Multi-level fine pointing test-bed for space applications

For space applications that need high accuracy pointing of the payload, fine pointing system is an indispensable tool. To reach high level of high accuracy pointing and tracking, proper synchronization between the attitude of the satellite and each stage of the pointing module should be considered. This study focuses on developing and demonstrating staging control for fine pointing system. Currently, an experimental model for multi-stage control has been built in the laboratory to be used as a testbed and a teaching tool to validate the control strategies. This paper gives a brief introduction of the study, experimental model design criteria and staging control strategy. An example of a controller synthesis for multi-stage actuators based on Hinfinity is also presented

Water-based Liquid Scintillator Detector as a New Technology Testbed for Neutrino Studies in Turkey

This study investigates the deployment of a medium-scale neutrino detector near Turkey's first nuclear power plant, the Akkuyu Nuclear Power Plant. The aim of this detector is to become a modular testbed for new technologies in the fields of new detection media and innovative photosensors. Such technologies include Water-based Liquid Scintillator (WbLS), Large Area Picosecond Photo-Detectors (LAPPDs), dichroic Winston cones, and large area silicon photomultiplier modules. The detector could be used for instantaneous monitoring of the Akkuyu Nuclear Power Plant via its antineutrino flux. In addition to its physics and technological goals, it would be an invaluable opportunity for the nuclear and particle physics community in Turkey to play a role in the development of next generation of particle detectors in the field of neutrino physics.Comment: V2, updated version with additional reference

Constraints on the Ultra-High Energy Neutrino Flux from Gamma-Ray Bursts from a Prototype Station of the Askaryan Radio Array

We report on a search for ultra-high-energy (UHE) neutrinos from gamma-ray bursts (GRBs) in the data set collected by the Testbed station of the Askaryan Radio Array (ARA) in 2011 and 2012. From 57 selected GRBs, we observed no events that survive our cuts, which is consistent with 0.12 expected background events. Using NeuCosmA as a numerical GRB reference emission model, we estimate upper limits on the prompt UHE GRB neutrino fluence and quasi-diffuse flux from $10^{7}$ to $10^{10}$ GeV. This is the first limit on the prompt UHE GRB neutrino quasi-diffuse flux above $10^{7}$ GeV.Comment: 14 pages, 8 figures, Published in Astroparticle Physics Journa

First Constraints on the Ultra-High Energy Neutrino Flux from a Prototype Station of the Askaryan Radio Array

The Askaryan Radio Array (ARA) is an ultra-high energy ($>10^{17}$ eV) cosmic neutrino detector in phased construction near the South Pole. ARA searches for radio Cherenkov emission from particle cascades induced by neutrino interactions in the ice using radio frequency antennas ($\sim150-800$ MHz) deployed at a design depth of 200 m in the Antarctic ice. A prototype ARA Testbed station was deployed at $\sim30$ m depth in the 2010-2011 season and the first three full ARA stations were deployed in the 2011-2012 and 2012-2013 seasons. We present the first neutrino search with ARA using data taken in 2011 and 2012 with the ARA Testbed and the resulting constraints on the neutrino flux from $10^{17}-10^{21}$ eV.Comment: 26 pages, 15 figures. Since first revision, added section on systematic uncertainties, updated limits and uncertainty band with improvements to simulation, added appendix describing ray tracing algorithm. Final revision includes a section on cosmic ray backgrounds. Published in Astropart. Phys.

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand

Artificial Intelligence can be applied to a wide variety of real world problems, with varying levels of complexity; nonetheless, real world problems often demand for capabilities that are difficult, if not impossible to achieve using a single Artificial Intelligence algorithm. This challenge gave rise to the development of hybrid systems that put together a combination of complementary algorithms. Hybrid approaches come at a cost however, as they introduce additional complications for the developer, such as how the algorithms should interact and when the independent algorithms should be executed. This research introduces a new algorithm called Cascading Genetic Network Programming (CGNP), which contains significant changes to the original Genetic Network Programming. This new algorithm has the facility to include any Artificial Intelligence algorithm into its directed graph network, as either a judgement or processing node. CGNP introduces a novel ability for a scalable multiple layer network, of independent instances of the CGNP algorithm itself. This facilitates problem subdivision, independent optimisation of these underlying layers and the ability to develop varying levels of complexity, from individual motor control to high level dynamic role allocation systems. Mechanisms are incorporated to prevent the child networks from executing beyond their requirement, allowing the parent to maintain control. The ability to optimise any data within each node is added, allowing for general purpose node development and therefore allowing node reuse in a wide variety of applications without modification. The abilities of the Cascaded Genetic Network Programming algorithm are demonstrated and proved through the development of a multi-behavioural robot soccer goal keeper, as a testbed where an individual Artificial Intelligence system may not be sufficient. The overall role is subdivided into three components and individually optimised which allow the robot to pursue a target object or location, rotate towards a target and provide basic functionality for defending a goal. These three components are then used in a higher level network as independent nodes, to solve the overall multi- behavioural goal keeper. Experiments show that the resulting controller defends the goal with a success rate of 91%, after 12 hours training using a population of 400 and 60 generations