Parameterised analytical models that describe the trimmed inflight behaviour of classical aircraft have been studied and are widely accepted by the flight dynamics community. Therefore, the primary role of aircraft parameter estimation is to quantify the parameter values which make up the models and define the physical relationship of the air vehicle with respect to its local environment. Nevertheless, a priori empirical predictions dependent on aircraft design parameters also exist, and these provide a useful means of generating preliminary values predicting the aircraft behaviour at the design stage. However, at present the only feasible means that exist to actually prove and validate these parameter values remains to extract them through physical experimentation either in a wind-tunnel or from a flight test. With the advancement of UAVs, and in particular smaller UAVs (less than 1m span) the ability to fly the full scale vehicle and generate flight test data presents an exciting opportunity. Furthermore, UAV testing lends itself well to the ability to perform rapid prototyping with the use of COTS equipment. Real-time system identification was first used to monitor highly unstable aircraft behaviour in non-linear flight regimes, while expanding the operational flight envelope. Recent development has focused on creating self-healing control systems, such as adaptive re-configurable control laws to provide robustness against airframe damage, control surface failures or inflight icing. In the case of UAVs real-time identification, would facilitate rapid prototyping especially in low-cost projects with their constrained development time. In a small UAV scenario, flight trials could potentialy be focused towards dynamic model validation, with the prior verification step done using the simulation environment. Furthermore, the ability to check the estimated derivatives while the aircraft is flying would enable detection of poor data readings due to deficient excitation manoeuvres or atmospheric turbulence. Subsequently, appropriate action could then be taken while all the equipment and personnel are in place. This thesis describes the development of algorithms in order to perform online system identification for UAVs which require minimal analyst intervention. Issues pertinent to UAV applications were: the type of excitation manoeuvers needed and the necessary instrumentation required to record air-data. Throughout the research, algorithm development was undertaken using an in-house Simulink© model of the Aerosonde UAV which provided a rapid and flexible means of generating simulated data for analysis. In addition, the algorithms were further tested with real flight test data that was acquired from the Cranfield University Jestream-31 aircraft G-NFLA during its routine operation as a flying classroom. Two estimation methods were principally considered, the maximum likelihood and least squares estimators, with the aforementioned found to be best suited to the proposed requirements. In time-domain analysis reconstruction of the velocity state derivatives ˙W and ˙V needed for the SPPO and DR modes respectively, provided more statistically reliable parameter estimates without the need of a α- or β- vane. By formulating the least squares method in the frequency domain, data issues regarding the removal of bias and trim offsets could be more easily addressed while obtaining timely and reliable parameter estimates. Finally, the importance of using an appropriate input to excite the UAV dynamics allowing the vehicle to show its characteristics must be stressed.

This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data. A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale can be considered be of rectangular shape or can be decomposed to a set of rectangles. Several approaches to rectangle fitting are presented and evaluated in Monte Carlo simulations. There are error-in-variables present and thus, geometric fitting is used. The objects can have parts that are subject to articulation. A modular least squares method with outlier rejection, that can handle articulated objects, is proposed. This method falls within the iterative closest point framework. Recognition when several similar models are available is discussed. The recognition method is applied in a query-based multi-sensor system. The system covers the process from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. In object detection and recognition based on laser radar data, the range value’s accuracy is important. A general direct-detection laser radar system applicable for hard-target measurements is modeled. Three time-of-flight estimation algorithms are analyzed; peak detection, constant fraction detection, and matched filter. The statistical distribution of uncertainties in time-of-flight range estimations is determined. The detection performance for various shape conditions and signal-tonoise ratios are analyzed. Those results are used to model the properties of the range estimation error. The detector’s performances are compared with the Cramér-Rao lower bound. The performance of a tool for synthetic generation of scanning laser radar data is evaluated. In the measurement system model, it is possible to add several design parameters, which makes it possible to test an estimation scheme under different types of system design. A parametric method, based on measurement error regression, that estimates an object’s size and orientation is described. Validations of both the measurement system model and the measurement error model, with respect to the Cramér-Rao lower bound, are presented.

Sounding rockets and small suborbital payloads provide a means for research in situ of the atmosphere and ionosphere. The trajectory and the attitude of the payload are critical for the evaluation of the scientific measurements and experiments. The trajectory refers the location of the measurement, while the attitude determines the orientation of the sensors. This thesis covers methods of trajectory and attitude reconstruction implemented in several experiments with small suborbital payloads carried out by the Department of Space and Plasma Physics in 2012--2016. The problem of trajectory reconstruction based on raw GPS data was studied for small suborbital payloads. It was formulated as a global least squares optimization problem. The method was applied to flight data of two suborbital payloads of the RAIN REXUS experiment. Positions and velocities were obtained with high accuracy. Based on the trajectory reconstruction technique, atmospheric densities, temperatures, and horizontal wind speeds below 80 km were obtained using rigid free falling spheres of the LEEWAVES experiment. Comparison with independent data indicates that the results are reliable for densities below 70 km, temperatures below 50 km, and wind speeds below 45 km. Attitude reconstruction of suborbital payloads from yaw-pitch-roll Euler angles was studied. The Euler angles were established by two methods: a global optimization method and an Unscented Kalman Filter (UKF) technique. The comparison of the results shows that the global optimization method provides a more accurate fit to the observations than the UKF. Improving the results of the falling sphere experiments requires understanding of the attitude motion of the sphere. An analytical consideration was developed for a free falling and axisymmetric sphere under aerodynamic torques. The motion can generally be defined as a superposition of precession and nutation. These motion phenomena were modeled numerically and compared to flight data. / <p>QC 20170510</p>

This dissertation explores the possibility of using an imaging approach to model classical pharmacokinetic (PK) problems. The kinetic parameters which describe the uptake rates of a drug within a biological system, are parameters of interest. Knowledge of the drug uptake in a system is useful in expediting the drug development process, as well as providing a dosage regimen for patients. Traditionally, the uptake rate of a drug in a system is obtained via sampling the concentration of the drug in a central compartment, usually the blood, and fitting the data to a curve. In a system consisting of multiple compartments, the number of kinetic parameters is proportional to the number of compartments, and in classical PK experiments, the number of identifiable parameters is less than the total number of parameters. Using an imaging approach to model classical PK problems, the support region of each compartment within the system will be exactly known, and all the kinetic parameters are uniquely identifiable. To solve for the kinetic parameters, an indirect approach, which is a two part process, was used. First the compartmental activity was obtained from data, and next the kinetic parameters were estimated. The novel aspect of the research is using listmode data to obtain the activity curves from a system as opposed to a traditional binned approach. Using techniques from information theoretic learning, particularly kernel density estimation, a non-parametric probability density function for the voltage outputs on each photo-multiplier tube, for each event, was generated on the fly, which was used in a least squares optimization routine to estimate the compartmental activity. The estimability of the activity curves for varying noise levels as well as time sample densities were explored. Once an estimate for the activity was obtained, the kinetic parameters were obtained using multiple cost functions, and the compared to each other using the mean squared error as the figure of merit.

The flight testing and evaluation of collected data must be efficient during intensive flight-test programs such as the ones conducted during development of new aircraft. The aim of this thesis has thus been to produce a first version of an aerodynamic derivative estimation program that is to be used during real-time flight tests. The program is to give a first estimate of the aerodynamic derivatives as well as check the quality of the data collected and thus serve as a decision support during tests. The work that has been performed includes processing of data in order to use it in computations, comparing a batch and a sequential estimation method using real-time data and programming a user interface. All computations and programming has been done in Matlab. The estimation methods that have been compared are both built on transforming data to the frequency domain using a Chirp z-transform and then estimating the aerodynamic derivatives using complex least squares with instrumental variables.The sequential frequency domain method performs estimates at a given interval while the batch method performs one estimation at the end of the maneuver. Both methods compared in this thesis produce equal results. The continuous updates of the sequential method was however found to be better suited for a real-time application than the single estimation of the batch method. The telemetric data received from the aircraft must be synchronized to a common frequency of 60 Hz. Missing samples of the data stream must be linearly interpolated and different units of measured parameters must be corrected in order to be able to perform these estimations in the real-time test environment.

This research project sought to explore, compare and develop chemometric methods with the goal of resolving chromatographically overlapped peaks though the use of spectral information gained from the four-way data sets associated with comprehensive two-dimensional liquid chromatography with diode array detection (LC ´ LC-DAD). A chemometric method combining iterative key set factor analysis (IKSFA) and multivariate curve resolution-alternating least squares (MCR-ALS) was developed. In the section of urine data analyzed, over 50 peaks were found, with 18 visually observable and 32 additional compounds found only after application of the chemometric method. Upon successful chemometric resolution of chromatographically overlapped peaks, accurate and precise quantification was then necessary. Of the compared methods for quantification, the manual baseline method was determined to offer the best precisions. Of the 50 found peaks from the urine analysis, 34 were successfully quantified using the manual baseline method with percent relative standard deviations ranging from 0.09 to 16. The accuracy of quantification was then investigated by the analysis of wastewater treatment plant effluent (WWTPE) samples. The chemometrically determined concentration of the unknown phenytoin sample was found to not exhibit a significant difference from the result obtained by the LC-MS/MS reference method, and the precision of the IKSFA-ALS method was better than that of the precision of the LC-MS/MS analysis. Chromatographic factors (data complexity, large dynamic range, retention time shifting, chromatographic and spectral peak overlap and background removal, were all found to affect the quantification results. The last part of this work focused on rapid screening methods that were capable of locating peaks between samples that exhibited significant differences in concentration. The aim here was to reduce the amount of data required to be resolved and quantified to only those peaks that were of interest. This would then reduce the time required to analyze large, complex samples by eliminating the need to first quantify all peaks in a given sample for many different samples. Both the similarity index (SI) method and the Fisher ratio (FR) method were found to fulfill this requirement in a rapid means of screening fifteen wine samples.

To deal with the increasing information overload and with the structured and unstructured data complexity, many organizations have implemented enterprise content management (ECM) systems. Published research on ECM so far is very limited and reports on ECM implementations have been scarce until recently (Tyrväinen et al. 2006). However, the little available ECM literature shows that many organizations using ECM focus on operational benefits while strategic decision-making benefits are rarely considered. Moreover, the strategic capabilities such as decision making capabilities of ECM are not fully investigated in the current literature. In addition, the literature lacks a strategic management framework (SMF) that links strategies, business objectives, and performance management although there are several published studies that discuss ECM strategy. A strategic management framework would seem essential to effectively manage ECM strategy formulation, implementation, and performance evaluation (Kaplan and Norton 1996; Ittner and Larcker 1997). The absence of an appropriate strategic management framework keeps organizations from effective strategic planning, implementation, and evaluation, which affects the organizational capabilities overall. Therefore, the objective of this dissertation is to determine the decision support capabilities of ECM, and specify how ECM strategies can be formulated, implemented, and evaluated in order to fully utilize the ECM strategic capabilities. Structural equation modeling as well as design science approaches will be adopted to achieve the dissertation objectives.

This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative. Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art algorithms. The training time improvement is achieved at no cost, in other words, the accuracy is kept at the same level. All the experiments that support this claim were conducted on various datasets within the strict double cross-validation scheme. DL2 SVM solved with NN ISDA has faster training time on both medium and large datasets. In addition to a comprehensive DL2 SVM model we introduce and derive its three variants. Three different solvers for the DL2's system of linear equations with nonnegativity constraints were implemented, presented and compared in this dissertation.

The increasing penetration of distributed generation, responsive loads and emerging smart metering technologies will continue the transformation of distribution systems from passive to active network conditions. In such active networks, State Estimation (SE) tools will be essential in order to enable extensive monitoring and enhanced control technologies. In future distribution management systems, the novel electrical power distribution system SE requires development in a scalable manner in order to accommodate small to massive size networks, be operable with limited real time measurements and a restricted time frame. Furthermore, a significant phase of new sensor deployment is inevitable to enable distribution system SE, since present-day distribution networks lack the required level of measurement and instrumentation. In the above context, the research presented in this thesis investigates five SE optimization solution methods with various case studies related to expected scenarios of future distribution networks to determine their suitability. Hachtel's Augmented Matrix method is proposed and developed as potential SE optimizer for distribution systems due to its potential performance characteristics with regard to accuracy and convergence. Differential Evolution Algorithm (DEA) and Overlapping Zone Approach (OZA) are investigated to achieve scalability of SE tools; followed by which the network division based OZA is proposed and developed. An OZA requiring additional measurements is also proposed to provide a feasible solution for voltage estimation at a reduced computation cost. Realising the requirement of additional measurements deployment to enable distribution system SE, the development of a novel meter placement algorithm that provides economical and feasible solutions is demonstrated. The algorithm is strongly focused on reducing the voltage estimation errors and is capable of reducing the error below desired threshold with limited measurements. The scalable SE solution and meter placement algorithm are applied on a multi-processor system in order to examine effective reduction of computation time. Significant improvement in computation time is observed in both cases by dividing the problem into smaller segments. However, it is important to note that enhanced network division reduces computation time further at the cost of accuracy of estimation. Different networks including both idealised (16, 77, 356 and 711 node UKGDS) and real (40 and 43 node EG) distribution network data are used as appropriate to the requirement of the applications throughout this thesis.

My final thesis firstly addresses basic knowledge of the theory of stochastic processes. This is firstly due to the author's effort to make the thesis more comprehensible, and also due to the need for introduction of key concepts. The autoregressive model AR(1) is defined in the thesis through basic linear time series models, and in this model, the estimation of model parameter by the method of least squares is introduced. For this estimation, the theoretical findings of the thesis are extended through the classical limit theory. Furthermore, the models with their parameter dependent on number of observations are introduced and models of NIAR (1) are defined. Classical limit theory for least squares estimation is then enriched by the limit theory in these models. The category of more general models is introduced and using the acquired knowledge, the features for the model AR (1) are derived. This thesis deals with this issue in models of NIAR (1) and its area of interest is also the bootstrap. The theoretical part of the thesis is supplemented by a practical part represented by numerical studies.