Contents: Foreword. Preface. What Are Statistical Decisions? Non-Parametric Measures of Sensitivity. Gaussian Distributions of Signal and Noise With Equal Variances. Gaussian Distributions of Signal and Noise With Unequal Variances. Conducting a Rating Scale Experiment. Choice Theory Approximations to Signal Detection Theory. Threshold Theory. The Laws of Categorical and Comparative Judgement. Appendices: Answers to Problems. Logarithms. Integration of the Expression for the Logistic Curve. Computer Programmes for Signal Detection Analysis. Tables.

A Primer of Signal Detection Theory; Table of D' and β

The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.

A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid

1953 Like much of Flannery O’Connor’s short fiction, ‘‘The Life You Save May Be Your Own’’ is set in the American South and contains characters whose most notable feature seems to be their ordinariness. Through imagery, dialogue, and moments of revelation, O’Connor explores the themes of morality and religion, both frequent concerns in her work. The story evoked critical praise upon its publication in the Kenyon Review in the spring of 1953. Within the sparse, apparently simple plot of the story, O’Connor constructs a world torn between renewal and emptiness, natural beauty and crass materialism, compassion and cruelty. In the end, O’Connor’s protagonist must choose between these extremes and attempt to experience the grace of God’s love.

The life you save may be your own

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

This paper highlights the role of humans in the next generation of driver assistance and intelligent vehicles. Understanding, modeling, and predicting human agents are discussed in three domains where humans and highly automated or self-driving vehicles interact: 1) inside the vehicle cabin, 2) around the vehicle, and 3) inside surrounding vehicles. Efforts within each domain, integrative frameworks across domains, and scientific tools required for future developments are discussed to provide a human-centered perspective on research in intelligent vehicles.

Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles

The process industry has always been faced with the difficult task of determining the required integrity of safeguarding systems such as safety instrumented systems (SISs). The ANSI/ISA S84.01-1996 and IEC 61508 safety standards provide guidelines for the design, installation, operation, maintenance, and test of SIS. However, in the field, there is a considerable lack of understanding of how to apply these standards to both determine and achieve the required safety integrity level (SIL) for SIS. Moreover, in certain situations, the SIL evaluation is further complicated due to the uncertainty on reliability parameters of SIS components. This paper proposes a new approach to evaluate the ldquoconfidencerdquo of the SIL determination when there is an uncertainty about failure rates of SIS components. This approach is based on the use of failure rates and fuzzy probabilities to evaluate the SIS failure probability on demand and the SIL of the SIS. Furthermore, we provide guidance on reducing the SIL uncertainty based on fuzzy probabilistic importance measures.

A Fuzzy Probabilistic Approach for Determining Safety Integrity Level

Many practical methods and different approaches have been proposed to assess Multi-State Systems (MSS) reliability measures. The universal generating function (UGF) method, introduced in 1986, is known to be a very efficient way of evaluating the availability of different types of MSSs. In this paper, we propose an extension of the UGF method considering epistemic uncertainties. This extended method allows one to model ill-known probabilities and transition rates, or to model both aleatory and epistemic uncertainty in a single model. It is based on the use of belief functions which are general models of uncertainty. We also compare this extension with UGF methods based on interval arithmetic operations performed on probabilistic bounds.

An extension of Universal Generating Function in Multi-State Systems Considering Epistemic Uncertainties

We introduce extended component importance measures (Birnbaum importance, RAW, RRW, and Criticality importance) considering aleatory and epistemic uncertainties. The Dempster-Shafer theory, which is considered to be a less restricted extension of probability theory, is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the component state. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system state, and to the epistemic uncertainty of importance measures of components. Affine arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures, avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty, and experts' judgments). The influence of the epistemic uncertainty on the components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided. A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.

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Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

This article presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real- world MSSs, an insufficiency of data makes it difficult to estimate precise values for component state probabilities. The proposed approach applies the transferable belief model interpretation of the Dempster–Shafer theory to represent component state beliefs and to evaluate the MSS reliability indices. The example of an oil transmission system is used to demonstrate the proposed approach and it is compared with the universal generating function method. The value of the Dempster–Shafer theory lies in its ability to use several combination rules in order to evaluate reliability indices for MSSs that depend on the reliability of the experts’ opinions as well as their independence.

/pdf/reliability-assessment-for-multi-state-systems-under-1modg9wyb1.pdf

Reliability assessment for multi-state systems under uncertainties based on the Dempster–Shafer theory

Dealing with uncertainty adds a further level of complexity to problems of reliability analysis. The uncertainties which impact reliability studies usually involve incomplete or imprecise reliability data and complex failure dependencies. This paper proposes an original methodology based on the transferable belief model (TBM) to include failure dependencies between components in the evaluation of the reliability of the whole system, given both epistemic and aleatory uncertainties. First, based on expert opinion and experimental data, basic probability assignments (BPAs) are assigned to reliability data components. TBM operations are then used to obtain the reliability of the whole system, for series, parallel, series–parallel, parallel–series, and bridge configurations. Implicit, explicit, and discounting approaches are presented for taking account of failure dependencies. Finally, the proposed model is applied to take into account common cause failures (CCFs) in a case study.

/pdf/transferable-belief-model-for-reliability-analysis-of-34h3oqv8os.pdf

Mohamed Sallak

Papers

A Fuzzy Probabilistic Approach for Determining Safety Integrity Level

An extension of Universal Generating Function in Multi-State Systems Considering Epistemic Uncertainties

Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

Reliability assessment for multi-state systems under uncertainties based on the Dempster–Shafer theory

Transferable belief model for reliability analysis of systems with data uncertainties and failure dependencies