published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

In today’s modern society, we are surrounded by a multitude of digital devices.The number of available digital devices is set to grow even more. As the trendcontinues, product life-cycle is a major issue in mass production of these devices.Testing and verification is responsible for a significant percentage of the productioncost of digital devices. Time efficient procedures for testing and characterization aretherefore sought for. Moreover, the need for flexible and low-cost solutions in thedesign architecture of radio frequency devices coupled with the demand for highdata rate has presented a challenge caused by interferences from the analog circuitparts. Study of digital signal processing based techniques which would alleviate theeffects of the analog impairments is therefore a pertinent subject. In the first part of this thesis, we address parameter estimation based on wave-form fitting. We look at the sinewave model for parameter estimation which iseventually used to characterize the performance of a device. The underlying goal isto formulate and analyze a set of new parameter estimators which provide a moreaccurate estimate than well known estimators. Specifically, we study the maximum-likelihood (ML) SNR estimator employing the three-parameter sine fit and derivealternative estimator based on its statistical distribution. We show that the meansquare error (MSE) of the alternative estimators is lower than the MSE of the MLestimator for a small sample size and a few of the new estimators are very close tothe Cramér-Rao lower bound (CRB). Simply put, the number of acquired measure-ment samples translate to measurement time, implying that the fewer the numberof samples required for a given accuracy, the faster the test would be. We alsostudy a sub-sampling approach for frequency estimation problem in a dual channelsinewave model with common frequency. Coprime subsampling technique is usedwhere the signals from both channels are uniformly subsampled with coprime pairof sparse samplers. Such subsampling technique is especially beneficial to lower thesampling frequency required in applications with high bandwidth requirement. TheCRB based on the co-prime subsampled data set is derived and numerical illus-trations are given showing the relation between the cost in performance based onthe mean squared error and the employed coprime factors for a given measurementtime. In the second part of the thesis, we deal with the problem of phase-noise (PHN).First, we look at a scheme in orthogonal frequency-division multiplexing (OFDM)system where pilot subcarriers are employed for joint PHN compensation, channelestimation and symbol detection. We investigate a method where the PHN statis-tics is approximated by a finite number of vectors and design a PHN codebook. Amethod of selecting the element in the codebook that is closest to the current PHNrealization with the corresponding channel estimate is discussed. We present simula-tion results showing improved performance compared to state-of-the art techniques.We also look at a sequential Monte-Carlo based method for combined channel im-pulse response and PHN tracking employing known OFDM symbols. Such techniqueallows time domain compensation of PHN such that simultaneous cancellation ofthe common phase error and reduction of the inter-carrier interference occurs. / <p>QC 20150529</p>

Designing high performance control of rotorcraft unmanned aerial vehicle (UAV) requires a mathematical model that describes the dynamics of the vehicle. The model is derived from first principle modelling, such as rigid-body dynamics, actuator dynamics and etc. It is found that simplified decoupled model of RUAV has slightly better data fitting compared with the complex model for helicopter attitude dynamics in hover or near hover flight condition. In addition, the simplified modelling approach has made the analysis of system dynamics easy. System identification method is applied to identify the unknown intrinsic parameters in the nominal model, where manual piloted flight experiment is carried out and input-output data about a nominal operating region is recorded for parameters identification process. Integral-based parameter identification algorithm is then used to identify model parameters that give the best matching between the simulation and measured output response. The results obtained show that the dominant dynamics is captured. The advantages of using integral-based method include the fast computation time, insensitive to initial parameter value and fast convergence rate in comparison with other contemporary system identification methods such as prediction error method (PEM), maximum likelihood method, equation error method and output error method. Besides, the integral-based parameter identification method can be readily extended to tackle slow time-varying model parameters and fast varying disturbances. The model prediction is found to be improved significantly when the iterative integral-based parameter identification is employed and thus further validates the minimal modelling approach. From the literature review, many control schemes have been designed and validated in simulation. However, few of them has really been implemented in real flight as well as under windy and severe conditions, where unpredictable large system parameters variations and unexpected disturbances are present. Therefore, the emphasis on this part will be on the control design that would have satisfactory reference sequence tracking or regulation capability in the presence of unmodelled dynamics and external disturbances. Generalised Predictive Controller (GPC) is particularly considered as the helicopter attitude dynamics control due to its insensitivity with respect to model mismatch and its capability to address the control problem of nominal model with deadtime. The robustness analysis shows that the robustness of the basic GPC is significantly improved using the Smith Predictor (SP) in place of optimal predictor in basic GPC. The effectiveness of the proposed robust GPC was well proven with the control of helicopter heading on the test rig in terms of the reference sequence tracking performance and the input disturbance rejection capability. The second motivation is the investigation of adaptive GPC from the perspective of performance improvements for the robust GPC. The promising experimental results prove the feasibility of the adaptive GPC controller, and especially evident when the underlying robust GPC is tuned with low robustness and legitimates the use of simplified model. Another approach of robust model predictive control is considered where disturbance is identified in real‐time using an iterative integral‐based method.

Hydraulic tomography has been tested at the field scale, lab scale and in synthetic experiments. Recently Illman and Berg have conducted studies at the lab scale. Using their data hydraulic tomography can be compared to homogeneous anisotropic solutions using one pumping well or multiple pumping wells. It has been found that hydraulic tomography out performs homogenous methods at predicting hydraulic head for validation pumping experiments. Also it has been shown in this study that homogenous anisotropic tests exhibit scenario dependent behavior. Additional tests performed to further validate the conclusions made in this experiment include spatial moment analysis, response surface analysis, and synthetic hydraulic tomography and show consistent results providing additional validation of these findings. Additional study examining the principle of reciprocity has proven inconclusive.

In this research, a recent version of the Boreal Ecosystem Productivity Simulator (BEPS), called BEPS-TerrainLab, was adapted to northern peatlands and evaluated using observations made at the Mer Bleue bog located near Ottawa, Ontario, and the Sandhill fen located near Prince Albert, Saskatchewan. The code was extended and modified with a major focus on the adequate representation of northern peatlands' multi-layer canopy and the associated processes related to energy, water vapour and carbon dioxide fluxes through remotely-sensed leaf area index (LAI) maps. An important prerequisite for the successful mapping of LAI based on remote sensing imagery is the accurate measurement of LAI in the field with a standard technique such as the LAI-2000 plant canopy analyzer. As part of this research, a quick and reliable method to determine shrub LAI with the LAI-2000 instrument was developed. This method was used to collect a large number of LAI data at the Mer Bleue bog for the development of a new remote sensing-based methodology using multiple endmember spectral unmixing that allows for separate tree and shrub LAI mapping in ombrotrophic peatlands. A slight modification of this methodology allows for its application to minerotrophic peatlands and their surrounding landscapes. These LAI maps were used to explicitly represent the tree and shrub layers of the Mer Bleue bog and the tree and shrub/sedge layers of the Sandill fen within BEPS-TerrainLab. The adapted version of BEPS-TerrainLab was used to investigate the in fluence of mescoscale topography (Mer Bleue bog) and macro- and mesoscale topography (Sandhill fen) on wetness, evapotranspiration, and gross primary productivity during the snow-free period of 2004. This research suggests that future peatland ecosystem modelling efforts at regional and continental scales should include a peatland type-specific differentiation of macro- and mesoscale topographic effects on hydrology, to allow for a more realistic simulation of peatlands' soil water balance. This is an important prerequisite for the reduction of currently existing uncertainties in wetlands' contribution to North America's carbon dioxide and methane annual fluxes from an ecosystem modelling perspective.

The primary aim of this thesis is the elucidation of covariate effects on the dependence structure of random variables in bivariate or multivariate models. We develop a unified approach via a conditional copula model in which the copula is parametric and its parameter varies as the covariate. We propose a nonparametric procedure based on local likelihood to estimate the functional relationship between the copula parameter and the covariate, derive the asymptotic properties of the proposed estimator and outline the construction of pointwise confidence intervals. We also contribute a novel conditional copula selection method based on cross-validated prediction errors and a generalized likelihood ratio-type test to determine if the copula parameter varies significantly. We derive the asymptotic null distribution of the formal test. Using subsets of the Matched Multiple Birth and Framingham Heart Study datasets, we demonstrate the performance of these procedures via analyses of gestational age-specific twin birth weights and the impact of change in body mass index on the dependence between two consequent pulse pressures taken from the same subject.

A steady-state mathematical model for the stripping section of an industrial EPDM rubber production process was developed for a three-tank process, and two four-tank processes. The experiments that were conducted to determine model parameters such as equivalent radius for EPDM particles, as well as solubility and diffusivity parameters for hexane and ENB in EPDM polymer are described. A single-particle multiple-tank model was developed first, and a process model that accounts for the residence-time distribution of crumb particles was developed second. Plant data as well as input data from an existing steady-state model was used to determine estimates for the tuning parameters used in the multiple-particle, multiple-tank model. Using plant data to assess the model’s predictive accuracy, the resulting three-tank and four-tank process B models provide accurate model predictions with a typical error of 0.35 parts per hundred resin (phr) and 0.12 phr. The four-tank process A model provides less-accurate model predictions for residual crumb concentrations in the second tank and has an overall typical error of 1.05 phr. Additional plant data from the three- and four-tank processes would increase the estimability of the parameter values for parameter ranking and estimations steps and thus, yield increased model predictive accuracy. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-10-23 21:06:05.509

The trend of making more profits for the owners, deregulation of the utility market and need for obtaining permission from regulatory agencies have forced electric power utilities to operate their systems close to the security limits of their generation, transmission and distribution systems. The result is that power systems are now exposed to substantial risks of experiencing voltage collapse. This phenomenon is complex and is localized in nature but has widespread adverse consequences. The worst scenario of voltage collapse is partial or total outage of the power system resulting in loss of industrial productivity of the country and major financial loss to the utility. On-line monitoring of voltage stability is, therefore becoming a vital practice that is being increasingly adopted by electric power utilities. The phenomenon of voltage collapse has been studied for quite some time, and techniques for identifying voltage collapse situations have been suggested. Most suggested techniques examine steady-state and dynamic behaviors of the power system in off-line modes. Very few on-line protection and control schemes have been proposed and implemented. In this thesis, a new technique for preventing voltage collapse is presented. The developed technique uses subset of measurements from local bus as well as neighbouring buses and considers not only the present state of the system but also future load and topology changes in the system. The technique improves the robustness of the local-based methods and can be implemented in on-line as well as off-line modes. The technique monitors voltages and currents and calculates from those measurements time to voltage collapse. As the system approaches voltage collapse, control actions are implemented to relieve the system to prevent major disturbances. The developed technique was tested by simulating a variety of operating states and generating voltage collapse situations on the IEEE 30-Bus test system. Some results from the simulation studies are reported in this thesis. The results obtained from the simulations indicates that the proposed technique is able to estimate the time to voltage collapse and can implement control actions as well as alert operators.

Doctor of Philosophy / Department of Agronomy / Stephen M. Welch / Global crop production must be doubled by 2050 to feed 9 billion people. Novel crop improvement methods and management strategies are the sine qua non for achieving this goal. This requires reliable quantitative methods for predicting the behavior of crop cultivars in novel, time-varying environments. In the last century, two different mathematical prediction approaches emerged (1) quantitative genetics (QG) and (2) ecophysiological crop modeling (ECM). These methods are completely disjoint in terms of both their mathematics and their strengths and weaknesses. However, in the period from 1996 to 2006 a method for melding them emerged to support breeding programs. The method involves two steps: (1) exploiting ECM’s to describe the intricate, dynamic and environmentally responsive biological mechanisms determining crop growth and development on daily/hourly time scales; (2) using QG to link genetic markers to the values of ECM constants (called genotype-specific parameters, GSP’s) that encode the responses of different varieties to the environment. This can require huge amounts of computation because ECM’s have many GSP’s as well as site-specific properties (SSP’s, e.g. soil water holding capacity). Moreover, one cannot employ QG methods, unless the GSP’s from hundreds to thousands of lines are known. Thus, the overall objective of this study is to identify better ways to reduce the computational burden without minimizing ECM predictability. The study has three parts: (1) using the extended Fourier Amplitude Sensitivity Test (eFAST) to globally identify parameters of the CERES-Sorghum model that require accurate estimation under wet and dry environments; (2) developing a novel estimation method (Holographic Genetic Algorithm, HGA) applicable to both GSP and SSP estimation and testing it with the CROPGRO-Soybean model using 182 soybean lines planted in 352 site-years (7,426 yield observations); and (3) examining the behavior under estimation of the anthesis data prediction component of the CERES-Maize model. The latter study used 5,266 maize Nested Associated Mapping lines and a total 49,491 anthesis date observations from 11 plantings. Three major problems were discovered that challenge the ability to link QG and ECM’s: 1) model expressibility, 2) parameter equifinality, and 3) parameter instability. Poor expressibility is the structural inability of a model to accurately predict an observation. It can only be solved by model changes. Parameter equifinality occurs when multiple parameter values produce equivalent model predictions. This can be solved by using eFAST as a guide to reduce the numbers of interacting parameters and by collecting additional data types. When parameters are unstable, it is impossible to know what values to use in environments other than those used in calibration. All of the methods that will have to be applied to solve these problems will expand the amount of data used with ECM’s. This will require better optimization methods to estimate model parameters efficiently. The HGA developed in this study will be a good foundation to build on. Thus, future research should be directed towards solving these issues to enable ECM’s to be used as tools to support breeders, farmers, and researchers addressing global food security issues.