State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Conceptual design of Americium nuclear battery for space power applications

This work presents simplified 242mAm-fueled nuclear battery concept design featuring direct fission products energy conversion and passive heat rejection. Optimization of the battery operating characteristics and dimensions was performed. The calculations of power conversion efficiency under thermal and nuclear design constraints showed that 5.6 W e/kg power density can be achieved, which corresponds to conversion efficiency of about 4%. A system with about 190 cm outer radius translates into 17.8 MT mass per 100 kW e. Total power scales linearly with the outer surface area of the battery through which the residual heat is rejected. Tradeoffs between the battery lifetime, mass, dimensions, power rating, and conversion efficiency are presented and discussed. The battery can be used in a wide variety of interplanetary missions with power requirements in the kW to MW range. Copyright © 2007 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.