<p>The following research was performed to establish an engineering knowledge management strategy for improving knowledge sharing concerning risk prediction models for hospital readmissions. The health care industry has been met with numerous approaches toward reducing preventable hospital readmissions; a recognized quality of care metric. Risk prediction models are increasingly being seen as a data-driven readmission reduction tactic that proactively inform health care teams of a patient?s risk for readmission by using a sophisticated blend of data derived from electronic health records and other pertinent sources. In an industry where effective communication is critical to the deployment, execution, and sustainment of delivering quality health care to patients, validated methods contributing to the improvement of communication processes are generously accepted and encouraged. Application of an engineering knowledge management strategy was identified as a novel approach to guiding the flow of knowledge regarding the use and interpretation of risk prediction models for readmissions throughout health care teams. An engineering management research method was linked to the process of developing a knowledge management strategy and ultimately resulted in a pilot (trial) to realize the impact of improved knowledge capturing and sharing activities on using risk prediction models as a readmission reduction tactic. Overall, application of this praxis is intended to be performed by health care consultants and industrial/systems engineers involved in deploying readmission-based risk prediction models in hospitals.

As the world energy demand increases, and the desire for cleaner fuels strengthens, a number of major oil and gas companies are developing Floating Liquefied Natural Gas (FLNG) vessels to harvest natural gas ‘stranded’ in reservoirs that have previously been considered too uneconomic to develop. A key requirement for this new generation of vessels is a high volume of low temperature seawater for process cooling. The aim of this research is to investigate whether the concepts underpinning free hanging cantilever seawater intake risers used on Floating, Production, Storage and Offloading (FPSO) vessels can be extended to the design of seawater intake risers for FLNG vessels in order to reach and import colder seawater from depths greater than has so far been achieved with these systems. The research focusses on establishing the physical, mechanical and fatigue properties of a number of material elements under consideration for this application and then investigates a number of combinations to determine the optimum configuration for a hybrid deep seawater intake riser. To demonstrate the strength and fatigue capabilities of the hybrid riser, the selected configuration is then subject to a more detailed analysis with consideration of a number of key aspects such as vessel motion, marine growth, vortex induced vibration, stability due to internal flow and excursion due to external fluid. A number of sensitivities are also performed with respect to riser damping, riser length, vessel size and geographical location. Additionally, the flow characteristics in terms of pressure loss and temperature gain are examined and a number of sensitivities performed to show that the cold seawater can be imported effectively. Finally, using published data for FLNG vessels currently under construction, an economic argument is presented to highlight the potential cost advantage of reaching and importing colder seawater by means of a deep seawater intake riser. As a result of this research, the solution being presented offers a significant technological advantage for these systems in the field enabling high volumes of seawater to be imported from greater depths whilst accommodating the loads induced by the environmental conditions and minimising the loads induced into the hull of the vessel. Furthermore, the solution is based on the concepts of a field proven system, thereby limiting the risks associated with untested technological advancements. The findings of this research enable the process efficiencies of FLNG vessels to be greatly enhanced thus contributing to the more efficient extraction of a cleaner fuel which, in a world with ever increasing energy demands, is critical to the global economy. The novelty of the research is demonstrated by two successful patent applications, one in relation to the improved features of existing seawater intake riser systems and the other in relation to the use of multiple material elements for a hybrid seawater intake riser. Both patents have been examined and granted in five jurisdictions, namely, Europe, Japan, China. South Korea and the USA.

There is a constant requirement to understand scour especially regarding its prevention, due to the potential impact and disastrous consequences. The installation of offshore wind turbines is haunted by scour mitigation and at the start of the offshore wind turbine boom in the early 2000’s this was achieved using overzealous amounts of rock armour. However, as investment and cost efficiency has increased, protection methods have been refined, but, there remains significant room for improvement. Research into offshore sediment dynamics has benefited greatly by computational advancements providing a greater understanding of processes and the driving mechanisms; leading to protection method improvements and reductions in environmental impact. The premise of this study is to push this knowledge further, by developing and validating a novel scour model within CFD software that can be used to simulate and analyse offshore scour; specifically, the scour around complex, new offshore wind turbine foundation geometries.

Stereo correspondence is used in the extraction of the three dimensional structure of a scene. This is possible because the difference or displacement in position of corresponding Image points (also known as disparity) is related to the three dimensional position of the object point. In the stereo correspondence problem, the object is to determine the locations in each image that are the projection of the same physical point in space. No general solution to the correspondence problem exists mainly due to ambiguities into what constitutes a match. To resolve these ambiguities constraints and assumptions must be made. This thesis approaches this problem using an optimal and dense image matching procedure. The procedure is based on a method where epipolar and ordering constraints are employed. New to this approach is that this inherently constrained problem is solved in an unconstrained manner. In addition, reliance on heuristics is significantly reduced. The thesis presents a one dimensional and a two dimensional approach for addressing this problem. The two dimensional approach is a generalization of the one dimensional method such that spatial correlations inherent in images can aid in the matching process of inherently noisy images.

abstract: Interest in Micro Aerial Vehicle (MAV) research has surged over the past decade. MAVs offer new capabilities for intelligence gathering, reconnaissance, site mapping, communications, search and rescue, etc. This thesis discusses key modeling and control aspects of flapping wing MAVs in hover. A three degree of freedom nonlinear model is used to describe the flapping wing vehicle. Averaging theory is used to obtain a nonlinear average model. The equilibrium of this model is then analyzed. A linear model is then obtained to describe the vehicle near hover. LQR is used to as the main control system design methodology. It is used, together with a nonlinear parameter optimization algorithm, to design a family multivariable control system for the MAV. Critical performance trade-offs are illuminated. Properties at both the plant output and input are examined. Very specific rules of thumb are given for control system design. The conservatism of the rules are also discussed. Issues addressed include What should the control system bandwidth be vis--vis the flapping frequency (so that averaging the nonlinear system is valid)? When is first order averaging sufficient? When is higher order averaging necessary? When can wing mass be neglected and when does wing mass become critical to model? This includes how and when the rules given can be tightened; i.e. made less conservative. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2015

abstract: Modern Complex electronic system include multiple power domains and drastically varying power consumption patterns, requiring the use of multiple power conversion and regulation units. High frequency switching converters have been gaining prominence in the DC-DC converter market due to their high efficiency. Unfortunately, they are all subject to higher process variations jeopardizing stable operation of the power supply. This research mainly focus on the technique to track changes in the dynamic loop characteristics of the DC-DC converters without disturbing the normal mode of operation using a white noise based excitation and correlation. White noise excitation is generated via pseudo random disturbance at reference and PWM input of the converter with the test signal being spread over a wide bandwidth, below the converter noise and ripple floor. Test signal analysis is achieved by correlating the pseudo-random input sequence with the output response and thereby accumulating the desired behavior over time and pulling it above the noise floor of the measurement set-up. An off-the shelf power converter, LM27402 is used as the DUT for the experimental verification. Experimental results show that the proposed technique can estimate converter's natural frequency and Q-factor within ±2.5% and ±0.7% error margin respectively, over changes in load inductance and capacitance. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

abstract: Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user. In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks. Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications. Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior. / Dissertation/Thesis / Masters Thesis Engineering 2015

abstract: ABSTRACT The problem of litigation and disputes in the construction sector is a major impediment to countries’ development goals. The purpose of this paper is to investigate the problem of high legal costs and long delays that arise due to litigation involving project owners, designers, contractors and other construction parties worldwide and in Saudi Arabia, as well as to give recommendation according to the outcomes of this research. The causes of litigious behavior in Saudi Arabia and other countries around the world were identified and documented, also the differences in litigation of the Saudi Arabian construction industry as compared to other countries were identified. Preliminary investigations revealed that there are some level of similarity in the nature of the causes. Thus, these causes were grouped into three main categories which are expectation factors, communications factors and documentation factors. Further research based on existing literature showed that the practices used to minimize litigation in the construction industry were investigated. The following delivery process were researched: design-build (DB) delivery method, Alliance Contracting, Construction Manager at Risk (CMAR), Best Value Approach, Integrated Project Delivery (IPD), and Public-Private Partnerships (PPPs), and the PIPS/PIRMS approach. These delivery methods were found to have issues, which means the methods by observation do not seem to be the ideal solution to minimize litigation in the construction industry. The only delivery method found to have no litigation issues was the PIPS/PIRMS approach. / Dissertation/Thesis / Masters Thesis Construction 2015

abstract: A roofing manufacturer wants to differentiate themselves from other roofing manufacturers based on performance information. However, construction industry has revealed poor performance documentation in the last couple of decades. With no current developed performance measurement model in the industry, two roofing manufacturers approached the research group to implement a warranty program that measures the performance information of their systems and applicators. Moreover, the success of any project in the construction industry heavily relies upon the capability of the contractor(s) executing the project. Low-performing contractors are correlated with increased cost and delayed schedules, resulting in end-user dissatisfaction with the final product. Hence, the identification and differentiation of the high performing contractors from their competitors is also crucial. The purpose of this study is to identify and describe a new model for measuring manufacturer performance and differentiating contractor performance and capability for two roofing manufacturers (Manufacturer 1 and Manufacturer 2) in the roofing industry. The research uses multiple years of project data and customer satisfaction data collected for two roofing manufacturers for over 1,000 roofing contractors. The performance and end-user satisfaction was obtained for over 7,000 manufacturers' projects and each contractor associated with that project for cost, schedule, and quality metrics. The measurement process was successfully able to provide a performance measurement for the manufacturer based on the customer satisfaction and able to identify low performing contractors. This study presents the research method, the developed measurement model, and proposes a new performance measurement process that entities in the construction industry can use to measure performance. / Dissertation/Thesis / Doctoral Dissertation Construction 2016

abstract: Since the inception of Internet of Things (IoT) framework, the amount of interaction between electronic devices has tremendously increased and the ease of implementing software between such devices has bettered. Such data exchange between devices, whether between Node to Server or Node to Node, has paved way for creating new business models. Wireless Video Sensor Network Platforms are being used to monitor and understand the surroundings better. Both hardware and software supporting such devices have become much smaller and yet stronger to enable these. Specifically, the invention of better software that enable Wireless data transfer have become more simpler and lightweight technologies such as HTML5 for video rendering, Common Gateway Interface(CGI) scripts enabling interactions between client and server and WebRTC from Google for peer to peer interactions. The role of web browsers in enabling these has been vastly increasing. Although HTTP is the most reliable and consistent data transfer protocol for such interactions, the most important underlying challenge with such platforms is the performance based on power consumption and latency in data transfer. In the scope of this thesis, two applications using CGI and WebRTC for data transfer over HTTP will be presented and the power consumption by the peripherals in transmitting the data and the possible implications for those will be discussed. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016