Modern power networks are being driven ever closer to both their physical and operational limits. As a result, control systems are being increasingly relied on to assure satisfactory system performance. Power system stabilizers (PSSs) are one example of such controllers. Their purpose is to increase system damping and they are typically designed using a model of the network that is valid during nominal operating conditions. The limitation of this design approach is that during off-nominal operating conditions, such as those triggered by daily load fluctuations, performance of the controller can degrade. The research presented in this report attempts to evaluate the possibility of employing an adaptive PSS as a means of avoiding the performance degradation precipitated by off-nominal operation. Conceptually, an adaptive PSS would be capable of identifying changes in the network and then adjusting its parameters to ensure suitable damping of the identified network. This work begins with a detailed look at the identification algorithm employed followed by a similarly detailed examination of the control algorithm that was used. The results of these two investigations are then combined to allow for a preliminary assessment of the performance that could be expected from an adaptive PSS. The results of this research suggest that an adaptive PSS is a possibility but further work is needed to confirm this finding. Testing using more complex network models must be carried out, details pertaining to control parameter tuning must be resolved and closed-loop time domain simulations using the adaptive PSS design remain to be performed. / May 2007

As wireless devices become more common, the ability to position a wireless device has become a topic of importance. Accurate positioning through technologies such as the Global Positioning System is possible for outdoor environments. Indoor environments pose a different challenge, and research continues to position users indoors. Due to the prevalence of wireless local area networks (WLANs) in many indoor spaces, it is prudent to determine their capabilities for the purposes of positioning. Signal strength and time based positioning systems have been studied for WLANs. Direction or angle of arrival (AOA) based positioning will be possible with multiple antenna arrays, such as those included with upcoming devices based on the IEEE 802.11n standard. The potential performance of such a system is evaluated. The positioning performance of such a system depends on the accuracy of the AOA estimation as well as the positioning algorithm. Two different maximum-likelihood (ML) derived algorithms are used to determine the AOA of the mobile user: a specialized simple ML algorithm, and the space- alternating generalized expectation-maximization (SAGE) channel parameter estimation algorithm. The algorithms are used to determine the error in estimating AOAs through the use of real wireless signals captured in an indoor office environment. The statistics of the AOA error are used in a positioning simulation to predict the positioning performance. A least squares (LS) technique as well as the popular extended Kalman filter (EKF) are used to combine the AOAs to determine position. The position simulation shows that AOA- based positioning using WLANs indoors has the potential to position a wireless user with an accuracy of about 2 m. This is comparable to other positioning systems previously developed for WLANs.

Shooter localization systems are used to detect and locate the origin of gunfire. A wireless sensor network is one possible implementation of such a system. A wireless sensor network is sensitive to synchronization errors. Localization techniques that rely on the timing will give less accurate or even useless results if the synchronization errors are too large. This thesis focuses on the influence of synchronization errors on the abilityto localize a shooter using a wireless sensor network. A localization algorithm is developed and implemented and the effect of synchronization errors is studied. The localization algorithm is evaluated using numerical experiments, simulations, and data from real gunshots collected at field trials. The results indicate that the developed localization algorithm is able to localizea shooter with quite good accuracy. However, the localization performance is to a high degree influenced by the geographical configuration of the network as well as the synchronization error. / Skottlokaliseringssystem används för att upptäcka och lokalisera ursprunget för avlossade skott. Ett trådlöst sensornätverk är ett sätt att utforma ett sådant system.Trådlösa sensornätverk är känsliga för synkroniseringsfel. Lokaliseringsmetoder som bygger på tidsobservationer kommer med för stora synkroniseringsfel ge dåliga eller helt felaktiga resultat. Detta examensarbete fokuserar på vilken inverkan synkroniseringsfel har på möjligheterna att lokalisera en skytt i ett trådlöst sensornätverk. En lokaliseringsalgoritm utvecklas och förmågan att korrekt lokalisera en skytt vid olika synkroniseringsfel undersöks. Lokaliseringsalgoritmen prövas med numeriska experiment, simuleringar och även för data från riktiga skottljud, insamlade vid fältförsök. Resultaten visar att lokaliseringsalgoritmen fungerar tillfredställande, men att lokaliseringsförmågan till stor del påverkas av synkroniseringsfel men även av sensornätverkets geografiska utseende.

Catastrophes such as floods, droughts and earthquakes have caused significant human and infrastructural losses throughout history. Nevertheless, researchers struggle to quantify macroeconomic impacts, and the existing literature is ambiguous in its findings. In this study I use econometric methods on panel data from Centre for Research on the Epidemiology of Disasters (CRED), and find that hydrometeorological, climatological and geophysical events all affect economic growth negatively in the short run. Second, while events typically linked to climate change tend to cause negative growth shocks the same year they occur, geophysical disasters do not alter overall economic performance before the next year. With respect to future global warming, these dynamic differences give important insights for the understanding of how economies might be affected by climate change. However, by means of two stage least square methods, I do not find that negative economic shocks caused by weather related disasters increase the likelihood of armed civil conflicts. This latter result is in contrast to conclusions in much of the seminal conflict literature, but similar to findings in other recent cross-country studies that use the instrument variable approach.

PolyTech AB in Malmköping manufactures gyro stabilized camera systems or helicopter applications. In this Master´s Thesis a shaker test rig for vibration testing of these systems is designed, implemented and evaluated. The shaker is required to have an adjustable frequency and displacement and different shakers that meet these requirements are treated in a literature study. The shaker chosen in the test rig is based on a mechanical solution that is described in detail. Additionally all components used in the test rig are described and modelled. The test rig is identified and evaluated from different experiments carried out at PolyTech, where the major part of the identification is based on data collected from accelerometers. The test rig model is used to develop a controller that controls the frequency and the displacement of the shaker. A three-phase motor is used to control the frequency of the shaker and a linear actuator with a servo is used to control the displacement. The servo controller is designed using observer and state feedback techniques. Additionally, the mount in which the camera system is hanging is modelled and identified, where the identification method is based on nonlinear least squares (NLS) curve fitting technique.

Power systems are protected by numerical relays that detect and isolate faults that may occur on power systems. The correct operation of the relay is very important to maintain the security of the power system. <p>Numerical relays that use voltage measurements from the power system provided by coupling capacitor voltage transformers (CCVT) have sometimes difficulty in correctly identifying a fault in the protected area. The fundamental frequency voltage phasor resulting from these CCVT measurements may result in a deviation from the true value and therefore may locate this phasor temporarily in the incorrect operating region. This phasor deviation is due to the CCVT behavior and the CCVT introduces spurious decaying and oscillating transient signal components on top of the original voltage received from the power system in response to sudden voltage changes produced during faults. Most of the existing methods for estimating the voltage phasor do not take advantage of the knowledge of the CCVT behavior that can be obtained from its design parameters.<p>A new least squares error method for phasor estimation is presented in this thesis, which improves the accuracy and speed of convergence of the phasors obtained, using the knowledge of the CCVT behavior. The characteristics of the transient signal components introduced by the CCVT, such as frequencies and time constants of decay, are included in the description of the curve to be fitted, which is required in a least squares fitting technique. Parameters such as window size and sampling rate for optimum results are discussed.<p>The method proposed is evaluated using typical power systems, with results that can be compared to the response if an ideal potential transformer (PT) were used instead of a CCVT. The limitations of this method are found in some specific power system scenarios, where the natural frequencies of the power system are close to that of the CCVT, but with longer time constants. The accuracy with which the CCVT parameters are known is also assessed, with results that show little impact compared to the improvements achievable.

A linear periodically time-varying (LPTV) system is a linear time-varying system with the coefficients changing periodically, which is widely used in control, communications, signal processing, and even circuit modeling. This thesis concentrates on identification of LPTV systems. To this end, the representations of LPTV systems are thoroughly reviewed. Identification methods are developed accordingly. The usefulness of the proposed identification methods is verified by the simulation results.<p> A periodic input signal is applied to a finite impulse response (FIR)-LPTV system and measure the noise-contaminated output. Using such periodic inputs, we show that we can formulate the problem of identification of LPTV systems in the frequency domain. With the help of the discrete Fourier transform (DFT), the identification method reduces to finding the least-squares (LS) solution of a set of linear equations. A sufficient condition for the identifiability of LPTV systems is given, which can be used to find appropriate inputs for the purpose of identification.<p> In the frequency domain, we show that the input and the output can be related by using the discrete Fourier transform (DFT) and a least-squares method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR-LPTV systems. The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)-LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.

This study examines whether different corporate real estate (CRE) strategies affect the stock outperformance and systemic risk of various companies. The sample of 443 listed companies of 17 industries in Taiwan during 2000 to 2010 was divided into four groups for the different corporate real estate strategies. The pairwise abnormal return and systemic risk of composite and business (without the affect from real estate market) series were empirically examined and compared using a partial adjustment model. This study also conducts the two-stage least squares procedure to determine whether four CRE strategies were considered diversifiable factors when evaluating the firm¡¦s value The results do not indicate an increasingly abnormal return performance associated with the company implementing a certain CRE strategy, but companies with a stable profession and consistent adjustment strategies are considered a good diversifier by stock investors. Aggressive adjustment strategies do not diversify the systematic risk to overall industry, otherwise the scale of total assets would be considered a diversification in companies with aggressive strategies. The companies using an aggressive profession strategy to increase leverage are regarded as risky phenomen for stock investors, and companies with stable profession strategies face higher systemic risk if their book value is greater than their market value. Therefore, this study determines that CRE strategies affect companies¡¦ systematical risk.

Singular value decomposition (SVD) is a popular decomposition method for solving least-squares estimation problems. However, for large datasets, SVD is very time consuming and memory demanding in obtaining least squares solutions. In this paper, we propose a least squares estimator based on an iterative divide-and-merge scheme for large-scale estimation problems. The estimator consists of several levels. At each level, the input matrices are subdivided into submatrices. The submatrices are decomposed by SVD respectively and the results are merged into smaller matrices which become the input of the next level. The process is iterated until the resulting matrices are small enough which can then be solved directly and efficiently by the SVD algorithm. However, the iterative divide-and-merge algorithms executed on a single machine is still time demanding on large scale datasets. We propose two distributed algorithms to overcome this shortcoming by permitting several machines to perform the decomposition and merging of the submatrices in each level in parallel. The first one is implemented in MapReduce on the Hadoop distributed platform which can run the tasks in parallel on a collection of computers. The second one is implemented on CUDA which can run the tasks in parallel using the Nvidia GPUs. Experimental results demonstrate that the proposed distributed algorithms can greatly reduce the time required to solve large-squares problems.

In economy, banking sector has been considered as the main issue for development. Using panel data analyzing to test the relationship between banks performance and economic freedom indexes for 9 emerging market countries in Asia, the paper¡¦s result shows that there is the positive effect between monetary freedom, business freedom, financial freedom and banks performance while investment freedom has a negative impact. It suggests that emerging market countries¡¦ government and the policy maker need to focus deeply on the operation, the efficiency of regulation and improving the economic freedom.