The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

Multi-output Gaussian process (MGP) regression plays an important role in the integrative
analysis of different but interrelated systems/units. Existing MGP approaches assume that …

We present a nonparametric prognostic framework for individualized event prediction based
on joint modeling of both time series and time-to-event data. Our approach exploits a …

In this paper, we propose FGPR: a Federated Gaussian process () regression framework
that uses an averaging strategy for model aggregation and stochastic gradient descent for …

Standard Bayesian learning is known to have suboptimal generalization capabilities under
misspecification and in the presence of outliers. Probably approximately correct (PAC) …

Prediction of events such as part replacement and failure events plays a critical role in
reliability engineering. Event stream data are commonly observed in manufacturing and …

Multi-output regression seeks to borrow strength and leverage commonalities across
different but related outputs in order to enhance learning and prediction accuracy. A …

This report develops three data analytics frameworks that solve the challenges in the
engineering system, with application to quality and reliability engineering.(i) Develo** …

Bayesian optimization (BO) offers a solution to intractable optimization problems.
Exploration and exploitation (E&E) are determined in BO using acquisition functions, in …

The production of antennas involves several stages, notably the design phase, which must
adhere to specific requirements. This process demands extensive testing and …