The increasing performance of computers, reduced prices and ability to connect systems with low cost gigabit ethernet LAN and ATM WAN networks make distributed database systems an attractive research area. However, the complexity of distributed database query optimization is still a limiting factor. Optimal techniques, such as dynamic programming, used in centralized database query optimization are not feasible because of the increased problem size. The recently developed genetic algorithm (GA) based optimization techniques presents a promising alternative. We compared the best known GA with a random algorithm and showed that it achieves almost no improvement over the random search algorithm generating an equal number of random solutions. Then, we analyzed a set of possible GA parameters and determined that two-point truncate technique using GA gives the best results. New mutation and crossover operators defined in our GA are experimentally analyzed within a synthetic distributed database having increasing the numbers of relations and nodes. The designed synthetic database replicated relations, but there was no horizontal/vertical fragmentation. We can translate a select-project-join query including a fragmented relation with N fragments into a corresponding query with N relations. Comparisons with optimal results found by exhaustive search are only 20% off the results produced by our new GA formulation showing a 50% improvement over the previously known GA based algorithm.

In this thesis work, minimization of platform-based coupling between the antennas of two VHF radios on an aircraft platform and two HF radios on a ship platform is aimed. For this purpose / an optimal antenna placement, which yields minimum average coupling between the antennas over the whole frequency band of operation is determined for each platform. Two important global optimization techniques, namely Genetic Algorithm Optimization and Particle Swarm Optimization, are used in determination of these optimal antenna placements. Aircraft &amp / ship platforms and antennas placed on them are modeled based on their real electrical and physical properties in CST &ndash / MWS (Microwave Studio) simulation tool. For each platform, antenna placements and coupling results determined by two different optimization techniques and performances of these optimization techniques are compared with each other. At the end of this thesis work / for each platform, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage.

Tsunami forecasting methodology developed by the United States National Oceanic and Atmospheric Administration&rsquo / s Center for Tsunami Research is based on the concept of a pre-computed tsunami database which includes tsunami model results from Mw 7.5 earthquakes called tsunami source functions. Tsunami source functions are placed along the subduction zones of the oceans of the world in several rows. Linearity of tsunami propagation in an open ocean allows scaling and/or combination of the pre-computed tsunami source functions. An offshore scenario is obtained through inverting scaled and/or combined tsunami source functions against Deep-ocean Assessment and Reporting of Tsunami (DART) buoy measurements. A graphical user interface called Genetic Algorithm for INversion (GAIN) was developed in MATLAB using general optimization toolbox to perform an inversion. The 15 November 2006 Kuril and 27 February 2010 Chile tsunamis are chosen as case studies. One and/or several DART buoy measurement(s) is/are used to test different error minimization functions with/without earthquake magnitude as constraint. The inversion results are discussed comparing the forecasting model results with the tide gage measurements.

The main goal in inverse and forward problems of electrocardiography (ECG) is to better understand the electrical activity of the heart. In the forward problem of ECG, one obtains the body surface potential (BSP) distribution (i.e., the measurements) when the electrical sources in the heart are assumed to be known. The result is a mathematical model that relates the sources to the measurements. In the inverse problem of ECG, the unknown cardiac electrical sources are estimated from the BSP measurements and the mathematical model of the torso. Inverse problem of ECG is an ill-posed problem, and regularization should be applied in order to obtain a good solution. Tikhonov regularization is a well-known method, which introduces a trade-off between how well the solution fits the measurements and how well the constraints on the solution are satisfied. This trade-off is controlled by a regularization parameter, which can be easily calculated by the L-curve method. It is theoretically possible to include more than one constraint in the cost function / however finding more than one regularization parameter to use with each constraint is a challenging problem. It is the aim of this thesis to use genetic algorithm (GA) optimization method to obtain regularization parameters to solve the inverse ECG problem when multiple constraints are used for regularization. The results are presented when there are two spatial constraints, when there is one spatial, one temporal constraint, and when there are two spatial one temporal constraints / the performances of these three applications are compared to Tikhonov regularization results and to each other. As a conlcusion, it is possible to obtain correct regularization parameters using the GA method, and using more than one constraints yields improvements in the results.

Excessive pumping in coastal aquifers results in seawater intrusion where optimal and efficient planning is essential. In this study, numerical solution of single potential solution by Strack is combined with genetic algorithm (GA) to find the maximum extraction amount in a coastal aquifer. Seawater intrusion is tracked with the potential value at the extraction well locations. A code is developed by combining GA and a subroutine repeatedly calling MODFLOW as a numerical solver to calculate the potential distribution for different configurations of solution (trial solutions). Potential distributions are used to evaluate the fitness values for GA. The developed model is applied to a previous work by Mantoglou. Another heuristic method, simulated annealing (SA) is utilized to compare the results of GA. Different seawater prevention methods (i.e. injection wells, canals) and decision variables related to those methods (i.e. location of the injection wells or canals) are added to model to further prevent the seawater intrusion and improve the coastal aquifer benefit. A method called &ldquo / Alternating Constraints Method&rdquo / is introduced to improve the solution for the cases with variable location. The results show that both proposed method and the regular solution with GA or SA prove to be successful methods for the optimal management of coastal aquifers.

The controller area network (CAN) is the de-facto standard for in-vehicle communication. The growth of time-critical applications in modern cars leads to a considerable increase in the message trac on CAN. Hence, it is essential to determine ecient message schedules on CAN that guarantee that all communicated messages meet their timing constraints. The aim of this thesis is to develop oset scheduling strategies that

The main purpose of this thesis is to design a positioning system that matches the demand of the high-accuracy and the high-speed positioning. Hereon, the linear DC motor will be chosen as the main body of the whole system. Individually, we design the controller for macro model and micro model. Among them, using the genetic algorithms¡]GA¡^to find the near-optimum controller parameters for PID controller to complete the macro target. And adopting the relay-feedback auto-tuning PID controller to carry out the micro region position control. Through the dynamic transition condition, the two-step position control system is integrated. We hope that the positioning results can achieve the position sensor resolution, , in 0.2 second¡]positioning distance <1.0cm¡^. By adopting the principle and operation procedure of the genetic algorithms to make a search for the near-optimum controller parameters, and through the process of selection, reproduction, crossover, and mutation of genes, and then the performance of the closed-loop system with PID controller is improved. According to the computer simulations and the experimental results, it is obvious that the GA-based near-optimal controller can satisfactorily control the linear DC motor system.

In this study, the use of modified genetic algorithms (MGA) in the parameterization of the Transmission Mechanisms is facilitated. The new algorithm is proposed from the genetic algorithm with some additional strategies, and yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to compare with the LMS (Least mean-squares) method and GA method. Then, this proposed algorithm is applied to the identification of the Transmission Mechanisms of DC motor. The parameters of the friction force and DC motor are estimated in a single identification experiment. It is also shown that this technique is capable of identifying the whole transmission system. Finally, the Minimum Variance Controller (MVC) is taken to track the desired speed trajectory and then a comparison to the conventional digital PID controller is shown. Experiment results are included to demonstrate the excellent performance of the MVC.

We use the adaptive probabilities of crossover and mutation, elitist strategy, and extinction and immigration strategy to improve the simple genetic algorithm in this study. We expect that the search technique can avoid falling into the local maximum due to the premature convergence, and the chance of finding the near-optimal parameter in the larger searching space could be obviously increased. The accelerometer is then taken as the sensor for output measurement, and the designed actuator and digital PID controller is implemented to actively suppress the vibration of the plain that is due to the excitation effect of the high-speed and precision positioning of the linear motor. From the computer simulations and the experimented results, it is obvious that the near-optimal digital PID controller designed by modified genetic approach can improve the effect of vibration suppression; the settling time is also decrease. For the vibration suppressions of high-speed precision positioning problems, the vibrating plain system can fastly be stabilized.

The operation speed of modern computer system has been upgraded from several hundred MHz to GHz. The instant current will pass to the power plane of the mother board by way of the IC pins and result in electromagnetic wave propagation between the power and ground plane, so called ¡§Ground bounce.¡¨ To prevent the ground bounce from IC operation, decoupling capacitors are used. In this thesis, an efficient numerical approach which is based on the two-dimensional (2D) finite-difference time-domain (FDTD) method and with a new recursive algorithm has been used for modeling the power/ground planes characteristics with SMT capacitors above them. By the way, we take several methods, such as Debye model, FDTD-SPICE, and telegrapher¡¦s equation, for modeling various mother board structures. Finally, we use the genetic algorithm for calculating the optimum capacitor placements to meet the expect ground bounce limitation.