Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

© 1963-2012 IEEE. This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force models can be seen as hybrid models that contain a first-principle physical model part and a nonparametric GP model part. We briefly review the statistical inference and learning methods for this kind of models, introduce stochastic control methodology for these models, and provide new theoretical observability and controllability results for them.The work of S. Sarkka was financially supported by the Academy of Finland. The work of M. A. Alvarez was supported in part by the EPSRC under Research Project EP/N014162/1

Robot Mapping and Localisation for Feature Sparse Water Pipes Using Voids as Landmarks

Robotic systems for water pipe inspection do not generally include navigation components for mapping the pipe network and locating damage. Such navigation systems would be highly advantageous for water companies because it would allow them to more effectively target maintenance and reduce costs. In water pipes, a major challenge for robot navigation is feature sparsity. In order to address this problem, a novel approach for robot navigation in water pipes is developed here, which uses a new type of landmark feature - voids outside the pipe wall, sensed by ultrasonic scanning. The method was successfully demonstrated in a laboratory environment and showed for the first time the potential of using voids for robot navigation in water pipes

Sanitation of blackwater via sequential wetland and electrochemical treatment

The discharge of untreated septage is a major health hazard in countries that lack sewer systems and centralized sewage treatment. Small-scale, point-source treatment units are needed for water treatment and disinfection due to the distributed nature of this discharge, i.e., from single households or community toilets. In this study, a high-rate-wetland coupled with an electrochemical system was developed and demonstrated to treat septage at full scale. The full-scale wetland on average removed 79 +/- 2% chemical oxygen demand (COD), 30 +/- 5% total Kjeldahl nitrogen (TKN), 58 +/- 4% total ammoniacal nitrogen (TAN), and 78 +/- 4% orthophosphate. Pathogens such as coliforms were not fully removed after passage through the wetland. Therefore, the wetland effluent was subsequently treated with an electrochemical cell with a cation exchange membrane where the effluent first passed through the anodic chamber. This lead to in situ chlorine or other oxidant production under acidifying conditions. Upon a residence time of at least 6 h of this anodic effluent in a buffer tank, the fluid was sent through the cathodic chamber where pH neutralization occurred. Overall, the combined system removed 89 +/- 1% COD, 36 +/- 5% TKN, 70 +/- 2% TAN, and 87 +/- 2% ortho-phosphate. An average 5-log unit reduction in coliform was observed. The energy input for the integrated system was on average 16 +/- 3 kWh/m(3), and 11 kWh/m(3) under optimal conditions. Further research is required to optimize the system in terms of stability and energy consumption

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

© 1963-2012 IEEE. This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force models can be seen as hybrid models that contain a first-principle physical model part and a nonparametric GP model part. We briefly review the statistical inference and learning methods for this kind of models, introduce stochastic control methodology for these models, and provide new theoretical observability and controllability results for them

Rao-Blackwellized particle mcmc for parameter estimation in spatio-temporal Gaussian processes

In this paper, we consider parameter estimation in latent, spatiotemporal Gaussian processes using particle Markov chain Monte Carlo methods. In particular, we use spectral decomposition of the covariance function to obtain a high-dimensional state-space representation of the Gaussian processes, which is assumed to be observed through a nonlinear non-Gaussian likelihood. We develop a Rao-Blackwellized particle Gibbs sampler to sample the state trajectory and show how to sample the hyperparameters and possible parameters in the likelihood. The proposed method is evaluated on a spatio-temporal population model and the predictive performance is evaluated using leave-one-out cross-validation

Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods

The paper considers an electromagnetic inverse problem of localizing dipolar neural current sources on brain cortex using magnetoencephalography (MEG) or electroencephalography (EEG) data. We aim to localize the unknown and time-varying number of dipolar current sources using data from multiple MEG coil sensors. In this work, we model the problem in a Bayesian framework, we propose a linear prior detection method as well as a probabilistic approach for target number estimation, and target state initiation/termination. We then use a sequential Monte Carlo (SMC) algorithm to numerically estimate location and moment of the dipolar current sources. We apply the algorithm in both simulated and measured data. Results show that the proposed approach is able to estimate and localize the unknown and time-varying number of dipoles in simulated data with reasonable tracking accuracy and efficiency. © 2013 ISIF ( Intl Society of Information Fusi