The problem of measurement indeterminacy in complex neurobiological movement systems

In the study of complex neurobiological movement systems, measurement indeterminacy has typically been overcome by imposing artificial modelling constraints to reduce the number of unknowns (e.g., reducing all muscle, bone and ligament forces crossing a joint to a single vector). However, this approach prevents human movement scientists from investigating more fully the role, functionality and ubiquity of coordinative structures or functional motor synergies. Advancements in measurement methods and analysis techniques are required if the contribution of individual component parts or degrees of freedom of these task-specific structural units is to be established, thereby effectively solving the indeterminacy problem by reducing the number of unknowns. A further benefit of establishing more of the unknowns is that human movement scientists will be able to gain greater insight into ubiquitous processes of physical self-organising that underpin the formation of coordinative structures and the confluence of organismic, environmental and task constraints that determine the exact morphology of these special-purpose devices.

Biotribological assessment for artificial synovial joints : the role of boundary lubrication

Biotribology, the study of lubrication, wear and friction within the body, has become a topic of high importance in recent times as we continue to encounter debilitating diseases and trauma that destroy function of the joints. A highly successful surgical procedure to replace the joint with an artificial equivalent alleviates dysfunction and pain. However, the wear of the bearing surfaces in prosthetic joints is a significant clinical problem and more patients are surviving longer than the life expectancy of the joint replacement. Revision surgery is associated with increased morbidity and mortality and has a far less successful outcome than primary joint replacement. As such, it is essential to ensure that everything possible is done to limit the rate of revision surgery. Past experience indicates that the survival rate of the implant will be influenced by many parameters, of primary importance, the material properties of the implant, the composition of the synovial fluid and the method of lubrication. In prosthetic joints, effective boundary lubrication is known to take place. The interaction of the boundary lubricant and the bearing material is of utmost importance. The identity of the vital active ingredient within synovial fluid (SF) to which we owe the near frictionless performance of our articulating joints has been the quest of researchers for many years. Once identified, tribo tests can determine what materials and more importantly what surfaces this fraction of SF can function most optimally with. Surface-Active Phospholipids (SAPL) have been implicated as the body’s natural load bearing lubricant. Studies in this thesis are the first to fully characterise the adsorbed SAPL detected on the surface of retrieved prostheses and the first to verify the presence of SAPL on knee prostheses. Rinsings from the bearing surfaces of both hip and knee prostheses removed from revision operations were analysed using High Performance Liquid Chromatography (HPLC) to determine the presence and profile of SAPL. Several common prosthetic materials along with a novel biomaterial were investigated to determine their tribological interaction with various SAPLs. A pin-on-flat tribometer was used to make comparative friction measurements between the various tribo-pairs. A novel material, Pyrolytic Carbon (PyC) was screened as a potential candidate as a load bearing prosthetic material. Friction measurements were also performed on explanted prostheses. SAPL was detected on all retrieved implant bearing surfaces. As a result of the study eight different species of phosphatidylcholines were identified. The relative concentrations of each species were also determined indicating that the unsaturated species are dominant. Initial tribo tests employed a saturated phosphatidylcholine (SPC) and the subsequent tests adopted the addition of the newly identified major constituents of SAPL, unsaturated phosphatidylcholine (USPC), as the test lubricant. All tribo tests showed a dramatic reduction in friction when synthetic SAPL was used as the lubricant under boundary lubrication conditions. Some tribopairs showed more of an affinity to SAPL than others. PyC performed superior to the other prosthetic materials. Friction measurements with explanted prostheses verified the presence and performance of SAPL. SAPL, in particular phosphatidylcholine, plays an essential role in the lubrication of prosthetic joints. Of particular interest was the ability of SAPLs to reduce friction and ultimately wear of the bearing materials. The identification and knowledge of the lubricating constituents of SF is invaluable for not only the future development of artificial joints but also in developing effective cures for several disease processes where lubrication may play a role. The tribological interaction of the various tribo-pairs and SAPL is extremely favourable in the context of reducing friction at the bearing interface. PyC is highly recommended as a future candidate material for use in load bearing prosthetic joints considering its impressive tribological performance.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies

One of the main aims in artificial intelligent system is to develop robust and efficient optimisation methods for Multi-Objective (MO) and Multidisciplinary Design (MDO) design problems. The paper investigates two different optimisation techniques for multi-objective design optimisation problems. The first optimisation method is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The second method combines the concepts of Nash-equilibrium and Pareto optimality with Multi-Objective Evolutionary Algorithms (MOEAs) which is denoted as Hybrid-Game. Numerical results from the two approaches are compared in terms of the quality of model and computational expense. The benefit of using the distributed hybrid game methodology for multi-objective design problems is demonstrated.

Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Investigation on parameters used in warning systems for rain-induced embankment instability

A number of instrumented laboratory-scale soil embankment slopes were subjected to artificial rainfall until they failed. The factor of safety of the slope based on real-time measurements of pore-water pressure (suction) and laboratory measured soil properties were calculated as the rainfall progressed. Based on the experiment measurements and slope stability analysis, it was observed that slope displacement measurements can be used to warn the slope failure more accurately. Further, moisture content/pore-water pressure measurements near the toe of the slope and the real-time factor of safety can also be used for prediction of rainfall-induced embankment failures with adequate accuracy.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.