A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

Wide-Area Control Schemes to Improve Small Signal Stability in Power Systems

One of the main concerns for the secure and reliable operation of power systems is the small signal stability problem. In the complex and highly interconnected structure of future power systems, relying solely on operator responses and conventional controls cannot assure reliability. Therefore, there is a need for advanced Wide-Area Control Schemes (WACS) that can automatically respond to degradation of reliability in the system. The main objective of this dissertation is to address two key challenges regarding the design and implementation of wide-area control schemes for damping inter-area oscillations. First is the high communication cost associated with optimal centralized control approaches. As power networks are large-scale systems, both the synthesis and the implementation of centralized controllers suggested by most of the previous studies are often impossible in practice. Second is the difficulty of obtaining accurate system-wide dynamic models for initiating and updating the control design. In this research, we introduced wide-area damping control strategies that not only ensure the small signal stability with the desired performance but also consider communication and model information limitations in the design. A state feedback formulation is proposed that aims to simultaneously optimize a standard Linear Quadratic Regulator (LQR) cost criterion and induce a pre-defined communication structure. We solved the proposed problem with three different objectives to target a specific wide-area damping control design challenge in each setting. First, the communication structure is enforced as a constraint in the optimization and solved for a large idealized power network with information symmetry. Second, to make the method suitable for systems with arbitrary structures and information patterns, we proposed a group-sparse regularization to be added to the optimization cost function. Applications of the method for inducing the desired communication network and finding effective measurement and control signal combinations were also investigated. Third, we paired the proposed optimal control with a real-time model identification approach, to create a wide-area control framework that is capable of dealing with model information limitations and inaccuracies in online implementation. The performances of the proposed wide-area damping control architectures are validated through nonlinear simulations on different test systems

Efficient Methods for Computational Light Transport

En esta tesis presentamos contribuciones sobre distintos retos computacionales relacionados con transporte de luz. Los algoritmos que utilizan información sobre el transporte de luz están presentes en muchas aplicaciones de hoy en día, desde la generación de efectos visuales, a la detección de objetos en tiempo real. La luz es una valiosa fuente de información que nos permite entender y representar nuestro entorno, pero obtener y procesar esta información presenta muchos desafíos debido a la complejidad de las interacciones entre la luz y la materia. Esta tesis aporta contribuciones en este tema desde dos puntos de vista diferentes: algoritmos en estado estacionario, en los que se asume que la velocidad de la luz es infinita; y algoritmos en estado transitorio, que tratan la luz no solo en el dominio espacial, sino también en el temporal. Nuestras contribuciones en algoritmos estacionarios abordan problemas tanto en renderizado offline como en tiempo real. Nos enfocamos en la reducción de varianza para métodos offline,proponiendo un nuevo método para renderizado eficiente de medios participativos. En renderizado en tiempo real, abordamos las limitacionesde consumo de batería en dispositivos móviles proponiendo un sistema de renderizado que incrementa la eficiencia energética en aplicaciones gráficas en tiempo real. En el transporte de luz transitorio, formalizamos la simulación de este tipo transporte en este nuevo dominio, y presentamos nuevos algoritmos y métodos para muestreo eficiente para render transitorio. Finalmente, demostramos la utilidad de generar datos en este dominio, presentando un nuevo método para corregir interferencia multi-caminos en camaras Timeof- Flight, un problema patológico en el procesamiento de imágenes transitorias.n this thesis we present contributions to different challenges of computational light transport. Light transport algorithms are present in many modern applications, from image generation for visual effects to real-time object detection. Light is a rich source of information that allows us to understand and represent our surroundings, but obtaining and processing this information presents many challenges due to its complex interactions with matter. This thesis provides advances in this subject from two different perspectives: steady-state algorithms, where the speed of light is assumed infinite, and transient-state algorithms, which deal with light as it travels not only through space but also time. Our steady-state contributions address problems in both offline and real-time rendering. We target variance reduction in offline rendering by proposing a new efficient method for participating media rendering. In real-time rendering, we target energy constraints of mobile devices by proposing a power-efficient rendering framework for real-time graphics applications. In transient-state we first formalize light transport simulation under this domain, and present new efficient sampling methods and algorithms for transient rendering. We finally demonstrate the potential of simulated data to correct multipath interference in Time-of-Flight cameras, one of the pathological problems in transient imaging.<br /

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

Piotr Omenzetter and Simon Hoell’s work on this paper within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen was supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.Peer reviewedPostprin