19 research outputs found
GPU Implementation of extended total Lagrangian explicit (gpuXTLED) for Surgical Incision Application
An extended total Lagrangian explicit dynamic (XTLED) is presented as a potential numerical method for simulating interactive or physics-based surgical incisions of soft tissues. The simulation of surgical incision is vital to the integrity of virtual reality simulators that are used for immersive surgical training. However, most existing numerical methods either compromise on computational speed for accuracy or vice versa. This is due to the challenge of modelling nonlinear behaviour of soft tissues, incorporating incision and subsequently updating topology to account for the incision. To tackle these challenges, XTLED method which combines the extended finite element method (XFEM) using total Lagrangian formulation with explicit time integration method was developed. The algorithm was developed and deformations of 3D geometries under tension, were simulated. An attempt was made to validate the XTLED method using silicon samples with different incision configuration and a comparison was made between XTLED and FEM. Results show that XTLED could potentially be used to simulate interactive soft tissue incision. However, further quantitative verification and validation are required. In addition, numerical analyses conducted show that solutions may not be obtainable due to simulation errors. However, it is unclear whether these errors are inherent in the XTLED method or the algorithm created for the XTLED method in this thesis
Tracing back the source of contamination
From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer
Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF
Bioinspired metaheuristic algorithms for global optimization
This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions