Footings are often founded on multi-layered soil profiles. Real soil profiles are often multi-layered with material constantly varying with depth, which affects the footing response significantly. Furthermore, the properties of the soil are known to vary with location. The spatial variability of soil can be described by random field theory and geostatistics. The research presented in this thesis focuses on quantifying the effect of soil variability on the bearing capacity of rough strip footings on single and two layered, purely-cohesive, spatially variable soil profiles. This has been achieved by using Monte Carlo analysis, where the rough strip footings are founded on simulated soil profiles are analysed using finite element limit analysis. The simulations of virtual soil profiles are carried out using Local Average Subdivision (LAS), a numerical model based on the random field theory. An extensive parametric study has been carried out and the results of the analyses are presented as normalized means and coefficients of variation of bearing capacity factor, and comparisons between different cases are presented. The results indicate that, in general, the mean of the bearing capacity reduces as soil variability increases and the worst case scenario occurs when the correlation length is in the range of 0.5 to 1.0 times the footing width. The problem of estimating the bearing capacity of shallow strip footings founded on multi-layered soil profiles is very complex, due to the incomplete knowledge of interactions and relationships between parameters. Much research has been carried out on single- and two-layered homogeneous soil profiles. At present, the inaccurate weighted average method is the only technique available for estimating the bearing capacity of footing on soils with three or more layers. In this research, artificial neural networks (ANNs) are used to develop meta-models for bearing capacity estimation. ANNs are numerical modelling techniques that imitate the human brain capability to learn from experience. This research is limited to shallow strip footing founded on soil mass consisting of ten layers, which are weightless, purely cohesive and cohesive-frictional. A large number of data has been obtained by using finite element limit analysis. These data are used to train and verify the ANN models. The shear strength (cohesion and friction angle), soil thickness, and footing width are used as model inputs, as they are influencing factors of bearing capacity of footings. The model outputs are the bearing capacities of the footings. The developed ANN-based models are then compared with the weighted average method. Hand-calculation design formulae for estimation of bearing capacity of footings on ten-layered soil profiles, based on the ANN models, are presented. It is shown that the ANN-based models have the ability to predict the bearing capacity of footings on ten-layered soil profiles with a high degree of accuracy, and outperform traditional methods. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368281 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

<p>Simulations are widely used for analysis and design of complex systems. Real-world complex systems are often too complex to be expressed with tractable mathematical formulations. Therefore simulations are often used instead of mathematical formulations because of their flexibility and ability to model real-world complex systems in some detail. Simulation models can often be complex and slow which lead to the development of simulation meta-models that are simpler and faster models of complex simulation models. Artificial neural networks (ANNs) have been studied for use as simulation meta-models with different results. This final year project further studies the use of ANNs as simulation meta-models by comparing the predictability of five different neural network architectures: feed-forward-, generalized feed-forward-, modular-, radial basis- and Elman artificial neural networks where the underlying simulation is of complex production system. The results where that all architectures gave acceptable results even though it can be said that Elman- and feed-forward ANNs performed the best of the tests conducted here. The difference in accuracy and generalization was considerably small.</p>

Στην παρούσα διπλωματική εργασία μελετάμε την ικανότητα των τεχνητών νευρωνικών δικτύων στη σύνθεση μουσικής και την αναγνώριση μουσικού συνθέτη. Συγκεκριμένα, στο πρώτο κεφάλαιο κάνουμε μία εισαγωγή στα τεχνητά νευρωνικά δίκτυα και ειδικά σε αυτά που χρησιμοποιούνται στα επόμενα κεφάλαια. Γίνεται αναφορά στα βασικά είδη των ΤΝΔ που υπάρχουν, εμπρόσθιας τροφοδότησης και αναδραστικά και περιγράφονται οι αλγόριθμοι εκπαίδευσής τους. Εξηγούμε την ικανότητα των αναδραστικών νευρωνικών δικτύων να έχουν δυναμική μνήμη, σε αντίθεση με αυτά που είναι εμπρόσθιας τροφοδότησης, πράγμα που τα καθιστά ικανά στην πρόβλεψη χρονοσειρών. Αυτή η ικανότητα των αναδραστικών δικτύων σε συνδυασμό με το γεγονός ότι ένα μουσικό κομμάτι μπορεί να χαρακτηριστεί σαν μία αλληλουχία γεγονότων χρονικής συνοχής (χρονοσειρά) δημιούργησε ένα ερευνητικό ρεύμα προς την κατεύθυνση της σύνθεσης μουσικής με τη χρήση ανδραστικών τεχνητών νευρωνικών δικτύων. Στο δεύτερο κεφάλαιο κάνουμε μία αναφορά στην αλγοριθμική σύνθεση μουσικής, ιδιαίτερα με χρήση πινάκων μετάβασης. Έπειτα ακολουθεί η περιγραφή του CONCERT, ενός αναδραστικού νευρωνικού δικτύου που κατασκευάστηκε για να συνθέτει μουσική με πρόβλεψη νότας προς νότα. Αναλύουμε επίσης την μοντελοποίηση των μουσικών αντικειμένων για την επεξεργασία και αναπαράστασή τους από το CONCERT η οποία βασίζεται σε ψυχοακουστικούς περιορισμούς αντίληψης των μουσικών αντικειμένων από τους ανθρώπους. Εξηγούμε τον τρόπο που εκπαιδεύεται το CONCERT έτσι ώστε να έχει όσο το δυνατόν μεγαλύτερη μνήμη και περιγράφουμε τις επιδόσεις του σε διάφορες δοκιμές που έγιναν, από την εκμάθηση μίας διατονικής κλίμακας μέχρι ενός κομματιού του J. S. Bach. Παρατηρώντας την ικανότητα του CONCERT να αντιλαμβάνεται τοπικές δομές (μοτίβα φράσεις) μα όχι καθολικές (μέρη του μουσικού κομματιού) αναφερόμαστε στην τεχνική της περιορισμένης περιγραφής που αποτελεί μια προσπάθεια για εκπαίδευση του δικτύου έτσι ώστε να αντιλαμβάνεται το μουσικό κομμάτι σε μία μεγαλύτερη κλίμακα. Στο τέλος του δευτέρου κεφαλαίου εξετάζουμε τη συνολική επίδοση του CONCERT και αναλύουμε τις κατευθύνσεις προς τις οποίες θα μπορούσαμε να κινηθούμε για τη βελτίωση των αποτελεσμάτων. Στο τρίτο κεφάλαιο αναφερόμαστε στην αναγνώριση του συνθέτη ενός μουσικού κομματιού με τη χρήση τεχνητών νευρωνικών δικτύων πάνω στην παρτιτούρα του κομματιού αυτού. Αρχικά γίνεται μία συζήτηση γύρω από το ποια στοιχεία της παρτιτούρας θεωρούμε σημαντικά, ποια από αυτά μπορούμε και ποια έχει νόημα να μοντελοποιήσουμε έτσι ώστε ένα ΤΝΔ να μπορεί να κάνει πρόβλεψη. Αναλύονται οι τεχνικές λεπτομέρειες των στοιχείων που χρειαζόμαστε για τη μοντελοποίηση μιας παρτιτούρας στον υπολογιστή και στη συνέχεια αναφερόμαστε στα δύο πειράματα που ελέγχουν την ορθότητα και αποτελεσματικότητα της παραπάνω προσέγγισης. Το ποια κομμάτια χρησιμοποιήθηκαν και από ποιους συνθέτες δε θα μπορούσε να είναι τυχαίο καθώς πρέπει να ικανοποιούνται διάφορες συνθήκες στατιστικής ομοιομορφίας έτσι ώστε η απάντηση του ΤΝΔ να είναι όσο το δυνατόν πιο αμερόληπτη. Αυτές οι συνθήκες, καθώς και οι κίνδυνοι που υπάρχουν σε πιθανή παράληψή τους εξηγούνται πριν τα πειράματα. Το πρώτο πείραμα πραγματεύεται την αναγνώριση συνθέτη ενός κομματιού που συντέθηκε από τον Chopin ή όχι (δηλαδή από τους Beethoven ή Mozart) ενώ στο δεύτερο οι εμπλεκόμενοι συνθέτες είναι οι Bach και Handel. Δοκιμάζονται διάφορες αρχιτεκτονικές ΤΝΔ και μετρούμε τη μέση και τη βέλτιστη επίδοσή τους. Τέλος συζητάμε τα αποτελέσματα των δύο πειραμάτων καθώς και τροποποιήσεις είτε του ΤΝΔ είτε της μοντελοποίησης που διαλέξαμε για την αναπαράσταση της παρτιτούρας στον υπολογιστή έτσι ώστε να έχουμε καλύτερα αποτελέσματα. / In this work we study the capability of artificial neural networks for composing music and musical composer recognition. To this end, in the first chapter the neural networks are introduced, especially the forms of those that are used later on. A reference is being made to the basic forms of neural networks, feedforward (FNN) and recursive (RNN), and their training algorithms. We explain the ability of the RNNs to have dynamic memory, in contrast to FNNs, which makes them suitable for predicting time series. This ability combined to the fact that a musical piece can be considered as a time series has urged researchers to explore music composition through RNNs. In the second chapter algorithmic music composition is being described, especially with the use of Markov chains. Then we describe CONCERT, a RNN constructed for composing music with note by note prediction. We also analyze the representation of musical objects which is based in how humans perceive them. CONCERT is trained with different musical patterns (from diatonic scales to Bach pieces) and its composing ability is being discussed. The fact that CONCERT lacks in capturing the global structure of a piece is not changed with the use of reduced description, which is thoroughly described. The second chapter concludes with thoughts on how a RNN could capture the global structure of a piece. The third chapter is devoted to composer recognition with the use of FNNs. Firstly we discuss which elements of a score are useful and which of them we can represent such that a FNN can identify a composer. The techniques that we use for the computer modeling of the problem and the manipulation of the pieces are thoroughly described. Two experiments are presented, in the first one the FNN is called to recognize Chopin from Mozart and Beethoven and in the second Bach from Handel. Finally a discussion is made on the results of the above experiments and how we could optimize them.

The human olfactory system can classify new odors in a dynamic environment with varying odor complexity and concentration, while simultaneously reducing the influence of stable background odors. Replication of this capability has remained an active area of research over the past 3 decades and has great potential to advance medical diagnostics, environmental monitoring and industrial monitoring, among others. New methods for rapid dynamic temporal evaluation of chemical sensor arrays for the monitoring of analytes is explored in this work. One such method is high and low bandpass filtering of changing sensor responses; this is applied to reduce the effects of background noise and sensor drift over time. Processed sensor array responses, coupled with principal component analysis (PCA), will be used to develop a novel approach to classify odors in the presence of changing sensor responses associated with evolving odor concentrations. These methods will enable the removal of noise and drift, as well as facilitating the normalization to decouple classification patterns from intensity; lastly, PCA and artificial neural networks (ANNs) will be used to demonstrate the capability of this approach to function under dynamic conditions, where concentration is changing temporally. / February 2016

The operation of large industrial scale combustion systems, such as furnaces and boilers is increasingly dictated by emission legislation and requirements for improved efficiency. However, it can be exceedingly difficult and time consuming to gather the information required to improve original designs. Mathematical modelling techniques have led to the development of sophisticated furnace representations that are capable of representing combustion parameters. Whilst such data is ideal for design purposes, the current power of computing systems tends to generate simulation times that are too great to embed the models into online control strategies. The work presented in this thesis offers the possibility of replacing such mathematical models with suitably trained Artificial Neural Networks (ANNs) since they can compute the same outputs at a fraction of the model's speed, suggesting they could provide an ideal alternative in online control strategies. Furthermore, artificial neural networks have the ability to approximate and extrapolate making them extremely robust when encountering conditions not met previously. In addition to improving operational procedures, another approach to increasing furnace system efficiency is to minimise the waste heat energy produced during the combustion process. One very successful method involves the implementation of a heat exchanger system in the exiting gas flue stream, since this is predominantly the main source of heat loss. It can be exceptionally difficult to determine which heat exchanger is best suited for a particular application and it can prove an even more arduous task to control it effectively. Furthermore, there are many factors that alter the performance characteristics of a heat exchanger throughout the duration of its operational life, such as fouling or unexpected systematic faults. This thesis investigates the modelling of an experimental heat exchanger system via artificial neural networks with a view to aiding the design and selection process. Moreover, the work presented offers a means to control heat exchangers subject to varying operating conditions more effectively, thus promoting savings in both waste energy and time.

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2003Bitstream added on 2014-06-13T20:22:35Z : No. of bitstreams: 1 nagaoka_me_dr_botfca.pdf: 501587 bytes, checksum: a5d05cfa41f21298548d31b5d95dc6b1 (MD5) / Este trabalho apresenta a aplicação de um sistema inteligente utilizando redes neurais artificiais para estimar valores de densidade do solo, a partir de parâmetros referentes à resistência do solo à penetração. Foram considerados solos preparados e não preparados, os não preparados foram os seguintes : teor de argila menor que 30 % (solo tipo 1), de 30 a 50 % (solo tipo 2) e maior que 50 % (solo tipo 3). Os preparados foram os seguintes: um com teor de argila menor que 30 % (solo tipo 1) e o outro com teor de argila maior que 50 % (solo tipo 3). O objetivo principal deste trabalho foi implementar diversas redes neurais do tipo perceptron multicamadas, alimentando-as com resistência do solo à penetração, teor de água e teor de argila, tendo como variável de saída a densidade do solo. Cada rede foi treinada variando o número de camadas escondidas e também variando o número de neurônios, de 10 a 40, em cada camada. Para cada arquitetura, a rede foi treinada 10 vezes, escolhendo-se no final do treinamento a arquitetura com menor erro relativo médio e menor variância em relação aos dados de validação. As análises realizadas mostraram que as arquiteturas de rede com apenas uma camada escondida forneceram melhores resultados. Todas as redes tiveram melhor desempenho em solo não preparado do que em solo preparado. A rede de arquitetura de 3 entradas, uma camada escondida com 30 neurônios e 1 saída forneceu excelente resultado para solo não preparado (com teor de argila entre 30 e 50 %). Constatou-se que a rede quando treinada com dados do solo preparado, juntamente com dados do solo não preparado, melhorou os resultados de estimação para o solo preparado, mas piorou para os solos não preparados. Constatou também que a rede quando treinada junto com dados que contém solo solto fornece resultados imprecisos. O mesmo ocorreu para dados com teor de água elevado. / This work presents the development of an intelligent system using artificial neural networks to estimate values of soil density. Prepared and non-prepared soils were considered in this work. The non-prepared soils were the following ones: clay content lesser than 30 % (soil type 1), 30 to 50 % (soil type 2) and larger than 50 % (soil type 3). The prepared soils were the following ones: soil with clay content lesser than 30 % (soil type 1) and soil with clay content larger than 50 % (soil type 3). The main objective of this work was to implement several neural networks of type multilayer perceptron, feeding them with data concerning to the soil compaction characteristics. The output computed by the neural network was the respective density of these soils. Each neural network was trained varying both number of hidden layers and number of neurons, which was changed from 10 to 40 neurons in each layer. In each architecture the network was trained 10 times and selected architecture was always that having either the least mean relative error or the least variance in relation to validation data. The carried out analyses showed that the neural architectures having only a hidden layer were those that provided the best results. All neural networks have presented more efficient results for non-prepared soils than prepared soils. The neural network constituted by three inputs and one output, having 30 neurons at hidden layer, has provided excellent results for non-prepared soils (clay content between 30 and 50 %). It was also verified that the neural network when trained with data referent to non-prepared and soils, which were put in the same data set, it became the results referent to prepared soils more efficient, but the results for non-prepared soils become worse. Another observed point was when the network had been trained with data constituted by soft soil... (Complete abstract, click electronic address below).

Made available in DSpace on 2014-06-11T19:34:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-10-11Bitstream added on 2014-06-13T18:45:51Z : No. of bitstreams: 1 almeida_lf_dr_guara.pdf: 1479231 bytes, checksum: d1b34509ec45ba1ae48a6450780e381d (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Um importante aspecto no processo produtivo é proporcionar o funcionamento das máquinas o maior tempo possível sem o comprometimento na qualidade final do produto. Nesse sentido, a utilização de uma política de manutenção adequada se torna necessária para o monitoramento do desgaste dos componentes das máquinas a fim de aumentar o tempo de sua utilização sem comprometer a qualidade do produto. A manutenção baseada em condição se apresenta como a abordagem mais apropriada para esse controle. Dentre as diversas abordagens utilizadas para o desenvolvimento de programas para esse tipo de manutenção, as técnicas baseadas em Inteligência Artificial vêm se destacando no que diz respeito ao seu desempenho. Diante desse contexto, essa tese propõe uma Rede Neural Artificial, a qual, devidamente parametrizada, possibilita sua aplicação tanto para análise de vibrações quanto análise de partículas de desgaste. Para tanto, foi implementado um protótipo denominado NEURALNET-CBM, subdividido em dois módulos, Vibrações e Partículas. Os resultados dos testes mostram a efetividade da rede proposta, com um índice de acerto acima de 90% na classificação e identificação de defeitos e partículas de desgaste. / An important aspect in the production process is to ensure the operability of a machine as long as possible without interfering on the final quality product. In this way, the use of a suitable maintenance policy is critical for monitoring the wear of the machine components in order to increase your useful life without any compromise of the product quality. The Condition-Based Maintenance is presented as the most appropriate approach for this control. Among several methods used to develop systems for this type of maintenance, techniques Artificial Intelligence has been standing out in relation their performance. Therefore, this thesis proposes a Artificial Neural Network, which, properly parameterized, it makes possible its application for both vibration and wear particle analysis. For this, we implemented a prototype named NEURALNET-CBM, divided into two modules: Vibration and Particle. The test results show the effectiveness of the proposed network, with accuracy rate greater than 90% in classifying and identification of defects and wear particles.

Master of Science / Department of Computer Science / William H. Hsu / Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets. The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin. This plays a vital role in making trading decisions. There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task. To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time series – that is, past prices of Bitcoin over several years. Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day. We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson’s correlation coefficient) on a continuation of the training data from each time series, held out for validation.

Made available in DSpace on 2014-06-11T19:22:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-07-29Bitstream added on 2014-06-13T19:07:53Z : No. of bitstreams: 1 ferreira_ms_me_prud.pdf: 1897420 bytes, checksum: 1aede604709494154b2f75d18806c9fc (MD5) / O conhecimento da distribuição espacial da concentração de componentes da água é de fundamental importância para inferir a respeito dos processos ecológicos que ocorrem num sistema hídrico sendo, entretanto, de difícil obtenção. Dentre as variáveis que merecem atenção no monitoramento de ambientes aquáticos, destaca-se a clorofila a, a qual é uma substância presente em algas responsáveis pela fotossíntese, organismos que constituem a base da cadeia alimentar nesses ambientes. Por se tratar de um pigmento fotossintetizante, a clorofila a apresenta a propriedade de interagir com a radiação eletromagnética, e dessa interação resultam diferentes processos, identificáveis por meio de sensores remotos. Assim sendo, a presente pesquisa se propôs a desenvolver um método de inferência da concentração de clorofila a utilizando Redes Neurais Artificiais (RNA). Utilizou-se como dados de entrada para a inferência combinações de bandas espectrais de uma imagem World View-2 e valores de concentração de clorofila a obtidos com um fluorômetro de campo, o qual possibilitou uma amostragem densa na área de estudos. A imagem multiespectral foi corrigida radiometricamente, eliminando efeitos de instrumentação e atmosféricos. Ainda, efetuou-se uma suavização espectral em cada uma das bandas e foi avaliado se esse tratamento na imagem possibilitaria... / The knowledge of the spatial distribution of water components concentrations is of fundamental importance to infer about the ecological processes that occur in an aquatic system, however, is difficult to obtain it. Among the variables that deserve attention in the monitoring of aquatic environments, cite the chlorophyll a, which is a substance of photosynthetic algae, organisms that are the basis of the food chain in these environments. Because it is a photosynthetic pigment, chlorophyll a has the property to interact with electromagnetic radiation, and it results in different processes, identifiable through remote sensing. Thus, this research intended to develop a chlorophyll a concentration inference method using Artificial Neural Networks (ANN). As input for the inference, it was used combinations of World View-2 spectral bands and chlorophyll a concentration values obtained with a field fluorometer, which allowed a dense sampling in the study area. The multispectral imagery was radiometrically corrected, eliminating the instrumentation and atmospheric effects. Still, it was performed a spectral smoothing in each of the spectral bands and evaluated whether this treatment would give... (Complete abstract click electronic access below)

Orientador: Maria de Lourdes Bueno Trindade Galo / Resumo: A utilização de dados de Sensoriamento Remoto em conjunto com técnicas de processamento digital e análise de imagens tem possibilitado o desenvolvimento de estudos integrados, com vistas ao monitoramento dos recursos naturais. Uma maneira de representar esses dados é através de mapas temáticos, obtidos por métodos de classificação multiespectral. Para a classificação de dados de Sensoriamento Remoto, a utilização de Redes Neurais Artifíciais tem se apresentado como uma alternativa vantajosa em relação aos classificadores baseados em conceitos estatísticos, uma vez que nenhuma hipótese prévia sobre a distribuição dos dados a serem classificados é exigida. Assim, esse trabalho teve como objetivo detectar a ocorrência e mapear a dispersão espacial de plantas aquáticas emersas em cinco reservatórios ao longo do rio Tietê-SP (Barra Bonita, Bariri, Ibitinga, Promissão e Nova Avanhandava) através da classificação por Redes Neurais Artifíciais...(Resumo completo, clicar acesso eletrônico abaixo) / Mestre