
A Probabilistic Model For Sensor Validation
The validation of data from sensors has become an important issue in the...
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Fault Detection and Identification using Bayesian Recurrent Neural Networks
In processing and manufacturing industries, there has been a large push ...
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Quantitative system risk assessment from incomplete data with belief networks and pairwise comparison elicitation
A method for conducting Bayesian elicitation and learning in risk assess...
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Selecting Fault Revealing Mutants
Mutant selection refers to the problem of choosing, among a large number...
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Automatic Channel Fault Detection and Diagnosis System for a Small Animal APDBased Digital PET Scanner
Fault detection and diagnosis is critical to many applications in order ...
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SoC Memory Management for Reducing Fault Problem from Reserved Memory Components
In this paper, the author proposes an optimal management for system on c...
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FADO: A Deterministic Detection/Learning Algorithm
This paper proposes and studies a detection technique for adversarial sc...
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Bayesian Assessment of a Connectionist Model for Fault Detection
A previous paper [2] showed how to generate a linear discriminant network (LDN) that computes likely faults for a noisy fault detection problem by using a modification of the perceptron learning algorithm called the pocket algorithm. Here we compare the performance of this connectionist model with performance of the optimal Bayesian decision rule for the example that was previously described. We find that for this particular problem the connectionist model performs about 97 procedure. We then define a more general class of noisy singlepattern boolean (NSB) fault detection problems where each fault corresponds to a single :pattern of boolean instrument readings and instruments are independently noisy. This is equivalent to specifying that instrument readings are probabilistic but conditionally independent given any particular fault. We prove: 1. The optimal Bayesian decision rule for every NSB fault detection problem is representable by an LDN containing no intermediate nodes. (This slightly extends a result first published by Minsky & Selfridge.) 2. Given an NSB fault detection problem, then with arbitrarily high probability after sufficient iterations the pocket algorithm will generate an LDN that computes an optimal Bayesian decision rule for that problem. In practice we find that a reasonable number of iterations of the pocket algorithm produces a network with good, but not optimal, performance.
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