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Faisal Khan

Researcher at Memorial University of Newfoundland

Publications -  785
Citations -  28657

Faisal Khan is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topics: Risk assessment & Risk analysis. The author has an hindex of 70, co-authored 705 publications receiving 21281 citations. Previous affiliations of Faisal Khan include Royal Hobart Hospital & Australian Maritime College.

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A general method to combine environmental and life-safety consequences of Arctic ship accidents

TL;DR: In this paper , the authors present a general method to combine multiple consequences posed by an Arctic ship accident, including ecological and socio-economic consequences of a potential oil spill, and life-safety consequences of potential ship evacuation.
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Assessment of minerals and iron-bearing phases present in hydrometallurgical residues from a nickel sulfide concentrate and availability of residue associated metals

TL;DR: In this article, the geochemical and mineralogical character of hydrometallurgical residues is important for the management of this type of industrial waste, and the results of both scanning electron microscope (SEM) and X-Ray Diffraction (XRD) analysis indicate that the minerals produced in the process are principally gypsum and the iron oxides, hematite and magnetite, iron hydroxides and residual sulfur and sulfides in the form of FeS, chalcopyrite, pyrrhotite and pentlandite.
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Probability of Detection and False Detection for Subsea Leak Detection Systems: Model and Analysis

TL;DR: A probabilistic formulation of the probability of detection and probability of false detection for a fiber optic-based LDS for subsea pipeline applications is presented.
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Safety and integrity management of operations in harsh environments

TL;DR: The 4th workshop and Symposium on Safety and Integrity Management of Operations in Harsh Environments (C-RISE 2019) as discussed by the authors focused on developing fuzzy cognitive maps and Bayesian networks model to learn and assess the likelihood of marine accidents.
Proceedings Article

Radial basis function neural network based rudder roll stabilization for ship sailing in waves

TL;DR: A rudder-roll stabilization system utilizing Radial Basis Function neural network (RBFNN) for course keeping and roll damping based on the RBFNNs is presented and the performance and robustness of the proposed control system is investigated by taking consideration of the effects of external disturbance.