Large marine vessels carry their loads all over the world. It can be a container ship carrying over 10 000 containers filled with foods, textiles and electronics or a bulk freighter carrying 400 000 tons of coal. Vessels usually have a ballast system that pumps water into ballast tanks to stabilize the vessel. The ballast system can be used to change the vessel’s trim and list angles. Trim and list are the ship equivalents of pitch and roll. By changing the trim angle the water resistance can be reduced and thus also the fuel consumption. Since the vessel is consuming a couple of hundred tons of fuel per day, a small reduction in fuel consumption can save a considerable amount of money, and it is good for the environment. In this thesis, the ship’s power consumption has been estimated using an artificial neural network, which is a mathematical model based on data. The name refers to certain structural similarities with the neural synapse system in animals. The idea with neural networks has been to create brain-like systems. For applications such as learning to interpret sensor data, artificial neural networks are an effective learning method. The goal is to estimate the ship power using a artificial neural network and then use it to calculate the trim angle, to be able to save fuel. The data used in the artificial neural network come from sensor systems mounted on a container ship sailing between Europe and Asia. The sensor data have been thoroughly preprocessed and this includes for example removing the parts when the ship is docked in harbour, data patching and synchronisation and outlier detection based on a Kalman filter. A physical model of a marine craft including wind, wave, hydrodynamic and hydrostatic effects, has also been introduced to help analyse the performance and behaviour of the artificial neural network. The artificial neural network developed in this thesis could successfully estimate the power consumption of the ship. Based on the developed networks it can be seen that the fuel consumption is reduced by trimming the ship by bow, i.e., the ship is angled so the bow is closer to the water line than the stern. The method introduced here could also be applied on other marine vessels, such as bulk freighters or tank ships.

<p>An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques.</p> <p>On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load.</p> <p>The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor &alpha; according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane.</p> <p>Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning.</p>

An isolated hybrid system comprised of a dispatchable and a non-dispatchable power generation sources, is proposed to supply the load of a remote village in the west coast region of The Gambia. The thesis presents an artificial neural network (ANN) based approach to tune the parameters of the frequency regulator in hybrid wind/diesel power system for isolated area power supply. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the frequency regulator in the simulation of hybrid system under different load and wind conditions. Using MATLAB/Simulink, dynamic simulations are performed to investigate the interaction between these two power sources for the load management, and the voltage and frequency behaviors during wind speed and load variations. Simulation results show that the wind turbine and the diesel generator can be operated suitably in parallel. During simulation, the frequency and voltage regulators used in the proposed hybrid system performed fairly well under wind speed variations and load changing conditions. A good frequency regulator interface, which is around 50Hz is observed for nearly the entire period of operation.

Reflection cracking is one of the main distresses in hot-mix asphalt (HMA) overlays. It has been a serious concern since early in the 20th century. Since then, several models have been developed to predict the extent and severity of reflection cracking in HMA overlays. However, only limited research has been performed to evaluate and calibrate these models. In this dissertation, mechanistic-based models are calibrated to field data of over 400 overlay test sections to produce a design process for predicting reflection cracks. Three cracking mechanisms: bending, shearing traffic stresses, and thermal stress are taken into account to evaluate the rate of growth of the three increasing levels of distress severity: low, medium, and high. The cumulative damage done by all three cracking mechanisms is used to predict the number of days for the reflection crack to reach the surface of the overlay. The result of this calculation is calibrated to the observed field data (severity and extent) which has been fitted with an S-shaped curve. In the mechanistic computations, material properties and fracture-related stress intensity factors are generated using efficient Artificial Neural Network (ANN) algorithms. In the bending and shearing traffic stress models, the traffic was represented by axle load spectra. In the thermal stress model, a recently developed temperature model was used to predict the temperature at the crack tips. This process was developed to analyze various overlay structures. HMA overlays over either asphalt pavement or jointed concrete pavement in all four major climatic zones are discussed in this dissertation. The results of this calculated mechanistic approach showed its ability to efficiently reproduce field observations of the growth, extent, and severity of reflection cracking. The most important contribution to crack growth was found to be thermal stress. The computer running time for a twenty-year prediction of a typical overlay was between one and four minutes.

Now Taiwan is not only the country that facts the corruption threat. The greedy politician and never satisfied merchant unceasingly perform the scandal in the whole world. The national economy and the people¡¦s wealth are also injured. The topic of this research is how to choose the important variable from the corruption case. In recent years the Data Mining technique application in the behavioral analysis of shopping, customer relations management, crime investigation is in fashion; however the Data Mining technique application in politics and social domain is still not enough. In this research, we attempt to introduce the concepts and techniques of Data Mining and use Data Mining technique to set up a selective model for the consideration for the government in the corruption preventing. It attempts to explore the opportunity for the social sciences research.

The study of system dynamics starts from model construction and simulation to understand and solve dynamical complicated problems. Traditionally approaches of modeling process depend on an expert¡¦s experiences and the trial & error procedure. Chen¡¦s research proposes a transformation method that could map a System Dynamics Model (SDM) to a specially designed Partial Recurrent Network (PRN). Thus he could use the neural network training algorithm to assist model construction and policy design. In this paper, we will introduce a Genetic Algorithm (GA) in the model building process, which encodes a PRN into a string and uses an evolution process to select a best solution. The algorithm not only improves the PRN training, but also generates more candidate models for consideration. Thus, it enhances the SDM-PRN transformation¡¦s usability.

Traffic congestion has become a menace to civilized society. It degrades air quality, jeopardizes safety and causes delay. Traffic congestion can be alleviated by providing an effective traffic control signal system. Closed-loop traffic control systems are an example of such a system. Closed-loop traffic control systems can be operated primarily in either of two modes: Time of Day Mode (TOD) or Traffic Responsive Plan Selection Mode (TRPS). TRPS mode, if properly configured, can easily handle time independent variation in traffic volumes. It can also reduce the effect of timing plan aging. Despite these advantages, TRPS mode is not used as frequently as TOD mode. The reason being a lack of methodologies and formal guidelines for predicting the factors and thresholds associated with TRPS mode. In this research, a new methodology is developed for determining the thresholds and factors associated with the TRPS mode. This methodology, when tested on a closed-loop system in Odem, Texas, produced a classification accuracy of 94%. The classification accuracy can be increased to 98% with a proposed TRPS architecture.

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