the use of clustering techniques. In this paper, the K-means clustering and the newly proposed Partially-Hidden Markov Model (PHMM) are exploited to analyse the data collected during mechanical tests on composite structures. The health assessment considered in this paper is made difficult by working in unconstrained environments. The presence of the noise is illustrated in several examples and is shown to distort strongly the results of clustering. A solution is proposed to filter out the noisy partition provided by the clustering methods. After filtering, the PHMM provides results which appeared closer to the expectations than the K-means. The PHMM offers the possibility to use uncertain and imprecise labels on the possible states, and thus covers supervised and unsupervised learning as special cases which makes it suitable for real applications

    A new hybrid approach to human error probability quantification-applications in maritime operations

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    Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors

    A methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

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    The aim of this thesis is to develop a methodology for the selection of a paradigm of reasoning under uncertainty for the expert system developer. This is important since practical information on how to select a paradigm of reasoning under uncertainty is not generally available. The thesis explores the role of uncertainty in an expert system and considers the process of reasoning under uncertainty. The possible sources of uncertainty are investigated and prove to be crucial to some aspects of the methodology. A variety of Uncertainty Management Techniques (UMTs) are considered, including numeric, symbolic and hybrid methods. Considerably more information is found in the literature on numeric methods, than the latter two. Methods that have been proposed for comparing UMTs are studied and comparisons reported in the literature are summarised. Again this concentrates on numeric methods, since there is more literature available. The requirements of a methodology for the selection of a UMT are considered. A manual approach to the selection process is developed. The possibility of extending the boundaries of knowledge stored in the expert system by including meta-data to describe the handling of uncertainty in an expert system is then considered. This is followed by suggestions taken from the literature for automating the process of selection. Finally consideration is given to whether the objectives of the research have been met and recommendations are made for the next stage in researching a methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

    Multi-source heterogeneous intelligence fusion

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    Fuzzy evidence theory and Bayesian networks for process systems risk analysis

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    YesQuantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.The research of Sohag Kabir was partly funded by the DEIS project (Grant Agreement 732242)

    Applying Bayesian networks to model uncertainty in project scheduling

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    PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge. An important component of Project Risk Management (PRM) is risk analysis, which attempts to measure risk and its impact on different project parameters such as time, cost and quality. By highlighting the trade-off between project parameters, the thesis concentrates on project time management under uncertainty. The earliest research incorporating uncertainty/risk in projects started in the late 1950’s. Since then, several techniques and tools have been introduced, and many of them are widely used and applied throughout different industries. However, they often fail to capture uncertainty properly and produce inaccurate, inconsistent and unreliable results. This is evident from consistent problems of cost and schedule overrun. The thesis will argue that the simulation-based techniques, as the dominant and state-of-the-art approach for modelling uncertainty in projects, suffers from serious shortcomings. More advanced techniques are required. Bayesian Networks (BNs), are a powerful technique for decision support under uncertainty that have attracted a lot of attention in different fields. However, applying BNs in project risk management is novel. The thesis aims to show that BN modelling can improve project risk assessment. A literature review explores the important limitations of the current practice of project scheduling under uncertainty. A new model is proposed which applies BNs for performing the famous Critical Path Method (CPM) calculation. The model subsumes the benefits of CPM while adding BN capability to properly capture different aspects of uncertainty in project scheduling

    Reliability assessment of rock slopes by evidence theory

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    El objetivo de este proyecto de investigación es desarrollar una metodología para efectuar análisis de confiabilidad de la estabilidad de taludes rocosos, teniendo en cuenta la incertidumbre cuando la información sobre los parámetros geomecánicos de entrada es limitada. En mecánica de rocas, los métodos determinísticos y probabilísticos son ampliamente utilizados en el proceso de toma decisiones. No obstante, el primero no considera la incertidumbre y el segundo tiene limitaciones para representar la incertidumbre epistémica y tiene que asumir la distribución de probabilidad de las variables de entrada. Por lo tanto, se recurre a la Teoría de la Evidencia como una herramienta para describir la incertidumbre aleatoria y epistémica de los parámetros geomecánicos y propagarla a través de modelos de equilibrio límite, en los que la geometría es controlada por la orientación de las discontinuidades. Para llevar a cabo una mejor descripción de la variabilidad en el macizo, el proyecto utilizó fotogrametría de corto alcance, lo que permitió obtener series de datos robustas y confiables de la geometría de las discontinuidades, que fue modelada como una variable aleatoria con distribución Kent. Además, se desarrolló un procedimiento para actualizar los análisis de confiabilidad teniendo en cuenta la distribución de probabilidad de la orientación de las discontinuidades. La aplicación de la metodología en un talud rocoso de una mina de arenisca mostró su aplicabilidad a proyectos reales. Consecuentemente, la principal contribución de este trabajo es la generación de un marco de referencia para efectuar la evolución de confiabilidad de taludes rocoso basado en la teoría de la evidencia que permite combinar las series robustas de la orientación de los planos de discontinuidad, con información limitada de sus parámetros de resistencia, que puede ser actualizada a medida que se genera nueva información.This research project aims to develop a methodology to perform rock slope stability analysis considering the aleatory and epistemic uncertainty when the information on geomechanical parameters is limited. In rock mechanics, deterministic and probabilistic approaches are widely used in the decision-making process. However, the earlier does not consider the uncertainty, and the latter has limitations to account for the epistemic uncertainty and requires assumptions on probability distributions when robust data sets are not available. Therefore, we resorted to the Evidence Theory as a tool to describe the epistemic and aleatory uncertainty of input geomechanical variables and propagate them trough limit equilibrium models, in which the geometry is controlled by the joints orientation. To perform a better description of the variability of the rock mas properties, the project utilized a short-range photogrammetry system, which allowed us to have robust and reliable data sets on joints geometry to be modeled as Kent distributed variables. Besides, we suggested a procedure to update the reliability analysis acknowledging that orientations follow a Kent distribution. The application of the methodology to a rock slope in a sandstone mine showed its suitability to be applied in actual engineering projects. Consequently, the main contribution of this project is an rock slope evidence theory reliability-based framework for combining robust data sets on joints orientation, with limited information on geomechanical parameters, that can be updated as new information is available.ColcienciasAnalisis Cuantitativo de Riesgo en Taludes MinerosLínea de Investigación: Geotecnia y Riesgos Geo ambientalesDoctorad

    Biomedical applications of belief networks

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    Biomedicine is an area in which computers have long been expected to play a significant role. Although many of the early claims have proved unrealistic, computers are gradually becoming accepted in the biomedical, clinical and research environment. Within these application areas, expert systems appear to have met with the most resistance, especially when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is necessary to provide the information needed to make rational judgements concerning the inferences the system has made. This entails an explanation of what inferences were made, how the inferences were made and how the results of the inference are to be interpreted. Furthermore there must be a consistent approach to the combining of information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses. Until recently ad hoc formalisms were seen as the only tractable approach to reasoning under uncertainty. A review of some of these formalisms suggests that they are less than ideal for the purposes of decision making. Belief networks provide a tractable way of utilising probability theory as an inference formalism by combining the theoretical consistency of probability for inference and decision making, with the ability to use the knowledge of domain experts.nowledge of domain experts. The potential of belief networks in biomedical applications has already been recog¬ nised and there has been substantial research into the use of belief networks for medical diagnosis and methods for handling large, interconnected networks. In this thesis the use of belief networks is extended to include detailed image model matching to show how, in principle, feature measurement can be undertaken in a fully probabilistic way. The belief networks employed are usually cyclic and have strong influences between adjacent nodes, so new techniques for probabilistic updating based on a model of the matching process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used to apply the belief network formalism to two application domains. The first application is model-based matching in fetal ultrasound images. The imaging modality and biological variation in the subject make model matching a highly uncertain process. A dynamic, deformable model, similar to active contour models, is used. A belief network combines constraints derived from local evidence in the image, with global constraints derived from trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of evidence occurring during the classification of objects on a cervical smear slide as part of an automated pre-screening system. A belief network provides both an explicit domain model and a mechanism for the incremental aggregation of evidence, two attributes important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features required of a decision support system with desirable qualitative features that will lead to improved acceptability of expert systems in the biomedical domain
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