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In order to investigate the use of fuzzy logic in decision-support systems (DSS) for dairy cattle breeding, a first-generation prototype software system was developed. The objectives were to determine the advantages and limitations of fuzzy logic for this type of application, and to establish a basis for the development of more complete DSS in the future. The goal of the prototype DSS was to make culling decisions on the basis of monthly production data. An analysis of the development process of this prototype demonstrated the importance of creating a thorough methodology for the elicitation and implementation of knowledge. A framework for the development of fuzzy decision-support systems was established, consisting of four phases: the project groundwork phase, elicitation of knowledge from the expert, implementation of that knowledge, and system validation. In this framework, it is proposed that, in the case of multiple experts, knowledge can be amalgamated or aggregated. Once this framework was established, a second-generation prototype DSS was developed. Contrary to the first-generation prototype, where the encoded expertise was limited to three experts from the same domain, the second-generation prototype considered the knowledge of two individuals from each of three domains (Dairy researchers, Producers, and Dairy herd improvement specialists). An aggregation approach was used which involved the development and maintenance of separate modules, each containing the compiled expertise of one of the six experts.

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One of the fundamental aspects of a virtual environment is the virtual agents that inhabit them. In many applications, virtual agents are required to perceive input information from their environment and make decisions appropriate to their task based on their programmed reaction to those inputs. The research presented in this thesis focuses on the reactive behaviour of the agents. We propose a new control architecture to allow agents to behave autonomously in navigation tasks in unknown environments. Our behaviour-based architecture uses fuzzy logic to solve problems of agent control and action selection and which can coordinate conflicts among different operations of reactive behaviours. A Fuzzy Associative Memory (FAM) is used as the process of encoding and mapping the input fuzzy sets to the output fuzzy set and to optimise the fuzzy rules. Our action selection algorithm is based on the fuzzy α-level method with the Hurwicz criterion. The main objective of the thesis was to implement agent navigation from point to point by a coordination of planning, sensing and control. However, we believe that the reactive architecture emerging from this research is sufficiently general that it could be applied to many applications in widely differing domains where real-time decision making under uncertainty is required. To illustrate this generality, we show how the architecture is applied to a different domain. We chose the example of a computer game since it clearly demonstrates the attributes of our architecture: real-time action selection and handling uncertainty. Experimental results are presented for both implementations which show how the fuzzy method is applied, its generality and that it is robust enough to handle different uncertainties in different environments. In summary, the proposed reactive architecture is shown to solve aspects of behaviour control for autonomous virtual agents in virtual environments and can be applied to various application domains.

This thesis describes the development and experimental results of a system to explore cloning of facial expressions between dissimilar face models, so new faces can be animated using the animations from existing faces. The system described in this thesis uses fuzzy membership functions and subtractive clustering to represent faces and expressions in an intermediate space. This intermediate space allows expressions for face models with different resolutions to be compared. The algorithm is trained for each pair of faces using particle swarm optimization, which selects appropriate weights and radii to construct the intermediate space. These techniques allow the described system to be more flexible than previous systems, since it does not require prior knowledge of the underlying implementation of the face models to function.

Unreinforced masonry construction is considered to be the most vulnerable forms of construction as demonstrated through recent earthquakes. In Canada, many densely populated cities such as (Vancouver, Montreal and Ottawa) have large inventories of seismically vulnerable masonry structures. Although measures have been taken to rehabilitate and increase the seismic resistance of important and historic structures, many existing unreinforced masonry structures have not been retrofitted and remain at risk in the event of a large magnitude earthquake. There is therefore a need to identify buildings at risk and develop tools for assessing the seismic vulnerability of existing unreinforced masonry structures in Canada. This thesis presents results from an ongoing research program which forms part of a multi-disciplinary effort between the University of Ottawa’s Hazard Mitigation and Disaster Management Research Centre and the Geological Survey of Canada (NRCAN) to assess the seismic vulnerability of buildings in dense urban areas such as Ottawa, Ontario. A risk-based seismic assessment tool (CanRisk) has been developed to assess the seismic vulnerability of existing unreinforced masonry and reinforced concrete structures. The seismic risk assessment tool exploits the use of fuzzy logic, a soft computing technique, to capture the vagueness and uncertainty within the evaluation of the performance of a given building. In order to conduct seismic risk assessments, a general building inventory and its spatial distribution and variability is required for earthquake loss estimations. The Urban Rapid Assessment Tool (Urban RAT) is designed for the rapid collection of building data in urban centres. This Geographic Information System (GIS) based assessment tool allows for intense data collection and revolutionizes the traditional sidewalk survey approach for collecting building data. The application of CanRisk and the Urban RAT tool to the City of Ottawa is discussed in the following thesis. Data collection of over 13,000 buildings has been obtained including the seismic risk assessment of 1,465 unreinforced masonry buildings. A case study of selected URM buildings located in the City of Ottawa was conducted using CanRisk. Data obtained from the 2011 Christchurch Earthquake in New Zealand was utilized for verification of the tool.

This thesis presents two modern control methods to address the Internet traffic congestion control issues. They are based on a distributed traffic management framework for the fast-growing Internet traffic in which routers are deployed with intelligent or optimal data rate controllers to tackle the traffic mass. The first one is called the IntelRate (Intelligent Rate) controller using the fuzzy logic theory. Unlike other explicit traffic control protocols that have to estimate network parameters (e.g., link latency, bottleneck bandwidth, packet loss rate, or the number of flows), our fuzzy-logic-based explicit controller can measure the router queue size directly. Hence it avoids various potential performance problems arising from parameter estimations while reducing much computation and memory consumption in the routers. The communication QoS (Quality of Service) is assured by the good performances of our scheme such as max-min fairness, low queueing delay and good robustness to network dynamics. Using the Lyapunov’s Direct Method, this controller is proved to be globally asymptotically stable. The other one is called the OFEX (Optimal and Fully EXplicit) controller using convex optimization. This new scheme is able to provide not only optimal bandwidth allocation but also fully explicit congestion signal to sources. It uses the congestion signal from the most congested link, instead of the cumulative signal from a flow path. In this way, it overcomes the drawback of the relatively explicit controllers that bias the multi-bottlenecked users, and significantly improves their convergence speed and throughput performance. Furthermore, the OFEX controller design considers a dynamic model by proposing a remedial measure against the unpredictable bandwidth changes in contention-based multi-access networks (such as shared Ethernet or IEEE 802.11). When compared with the former works/controllers, such a remedy also effectively reduces the instantaneous queue size in a router, and thus significantly improving the queueing delay and packet loss performance. Finally, the applications of these two controllers on wireless local area networks have been investigated. Their design guidelines/limits are also provided based on our experiences.

Wireless Sensor Network routing protocols rely on the estimation of the quality of the links between nodes to determine a suitable path from the data source nodes to a data-collecting node. Several link estimators have been proposed, but most of these use only one link property. Fuzzy logic based link quality estimators have been recently proposed which consider a number of link quality metrics. The fuzzification of crisp values to fuzzy values is done through membership functions. The shape of the fuzzy link quality estimator membership functions is primarily performed leveraging qualitative knowledge and an improper assignment of fuzzy membership functions can lead to poor route selection and hence to unacceptable packet losses. This thesis evaluated the Channel Quality membership function of, an existing fuzzy link quality estimator and it was seen that this membership function didn???t perform as well as expected. This thesis presents an experimental approach to determine a suitable Channel Quality fuzzy membership function based on varying the shape of the fuzzy set for a multipath wireless sensor network scenario and choosing an optimum shape that maximizes the Packet Delivery Ratio of the network. The computed fuzzy set membership functions were evaluated against an existing fuzzy link quality estimator under more complex scenarios and it is shown the performance of the experimental refined membership function was better in terms of packet reception ratio and end to end delay.The fuzzy link quality estimator was applied in WiseRoute (a simple converge cast based routing protocol) and shown that this SNR based fuzzy link estimator performed better than the original implemented RSSI based link quality used in WiseRoute.