UTSGUniversities' Transport Study Group (UK)
UTSGU-Tube Steam Generator (engineering)
UTSGUrban Transportation Systems Group (University of Pennsylvania)
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References in periodicals archive ?
All possible combinations of multiple component failures, each represented by a MVL vector of time, magnitude, and order of occurrence, lead to a total of N = 100509 accidental MVL scenarios to be treated for the quantification of the risk related to the UTSG operation.
For postprocessing the N = 100509 multivalued dynamic scenarios of the UTSG, we resort to a Semi-Supervised Self-Organizing Map (SSSOM) based on the Manhattan distance (shown in Figure 2(c)).
A SSSOM of M = 3025 neurons C = [[c.sub.1], [c.sub.2], ..., [c.sub.M]], each of which is assigned a weight vector [[bar.w].sub.m] = [[w.sub.1], [w.sub.2], ..., [w.sub.d]], is trained on the N = 100509 UTSG dynamic scenarios [bar.X] belonging to a d = 12-dimensional space, say [??] = [[[bar.X].sub.1], [[bar.X].sub.2], ..., [[bar.X].sub.N]], where the nth sample is [[bar.X].sub.n] = [[x.sub.1], [x.sub.2], ..., [x.sub.d]].
The four classifiers of Sections 3.1.2-3.1.5 are compared to the stand-alone SSSOM of [14], on the UTSG scenario postprocessing task.