Towards extracting 3-D structural representations of AGR core fuel channels from 2-D in-core inspection videos

Remote Visual Inspection (RVI) of Advanced Gas-cooled Reactor (AGR) nuclear power stations allows engineers to gain an understanding of the AGR graphite core health by investigating the incorporated fuel channels. During planned, periodic outages, video footage of the pre-selected fuel channels is acquired using specialist inspection tools and is subsequently taken offline for further analysis using visualization techniques. Current methods of visualization however provide limited structural information due to the loss of depth information as a direct result of the image acquisition process. This paper introduces a new bespoke 3-D reconstruction framework to recover lost depth information to produce 3-D point cloud reconstructions of fuel channels from inspection videos. We also present here a new, lab based, experimental rig setup with which we effectively captured data under lab controlled conditions to verify our 3-D reconstruction algorithms. Our proposed method is tested on 2-D in core inspection videos in addition to the footage captured within laboratory conditions and outperforms state-of-the-art incremental reconstruction frameworks whilst producing a more representative 3-D point cloud for improved in-core visualization

Spatial encoding in primate hippocampus during free navigation.

The hippocampus comprises two neural signals-place cells and θ oscillations-that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)-rather than continuously-and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals

Model Estimation Within Planning and Learning

Risk and reward are fundamental concepts in the cooperative control of unmanned systems. In this research, we focus on developing a constructive relationship between cooperative planning and learning algorithms to mitigate the learning risk, while boosting system (planner & learner) asymptotic performance and guaranteeing the safety of agent behavior. Our framework is an instance of the intelligent cooperative control architecture (iCCA) where the learner incrementally improves on the output of a baseline planner through interaction and constrained exploration. We extend previous work by extracting the embedded parameterized transition model from within the cooperative planner and making it adaptable and accessible to all iCCA modules. We empirically demonstrate the advantage of using an adaptive model over a static model and pure learning approaches in an example GridWorld problem and a UAV mission planning scenario with 200 million possibilities. Finally we discuss two extensions to our approach to handle cases where the true model can not be captured exactly through the presumed functional form.United States. Air Force Office of Scientific Research (FA9550-09-1-0522)Natural Sciences and Engineering Research Council of CanadaUSAF (FA9550-09-1-0522