A self-­‐learning Audio Player was built to learn users habits by analyzing operations the user does when listening to music. The self-­‐learning component is intended to provide a better music experience for the user by generating a special playlist based on the prediction of users favorite songs. The rough set core characteristics are used throughout the learning process to capture the dynamics of changing user interactions with the audio player. The engine is evaluated by simulation data. The simulation process ensures the data contain specific predetermined patterns. Evaluation results show the predictive power and stability of the hybrid engine for learning a users habits and the increased intelligence achieved by combining rough sets and NN when compared with using NN by itself.

Control of the contact force between a robot manipulator and a workpiece is critical for successful execution of tasks where the robot’s end effector must perform a contact operation along the contour of a workpiece. Representative tasks include polishing, grinding and deburring. Considerable research has been conducted on force control with electric robots. By contrast, little research has been conducted on force control with pneumatic robots. The later has the potential to be considerably cheaper. However, the compressible nature of air as the working fluid and relatively high friction means pneumatic robots are more difficult to control. The subject of this thesis is the design and testing of a controller that regulates the normal contact force and tangential velocity of the end effector of a pneumatic gantry robot while tracking the contour of a planar workpiece. Both experimental and simulation results are presented. A PI Force Velocity (FV) controller for contour tracking was designed and tested experimentally. Three different workpiece edge geometries were studied: straight, inclined and curved. The tracking performance with the PI FV controller was comparable to the performance reported by other researchers with a similar controller implemented with an electric robot. This result confirms the potential of pneumatically actuated robots in force control applications. A system model was developed and validated in order to investigate the parameters that affect performance. A good match between experiment and simulation was achieved when the friction of the z-axis cylinder was modeled with a Displacement Dependent Friction Model (DDFM) instead of a Velocity Dependent Friction Model (VDFM). Subsequently, a DDFM based friction compensator was designed and tested. However, it was found that performance could not be improved even with perfect friction compensation, due to the effects of system lag. Two Neural Network (NN) compensators were designed to compensate for both the lag and friction in the system. Simulation results for straight and curved edges were used to examine the effectiveness of the NN compensators. The performance of the PI FV controller was found to improve significantly when a NN compensator was added. This result confirms the value of NN’s in control compensation for tracking applications with pneumatic actuation. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2008-09-16 12:29:44.679

Considerable research has been conducted on the control of pneumatic systems due to their potential as a low-cost, clean, high power-to-weight ratio actuators. However, nonlinearities such as those due to compressibility of air continue to limit their accuracy. Among the nonlinearities in a pneumatic system, friction can have a significant effect on tracking performance, especially in applications that use rodless cylinders which have higher Coulomb friction than rodded cylinders. Compensation for nonlinearities in pneumatic systems has been a popular area of research in pneumatic system control. Most advanced nonlinear control strategies are based on a detailed mathematical model of the system. If a simplified mathematical model is used, then performance is sensitive to uncertainties and parameter variations in the robot. Although they show relatively good results, the requirement for model parameter identification has made these methods difficult to implement. This highlights the need for an adaptive controller that is not based on a mathematical model. The objective of this thesis was to design and evaluate a position and velocity controller for application to a pneumatic gantry robot. An Adaptive Neural Network (ANN) structure was implemented as both a controller and as a compensator. The implemented ANN had online training as this was considered to be the algorithm that had the greatest potential to enhance the performance of the pneumatic system. One axis of the robot was used to obtain results for the cases of velocity and position control. Seven different velocity controllers were tested and their performance compared. For position control, only two controllers were examined: conventional PID and PID with an ANN Compensator (ANNC). The position controllers were tuned for step changes in the setpoint. Their performance was evaluated as applied to sinusoid tracking. It was shown that the addition of ANN as a compensator could improve the performance of both position and velocity control. For position control, the ANNC improved the tracking performance by over 20%. Although performance was better than with conventional PID control, it was concluded that the level of improvement with ANNC did not warrant the extra effort in tuning and implementation. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2010-09-08 15:02:28.177

Real-time tracking and object recognition is a large field with many possible applications. In this thesis we present a technical demo of a stereoscopic tracking system using artificial neural networks (ANN) and also an overview of the entire system, and its core functions. We have implemented a system able of tracking an object in real time at 60 frames per second. Using stereo matching we can extract the object coordinates in each camera, and calculate a distance estimate from the cameras to the object. The system is developed around the Xilinx ZC-706 evaluation board featuring a Zynq XC7Z045 SoC. Performance critical functions are implemented in the FPGA fabric. A dual-core ARM processor, integrated on the chip, is used for support and communication with an external PC. The system runs at moderate clock speeds to decrease power consumption and provide headroom for higher resolutions. A toolbox has been developed for prototyping and the aim has been to run the system with a one-push-button approach. The system can be taught to track any kind of object using an eight bit 32 × 16 pixel pattern generated by the user. The system is controlled over Ethernet from a regular workstation PC, which enables it to be very user-friendly.

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This thesis describes hydrocarbon vapour sensing using Langmuir-Blodgett films prepared from: a co-ordination polymer; substituted phthalocyanines containing copper and zinc as the central metal ions; and a polysiloxane. The physical and chemical properties of the co-ordination polymer, 5,5'-methylenebis (N- hexadecylsalicylidenamine), at the air water interface were investigated using Brewster angle microscopy and surface pressure versus area measurements. Langmuir-Blodgett films were built-up on a variety of substrates. The addition of copper acetate to the subphase caused a change in both the physical and optical properties of the Langmuir- Blodgett layers. Film thickness data suggest that a true monolayer (thickness ca 2 nm) is only formed under these conditions. The multilayer films were studied using X-ray diffraction, UV/Visible spectroscopy, ellipsometry, surface plasmon resonance, surface profiling and electron spin resonance. The response of each film when exposed to, benzene, toluene, ethanol and water vapours were recorded. Two optical systems were used, both based on surface plasmon resonance. The first incorporated a silicon photodiode to record the intensity of the reflected light. The second was similar to that of surface plasmon microscopy, using a charge coupled device camera to monitor the reflected light intensity from the Langmuir-Blodgett film/metal interface. The co-ordination polymer was found to be most sensitive to benzene and could reliably detect concentrations of this vapour down to 100 vapour parts per million. Data obtained when the co-ordination polymer was exposed to benzene and water vapour (using the latter system) were presented to a neural network for recognition.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.