The inverted beta loss function: properties and applications

Leung, Bartholomew P.K.; Spiring, Fred

doi:10.1080/07408170208928938

Cited by 25 publications

(12 citation statements)

References 3 publications

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“…On the other hand, trade-off on parameter settings is also a crucial aspect in the manufacturing process. To achieve this goal, Leung and Spiring (2002) have introduced the concept of the inverted beta loss function (IBLF), which is a further deduction of the Taguchi loss function (Taguchi, 1986) in the industrial domain, helping to balance the possible loss resulting from the use of different process parameter combinations along the entire process. However, another substantial issue, prediction of the parameter settings, has not been strongly emphasized by scholars.…”

Section: Literature Reviewsmentioning

confidence: 99%

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A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

Lau

Tang

et al. 2009

Expert Systems with Applications

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Section: Literature Reviewsmentioning

confidence: 99%

“…Thus the Taguchi loss function is further developed by the inverted beta loss function (Leung & Spiring, 2002) to better describe the following circumstances. Therefore a family of symmetric, asymmetric and half-bell loss functions could be plotted based on an inverted beta pdf.…”

Section: Performance Trade-offmentioning

confidence: 99%

A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

Lau

Tang

et al. 2009

Expert Systems with Applications

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“…For various values of γ, ranging from

Δ / 0.1

Δ / 5

, the MINLF approximates the QLF to INLF through SINLF. Other forms of univariate and inverted probability LFs in use include the inverted beta loss function (IBLF), uniform distribution, Tukey's symmetric Lambda distribution, Laplace distribution, and the inverted gamma loss function (IGLF). The essence of these inverted probability LFs is to enable varieties and better representation of actual process losses …”

Section: Consequence Modeling Using Loss Functionmentioning

confidence: 99%

Dynamic Blowout Risk Analysis Using Loss Functions

Abimbola

Khan

2017

Risk Analysis

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Most risk analysis approaches are static; failing to capture evolving conditions. Blowout, the most feared accident during a drilling operation, is a complex and dynamic event. The traditional risk analysis methods are useful in the early design stage of drilling operation while falling short during evolving operational decision making. A new dynamic risk analysis approach is presented to capture evolving situations through dynamic probability and consequence models. The dynamic consequence models, the focus of this study, are developed in terms of loss functions. These models are subsequently integrated with the probability to estimate operational risk, providing a real-time risk analysis. The real-time evolving situation is considered dependent on the changing bottom-hole pressure as drilling progresses. The application of the methodology and models are demonstrated with a case study of an offshore drilling operation evolving to a blowout.

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“…A key variable for each event will be identified and a loss function will be defined to estimate the loss associated with an event occurrence under the current conditions of the key variable. Use of the inverted beta loss function, IBLF, [15] is considered as a suitable option to model consequences for alarm syetem design due to its flexibility to define loss model parameters [16]. The IBLF can be defined as…”

Section: Alarm Annunciation Philosophymentioning

confidence: 99%

Conceptual framework for an event-based plant alarm system

Ahmed

Dalpatadu

Khan

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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An event-based plant alarm system is proposed as an alternative to the current variable-based alarm system used in the process industry. While a traditional variablebased process alarm indicates an unsafe deviation of a process variable, an event alarm will be a direct indicator of an undesired event. A conceptual framework for the design of such an alarm processor is outlined in this article. A methodology for allocation of alarms based on the information content of variables is highlighted. The annunciation philosophy for event alarms is detailed, and alarm prioritization method and operator guidance are discussed. The method uses Bayesian network and Bayesian inference to estimate probability of an event and to identify its root causes.

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The inverted beta loss function: properties and applications

Cited by 25 publications

References 3 publications

A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

Dynamic Blowout Risk Analysis Using Loss Functions

Conceptual framework for an event-based plant alarm system

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