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The design optimization problem of optical component allocation for a point to point optical link is considered.
The proposed analysis and the algorithm simplify the process of finding the optimal configuration of point to point optical links and provide an optimized component allocation with an improved ASE noise performance.
Note that a realization of the collection of component allocation variables, defines a clustering of the data (i.e. if then and are clustered together).
For clarity, note that is the component allocation variable associated with gene i in model k, and that is the mixture proportion associated with component in model k.
As is common in mixture modelling (e.g. Dempster et al., 1977; see also Friedman et al., 2004 for a graphical model perspective), we introduce latent component allocation variables such that is the component responsible for observation.
If gene i is a member of, this simply tells us that the component allocation variables tend to be equal (i.e. gene i tends to be allocated to the same component across datasets ).
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From our Gibbs sampler, we have a collection of sampled component allocations for each member of.
We identify a final clustering for the set of fused genes by searching amongst the sampled component allocations to find the one that maximizes the posterior expected adjusted Rand index (ARI; Fritsch and Ickstadt, 2009).
Fixson and Park [29] used two dimensions of the function-component allocation and interface to measure difference between the integral and modular structure.
The uniqueness of the proposed fuzzy CLSC optimization model lies in providing an integrated decision making framework that can aid the manufacturer in making crucial strategic, tactical, and operational decisions of optimal selection of suppliers, component order allocation, recovery flow allocation, and vehicle routing planning.
One modification that does appear to be utilised regularly is to add in a random component; thus, allocation is no longer completely deterministic.
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