Actor-observer bias är benägenheten för människor att attribuera sitt eget beteende utifrån omständigheter och andras beteende utifrån deras person. Vi tenderar dessutom att favorisera och vara mer förlåtande i attributionerna till de som tillhör vår ingrupp. Studiens syfte var att undersöka huruvida graden av tillhörighet till ingruppen, kön, påverkar hur vi attribuerar. Studien genomfördes med 102 deltagare, 51 kvinnor och 51 män. En konfliktsituation mellan ett par presenterades för deltagarna där den ena parten i förhållandet (X) beter sig illa mot den andra. Enkäten förekom i tre versioner med X som kvinna, man eller deltagaren själv. Det predicerades bl a att deltagare som upplever hög identifiering med sin ingrupp kommer attribuera övervägande externt då X hör till dennes ingrupp. Resultatet visade inget signifikant stöd för denna hypotes. Tendenser för att kvinnans beteende bedömdes hårdare kunde dock urskiljas. Betydelsen av detta, den eventuella inverkan av könsroller samt framtida forskning diskuteras.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 129-135).

With advances in technology and the increasing use of power electronic components in the design of household and industrial equipment, harmonic distortion has become one of the major power quality problems in power systems. Identifying the harmonic sources and quantifying the contributions of these harmonic sources provides utility companies with the information they require to effectively mitigate harmonics in the system. This thesis proposes the use of observers for harmonic estimation and harmonic source identification. An iterative observer algorithm is designed for performing harmonic estimation in measured voltage or current signals taken from a power distribution system. The algorithm is based on previous observer designs for estimating the power system states at the fundamental frequency. Harmonic estimation is only carried out when the total harmonic distortion (THD) exceeds a specified threshold. In addition, estimation can be performed on multiple measurements simultaneously. Simulations are carried out on an IEEE distribution test feeder. A number of scenarios such as changes in harmonic injections with time, variations in fundamental frequency and measurement noise are simulated to verify the validity and robustness of the proposed iterative observer algorithm. Furthermore, an observer-based algorithm is proposed for identifying the harmonic sources in power distribution systems. The observer is developed to estimate the system states for a combination of suspicious nodes and the estimation error is analysed to verify the existence of harmonic sources in the specified node combinations. This method is applied to the identification of both single and multiple harmonic sources. The response of the observer-based algorithm to time varying load parameters and variations in harmonic injections with time is investigated and the results show that the proposed harmonic source identification algorithm is able to adapt to these changes. In addition, the presence of time delay in power distribution system measurements is taken into consideration when identifying harmonic sources. An observer is designed to estimate the system states for the case of a single time delay as well as multiple delays in the measurements. This observer is then incorporated into the observer-based harmonic source identification algorithm to identify harmonic sources in the presence of delayed measurements. Simulation results show that irrespective of the time delay in the measurements, the algorithm accurately identifies the harmonic sources in the power distribution system.

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.

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.

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.

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.

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.

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

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