In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty. In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC. Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data. In another direction, the stability of data driven predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. Finally, I address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The effectiveness of all of the proposed methods are illustrated through simulations on nonlinear systems. / Dissertation / Doctor of Philosophy (PhD)

The clinical signs that a physician can identify from the appearance of a patient represent an important source of information, upon which the diagnostic decision is nominally based. Most of the research in medical education emphasizes the organization of medical knowledge or the reasoning processes based on these signs. This emphasis carries the implicit assumptions that identifying features is not the major problem and that evaluation of the clinical signs occurs largely independently of consideration of the diagnosis. However, there is accumulating evidence to suggest that the identification of these clinical signs can be influenced by the diagnosis being evaluated. The studies in this thesis contribute to this body of research by investigating the underlying processes by which the diagnosis being considered influences feature identification. Participants in these experiments were asked to identify the clinical signs from photographs of patients or electrocardiogram strips after having been biased towards the correct or an alternate diagnosis. It was found that the availability of a diagnosis served both to change the probability of reporting relevant clinical signs as well as to influence the identification of ambiguous signs. Manipulating the credibility of the suggested diagnosis, subsequently suggesting a second diagnosis, or decreasing the size of the pool of alternatives available to the diagnostician had a large impact on diagnostic conclusions, but produced relatively small effects on the features reported. These results suggest that changing the degree of focus that a clinician places on the suggested diagnosis has a small effect on the identification of the features by comparison to the substantial effect of merely suggesting a diagnosis. Furthermore, it was found that the subsequent suggestion of a competing diagnosis did not lead to a reinterpretation of the data. This indicates that once clinicians have seen the evidence one way, they are unlikely to see and label it differently. The implication of these findings for research on medical decision making, the mental organization of medical categories, as well as medical education are discussed. / Thesis / Doctor of Philosophy (PhD)

Language identification is the task of automatically detecting the languages(s) written in a text or a document given, and is also the very first step of further natural language processing tasks. This task has been well-studied over decades in the past, however, most of the works have focused on long texts rather than the short that is proved to be more challenging due to the insufficiency of syntactic and semantic information. In this work, we present approaches to this problem based on deep learning techniques, traditional methods and their combination. The proposed ensemble model, composed of a learning based method and a dictionary based method, achieves 89.6% accuracy on our new generated gold test set, surpassing Google Translate API by 3.7% and an industry leading tool Langid.py by 26.1%. / Thesis / Master of Applied Science (MASc)

Sensor-embedded tires, known as intelligent tires, have been widely studied because they are believed to provide reliable and crucial information on tire-road contact characteristics e.g., slip, forces and deformation of tires. Vehicle control systems such as ABS and VSP (Vehicle Stability Program) can be enhanced by leveraging this information since control algorithms can be updated based on directly measured parameters from intelligent tire rather than estimated parameters based on complex vehicle dynamics and on-board sensor measurements. Moreover, it is also expected that intelligent tires can be utilized for the purpose of the analysis of tire characteristics, taking into consideration that the measurements from the sensors inside the tire would contain considerable information on tire behavior in the real driving scenarios. In this study, estimation methods for the tire-road contact features by utilizing intelligent tires are investigated. Also, it was discussed how to identify key tire parameters based on the fusion technology of intelligent tire and tire modeling. To achieve goals, extensive literature reviews on the estimation methods using the intelligent tire system was conducted at first. Strain-based intelligent tires were introduced and tested in the laboratory for this research. Based on the literature review and test results, estimation methods for diverse tire-road contact characteristics such as slippages and contact forces have been proposed. These estimation methods can be grouped into two categories: statistical regressions and model based methods. For statistical regressions, synthetic regressors were proposed for the estimation of contact parameters such as contact lengths, rough contact shapes, test loads and slip angles. In the model-based method, the brush type tire model was incorporated into the estimation process to predict lateral forces. Estimated parameters using suggested methods agreed well with measured values in the laboratory environment. By utilizing sensor measurements from intelligent tires, the tire physical characteristics related to in-plane dynamics of the tire, such as stiffness of the belt and sidewall, contact pressure distribution and internal damping, were identified based on the combination of strain measurements and a flexible ring tire model. The radial deformation of the tread band was directly obtained from strain measurements based on the strain-deformation relationship. Tire parameters were identified by fitting the radial deformations from the flexible ring model to those derived from strain measurements. This approach removed the complex and repeated procedure to satisfy the contact3 constraints between the tread and the road surface in the traditional ring model. For tires with different specifications, identification using the suggested method was conducted and their results are compared with results from conventional methods and tests, which shows good agreements. This approach is available for the tire standing still or rolling at low speeds. For tires rolling at high speeds, advanced tire model was implemented and associated with strain measurements to estimate dynamic stiffness, internal damping effects as well as dynamic pressure distributions. Strains were measured for a specific tire under various test conditions to be used in suggested identification methods. After estimating key tire parameters step by step, dynamic pressure distributions was finally estimated and used to update the estimation algorithm for lateral forces. This updated estimation method predicted lateral forces more accurately than the conventional method. Overall, this research will serve as a stepping stone for developing a new generation of intelligent tire capable of monitoring physical tire characteristics as well as providing parameters for enhanced vehicle controls. / PHD

The surface-mining worker fatalities are unacceptably high as compared to other private industries in United States such as construction, manufacturing, agriculture, forestry, fishing and hunting. Although many studies about generating a safe work environment and developing safety training through intervention modules have tried to reduce the number of worker fatalities by utilizing an administrative approach to the development of safety controls, a rigorous study about understanding craftworkers' perceptions of a safe work environment is lacking. This study particularly investigated quarry workers' safety behaviors regarding work hazards by adapting findings from construction safety research because construction and mining industries have similarities in work hazards and environment. Despite their similarities, the work fatality rate in the mining industry is greater than in the construction industry (BLS, 2018). To gain a greater understanding of how quarry workers identify and interpret work hazards and employ strategies to prevent accidents, this study explored quarry workers' perceptions of work hazards (i.e., hazard identification, severity interpretation) and their preventive strategies to develop a safer working environment at the workplace. To accomplish this, a single descriptive case study was conducted with multiple sources of data such as interviews with photo elicitation, field notes, site photographs, and precursory meeting with safety managers. These sources of data were used to investigate quarry workers' identification and interpretation skills and utilization of preventive strategies. This study will contribute to improve safety of workers by incorporating workers' perceptions of work hazards and by exploring their experience to the development of safety training. / Master of Science / The surface-mining industry is one of the private industries with a high fatality rate in the United States. To reduce the number of accidents and help workers to perform safely in the workplace, previous studies have investigated different strategies to promote workers’ safety performance. These strategies include safety interventions such as safety training and safety inspections. However, a lack of study exists to understand workers’ behaviors and perceptions regarding safety and work hazards. To better explore workers’ behaviors and perceptions, this study adapted findings from construction safety research. Because the number of surface-mining safety research is relatively small and the work environment and types of work hazards in both the surface-mining and construction industries share similarities, the findings in construction safety research can be used to promote a safer workplace for surface-mine workers. By adapting findings in construction safety research, this study further investigated workers’ perceptions of work hazards, which is operationalized in two approaches: hazard identification and severity interpretation. This study also examines workers’ execution of strategies to prevent accidents. These observations were gathered through the utilization of a case study of quarry, which gave a guideline to the researcher to collect data from multiple sources (e.g., precursory meeting with safety managers, field notes, photographs, interviews) and analyze the findings by primarily using participants’ responses in the interviews with photo elicitation. This study will contribute to improve a safer workplace in the surface mining industry by incorporating workers’ perceptions and experience and by emphasizing workers’ involvement in tandem with company commitment to develop safety training.

Studies have concluded that youth sports programs have a bias selection process in identifying player talent. Athletes that are identified as talented are more likely to be born in the first three months after the eligibility cut-off for a program's particular age group. This is referred to as the relative age effect (RAE) and has been identified in many youth sports. However, it is not known if the RAE carries over into elite, adult competition. The purpose of this study was to determine if the RAE exists in Olympic competition and to compare the RAE between genders, team vs individual sports, weight class vs non-weight class sports, and medalists vs non-medalists. Data on Olympians competing in the 2012 London and 2016 Rio de Janeiro Olympics were gathered from publicly available databases. Lorenz curves were constructed and Gini coefficients calculated to detect unexpected distributions of birth months. In addition, linear regression was used to determine a directional distribution. A negative Gini coefficient and a statistically significant negative slope of the birth month distribution suggested the existence of a RAE. The results showed that there was a RAE in Olympic competition. For all athletes, the Gini coefficient was -0.0324 and the slope of -0.0014 fraction of athletes born per month. Within specific sports, the RAE varies considerably with some showing a positive RAE. Further, the RAE in Olympic athletes does not seem to be influenced by gender, type of team or success of the athlete. / Master of Science

Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle, a dynamic particle, and a dynamic rigid body models are developed to measure wind velocity in hovering flight. Wind tunnel and steady level flight tests are used to characterize kinematic and dynamic particle models. System identification stepwise regression and output error algorithms are used to determine the model structure and parameter estimates of rigid body models. The comparison of all three models demonstrates the rigid body model to have higher performance resolving slow-varying winds based on a frequency response analysis and field experiments conducted next to a 3-D sonic anemometer. The dissertation also presents an extension of the rigid body wind estimation framework to profile the horizontal components of wind velocity in vertical steady ascending flight. The extension employed system identification to characterize five rigid body models for steady-ascending flight speeds increasing from 0 to 2 m/s in intervals of 0.5~m/s. State observers for wind profiling were synthesized using all five rigid body models. Performance assessments employing wind observations from in situ and remote sensors demonstrated model-based wind profiling results to be be in close agreement with ground-truth wind observations. Finally, the rigid body wind sensing framework developed in this dissertations for multirotor UAS is employed to support science objectives for the Advanced Lagrangian Predictions for Hazards Assessment Project. Quadrotor wind measurements sampled at 10 m above sea level were used to characterize the leeway of a person in water for search and rescue scenarios. Leeway values determined from quadrotor wind measurements were found to be in close to leeway parameters previous published in the literature. This results demonstrates the utility of model-based wind sensing for multirotor UAS for providing wind velocity observations in complex environments where conventional wind observations are not readily available. / Doctor of Philosophy / Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle model, a dynamic particle model, and a dynamic rigid body model, are characterized to measure wind velocity in hovering flight. Parameter characterizations are realized using data from wind tunnel, steady level flight tests and system identification experiments. Model-based wind estimations algorithms are developed using the kinematic particle model directly and by synthesizing state observers for the dynamic particle and rigid body models separately. For comparison purposes, the frequency response characteristic of the dynamic particle and rigid body models is examined to determine the range of wind fluctuations that each model can resolve. Performance comparisons demonstrate that the rigid body model to resolve higher wind fluctuations and yield more accurate wind estimates. The dissertation extends the rigid body wind estimation algorithm to estimate wind velocity profiles of the horizontal wind vector. The rigid body wind estimation algorithms is used to answer science questions about about the drift of a person in water.

The effects of visual representations of the thin ideal in movies, magazines, and television have been widely explored, but strictly textual representations of the thin ideal in novels have received scant attention. The genre of chick literature has been criticized for depicting characters that constantly worry about their body weight and have poor body esteem. Excerpts from two popular chick lit novels were used to examine the effect of a protagonist's body weight and body esteem on participants' overall body esteem, sexual attractiveness, weight concern, and physical condition. In Study 1 (N = 159), underweight protagonists made participants feel less sexually attractive. Furthermore, protagonists with low body esteem caused participants to report significantly low body esteem scores compared to protagonists with high body esteem. Study 2 (N = 251) examined the role of transportation and identification with the protagonist. Identification with the protagonist with low body esteem was a significant predictor of participants' lower reported scores of body esteem, lower evaluations of appearance, and increased weight concern. The results suggest that textual representations of body size and body esteem in novels have an effect that is similar to the effect of visual images prevalent in movies, television, and magazines. Implications and future directions are discussed. / Master of Arts

Swietenia, Cedrela, Khaya et Toona sont des genres d'arbres tropicaux à bois précieux. Une identification précise du bois est nécessaire pour contrôler l'exploitation forestière illégale et le commerce connexe ainsi qu'à des fins de certification dans l'industrie des produits forestiers et du bois. Swietenia mahagoni, Swietenia macrophylla et Cedrela odorata ont été inscrites à l'Annexe II de la Convention sur le commerce international des espèces de faune et de flore menacées d'extinction (CITES) et le commerce de ces espèces a été restreint par l'imposition de documentation spécifique et de permis pour l'importation et l'exportation de marchandises. En conséquence, certaines espèces (par exemple, Khaya ivorensis et Toona ciliata) ont été introduites comme substituts de ces espèces et leur identification est maintenant un problème difficile pour l'application de la réglementation liée à la CITES à travers le monde. Ainsi, le but de l'étude est d'identifier et de différencier des espèces étroitement apparentées de la famille des Meliaceae, à savoir Swietenia mahagoni, Swietenia macrophylla, Cedrela odorata, Khaya ivorensis et Toona ciliata sur la base d'analyses anatomiques, de vision et d'intelligence artificielles et de caractérisation chimique du bois. Dans cette étude, trois approches ont été utilisées, la structure du bois par microscopie optique, le système de vision artificielle et le profilage du métabolome par chromatographie en phase gazeuse bidimensionnelle combinée à la spectrométrie de masse à temps de vol (GC*GC-TOFMS). Au total, nous avons analysé 50 échantillons de bois des espèces susmentionnées provenant des xylothèques du Service canadien des forêts et du Centre de recherche sur les matériaux renouvelables de l'Université Laval. Des variations considérables ont été observées dans les caractéristiques anatomiques du bois de ces espèces, mais ces variations n'étaient pas suffisantes pour les différencier au niveau de l'espèce. De même, le système de vision artificielle (Xylotron) est simple et efficace pour l'identification du bois, mais cet outil est encore en développement et les bases de données de référence sont encore insuffisantes pour pouvoir différencier efficacement les espèces étudiées. En revanche, la technique de GC*GC-TOFMS combinée à des statistiques non supervisées (Analyse en Composantes Principales) a montré des signatures chimiques distinctes à partir d'échantillons de bois des cinq espèces, ce qui a permis une identification rapide au niveau de l'espèce. Ainsi, GC*GC-TOFMS fournit une méthode rapide d'identification du bois qui peut être facilement appliquée à de petits échantillons de bois de duramen et peut avoir des applications potentielles dans un contexte d'application de la loi et d'analyse/identification judiciaire des bois illégaux, et dans diverses recherches liées à la foresterie, aux sciences du bois et à l'écophysiologie des arbres. / Swietenia, Cedrela, Khaya and Toona are tree genera providing valuable tropical timber. Accurate identification of timber is necessary to control illegal logging and related trade as well as for certification purposes in the forest and wood products industry. Swietenia mahagoni, Swietenia macrophylla and Cedrela odorata have been included in Appendix II of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and trade in these species has been restricted by the imposition of specific documentation and permits for importation and exportation of goods. As a result of these essential restrictions to protect species survival, other closely related non-CITES species including Khaya ivorensis and Toona ciliata have been introduced as substitutes for these CITES- regulated species and their accurate identification represents a challenge for the application of CITES regulation worldwide. Thus, the aim of the study is to evaluate identification methods to identify and differentiate closely related species of the Meliaceae family namely Swietenia mahagoni, Swietenia macrophylla, Cedrela odorata, Khaya ivorensis and Toona ciliata based on anatomical, machine-vision and chemical analysis of the wood. In this study, three approaches were used, analysis of the wood structure by optical microscopy, machine-vision system and metabolome profiling by two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC*GC-TOFMS). In total, 50 wood samples of the above-mentioned species from the Xylaria of the Canadian Forest Service and the Centre de recherche sur les matériaux renouvelables, Université Laval were analyzed. Considerable variations were observed in the anatomical characteristics of the wood of these species, but these variations were not sufficient to differentiate them at the species level. Similarly, machine-vision system (Xylotron) is simple and effective in wood identification, but this tool is still in development and require the construction of sufficient reference databases to cover a greater number of wood species. In contrast, GC*GC-TOFMS combined with unsupervised statistics (Principal Component Analysis) showed distinct chemical signatures from wood samples of five species, which allowed rapid identification to the species level. Thus, GC*GC-TOFMS provides a rapid method for wood identification that can be easily applied to small samples of heartwood and forensics and have potential applications in a context of law enforcement analysis/identification of illegal wood, and in related to forestry, wood science and tree ecophysiology.

Yes / Background: This study aims at characterizing the in vitro metabolism of cryptolepine using human and rat hepatocytes, identifying metabolites in rat plasma and urine after a single cryptolepine dose, and evaluating the single-dose oral and intravenous pharmacokinetics of cryptolepine in male Sprague Dawley (SD) rats. Methods: The in vitro metabolic profiles of cryptolepine were determined by LC-MS/MS following incubation with rat and human hepatocytes. The in vivo metabolic profile of cryptolepine was determined in plasma and urine samples from Sprague Dawley rats following single-dose oral administration of cryptolepine. Pharmacokinetic parameters of cryptolepine were determined in plasma and urine from Sprague Dawley rats after single-dose intravenous and oral administration. Results: Nine metabolites were identified in human and rat hepatocytes, resulting from metabolic pathways involving oxidation (M2-M9) and glucuronidation (M1, M2, M4, M8, M9). All human metabolites were found in rat hepatocyte incubations except glucuronide M1. Several metabolites (M2, M6, M9) were also identified in the urine and plasma of rats following oral administration of cryptolepine. Unchanged cryptolepine detected in urine was negligible. The Pharmacokinetic profile of cryptolepine showed a very high plasma clearance and volume of distribution (Vss) resulting in a moderate average plasma half-life of 4.5 h. Oral absorption was fast and plasma exposure and oral bioavailability were low. Conclusions: Cryptolepine metabolism is similar in rat and human in vitro with the exception of direct glucuronidation in human. Clearance in rat and human is likely to include a significant metabolic contribution, with proposed primary human metabolism pathways hydroxylation, dihydrodiol formation and glucuronidation. Cryptolepine showed extensive distribution with a moderate half-life. / Funded by Novartis Pharma under the Next Generation Scientist Program.