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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

SUPPRESSION OF HARMONIC TORQUE AND HARMONIC CURRENT IN PERMANENT MAGNET SYNCHRONOUS MOTOR

Abou Qamar, Nezar Yehya 01 May 2018 (has links)
In this dissertation harmonic current, harmonic torque originated at the load and harmonic torque originated at the motor, where modeled and treated via closed loop control. The dissertation propose a remedy for cancelling harmonic current by placing the proposed adaptive feedforward controller (AFC) in parallel with the FOC current control. Similarly, harmonic torque load was cancelled by proposing an AFC in parallel with the speed control loop. Harmonic torque originated in the motor mainly due to harmonic flux where cancelled through the estimation of harmonic flux, which was achieved by a novel Minimal Parameter Harmonic Flux Estimator (MPHFE). The latter is formulated such that the inductance, resistance, and stator current and its derivative are not necessary for the estimation of the harmonic eflux. This was achieved by forcing the harmonic current induced by the harmonic flux component to zero through the combined action of a Field-Oriented Controller (FOC) and a feed-forward controller. Subsequently, the harmonic flux can be obtained directly from the estimated harmonic back-EMF without the involvement of other motor parameters. Finally, the estimated flux is used in conjunction with a comprehensive analysis of the motor harmonic torque to determine the stator current compensation to eliminate the torque harmonic. A systematic approach to assign the parameter of the AFC controller were developed in this dissertation. Furthermore, multiple experiments were conducted to demonstrate the efficacy of the proposed control schemes harmonics.
12

Development of a model of work-personality

Owens, Courtney Elizabeth January 2019 (has links)
Personality is important to job performance; meta-analyses published over the years repeatedly showed that self-rated personality traits can significantly predict overall job performance (Barrick & Mount, 1991; Barrick, Mount, & Judge, 2001). Despite their significance, these same meta-analyses, generally showed personality only had a small effect on overall job performance. The exception was conscientiousness, which had a less than medium effect. However, there is also a growing body of evidence suggesting that other-ratings of personality can show higher concurrent validities than self-ratings. Meta-analytic results showed that personality can have a large effect on overall job performance, if the personality traits are rated by others (Connelly & Ones, 2010). Moreover, concurrent validities increased when utilising narrow measures of both personality (Judge, Rodell, Klinger, Simon, & Crawford, 2013) and job performance (Bartram, 2005). In this study, the author examined the suggestion from meta-analyses that observer-ratings, rather than self-ratings, provide greater explanatory power when predicting job performance. Further, the concurrent validities of using narrow personality traits (facets) as predictors of narrow measures of job performance were investigated. This study comprised 1,041 participants, of which 92% were employed in a UK police organisation. Employees provided self-ratings and identified two co-workers and a manager who could provide other-ratings of personality and job performance. Online questionnaires measured 71 personality facets of the 11+ Factor Model (Irwing & Booth, 2013) and Bartram's (2005) Great Eight factors of job performance. Arguably the most comprehensive measure of personality, the 11+ Factor Model is comprised of 11 factors and 74 facets. Items from the International Personality Item Pool (IPIP; Goldberg, 1999) were utilised to create scales for each of the 74 personality facets. A planned missing data design was implemented to improve response rates (Graham, Taylor, Olchowski, & Cumsille, 2006). Measurement models were estimated first, followed by testing of the structural models (J. C. Anderson & Gerbing, 1988) to estimate the combined effects of personality facets on each of the job performance outcomes. Since cross-validation is a powerful approach for evaluating models (Millsap & Meredith, 2007), all models were cross-validated on two datasets. Fifty-two personality facets were identified and cross-validated. Some of these facets provided superior prediction over factors, when predicting narrow measures of job performance. The facets of integrity, leadership, harm avoidance and empathy explained much of the variance in the Great Eight job competencies. In some cases, self-ratings of personality provided superior prediction over other-ratings.
13

An Inverse Source Problem for a One-dimensional Wave Equation: An Observer-Based Approach

Asiri, Sharefa M. 25 May 2013 (has links)
Observers are well known in the theory of dynamical systems. They are used to estimate the states of a system from some measurements. However, recently observers have also been developed to estimate some unknowns for systems governed by Partial differential equations. Our aim is to design an observer to solve inverse source problem for a one dimensional wave equation. Firstly, the problem is discretized in both space and time and then an adaptive observer based on partial field measurements (i.e measurements taken form the solution of the wave equation) is applied to estimate both the states and the source. We see the effectiveness of this observer in both noise-free and noisy cases. In each case, numerical simulations are provided to illustrate the effectiveness of this approach. Finally, we compare the performance of the observer approach with Tikhonov regularization approach.
14

A graphical methodology for describing interrater variability in ordinal assessments among many raters /

Nelson, Jennifer Clark. January 1999 (has links)
Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 129-135).
15

Observers on linear Lie groups with linear estimation error dynamics

Koldychev, Mikhail January 2012 (has links)
A major motivation for Lie group observers is their application as sensor fusion algorithms for an inertial measurement unit which can be used to estimate the orientation of a rigid-body. In the first part of this thesis we propose several types of nonlinear, deterministic, locally exponentially convergent, state observers for systems with all, or part, of their states evolving on the general linear Lie group of invertible matrices. Our proposed Lie group observer with full-state measurement is applicable to left-invariant systems on linear Lie groups and yields linear estimation error dynamics. We also propose a way to extend our full-state observer, to build observers with partial-state measurement, i.e., only a proper subset of the states are available for measurement. Our proposed Lie group observer with partial-state measurement is applicable when the measured states are evolving on a Lie group and the rest of the states are evolving on the Lie algebra of this Lie group. We illustrate our observer designs on various examples, including rigid-body orientation estimation and dynamic homography estimation. In the second part of this thesis we propose a nonlinear, deterministic state observer, for systems that evolve on real, finite-dimensional vector spaces. This observer uses the property of high-gain observers, that they are approximate differentiators of the output signal of a plant. Our new observer is called a composite high-gain observer because it consists of a chain of two or more sub-observers. The first sub-observer in the chain differentiates the output of the plant. The second sub-observer in the chain differentiates a certain function of the states of the first sub-observer. Effectiveness of the composite observer is demonstrated via simulation.
16

Observers on linear Lie groups with linear estimation error dynamics

Koldychev, Mikhail January 2012 (has links)
A major motivation for Lie group observers is their application as sensor fusion algorithms for an inertial measurement unit which can be used to estimate the orientation of a rigid-body. In the first part of this thesis we propose several types of nonlinear, deterministic, locally exponentially convergent, state observers for systems with all, or part, of their states evolving on the general linear Lie group of invertible matrices. Our proposed Lie group observer with full-state measurement is applicable to left-invariant systems on linear Lie groups and yields linear estimation error dynamics. We also propose a way to extend our full-state observer, to build observers with partial-state measurement, i.e., only a proper subset of the states are available for measurement. Our proposed Lie group observer with partial-state measurement is applicable when the measured states are evolving on a Lie group and the rest of the states are evolving on the Lie algebra of this Lie group. We illustrate our observer designs on various examples, including rigid-body orientation estimation and dynamic homography estimation. In the second part of this thesis we propose a nonlinear, deterministic state observer, for systems that evolve on real, finite-dimensional vector spaces. This observer uses the property of high-gain observers, that they are approximate differentiators of the output signal of a plant. Our new observer is called a composite high-gain observer because it consists of a chain of two or more sub-observers. The first sub-observer in the chain differentiates the output of the plant. The second sub-observer in the chain differentiates a certain function of the states of the first sub-observer. Effectiveness of the composite observer is demonstrated via simulation.
17

Citizen science data quality: Harnessing the power of recreational SCUBA divers for rockfish (Sebastes spp.) conservation

Gorgopa, Stefania M. 30 August 2018 (has links)
Monitoring rare or elusive species can be especially difficult in marine environments, resulting in poor data density. SCUBA-derived citizen science data has the potential to improve data density for conservation. However, citizen science data quality may be perceived to be of low quality relative to professional data due to a lack of ‘expertise’ and increased observer variability. We evaluated the quality of data collected by citizen science scuba divers for rockfish (Sebastes spp.) conservation around Southern Vancouver Island, Canada. An information-theoretic approach was taken in two separate analyses to address the overarching question: ‘what factors are important for SCUBA-derived citizen science data quality?’. The first analysis identified predictors of variability in precision between paired divers. We found that professional scientific divers did not exhibit greater data precision than recreational divers. Instead, precision variation was best explained by study site and divers’ species identification or recreational training. A second analysis identified what observer and environmental factors correlated with higher resolution identifications (i.e. identified to the species level rather than family or genus). We found divers provided higher resolution identifications on surveys when they had high species ID competency and diving experience. Favorable conditions (high visibility and earlier in the day) also increased taxonomic resolution on dive surveys. With our findings, we are closer to realizing the full potential of citizen science to increase our capacity to monitor rare and elusive species. / Graduate
18

Adaptive distributed observers for a class of linear dynamical systems

Heydari, Mahdi 29 April 2015 (has links)
The problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of continuous-time linear systems is considered. Distributed estimation strategies improve estimation and robustness of the sensors to environmental obstacles and sensor failures in a sensor network. In particular, this dissertation focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters, distributed unknown input observers, and distributed functional observers. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov-redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of estimates as a function of their agreement, thereby enforcing an interconnection-dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge-dependent differences in the estimates. Accounting for node-specific differences, a special case of the scheme is presented where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of estimates in the interconnection term. In the case of distributed Kalman filters, the filter gains can be designed either by standard Kalman or Luenberger observers to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown and it is independent of the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes compared to other distributed Kalman filters. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters, adaptive distributed unknown input observers, and adaptive distributed functional observers.
19

Modeling and estimation for stepped automatic transmission with clutch-to-clutch shift technology

Watechagit, Sarawoot 30 September 2004 (has links)
No description available.
20

Fault estimation algorithms : design and verification

Su, Jinya January 2016 (has links)
The research in this thesis is undertaken by observing that modern systems are becoming more and more complex and safety-critical due to the increasing requirements on system smartness and autonomy, and as a result health monitoring system needs to be developed to meet the requirements on system safety and reliability. The state-of-the-art approaches to monitoring system status are model based Fault Diagnosis (FD) systems, which can fuse the advantages of system physical modelling and sensors' characteristics. A number of model based FD approaches have been proposed. The conventional residual based approaches by monitoring system output estimation errors, however, may have certain limitations such as complex diagnosis logic for fault isolation, less sensitiveness to system faults and high computation load. More importantly, little attention has been paid to the problem of fault diagnosis system verification which answers the question that under what condition (i.e., level of uncertainties) a fault diagnosis system is valid. To this end, this thesis investigates the design and verification of fault diagnosis algorithms. It first highlights the differences between two popular FD approaches (i.e., residual based and fault estimation based) through a case study. On this basis, a set of uncertainty estimation algorithms are proposed to generate fault estimates according to different specifications after interpreting the FD problem as an uncertainty estimation problem. Then FD algorithm verification and threshold selection are investigated considering that there are always some mismatches between the real plant and the mathematical model used for FD observer design. Reachability analysis is drawn to evaluate the effect of uncertainties and faults such that it can be quantitatively verified under what condition a FD algorithm is valid. First the proposed fault estimation algorithms in this thesis, on the one hand, extend the existing approaches by pooling the available prior information such that performance can be enhanced, and on the other hand relax the existence condition and reduce the computation load by exploiting the reduced order observer structure. Second, the proposed framework for fault diagnosis system verification bridges the gap between academia and industry since on the one hand a given FD algorithm can be verified under what condition it is effective, and on the other hand different FD algorithms can be compared and selected for different application scenarios. It should be highlighted that although the algorithm design and verification are for fault diagnosis systems, they can also be applied for other systems such as disturbance rejection control system among many others.

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