This thesis analyzes the relationship between users&rsquo / characteristics and users&rsquo / interface preferences. An online survey is developed for this study. This survey composed of two types of questions: (1) users&rsquo / personal information such as age, gender, country, cognitive structure, and also computer experience and (2) user interface elements. More than 2,500 participants from 120 different countries throughout the world completed our survey. Results were analyzed using cross tables. Our findings show that there is a relationship between users&rsquo / characteristics and users&rsquo / interface preferences. In the presence of this relationship, an artificial neural network model is developed for the estimation of the interface preferences based on the user characteristics.

Apatite group of minerals have been widely used in applications like detoxification of wastes, disposal of nuclear wastes and energy applications in addition to biomedical applications like bone repair, substitution, and coatings for metal implants due to its resemblance to the mineral part of the bone and teeth. X-ray diffraction patterns of bone are similar to mineral apatites such as hydroxyapatite and fluorapatite. Formation and physicochemical properties of apatites can be understood better by computer modeling. For this reason, lattice parameters of possible apatite compounds (A10(BO4)6C2), constituted by A: Na+, Ca2+, Ba2+, Cd2+, Pb2+, Sr2+, Mn2+, Zn2+, Eu2+, Nd3+, La3+, Y3+ / B: As+5, Cr+5, P5+, V5+, Si+4 / and C: F-, Cl-, OH-, Br-1 were predicted from their elemental ionic radii by artificial neural networks techniques. Using artificial neural network techniques, prediction models of lattice parameters a, c and hexagonal lattice volumes were developed. Various learning methods, neuron numbers and activation functions were used to predict lattice parameters of apatites. Best results were obtained with Bayesian regularization method with four neurons in the hidden layer with &lsquo / tansig&rsquo / activation function and one neuron in the output layer with &lsquo / purelin&rsquo / function. Accuracy of prediction was higher than 98% for the training dataset and average errors for outputs were less than 1% for dataset with multiple substitutions and different ionic charges at each site. Non-stoichiometric apatites were predicted with decreased accuracy. Formulas were derived by using ionic radii of apatites for lattice parameters a and c.

In this study a hybrid Pore Network (PN) model that simulates two-phase (water-oil) drainage and imbibition mechanisms is developed. The developed model produces Nuclear Magnetic Resonance (NMR) T2 relaxation times using correlations available in the literature. The developed PN was calibrated using experimental relative permeability data obtained for Berea Sandstone, Kuzey Marmara Limestone, Yenik&ouml / y Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from serial computerized tomography scans and thin section images. It was observed that pore body and throat sizes were not statistically correlated. It was also observed that the developed PN model can be used to model different displacement mechanisms. By using the synthetic data obtained from PN model, an Artificial Neural Network (ANN) model was developed and tested. It has been observed that the developed ANN tool can be used to estimate oil &ndash / water relative permeability data very well (with less than 0.05 mean square error) given a T2 signal. It was finally concluded that the developed tools can be used to obtain multiphase flow functions directly from an NMR well log such as Combinable Magnetic Resonance (CMR).

This study aims to develop business failure prediction models using the data of selected firms from ISE markets. The sample data comprise ten selected financial ratios for 27 non-going concerns (failed businesses) and paired 27 going concerns. Two non-parametric classification methods are used in the study: Artificial Neural Networks (ANN) and Decision Trees. The classification results show that there is equilibrium in the classification of the training samples by the models, but ANN model outperform the decision tree model in the classification of the testing samples. Further, the potential usefulness of ANN and Decision Tree type data mining techniques in the analysis of complex and non-linear relationships are observed.

In this dissertation, 3D surface seismic data was integrated with the well logs to be able to define the properties in every location for the reservoir under investigation. To accomplish this task, geostatistical and artificial neural networks (ANN) techniques were employed. First, missing log sets in the study area were estimated using common empirical relationships and ANN. Empirical estimations showed linear dependent results that cannot be generalized. On the other hand, ANNs predicted missing logs with an very high accuracy. Sonic logs were predicted using resistivity logs with 90% correlation coefficient. Second, acoustic impedance property was predicted in the study area. AI estimation first performed using sonic log with GRNN and 88% CC was obtained. AI estimation was repeated using sonic and resistivity logs and the result were improved to 94% CC. In the final part of the study, SGS technique was used with collocated cokriging techniques to estimate NPHI property. Results were varying due to nature of the algorithm. Then, GRNN and RNN algorithms were applied to predict NPHI property. Using optimized GRNN network parameters, NPHI was estimated with high accuracy. Results of the study were showed that ANN provides a powerful solution for reservoir parameter prediction in the study area with its flexibility to find out nonlinear relationships from the existing available data.

In the design and plan of the coast engineering, long-term and continual tidal database represent the indispensable role. This paper collect the tidal database, their locations include the ocean around the Taiwan and the South China Sea. Use the artificial neural networks (ANN) to build model and find the relationship between neighbor tidal observation stations. There are many reasons to cause the tide phenomenon, include the tide generating force, season, coastal geography, geography of sea floor, resonance of gulf or estuary, change depth of sea, and so on, it will be determined by local environment. The tide analysis and prediction usually use the harmonic analysis method. This method need long-term and continual tidal record, and the theory depend on the tide generating force, it has limit about accuracy. The application of artificial neural networks is used in nonlinear science problems in general cases. The back propagation (BP) networks is the one model of the artificial neural networks, this paper use ANN-BP model to build the relationship from different tide observed stations, and verify the quality of model. From the result of verified models, the ANN-BP model can predict and supplement the tide record very well. The items of research include: ¡i1¡j the relationship between two neighbor tide observed stations. (one station input, one station output) ¡i2¡jthe relationship between three neighbor tide observed stations. (two station input, one station output) ¡i3¡j input several tide observed stations and output one station. ¡i4¡j the correlation of connected weight and threshold between different models. ¡i5¡j change the parameters of ANN-BP model and discus the affect of model¡¦s quality. ¡i6¡j application of truly case. From the result of this paper, in the design and plan of the coast engineering, the long-term tide observed record can be predict from the ANN-BP model and tide record of neighbor observed stations. When the tide record has miss or lost cause by machine or other reasons, the ANN-BP model can supplement the lost tide record well. This paper show the ANN-BP model can be apply to predict and supplement the tide record very well, and will be possible applied method.

Man-made farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing and bring with it the loss of this refuge function. It is ecologically significant because one fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon-Wiener diversity index (H’). Results indicate that ponds with regular shape or the ones with larger size possess a strong positive correlation with H’. Farm ponds adjacent to farmland benefited waterside bird diversity. On the other hand, urban development was shown to cause the reduction of farmland and pond numbers, which in turn reduced waterside bird diversity. By running the ANN model with four neurons, the resulting H’ index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28). Analysis of historical pond occurrence to the present showed that ponds with larger size and a long perimeter were less likely to disappear. Smaller (< 0.1 ha) and more curvilinear ponds had a more drastic rate of disappearance. Based on this finding, a logistic regression was constructed to predict pond-loss likelihood in the future and to help identify ponds that should be protected. Overlaying results from ANN and form logistic regression enabled the creation of pond-diversity maps for these simulated scenarios of development intensities with respective to pond-loss trends and the corresponding dynamics of bird diversity.

<p>The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any system, the results show that ANNs are able to satisfactory distinguish different workload scenarios into those that cause future overloads from those that do not. Two ANN architectures have been evaluated, one standard feed-forward ANN and one recurrent ANN. These ANNs were trained and tested on sporadic workloads with different average arrival rates. At best the ANNs are able to predict up to 85% of the transient overloads in the test workload, while causing around 10% false alarms.</p>

<p>Admission controllers in dynamic real-time systems perform traditional schedulability tests in order to determine whether incoming tasks will meet their deadlines. These tests are computationally expensive and typically run in n * log n time where n is the number of tasks in the system. An incoming task might therefore miss its deadline while the schedulability test is being performed, when there is a heavy load on the system. In our work we evaluate a new approach for admission control in firm real-time systems. Our work shows that ANNs can be used to perform a schedulability test in order to work as an admission controller in firm real-time systems. By integrating the ANN admission controller to a real-time simulator we show that our approach provides feasible performance compared to a traditional approach. The ANNs are able to make up to 86% correct admission decisions in our simulations and the computational cost of our ANN schedulability test has a constant value independent of the load of the system. Our results also show that the computational cost of a traditional approach increases as a function of n log n where n is the number of tasks in the system.</p>

<p>This dissertation evaluates evolutionary methods for evolving cooperative teams of robots. Cooperative robotics is a challenging research area in the field of artificial intelligence. Individual and autonomous robots may by cooperation enhance their performance compared to what they can achieve separately. The challenge of cooperative robotics is that performance relies on interactions between robots. The interactions are not always fully understood, which makes the designing process of hardware and software systems complex. Robotic soccer, such as the RoboCup competitions, offers an unpredictable dynamical environment for competing robot teams that encourages research of these complexities. Instead of trying to solve these problems by designing and implement the behavior, the robots can learn how to behave by evolutionary methods. For this reason, this dissertation evaluates evolution of neural controllers for a team of two robots in a competitive soccer environment. The idea is that evolutionary methods may be a solution to the complexities of creating cooperative robots. The methods used in the experiments are influenced by research of evolutionary algorithms with single autonomous robots and on robotic soccer. The results show that robot teams can evolve to a form of cooperative behavior with simple reactive behavior by relying on self-adaptation with little supervision and human interference.</p>