We use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates of soil texture and bulk density data across the site are then used as input into a pedotransfer function to produce estimates of soil hydraulic parameter (saturated and residual water content θs and θr, saturated hydraulic conductivity Ks, van Genuchten parameters αand n) distributions across the site in three dimensions. We compare these estimates with laboratory-measured values of these same hydraulic parameters and find the estimated parameters match the measured well for θs, n and Ks but not well for θr nor α, while some measured extreme values are not captured. Finally the estimated soil hydraulic parameters are put into a numerical simulator to test the reliability of the models. Resultant simulated water contents do not agree well with those observed, indicating inverse calibration is required to improve the modeling performance. The results of this research conform to a previous work by Wang et al. at 2003. Also this research covers the gaps of Wang’s work in sense of generating 3-D heterogeneous fields of soil texture and bulk density by cokriging and providing comparisons between estimated and measured soil hydraulic parameters with new field and laboratory measurements of water retentions datasets.

A self-­‐learning Audio Player was built to learn users habits by analyzing operations the user does when listening to music. The self-­‐learning component is intended to provide a better music experience for the user by generating a special playlist based on the prediction of users favorite songs. The rough set core characteristics are used throughout the learning process to capture the dynamics of changing user interactions with the audio player. The engine is evaluated by simulation data. The simulation process ensures the data contain specific predetermined patterns. Evaluation results show the predictive power and stability of the hybrid engine for learning a users habits and the increased intelligence achieved by combining rough sets and NN when compared with using NN by itself.

This thesis discusses aspects of design, implementation and theory of expert systems, which have been constructed in a novel way using techniques derived from several existing areas of Artificial Intelligence research. In particular, it examines the philosophical and technical aspects of combining techniques derived from the traditional rule-based methods for knowledge representation, with others taken from connectionist (more commonly described as Artificial Neural Network) approaches, into one homogenous architecture. Several issues of viability have been addressed, in particular why an increase in system complexity should be warranted. The kind of gain that can be achieved by such hybrid systems in terms of their applicability to general problem solving and ability to continue working in the presence of noise, are discussed. The first aim of this work has been to assess the potential benefits of building systems from modular components, each of which is constructed using different internal architectures. The objective has been to progress the state of knowledge of the operational capabilities of a specific system. A hybrid architecture containing multiple neural nets and a rule-based system has been designed, implemented and analysed. In the course of, and as an aid to the development of the system, an extensive simulation work-bench has been constructed. The overall system, despite its increased internal complexity provides many benefits including ease of construction and improved noise tolerance, combined with explanation facilities. In terms of undesirable features inherited from the parent techniques the losses are low. The project has proved successful in its stated aims and has succeeded in contributing a working hybrid system model and experimental results derived from the comparison of this new approach with the two, primary, existing techniques.

Artificial neural networks (ANNs) have been used in many fault detection and identification (FDI) applications due to their pattern recognition abilities. In this study, two ANNs, a supervised network based on Backpropagation (BP) learning and an unsupervised network based on Adaptive Resonance Theory (ART-2A), were tested for FDI on an automated assembly machine and compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches were used as cost-effective sensors to monitor the machine's operating condition. To test each method, sensor data were collected while the machine operated under normal conditions, as well as 10 fault conditions. Features were selected from the raw sensor data to create data sets for training and testing. The performance of the methods was evaluated with respect to their ability to detect and identify known, unknown and multiple faults. Their modelling and computational requirements were also considered as performance measures. Results showed that all three methods were able to achieve perfect classification with the test data sets; however, the BP method could not classify unknown or multiple faults. In all cases, the performance depended on careful tuning of each method’s parameters. The BP method required an ideal number of neurons in the hidden layer and good initialization. The ART-2A method required tuning of its classification parameter. The rule-based method required tuning of its thresholds. Although it was found that the rule-based system required more effort to set up, it was judged to be more useful when unknown or multiple faults were present. The ART-2A network created new outputs for these conditions, but it could not give any more information as to what the new fault was. By contrast, the rule-based method was able to generate symptoms that clearly identified the unknown and multiple fault conditions. Thus, the rule-based method was judged to be the best overall method for this type of application. It is recommended that future work examine the application of computer vision-based techniques to FDI with the assembly machine. The results from this study, using cost-effective sensors, could then be used as a performance benchmark for image-based sensors. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2014-05-16 17:21:13.676

This thesis investigates the viability of using dynamic-based ‘damage fingerprints’ in combination with artificial neural network (ANN) techniques and principal component analysis (PCA) to identify defects in civil engineering structures. Vibration-based damage detection techniques are global methods and are based on the principle that damage alters both the physical properties, such as mass, stiffness and damping, as well as the dynamic properties of a structure. It is therefore feasible to utilise measured dynamic quantities, such as time histories, frequency response functions (FRFs) and modal parameters, from structural vibration to detect damage. Damage identification based on vibrational characteristics is essentially a form of pattern recognition problem, which looks for the discrimination between two or more signal categories, e.g., before and after a structure is damaged, or differences in damage levels or locations. Artificial neural networks are capable of pattern recognition, classification, signal processing and system identification, and are therefore an ideal tool in complementing dynamic-based damage detection techniques. Likewise, PCA has pattern recognition abilities and is capable of data reduction and noise filtering. With these characteristics, both techniques can help overcome limitations associated with previously developed vibration-based methods and assist in delivering more accurate and robust damage identification results. In this study, two types of dynamic-based damage identification methods are proposed. The first is based on the damage index (DI) method (initially proposed by Stubbs et al.), while the second approach uses changes in FRF data as damage fingerprints. The advantage of using damage patterns from the DI method, which is based on changes in modal strain energies, is that only measured mode shapes are required in the damage identification, without having to know the complete stiffness and mass matrices of the structure. The use of directly measured FRF data, which provide an abundance of information, is further beneficial as the execution of experimental modal analysis is not required, thus greatly reducing human induced errors. Both proposed methods utilise PCA and neural network techniques for damage feature extraction, data reduction and noise filtering. A hierarchical network training scheme based on network ensembles is proposed to take advantage of individual characteristics of damage patterns obtained from different sources (different vibrational modes for the DI-based method and different sensor locations for the FRF-based method). In the ensemble, a number of individual networks are trained in parallel, which optimises the network training and delivers improved damage identification outcomes. Both methods are first tested on a simple beam structure to assess their feasibility and performance. Then, the FRF-based method is applied to a more complicated structure, a two-storey framed structure, for validation purposes. The two methods are verified by numerical simulations and laboratory testing for both structures. As defects, notch type damage of different severities and locations are investigated for the beam structure. For the two-storey framed structure, three different types of structural change are studied, i.e. boundary damage, added mass changes and section reduction damage. To simulate field-testing conditions, the issue of limited sensor availability is incorporated into the analysis. For the DI-based method, sensor network limitations are compensated for by refining coarse mode shape vectors using cubic spline interpolation techniques. To simulate noise disturbances experienced during experimental testing, for the numerical simulations, measurement data are polluted with different levels of white Gaussian noise. The damage identifications of both methods are found to be accurate and reliable for all types of damage. For the DI-based method, the results show that the proposed method is capable of overcoming limitations of the original DI method associated with node point singularities and sensitivities to limited number of sensors. For the FRF-based method, excellent results are obtained for damage identification of the beam structure as well as of the two-storey framed structure. A major contribution is the training of the neural networks in a network ensemble scheme, which operates as a filtering mechanism against individual networks with poor performance. The ensemble network, which fuses results of individual networks, gives results that are in general better than the outcomes of any of the individual networks. Further, the noise filtering capabilities of PCA and neural networks demonstrate great performance in the proposed methods, especially for the FRF-based identification scheme.

This thesis investigates the viability of using dynamic-based ‘damage fingerprints’ in combination with artificial neural network (ANN) techniques and principal component analysis (PCA) to identify defects in civil engineering structures. Vibration-based damage detection techniques are global methods and are based on the principle that damage alters both the physical properties, such as mass, stiffness and damping, as well as the dynamic properties of a structure. It is therefore feasible to utilise measured dynamic quantities, such as time histories, frequency response functions (FRFs) and modal parameters, from structural vibration to detect damage. Damage identification based on vibrational characteristics is essentially a form of pattern recognition problem, which looks for the discrimination between two or more signal categories, e.g., before and after a structure is damaged, or differences in damage levels or locations. Artificial neural networks are capable of pattern recognition, classification, signal processing and system identification, and are therefore an ideal tool in complementing dynamic-based damage detection techniques. Likewise, PCA has pattern recognition abilities and is capable of data reduction and noise filtering. With these characteristics, both techniques can help overcome limitations associated with previously developed vibration-based methods and assist in delivering more accurate and robust damage identification results. In this study, two types of dynamic-based damage identification methods are proposed. The first is based on the damage index (DI) method (initially proposed by Stubbs et al.), while the second approach uses changes in FRF data as damage fingerprints. The advantage of using damage patterns from the DI method, which is based on changes in modal strain energies, is that only measured mode shapes are required in the damage identification, without having to know the complete stiffness and mass matrices of the structure. The use of directly measured FRF data, which provide an abundance of information, is further beneficial as the execution of experimental modal analysis is not required, thus greatly reducing human induced errors. Both proposed methods utilise PCA and neural network techniques for damage feature extraction, data reduction and noise filtering. A hierarchical network training scheme based on network ensembles is proposed to take advantage of individual characteristics of damage patterns obtained from different sources (different vibrational modes for the DI-based method and different sensor locations for the FRF-based method). In the ensemble, a number of individual networks are trained in parallel, which optimises the network training and delivers improved damage identification outcomes. Both methods are first tested on a simple beam structure to assess their feasibility and performance. Then, the FRF-based method is applied to a more complicated structure, a two-storey framed structure, for validation purposes. The two methods are verified by numerical simulations and laboratory testing for both structures. As defects, notch type damage of different severities and locations are investigated for the beam structure. For the two-storey framed structure, three different types of structural change are studied, i.e. boundary damage, added mass changes and section reduction damage. To simulate field-testing conditions, the issue of limited sensor availability is incorporated into the analysis. For the DI-based method, sensor network limitations are compensated for by refining coarse mode shape vectors using cubic spline interpolation techniques. To simulate noise disturbances experienced during experimental testing, for the numerical simulations, measurement data are polluted with different levels of white Gaussian noise. The damage identifications of both methods are found to be accurate and reliable for all types of damage. For the DI-based method, the results show that the proposed method is capable of overcoming limitations of the original DI method associated with node point singularities and sensitivities to limited number of sensors. For the FRF-based method, excellent results are obtained for damage identification of the beam structure as well as of the two-storey framed structure. A major contribution is the training of the neural networks in a network ensemble scheme, which operates as a filtering mechanism against individual networks with poor performance. The ensemble network, which fuses results of individual networks, gives results that are in general better than the outcomes of any of the individual networks. Further, the noise filtering capabilities of PCA and neural networks demonstrate great performance in the proposed methods, especially for the FRF-based identification scheme.

A new methodology was developed for flow regime identification in pipes. The method utilizes the pattern recognition abilities of Artificial Neural Networks and the unprocessed time series of a system-monitoring-signal. The methodology was tested with synthetic data from a conceptual system, liquid level indicating capacitance signals from a Horizontal flow system and with a pressure difference signal from a S-shape riser. The results showed that the signals that were generated for the conceptual system had all their patterns identified correctly with no errors whatsoever. The patterns for the Horizontal flow system were also classified very well with a few errors recorded due to original misclassifications of the data. The misclassifications were mainly due to subjectivity and due to signals that belonged to transition regions, hence a single label for them was not adequate. Finally the results for the S-shape riser showed also good agreement with the visual observations and the few errors that were identified were again due to original misclassifications but also to the lack of long enough time series for some flow cases and the availability of less flow cases for some flow regimes than others. In general the methodology proved to be successful and there were a number of advantages identified for this neural network methodology in comparison to other ones and especially the feature extraction methods. These advantages were: Faster identfication of changes to the condition of the system, inexpensive suitable for a variety of pipeline geometries and more powerful on the flow regime identification, even for transitional cases.

Orientador: Carlos Roberto Minussi / Banca: Francisco Villarreal Alvarado / Banca: Nobuo Oki / Banca: Geraldo Roberto Martins da Costa / Banca: Mário Oleskovicz / Resumo: Nos dias atuais, principalmente pelo fato de alguns sistemas serem desregulamentados, o estudo dos problemas de análise, planejamento e operação de sistemas de energia elétrica é de extrema importância para o funcionamento do sistema. Para isso é necessário que se obtenha, com antecedência, o comportamento da carga elétrica com o propósito de garantir o fornecimento de energia aos consumidores de forma econômica, segura e contínua. Este trabalho propõe o desenvolvimento de redes neurais artificiais utilizadas para resolver o problema de previsão de cargas elétricas. Para tanto, inicialmente, propôs-se a introdução de melhorias na rede neural feedforward com treinamento realizado utilizando o algoritmo retropropagação. Neste caso, foi desenvolvida/implementada a adaptação dos parâmetros de inclinação e translação da função sigmóide (função de ativação da rede neural). A inclusão desta nova estrutura de redes neurais produziu melhores resultados, se comparado à rede neural retropropagação convencional. Essas arquiteturas proporcionam bons resultados, porém, são estruturas de redes neurais que possuem o problema de convergência. O problema de previsão de cargas elétricas a curto-prazo necessita de uma rede neural que forneça uma saída de forma rápida e eficaz. No intuito de solucionar os problemas encontrados com o algoritmo retropropagação foi desenvolvida/implementada uma rede neural baseada na arquitetura ART (Adaptive Rossonance Theory), denominada rede neural ART&ARTMAP nebulosa, aplicada ao problema de previsão de carga elétrica. Trata-se, por conseguinte, da principal contribuição desta tese. As redes neurais, baseadas na arquitetura ART, possuem duas características fundamentais que são de extrema importância para o desempenho da rede (estabilidade e plasticidade), que permite a implementação do treinamento de modo contínuo...(Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Nowadays due to the deregulamentation it is very important to study the problems of analyzing, planning and operation of electric power systems. For a reliable operation it is necessary to know previously the behavior of the load to guarantee the energy providing to the users with security and continuity and in an economic way. This work proposes to develop artificial neural networks to solve the problem of electric load forecasting. First, it is introduced some improvements on the feedforward neural network, with the training effectuated with the backpropagation algorithm. The improvement was the adaptation of the inclination and translation parameters of the sigmoid function (activation function of the neural network). The inclusion of this new structure provides better results if compared to the conventional backpropagation algorithm. These architectures provide good results, although they are structures that have some convergence problems. The short term electric load forecasting problem needs a neural network that provide a fast and efficient output. To solve this problem a neural network based on the ART (Adaptive Ressonance Theory), called_ fuzzy ART&ARTMAP applied to the load-forecasting problem, was developed and implemented._This is one of the contributions of this work. Neural networks based on the ART architecture have two important characteristics for the network performance, which are stability and plasticity, allowing the continuous training. The fuzzy ART&ARTMAP neural network reduces the imprecision of the results by a mechanism that separates the binary and analogical data and processing them separately. This represents a quality and an improvement on the results (reduction of the processing time and better precision), if compared to the neural network with backpropagation training (often considered as a benchmark in precision by the specialized...(Complete abastract click electronic access below) / Doutor

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / Electricity generation capacity from different renewable sources has been significantly growing worldwide in recent years, specially wind power. Fast dispatch of wind power provides flexibility for spinning reserve. However, wind is intermittent in nature. Thus, stable grid operations and energy management are becoming more challenging with the increasing penetration of wind in power systems. Efficient forecast methods can help the scenario. Many wind forecast models have been developed over the years. Highly effective models with the combination of numerical weather prediction and statistical models also exist at present. This study intends to develop a model to forecast hourly wind speed using an artificial neural network (ANN) approach for effective and fast operation with minimum data. The procedure is outlined in this work and the performance of the ANN model is compared with the persistence forecast model.

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.