Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Mapping experience in organizations: A learning process for strategic technology planning

Today's fast-paced, dynamic environments mean that for organizations to keep "ahead of the game", engineering managers need to maximize current opportunities and avoid repeating past mistakes. This article describes the development study of a collaborative strategic management tool - the Experience Scan to capture past experience and apply learning from this to present and future situations. Experience Scan workshops were held in a number of different technology organizations, developing and refining the tool until its format stabilized. From participants' feedback, the workshop-based tool was judged to be a useful and efficient mechanism for communication and knowledge management, contributing to organizational learning.

Numerical study of the impact of process variations on the motional resistance of weakly coupled MEMS resonators

This paper presents a numerical study of the impact of process-induced variations on the achievable motional resistance Rx of one-dimensional, cyclic and cross-coupled architectures of electrostatically transduced MEMS resonators operating in the 250 kHz range. Monte Carlo numerical simulations which accounted for up to 0.75% variation in critical resonator feature sizes were initiated on 1, 2, 3, 4, 5 and 9 coupled MEMS resonators for three distinct coupling architectures. Improvements of 100X in the spread of Rx and 2.7X in mean achievable Rx are reported for the case of 9 resonators when implemented in the cross-coupled topology, as opposed to the traditional one-dimensional chain. © 2013 IEEE.