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This dissertation presents new fuzzy logic techniques for designing control systems for a wide class of complex systems. The methods are developed in detail for a crane system which contains one rigid-body and one oscillation mode. The crane problem is to transfer the rigid body a given distance such that the pendulation of the oscillation mode is regulated at the final time using a single control input. The investigations include in-depth studies of the time-optimal crane control problem as an integral part of the work. The main contributions of this study are: (1) Development of rule-based systems (both fuzzy and crisp) for the design of optimal controllers. This development involves control variable parametrization, rule derivation with parameter perturbation methods, and the design of rule based controllers, which can be combined with model-based feedback control methods. (2) A thorough investigation and analysis of the solutions for time-optimal control problems of oscillation mode systems, with particular emphasis on the use of phase-plane interpretation. (3) Development of fuzzy logic control system methodology using expert rules obtained through energy reducing considerations. In addition, dual mode control is a "spin-off" design method which, although no longer time optimal, can be viewed as a near-optimal control method which may be easier to implement. In both types of design optimization of the fuzzy logic controller can be used to improve performance. / Ph. D.

Flotation is an important separation process used in the mining industry. The process is based on hydrophobizing a selected mineral using an appropriate surfactant, so that an air bubble can spontaneously adhere on the mineral surface. The bubble-particle adhesion is possible only when the thin film of water between the bubble and particle ruptures, just like when two colloidal particles or air bubbles adhere with each other. Under most flotation conditions, however, both the double-layer and dispersion forces are repulsive, which makes it difficult to model the rupture of the wetting films using the DLVO theory. In the present work, we have measured the kinetics of film thinning between air bubble and flat surfaces of gold and silica. The former was hydrophobized by ex-site potassium amyl xanthate, while the latter by in-site Octadecyltrimetylammonium chlroride. The kinetics curves obtained with and without theses hydrophobizing agents were fitted to the Reynolds lubrication theory by assuming that the driving force for film thinning was the sum of capillary pressure and the disjoining pressure in a thin film. It was found that the kinetics curves obtained with hydrophilic surfaces can be fitted to the theory with the disjoining pressure calculated from the DLVO theory. With hydrophobized surfaces, however, the kinetics curves can be fitted only by assuming the presence of a non-DLVO attractive force (or hydrophobic force) in the wetting films. The results obtained in the present work shows that long-range hydrophobic forces is responsible for the faster drainage of wetting film. It is shown that the changes in hydrophobic forces upon the thin water film between air bubble and hydrophobic surface is dependent on hydrophobizing agent concentration, immersion time and the electrolyte concentration in solution. The obtained hydrophobic forces constant in wetting film K132 is compared with the hydrophobic forces constant between two solid surfaces K131 to verify the combining rule for flotation. / Master of Science

Yes / The rule of law, that is, the fair, competent, effective, and predictable application of laws that enhance, rather than undermine, social accountability and fundamental human rights, is a core function of the state, and forms part of its social contract with the citizenry. However, ensuring that a government upholds the rule of law requires a number of checks and balances. Some of this accountability and enforcement function lies with the other branches of government: oversight of the executive by the legislative branch through its committees and reports, and by the judiciary, which has its own proactive powers and can be petitioned by citizens and their representatives. But this republican structure can still be unresponsive or resistant to scrutiny, particularly when elites across the branches of government are indifferent to, or collude in, maintaining chronic problems in the justice system. Active non-governmental organisations (NGOs) are therefore recognised as a crucial component in the effective application of the rule of law due to their independence from government and their often-different perspective on the impact of unevenly applied and unjust laws and law enforcement through direct contact with the victims of arbitrary treatment. This chapter explores ways in which NGOs (both international and local) can contribute to strengthening rule of law through a case study of how the Open Society Institute and its Justice Initiative (OSJI) and a network of Brazilian NGOs developed a campaign to reduce the excessive use of pre-trial detention. It demonstrates how NGOs can fulfil important watchdog functions and are able to change laws, policies and practices that significantly improve the rule of law by working strategically with one another, with international partners and with sympathetic state actors.

Melting and vaporization of solids occur very often in nature and in man-made processes. Many analytical and numerical solutions exist for solving the temperature field in the liquid and solid regions, but inaccuracies persist in tracking the phase change interfaces and the numerical solution of the temperature field is usually cumbersome. The Boundary Element Method is proposed as an accurate, efficient way to solve for the temperature field and the interface positions in a phase change problem involving combined melting and vaporization. When comparing to specific one-dimensional test cases, accurate results arc obtained when using a sufficiently small time step. A comparison is made to existing data from a laser drilling experiment. The anticipated physical effects which occur on semi-infinite and finite domains arc confirmed. Consequently, this method can be used to model natural and industrial phenomena involving phase change. / Master of Science

An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand. Vehicle testing validates that the hybrid powertrain meets acceleration response targets while delivering an enjoyable driving experience. Simulation testing shows that the energy management strategy improved fuel economy in most drive cycles with improvements of 8.8% for US06, 9.8% for HWFET, and 0.1% for the EcoCAR Mobility Challenge Cycle. Battery state of charge management behavior is robust across a variety of drive cycles using inputs from both simulated and test drivers. The resulting energy management strategy delivers an efficient, responsive, and reliable hybrid electric vehicle. / Master of Science / A control strategy for a hybrid vehicle is developed to improve fuel efficiency without sacrificing vehicle responsiveness. Efficiency improvements are achieved by the strategy intelligently selecting to use the engine, motor, or a combination of the two to minimize fuel consumption. The strategy also handles the important tasks of maintaining the battery pack charge and smoothly transitioning between the engine and motor power. All together, this results in a hybrid vehicle with both improved fuel economy and an enjoyable driving experience.

Gillespie's stochastic simulation algorithm (SSA) has been a conventional method for stochastic modeling and simulation of biochemical systems. However, its population-based scheme faces the challenge from multi-state situations in many biochemical models. To tackle this problem, Morton-Firth and Bray's stochastic simulator (StochSim) was proposed with a particle-based scheme. The thesis first provides a detailed comparison between these two methods, and then proposes improvements on StochSim and a hybrid method to combine the advantages of the two methods. Analysis and numerical experiment results demonstrate that the hybrid method exhibits extraordinary performance for systems with both the multi-state feature and a high total population. In order to deal with the combinatorial complexity caused by the multi-state situation, the rules-based modeling was proposed by Hlavacek's group and the particle-based Network-Free Algorithm (NFA) has been used for its simulation. In this thesis, we improve the NFA so that it has both the population-based and particle-based features. We also propose a population-based method for simulation of the rule-based models. The bacterial chemotaxis model has served as a good biological example involving multi-state species. We implemented different simulation methods on this model. Then we constructed a graphical interface and compared the behaviors of the bacterium under different mechanisms, including simplified mathematical models and chemically reacting networks which are simulated stochastically. / Master of Science

The advancement in the field of artificial intelligence has been rapid in recent years and has revolutionized various industries. For example, convolutional neural networks (CNNs) perform image classification at a level equivalent to that of humans on many image datasets. These state-of-the-art networks reached unprecedented success using complex architectures with billions of parameters, numerous kernel configurations, weight initialization and regularization methods. This transitioned the models into black-box entities with little to no information on the decision-making process. This lack of transparency in decision making and started raising concerns amongst some sectors of user community such as the sectors, amongst others healthcare, finance and justice. This challenge motivated our research where we successfully produced human interpretable influential features from CNN for image classification and captured the interactions between these features by producing a concise decision tree making accurate classification decisions. The proposed methodology made use of pre-trained VGG16 with finetuning to extract feature maps produced by learnt filters. A decision tree was then induced on these extracted features that captured important interactions between the features. On the CelebA image dataset, we successfully produced human interpretable rules capturing the main facial landmarks responsible for segmenting males from females with the use of a decision tree which achieved 89.57% accuracy, while on the Cats vs Dogs dataset 87.55% accuracy was achieved.