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.

While prior studies on ERP implementation have largely focused on the importance of best practices, this paper examines the impact of knowledge transfer climate and the competence of the players (i.e., the implementing firm and the consultant team). The model divides factors that influence the result of knowledge transfer during ERP implementation into three categories: those implemented by the firm, those implemented by the consultant, and those related to the impact of the knowledge transfer climate. Competence factors from the first two aspects facilitate the building of a better knowledge transfer climate. Survey results from 101 respondents were subjected to multivariate analysis. The significance of player competence factors is verified, and an understanding of the role that the knowledge transfer climate plays in the knowledge transfer process and the impact on the transfer process is developed. This paper provides a broader, richer model of knowledge transfer networks to promote insight into successful ERP implementation. In practice, the key to effective knowledge transfer is the establishment a positive knowledge transfer climate. To achieve a successful ERP implementation, practitioners should focus on possessing robust competences with ERP implementation partners. Additional research may help ERP implementation project teams more effectively promote knowledge transfer in a wider range of conditions with greater confidence and precision.

In this research, least-squares based finite element formulations and their applications in fluid mechanics are presented. Least-squares formulations offer several computational and theoretical advantages for Newtonian as well as non-Newtonian fluid flows. Most notably, these formulations circumvent the inf-sup condition of Ladyzhenskaya-Babuska- Brezzi (LBB) such that the choice of approximating space is not subject to any compatibility condition. Also, the resulting coefficient matrix is symmetric and positive-definite. It has been observed that pressure and velocities are not strongly coupled in traditional leastsquares based finite element formulations. Penalty based least-squares formulations that fix the pressure-velocity coupling problem are proposed, implemented in a computational scheme, and evaluated in this study. The continuity equation is treated as a constraint on the velocity field and the constraint is enforced using the penalty method. These penalty based formulations produce accurate results for even low penalty parameters (in the range of 10-50 penalty parameter). A stress based least-squares formulation is also being proposed to couple pressure and velocities. Stress components are introduced as independent variables to make the system first order. The continuity equation is eliminated from the system with suitable modifications. Least-squares formulations are also developed for viscoelastic flows and moving boundary flows. All the formulations developed in this study are tested using several benchmark problems. All of the finite element models developed in this study performed well in all cases. A method to exploit orthogonality of modal bases to avoid numerical integration and have a fast computation is also developed during this study. The entries of the coefficient matrix are calculated analytically. The properties of Jacobi polynomials are used and most of the entries of the coefficient matrix are recast so that they can be evaluated analytically.

The conventional finite element models (FEM) of problems in structural mechanics are based on the principles of virtual work and the total potential energy. In these models, the secondary variables, such as the bending moment and shear force, are post-computed and do not yield good accuracy. In addition, in the case of the Timoshenko beam theory, the element with lower-order equal interpolation of the variables suffers from shear locking. In both Euler-Bernoulli and Timoshenko beam theories, the elements based on weak form Galerkin formulation also suffer from membrane locking when applied to geometrically nonlinear problems. In order to alleviate these types of locking, often reduced integration techniques are employed. However, this technique has other disadvantages, such as hour-glass modes or spurious rigid body modes. Hence, it is desirable to develop alternative finite element models that overcome the locking problems. Least-squares finite element models are considered to be better alternatives to the weak form Galerkin finite element models and, therefore, are in this study for investigation. The basic idea behind the least-squares finite element model is to compute the residuals due to the approximation of the variables of each equation being modeled, construct integral statement of the sum of the squares of the residuals (called least-squares functional), and minimize the integral with respect to the unknown parameters (i.e., nodal values) of the approximations. The least-squares formulation helps to retain the generalized displacements and forces (or stress resultants) as independent variables, and also allows the use of equal order interpolation functions for all variables. In this thesis comparison is made between the solution accuracy of finite element models of the Euler-Bernoulli and Timoshenko beam theories based on two different least-square models with the conventional weak form Galerkin finite element models. The developed models were applied to beam problems with different boundary conditions. The solutions obtained by the least-squares finite element models found to be very accurate for generalized displacements and forces when compared with the exact solutions, and they are more accurate in predicting the forces when compared to the conventional finite element models.