Passive Two-Camera System to Generate a Depth Map of a Scene and Depth Estimation using Optical Flow Techniques.

This paper presents a method to recover depth information from a 2-D image taken from different viewpoints. An implementation of a passive two-camera system and a regionbased matching algorithm implemented in Matlab generates a depth map of a scene based on a pair of stereo images. Matlab scripts implementing optical flow algorithms were written. The optical flow for a motion sequence was computed and the vector field that shows both the direction and magnitude of the corresponding motion displayed

Computer vision techniques for a robot-assisted emergency neurosurgery system

This thesis presents the development of computer vision techniques for a robot-assisted emergency neurosurgery system that is being developed by the Mechatronics in Medicine group at Loughborough University, UK, and situates them within the context of the overall project. There are two main contributions in this thesis. The first is the development of a registration framework, to establish spatial correspondence between a preoperative plan of a patient (based on computed tomography images) and the patient. The registration is based on the rigid transformation of homologous anatomical soft tissue point landmarks of the head, the medial canthus and tragus, in CT and patient space. As a step towards automating the registration, a computational framework to localise these landmarks in CT space, with performance comparable to manual localisation, has been developed. The second contribution in this thesis is the development of computer vision techniques for a passive intraoperative supervisory system, based on visual cues from the operative site. Specifically, the feasibility of using computer vision to assess the outcome of a surgical intervention was investigated. The ability to mimic and embody part of a surgeon s visual sensory and decision-making capability is aimed at improving the robustness of the robotic system. Low-level image features to distinguish the two possible outcomes, complete and incomplete, were identified. Encouraging results were obtained for the surgical actions under consideration, which have been demonstrated by experiments on cadaveric pig heads. The results obtained are suggestive of the potential use of computer vision to assist surgical robotics in an operating theatre. The computational approaches developed, to provide greater autonomy to the robotic system, have the potential to improve current practice in robotic surgery. It is not inconceivable that the state of the art in surgical robotics can advance to a stage where it is able to emulate the ability and interpretation process of a surgeon.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Experimental study on hydrodynamic characteristics of underwater glider

1091-1097The enhancement of the hydrodynamic characteristics of autonomous underwater gliders (AUGs) is an important factor because of their weak inner propulsion system and altitude control. Moreover, resistance forces acting on the glider limit its operational range and increase energy utilisation. In this paper, towing tanks experiments were conducted to investigate the hydrodynamic characteristic of a newly developed underwater glider with fixed wings and a tail rudder. Specifically, this work presents the hydrodynamic performance of a newly developed AUG in a horizontal plane towed tank environment. This hydrodynamic study investigates the glider performance at a wide range of speed (0.3-0.7 m/sec) and drift angles (0-18o). The resistance forces were measured by internal strain gauges, mounted on the towing carriage. The experimental results were used to analyse the resistance with variation in Froude’s number and drift angles, using Reynold’s Average Navier Stoke equation in Ansys FLUENT. Both experimental and simulation are well corroborated and show that resistance force is a strong function of the drift angle. The results are useful for the potential development of AUGs and their control surfaces

Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging fro

Modeling and Simulation of PMSG Wind Energy Conversion System using Active Disturbance Rejection Control

Electrical power generated from wind turbines inherently fluctuates due to changing wind speeds. Without proper control, disturbances such as changing wind speeds can degrade the power quality factor and robustness of the electrical grid. To ensure good power quality factor, high performance and robustness of the grid against internal and external disturbances, the use of Active Disturbance Rejection Control with an extended state observer ESO for a PMSG Wind Energy Conversion System is investigated. The system has been simulated in MATLAB/Simulink at various wind speeds. The obtained simulation results indicate that the controller maintains constant DC voltage at the interface of the generator-side converter and grid-side converters and achieves maximum power. The results also show that the system performance has good stability, precision and rejection of internal disturbances, with an overall system efficiency of 98.65%

A New Roll and Pitch Control Mechanism for an Underwater Glider

In this paper, a new roll and pitch control mechanism for an underwater glider is described. The mechanism controls the glider’s pitch and roll without the use of a conventional buoyancy engine or movable mass. It uses water as trim mass, with a high flow rate water pump to shift water from water bladders located at the front, rear, left, and right of the glider. By shifting water between the left and right water bladder, a roll moment is induced. Similarly, pitch is achieved by shifting water between the front and rear water bladders. The water bladders act not only as a means for roll and pitch control but as a buoyancy engine as well. This eliminates the use of a dedicated mechanism for pitch and roll, thereby improving gliding efficiency and energy consumption, as the glider's overall size is decreased since the hardware required is reduced. The dynamics of the system were derived and simulated, as well as validated experimentally. The glider is able to move in a sawtooth pattern with a maximum pitch angle of 43.5˚, as well as a maximum roll angle of 43.6˚ with pitch and roll rates increase with increasing pump rate

ANN- and FEA-Based Assessment Equation for a Corroded Pipeline with a Single Corrosion Defect

Most of the standards available for the assessment of the failure pressure of corroded pipelines are limited in their ability to assess complex loadings, and their estimations are conservative. To overcome this research gap, this study employed an artificial neural network (ANN) model trained with data obtained using the finite element method (FEM) to develop an assessment equation to predict the failure pressure of a corroded pipeline with a single corrosion defect. A finite element analysis (FEA) of medium-toughness pipelines (API 5L X65) subjected to combined loads of internal pressure and longitudinal compressive stress was carried out. The results from the FEA with various corrosion geometric parameters and loads were used as the training dataset for the ANN. After the ANN was trained, its performance was evaluated, and its weights and biases were obtained for the development of a corrosion assessment equation. The prediction from the newly developed equation has a good correlation value, R2 of 0.9998, with percentage errors ranging from −1.16% to 1.78%, when compared with the FEA results. When compared with the failure pressure estimates based on the Det Norske Veritas (DNV-RP-F101) guidelines, the standard was more conservative in its prediction than the assessment equation developed in this study

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intra-operative space. CT images are used in the pre-operative (planning) stage, whilst white light (TV camera) images are used to capture the intra-operative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a Polynomial Neural Network (PNN) classifier based on Gabor features for the detection and localisation of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks

EVALUATION OF K-EPSILON MODEL FOR TURBULENT BUOYANT JET

The modelling of a turbulent buoyant jet is challenging due to the complex nature of such flow, which consists of two fluids with different densities, as well as the multi-scale flow phenomena associated in both space and time. In this paper, the k-epsilon turbulence model is applied to model a turbulent buoyant jet at different flow regimes including laminar and turbulent. The velocity field and centerline velocity are in good agreement with the experiments, as well as the expected results based on jet theory. Moreover, the distribution of the radial velocity matches with Gaussian distribution. The k-epsilon model is an appropriate turbulent model that can be applied for larger Reynolds number flow simulation

Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN)

Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (R2) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress