M.Ing. (Electrical And Electronic Engineering) / This dissertation describes the development of a system of Artificial Neural Networks that enables the incremental training of feed forward neural networks using supervised training algorithms such as back propagation. It is argued that incremental learning is fundamental to the adaptive learning behavior observed in human intelligence and constitutes an imperative step towards artificial cognition. The importance of developing incremental learning as a system of ANNs is stressed before the complete system is presented. Details of the development and implementation of the system is complemented by the description of two case studies. In conclusion the role of the incremental learning system as basis for further development of fundamental elements of cognition is projected.

Analytical solutions such as the Theis solution have historically been utilized to forecast changes in aquifer water levels resulting from human-driven withdrawals using pumping wells. This method, however, suffers from a number of disadvantages, such as long data acquisition times, model uncertainty, and trial-and-error calibrations. This study illustrated the effectiveness of alternate forecasting methods that utilized machine learning principles. The groundwater level dynamics of two sites located at the Virginia Eastern Shore were predicted using historical groundwater level below land surface (GWLBLS) data as the endogenous variable and local pumping data as the exogenous variable. Predicting the local pumping data from the GWLBLS values was also implemented, to not only enforce reliability of the model, but also to highlight the capability of verifying and enforcing permitted pumping data. The machine learning methods chosen for this study were the Random Forest and SARIMAX models. Historical datasets were divided into training/calibration and testing/validation sets, and the respective models were fit to the data. These calibrated models were then compared to the performance of the Theis solution. Across both study sites, the Random Forest performed best at forecasting groundwater level over time given the pumping data as an exogenous variable, with SARIMAX performing similarly to the Theis solution. The Theis solution, however, did perform well in terms of generalization ability (GA). / Master of Science / Groundwater is a vital resource for drinking, agriculture, and industry. In order to ensure aquifer health for future use, it is crucial to be able to forecast well water level in the midst of groundwater pumping. Currently, analytical solutions such as the Theis solution are utilized to predict water level over time, but data acquisition is time-consuming and many of the calibrations have to be based on trial-and-error. In this thesis, machine learning methods were explored as alternatives to the current analytical methods. The groundwater level dynamics of the two study sites, Oyster, Virginia and Temperanceville, Virginia, were used to calibrate corresponding machine learning models, called the Random Forest (RF) and SARIMAX models. While the Theis showed that it was the most adaptable model, the RF performed the best overall in terms of root mean square error and R2 scores, which were used as reliability metrics. This study provides a range of substitutes for the Theis solution that have the capability to perform better when calibrated on a site-by-site basis.

This dissertation is a systematic study of artificial neural networks (ANN) applications in power system restoration (PSR). PSR is based on available generation and load to be restored analysis. A literature review showed that the conventional PSR methods, i.e. the pre-established guidelines, the expert systems method, the mathematical programming method and the petri-net method have limitations such as the necessary time to obtain the PSR plan. ANN may help to solve this problem presenting a reliable PSR plan in a smaller time. Based on actual and past experiences, a PSR engine based on ANN was proposed and developed. Data from the Iowa 162 bus power system was used in the implementation of the technique. Reactive and real power balance, fault location, phase angles across breakers and intentional islanding were taken into account in the implementation of the technique. Constraints in PSR as thermal limits of transmission lines (TL), stability issues, number of TL used in the restoration plan and lockout breakers were used to create feasible PSR plans. To compare the time necessary to achieve the PSR plan with another technique a PSR method based on a breadth-search algorithm was implemented. This algorithm was also used to create training and validation patterns for the ANN used in the scheme. An algorithm to determine the switching sequence of the breakers was also implemented. In order to determine the switching sequence of the breakers the algorithm takes into account the most priority loads and the final system configuration generated by the ANN. The PSR technique implemented is composed by several pairs of ANN, each one assigned to an individual island of the system. The restoration of the system is done in parallel in each island. After each island is restored the tie lines are closed. The results encountered shows that ANN based schemes can be used in PSR helping the operators restore the system under the stressful conditions following a blackout. / Ph. D.

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The thesis outlines the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is that the network can learn quickly. Fast training methods may be used in certain Control Systems and it has been suggested that they could find application in ‘disaster control,’ where a potentially dangerous system (for example, suffering a mechanical failure) needs to be controlled quickly. Previous work on the methods has shown that they suffer problems when used with multi-layer networks. The thesis discusses the reasons for these problems and reports on several successful techniques for overcoming them. These techniques are based on the consideration of the neuron, rather then the individual weight, as a factor to be optimised. The applications of technique and further work are also discussed.

Diabetes Mellitus is a major health problem which affects about 200 million people worldwide. Diabetics require their blood glucose levels to be kept within the normal range in order to prevent diabetes-related complications from occurring. Blood glucose measurement is therefore of vital importance. The current glucose measurement techniques are, however, painful, inconvenient and episodic. This document provides an investigation into the use of near-infrared spectroscopy for continuous, non-invasive measurement of blood glucose. Artificial neural networks are used for the development of multivariate calibration models which predict glucose concentrations based on the near-infrared spectral data. Simulations have been performed which make use of simulated spectral data generated from the characteristic spectra of many of the major components of human blood. The simulations show that artificial neural networks are capable of predicting the glucose concentrations of complex aqueous solutions with clinically relevant accuracy. The effect of interference, such as temperature changes, pathlength variations, measurement noise and absorption due other analytes, has been investigated and modelled. The artificial neural network calibration models are capable of providing acceptably accurate predictions in the presence of multiple forms of interference. It was found that the performance of the measurement technique can be improved through careful selection of the optical pathlength and wavelength range for the spectroscopic measurements, and by using preprocessing techniques to reduce the effect of interference. Although the simulations suggest that near-infrared spectroscopy is a promising method of blood glucose measurement, which could greatly improve the quality of life of diabetics, many further issues must be resolved before the long-term goal of developing a continuous non-invasive home glucose monitor can be achieved.

This research provides a robust solution based on artificial intelligence (AI) techniques to overcome the challenges in Software Defined Networks (SDNs) that can jeopardise the overall performance of the network. The proposed approach, presented in the form of an intelligent agent appended to the SDN network, comprises of a new hybrid intelligent mechanism that optimises the performance of SDN based on heuristic optimisation methods under an Artificial Neural Network (ANN) paradigm. Evolutionary optimisation techniques, including Particle Swarm Optimisation (PSO) and Genetic Algorithms (GAs) are deployed to find the best set of inputs that give the maximum performance of an SDN-based network. The ANN model is trained and applied as a predictor of SDN behaviour according to effective traffic parameters. The parameters that were used in this study include round-trip time and throughput, which were obtained from the flow table rules of each switch. A POX controller and OpenFlow switches, which characterise the behaviour of an SDN, have been modelled with three different topologies. Generalisation of the prediction model has been tested with new raw data that were unseen in the training stage. The simulation results show a reasonably good performance of the network in terms of obtaining a Mean Square Error (MSE) that is less than 10−6 [superscript]. Following the attainment of the predicted ANN model, utilisation with PSO and GA optimisers was conducted to achieve the best performance of the SDN-based network. The PSO approach combined with the predicted SDN model was identified as being comparatively better than the GA approach in terms of their performance indices and computational efficiency. Overall, this research demonstrates that building an intelligent agent will enhance the overall performance of the SDN network. Three different SDN topologies have been implemented to study the impact of the proposed approach with the findings demonstrating a reduction in the packets dropped ratio (PDR) by 28-31%. Moreover, the packets sent to the SDN controller were also reduced by 35-36%, depending on the generated traffic. The developed approach minimised the round-trip time (RTT) by 23% and enhanced the throughput by 10%. Finally, in the event where SDN controller fails, the optimised intelligent agent can immediately take over and control of the entire network.

This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.

Le principal objectif de ce travail est l’étude de l’influence de la pesanteur sur la mécanique respiratoire et le contrôle de la respiration, ainsi que sur les interactions cardio-respiratoires pendant les différents stades du sommeil. Le chapitre introductif présente le contexte général et les objectifs de la thèse. Des sections abordant le sommeil, la respiration, et l’interaction cardio-respiratoire y sont présentées, résumant l’état actuel des connaissances sur les effets de la pesanteur sur chacun de ces systèmes. Dans le deuxième chapitre, l’expérience “Sleep and Breathing in microgravity”, qui constitue la source des données à la base de ce travail, est présentée en détail. L’étude des signaux de longue durée requiert avant tout de disposer d’outils performants d’analyse des signaux. La première partie de la thèse présente en détail deux algorithmes : un algorithme de détection automatique d’événements respiratoires (inspiration / expiration) basé sur des réseaux neuronaux artificiels, et un algorithme de quantification de l’amplitude et de la phase de l’arythmie sinusale pendant le sommeil, utilisant la méthode des ondelettes. La validation de chaque algorithme est présentée, et leur performance évaluée. Cette partie inclut aussi des courtes introductions théoriques aux réseaux de neurones artificiels ainsi qu’aux méthodes d’analyse temps–fréquence (Fourier et ondelettes). Une approche similaire à celle utilisée pour la détection automatique d’événements respiratoires a été appliquée à la détection d’événements dans des signaux de vitesse du sang dans l’artère cérébrale moyenne, mesures obtenues par Doppler transcrânien. Ceci est le sujet de la thèse annexe. Ces deux algorithmes ont été appliqués aux données expérimentales pour extraire des informations physiologiques quant à l’impact de la pesanteur sur la mécanique respiratoire et l’interaction cardio-respiratoire. Ceci constitue la deuxième partie de la thèse. Un chapitre est consacré aux effets de l’apesanteur sur la mécanique respiratoire pendant le sommeil. Ce chapitre a mis en évidence, pour tous les stades de sommeil, une augmentation de la contribution abdominale en microgravité, suivi d’un retour progressif vers des valeurs observées avant le vol. L’augmentation initiale était attendue, mais l’adaptation progressive observée ne peut pas être expliquée par un effet purement mécanique, et nous suggère la présence d’un mécanisme d’adaptation central. Un deuxième chapitre présente les résultats comparant l’arythmie sinusale pendant le sommeil avant le vol, en apesanteur et après le retour sur terre. Le rythme cardiaque pendant le sommeil dans l’espace présente une moindre variabilité. Les différences NREM–REM observées sur terre pour les influences vagales et sympathiques sont accentuées dans l’espace. Aucun changement significatif n’est présent pour le gain et la différence de phase entre les les signaux cardiaque et respiratoire en comparant le sommeil sur terre et en apesanteur. La dissertation termine par une discussion générale du travail effectué, incluant les prin- cipales conclusions ainsi que les perspectives qui en découlent.