The Finite-Difference Time-Domain Method (FDTD) is a very powerful numerical method for the full wave analysis electromagnetic phenomena. Due to its flexibility, it can be used to solve numerous electromagnetic scattering problems on microwave circuits, dielectrics, and electromagnetic absorption in biological tissue at microwave frequencies. However, it needs so much computation time to simulate microwave integral circuits by applying the FDTD method. If the structure we simulated is complicated and we want to obtain accurate frequency domain scattering parameters, the simulation time will be so much longer that the efficiency of simulation will be bad as well. Therefore, in the thesis, we introduce an artificial neural networks (ANN) method called ¡§Infinite Impulse Response Neural Networks (IIRNN)¡¨ can speed up the FDTD simulation time. In order to boost the efficiency of the FDTD simulation time by stopping the simulation after a sufficient number of time steps and using FIRNN as a predictor to predict time series signal.

Metamodeling is a very popular approach for the approximation of complex systems. Metamodeling techniques can be categorized according to the type of regression method employed as linear and nonlinear models. The Response Surface Methodology (RSM) is an example of linear regression. In classical RSM metamodels, parameters are estimated using the Least Squares (LS) Method. Robust regression techniques, such as Least Absolute Deviation (LAD) and M-regression, are also considered in this study due to the outliers existing in data sets. Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS) are examples for non-linear regression technique. In this thesis these two nonlinear metamodeling techniques are constructed and their performances are compared with the performances of linear models.

<p>The human brain is always trying to predict ahead in time. Many say that it is possible to take actions only based on internal simulations of the brain. A recent trend in the field of Artificial Intelligence is to provide agents with an “inner world” or internal simulations. This inner world can then be used instead of the real world, making it possible to operate without any inputs from the real world.</p><p>This final year project explores the possibility to navigate collision-free in a dynamic environment, using only internal simulation of sensor input instead of real input. Three scenarios will be presented that show how internal simulation operates in a dynamic environment. The results show that it is possible to navigate entirely based on predictions without a collision.</p>

<p>Germund Hesslows simuleringshypotes består av tre olika komponenter. För att modellera den första av dessa komponenter som postulerar en mekanism för intern perceptionssimulering, har det tidigare utförts ett antal experimentella undersökningar. En simulerad Kheperarobot har i dessa tidigare experiment med viss framgång styrts med hjälp av ett artificiellt neuronnät. Detta skedde genom att roboten utifrån den miljö i vilken den befann sig predicerade nästkommande sensortillstånd via neuronnätet. Sensorerna stängdes därefter av och de predicerade värdena återkopplades in i nätverket som indata. Roboten agerade därefter blint i miljön utifrån denna inre värld, men i dessa tidigare undersökningar var det endast ett nätverk som lyckades tränas för att utföra intern perceptionssimulering. Denna experimentella undersökning ämnar att mer systematiskt analysera en implementering av intern perceptionssimulering i en förenklad värld med enklare sensorer än de som har används i tidigare undersökningar. Fem olika nätverkstopologier jämförs via två olika träningsförfaranden, dels ett träningsförfarande där nätverkens vikter tränas med en evolutionsalgoritm, och dels via ett tvådelat evolveringsförfarande där olika delar av nätverket tränas separat. Experimentet gav upphov till ett flertal individer som bedömdes klara den interna perceptionssimuleringen. Resultaten indikerar också på att det integrerade träningsförfarandet verkar vara bättre än det uppdelade</p>

<p>I dagens samhälle finns det en mängd olika maskiner för att underlätta ardagssysslorna, såsom batteridrivna dammsugare och gräsklippare. Gräsklippardomänen används i detta projekt, för att undersöka vilken av monolitisk och hierarkisk kontrollarkitektur i en batteridriven gräsklippare som har bäst generaliseringsförmåga. Gräsklippardomänen används som testdomän därför att det finns en oändlig mängd olika yt-fromer. Med generalisering menas hur bra gräsklipparen klipper på ytor som den nyligen eller aldrig tränats på. Experiment har utförs på båda kontrollarkitekturerna i en simulator. Val av kontrollarkitektur spelar inte någon större roll för gräsklipparen. Bidraget med detta arbete är att undersöka hur bra de olika arkitekturerna generaliserar.</p>

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 18, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Elisa Glick. Includes bibliographical references.

Siemens gas turbines are monitored and controlled by a large number of sensors and actuators. Process information is stored in a database and used for offline calculations and analyses. Before storing the sensor readings, a compression algorithm checks the signal and skips the values that explain no significant change. Compression of 90 % is not unusual. Since data from the database is used for analyses and decisions are made upon results from these analyses it is important to have a system for validating the data in the database. Decisions made on false information can result in large economic losses. When this project was initiated no sensor validation system was available. In this thesis the uncertainties in measurement chains are revealed. Methods for fault detection are investigated and finally the most promising methods are put to the test. Linear relationships between redundant sensors are derived and the residuals form an influence structure allowing the faulty sensor to be isolated. Where redundant sensors are not available, a gas turbine model is utilized to state the input-output relationships so that estimates of the sensor outputs can be formed. Linear parametric models and an ANN (Artificial Neural Network) are developed to produce the estimates. Two techniques for the linear parametric models are evaluated; prediction and simulation. The residuals are also evaluated in two ways; direct evaluation against a threshold and evaluation with the CUSUM (CUmulative SUM) algorithm. The results show that sensor validation using compressed data is feasible. Faults as small as 1% of the measuring range can be detected in many cases.

This thesis explores the evolution, growth and sudden decline of the East Washington community, located in East Point, Georgia. This African-American community was strategically created in 1912, when the city council passed its first residential segregation ordinance. This research uses oral histories and other documents to analyze the survival techniques that enabled East Washington to endure the turmoil of Jim Crow racial segregation from its 1912 inception to its 1962 transformation due to urban renewal. First, it identifies the people who chose to migrate to this area, where they came from and what enticed them to settle in East Point. Second, it discusses the network of institutions that they built and depended upon, including businesses, schools and churches, in order to maintain their largely autonomous community. Finally, it illuminates East Washington’s demise through urban renewal.

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