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Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. The neural network is trained using the Levenberg-Marquardt algorithm and the back propagation method by comparing the estimated off-board magnetic field intensity to the true value. This research also investigates sensitivity, scalability, and implementation issues of the neural network for signature estimation in a practical environment. / Electrical and Computer Engineering

Indiana University-Purdue University Indianapolis (IUPUI) / Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / In this paper, a method for digital correlation detector that takes advantage of the frame-synchronization-pattern feature of coincidence rate and adopts a multiple-bit detection window is proposed. Based on this method, a new digital correlation detector with a neural network is designed. It can recognizes frame-synchronization-pattern with error bits and slippage bits correctly, which has been approved practically according to the experimental results.

This thesis investigates novel methodologies for modelling, simulation and control of gas turbines using ANNs. In the field of modelling and simulation, two different types of gas turbines are modelled and simulated using both Simulink and neural network based models. Simulated and operational data sets are employed to demonstrate the capability of neural networks in capturing complex nonlinear dynamics of gas turbines. For ANN-based modelling, the application of both static (MLP) and dynamic (NARX) networks are explored. Simulink and NARX models are set up to explore both steady-state and transient behaviours. To develop an offline ANN-based system identification methodology for a low-power gas turbine, comprehensive computer program code including 18720 different ANN structures is generated and run in MATLAB to create and train different ANN models with feedforward multi-layer perceptron (MLP) structure. The results demonstrate that the ANN-based method can be applied accurately and reliably for the system identification of gas turbines. In this study, Simulink and NARX models are created and validated using experimental data sets to explore transient behaviour of a heavy-duty industrial power plant gas turbine (IPGT). The results show that both Simulink and NARX models successfully capture dynamics of the system. However, NARX approach can model gas turbine behaviour with a higher accuracy compared to Simulink approach. Besides, a separate complex model of the start-up operation of the same IPGT is built and verified by using NARX models. The models are set up and verified on the basis of measured time-series data sets. It is observed that NARX models have the potential to simulate start-up operation and to predict dynamic behaviour of gas turbines. In the area of control system design, a conventional proportional-integral-derivative (PID) controller and neural network based controllers consisting of ANN-based model predictive (MPC) and feedback linearization (NARMA-L2) controllers are designed and employed to control rotational speed of a gas turbine. The related parameters for all controllers are tuned and set up according to the requirements of the controllers design. It is demonstrated that neural network based controllers (in this case NARMA-L2) can perform even better than conventional controllers. The settling time, rise time and maximum overshoot for the response of NARMA-L2 is less than the corresponding factors for the conventional PID controller. It also follows the input changes more accurately than the PID. Overall, it is concluded from this thesis that in spite of all the controversial issues regarding using artificial neural networks for industrial applications, they have a high and strong potential to be considered as a reliable alternative to the conventional modelling, simulation and control methodologies. The models developed in this thesis can be used offline for design and manufacturing purposes or online on sites for condition monitoring, fault detection and trouble shooting of gas turbines.

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