published_or_final_version / abstract / toc / Ecology and Biodiversity / Doctoral / Doctor of Philosophy

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This work explores the use of a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, for an unsupervised machine learning technique, K-Means, applied to encrypted traffic identification (SSH). The performance of the proposed model is benchmarked against other unsupervised learning techniques existing in the literature: Basic K-Means, semi-supervised K-Means, DBSCAN, and EM. Results show that the proposed MOGA, not only outperforms the other models, but also provides a good trade off in terms of detection rate, false positive rate, and time to build and run the model. A hierarchical version of the proposed model is also implemented, to observe the gains, if any, obtained by increasing cluster purity by means of a second layer of clusters. Results show that with the hierarchical MOGA, significant gains are observed in terms of the classification performances of the system.

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The thesis has been conducted at Hetvägg AB and the aim is to develop a combined PID and Model Predictive Controller (MPC) controller for an air to water heat pump system that supplies domestic hot water (DHW) to the users. The current control system is PLC based but because of its big size and expensive maintenance it must be replaced with a robust controller for the heat pump. The main goal of this project has been to find a suitable improvement strategy. By constructing a model of the system, the control system has been evaluated. First a model of the system is derived using system identification techniques in Matlab-Simulink; since the system is nonlinear and dynamic a model of the system is needed before the controller is implemented. The data has been estimated and validated for the final selection of the model in system identification toolbox and then the controller is designed for the selected model. The combined PID and MPC controller utilizes the obtained model to predict the future behavior of the system and by changing the constraints an optimal control of the system is achieved. In this thesis work, first the PID and MPC controller are evaluated and their results are compared using transient and frequency response plots. It is seen that the MPC obtained better control action than the PID controller, after some tuning the MPC controller is capable of maintaining the outlet water temperature to the reference or set point value. Both the controllers are combined to remove the minor instabilities from the system and also to obtain a better output. From the transient response behavior it is seen that the combined MPC and PID controller delivered good output response with minimal overshoot, rise time and settling time.

This program of research examined whether mock-jurors could more accurately discriminate between correct and false eyewitness identifications after exposure to the identification procedure instead of—or in addition to—the witness’s testimony. In Experiment 1, 332 eyewitnesses exposed to staged crime videos attempted to identify the “criminal” from lineups. Lineups contained either the “criminal” or a replacement foil, were presented simultaneously or sequentially, and were conducted under double-blind, single-blind, or post-identification feedback conditions. In Experiment 2, 432 mock-jurors viewed a subset of the eyewitnesses from Experiment 1 (n = 48). Each mock-juror viewed a single eyewitness making their identification decision and/or testifying about the crime, their identification, and the officer. More mock-jurors believed that the eyewitnesses had made correct identifications if they viewed the testimony—with or without the identification procedure—compared to exposure to the identification procedure alone. Furthermore, more mock-jurors believed eyewitnesses who received post-identification feedback or had made their identifications from sequential lineups. These differences in belief, however, did not translate into a difference in accuracy; overall, mock-jurors believed 62.96% of correct identifications and 56.48% of false identifications. Exposure to the identification procedure did not improve mock-jurors ability to determine the accuracy of an identification; however, these mock-jurors were more aware of the post-identification feedback. Videotaping identification procedures may make triers of fact more aware of biased lineup procedures; nonetheless, exposure to these videotapes will not improve the accuracy of mock-jurors’ decisions. / Thesis (Ph.D, Psychology) -- Queen's University, 2008-07-24 15:00:30.512

This work presents three techniques for parameter identification for nonlinear systems. The methods presented are expanded from those presented in Adetola and Guay [3, 4, 5] and are intended to improve the performance of existing adaptive control systems. The first two methods exactly recover open-loop system parameters once a defined convergence condition is met. In either case, the true parameters are identified when the regressor matrix is of full rank and can be inverted. The third case uses a novel method developed in Adetola and Guay [5] to define a parameter uncertainty set. The uncertainty set is periodically updated to shrink around the true value of the parameters. Each method is shown to be applicable to a large class of linearly parameterized nonlinear discrete-time system. In each case, parameter convergence is guaranteed subject to an appropriate convergence condition, which has been related to a classical persistence of excitation condition. The effectiveness of the methods is demonstrated using a simulation example. The application of the uncertainty set technique to nonlinearly parameterized systems constitutes the main contribution of the thesis. The parameter uncertainty set method is generalized to the problem of adaptive estimation in nonlinearly parameterized systems, for both continuous-time and discrete-time cases. The method is demonstrated to perform well in simulation for a simplified model of a bioreactor operating under Monod kinetics. / Thesis (Master, Chemical Engineering) -- Queen's University, 2010-10-19 10:58:24.888

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