Browsing by Author "Koohestani, Mohsen"
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Item Open Access Application of probabilistic models for multitone electromagnetic immunity analysis(IEEE, 2022-10-12) Devaraj, Lokesh; Khan, Qazi Mashaal; Ruddle, Alastair R.; Duffy, A. P.; Perdriau, Richard; Koohestani, MohsenThe operational environment of modern electronic systems may include multiple frequency electromagnetic dis- turbances. However, immunity measurements usually employ single frequency continuous waveforms (i.e. single-tones). The performance of two oscillator circuits with different topologies (one simulated and one measured) were used as case studies to in- vestigate immunity to simultaneous single-tone disturbances (i.e. multitones) using probabilistic Bayesian network models. For the multitone analysis, the noisy-OR model was first used to identify the type of causal interactions between simultaneously occurring single-tones. Probabilistic theories derived from the recursive noisy-OR model, which inherits the independence assumptions of the noisy-OR and any known causal dependence between simultaneously occurring single-tones, were then used to predict the probability of higher order multitone failures. For the two case studies, the probability of three-tone failures was estimated using the single-tone and two-tone failure probability values. An improved adaptive recursive noisy-OR model was also proposed to overcome the practical difficulties of obtaining multitone failure probabilities, from either simulations or measurements.Item Metadata only Improvements proposed to noisy-OR derivatives for multi-causal analysis: A case study of simultaneous electromagnetic disturbances(Elsevier, 2023-11-09) Devaraj, Lokesh; Khan, Qazi Mashaal; Ruddle, Alastair R.; Duffy, A. P.; Perdriau, Richard; Koohestani, MohsenIn multi-causal analysis, the independence of causal influence (ICI) assumed by the noisy-OR (NOR) model can be used to predict the probability of the effect when several causes are present simultaneously, and to identify (when it fails) inter-causal dependence (ICD) between them. The latter is possible only if the probability of observing the multi-causal effect is available for comparison with a corresponding NOR estimate. Using electromagnetic interference in an integrated circuit as a case study, the data corresponding to the probabilities of observing failures (effect) due to the injection of individual (single cause) and simultaneous electromagnetic disturbances having different frequencies (multiple causes) were collected. This data is initially used to evaluate the NOR model and its existing derivatives, which have been proposed to reduce the error in predictions for higher-order multi-causal interactions that make use of the available information on lower-order interactions. Then, to address the identified limitations of the NOR and its existing derivatives, a new deterministic model called Super-NOR is proposed, which is based on correction factors estimated from the available ICD information.