Inside IISE Journals

This month we highlight two articles from IISE Transactions. The first article studies how to learn the true sources of variation from high-dimensional data. The authors developed an automated method that uses deep auto-associative neural networks to discover and visualize nonlinear sources of variation and further demonstrated their method by analyzing data from a manufacturing system’s optical coordinate measuring machines. The second article investigates how to assess the reliability of a multistate system (MSS) that is under hybrid uncertainty. The authors proposed a model of a hybrid uncertain variable (HUV) to help in modeling the hybrid uncertainty of the multistate system in which both state performance and the associated probability value are allowed to be uncertain. They further developed a computational framework for assessing the reliability of the MSS and demonstrated its effectiveness in a data transmission system. These articles will appear in the December 2018 issue of IISE Transactions (Vol 50, No. 12).

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