Spectral-Spatial Analysis of Remote Sensing Data: An Image Model and A Procedural Design

The distinguishing property of remotely sensed data is the multivariate information coupled with a two-dimensional pictorial representation amenable to visual interpretation. The contribution of this work is the design and implementation of various schemes that exploit this property. This dissertation comprises two distinct parts. The essence of Part One is the algebraic solution for the partition function of a high-order lattice model of a two dimensional binary particle system. The contribution of Part Two is the development of a procedural framework to guide multispectral image analysis. The characterization of binary (black and white) images with little semantic content is discussed in Part One. Measures of certain observable properties of binary images are proposed. A lattice model is introduced, the solution to which yields functional mappings from the model parameters to the measurements on the image. Simulation of the model is explained, as is its usage in the design of Bayesian priors to bias classification analysis of spectral data. The implication of such a bias is that spatially adjacent remote sensing data are identified as belonging to the same class with a high likelihood. Experiments illustrating the benefit of using the model in multispectral image analysis are also discussed. The second part of this dissertation presents a procedural schema for remote sensing data analysis. It is believed that the data crucial to a succc~ssful analysis is provided by the human, as an interpretation of the image representation of the remote sensing spectral data. Subsequently, emphasis is laid on the design of an intelligent implementation of existing algorithms, rather than the development of new algorithms for analysis. The development introduces hyperspectral analysis as a problem requiring multi-source data fusion and presents a process model to guide the design of a solution. Part Two concludes with an illustration of the schema as used in the classification analysis of a given hyperspectral data set

Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm

The primary focus of this paper is centered on the numerical analysis and optimal control of vertical axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically tackle small-scale wind turbines with permanent magnet synchronous generator, which are well-suited to local and compact production of electrical energy in small scale such as urban and rural infrastructure installations. Through this work, we formulate and implement an RL strategy using Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of the wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, where the designer does not have to know the precise dynamics of the plant and their uncertainties. The method specifically overcomes the shortcomings typically associated with conventional solutions including but not limited to component aging, modeling errors and inaccuracies in the estimation of wind speed patterns. It has been observed to be especially successful in capturing power from wind transients; it modulates the generator load and hence rotor torque load so that the rotor tip speed reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method is shown to be superior to the classical maximum power point tracking method

Examining average and discounted reward optimality criteria in reinforcement learning

In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality criterion is fundamentally important. Two major optimality criteria are average and discounted rewards, where the later is typically considered as an approximation to the former. While the discounted reward is more popular, it is problematic to apply in environments that have no natural notion of discounting. This motivates us to revisit a) the progression of optimality criteria in dynamic programming, b) justification for and complication of an artificial discount factor, and c) benefits of directly maximizing the average reward. Our contributions include a thorough examination of the relationship between average and discounted rewards, as well as a discussion of their pros and cons in RL. We emphasize that average-reward RL methods possess the ingredient and mechanism for developing the general discounting-free optimality criterion (Veinott, 1969) in RL.Comment: 14 pages, 3 figures, 10-page main conten

Robots that Learn and Plan — Unifying Robot Learning and Motion Planning for Generalized Task Execution

Robots have the potential to assist people with a variety of everyday tasks, but to achieve that potential robots require software capable of planning and executing motions in cluttered environments. To address this, over the past few decades, roboticists have developed numerous methods for planning motions to avoid obstacles with increasingly stronger guarantees, from probabilistic completeness to asymptotic optimality. Some of these methods have even considered the types of constraints that must be satisfied to perform useful tasks, but these constraints must generally be manually specified. In recent years, there has been a resurgence of methods for automatic learning of tasks from human-provided demonstrations. Unfortunately, these two fields, task learning and motion planning, have evolved largely separate from one another, and the learned models are often not usable by motion planners. In this thesis, we aim to bridge the gap between robot task learning and motion planning by employing a learned task model that can subsequently be leveraged by an asymptotically-optimal motion planner to autonomously execute the task. First, we show that application of a motion planner enables task performance while avoiding novel obstacles and extend this to dynamic environments by replanning at reactive rates. Second, we generalize the method to accommodate time-invariant model parameters, allowing more information to be gleaned from the demonstrations. Third, we describe a more principled approach to temporal registration for such learning methods that mirrors the ultimate integration with a motion planner and often reduces the number of demonstrations required. Finally, we extend this framework to the domain of mobile manipulation. We empirically evaluate each of these contributions on multiple household tasks using the Aldebaran Nao, Rethink Robotics Baxter, and Fetch mobile manipulator robots to show that these approaches improve task execution success rates and reduce the amount of human-provided information required.Doctor of Philosoph

Non-parametric modeling in non-intrusive load monitoring

Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM