Orientador: Daniel Barrera Arellano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos / Made available in DSpace on 2018-08-25T11:25:07Z (GMT). No. of bitstreams: 1 Garcia_RitadeKassiadeAlmeida_D.pdf: 2621947 bytes, checksum: 1a1aa809676a39c098a38069d013cf39 (MD5) Previous issue date: 2014 / Resumo: Óleos e gorduras são submetidos ao processo de blending para alcançar características específicas, visando sua aplicação em variados produtos. Redes neurais artificiais (RNA) têm sido utilizadas para otimizar o processo de formulação de gorduras baseado no conteúdo de gordura sólida (SFC). Além do SFC, a cinética de cristalização das gorduras ou blends influencia diretamente nas condições de processamento, bem como nas características e qualidade dos produtos elaborados. Nesse contexto, o objetivo deste trabalho foi construir e treinar RNAs capazes de prever parâmetros de cristalização de blends de gorduras. Foram treinadas duas RNAs usando blends contendo gorduras interesterificadas de soja, óleos de soja, palma e palmíste como matérias-primas. No treinamento, além dos dados de SFC, foram utilizados os parâmetros de cristalização tempo de indução (T1), tempo médio (T2), tempo final (T3) e SFC máximo (%), obtidos pelas isotermas de cristalização a 25°C. Além disso, como avaliação, foi verificada a capacidade das RNAs em predizer os parâmetros de cristalização de formulações sugeridas pelas RNAs para aplicação em recheio de biscoitos e uso geral. Como resultados, as RNAs se mostraram capazes de prever os parâmetros de cristalização para os blends elaborados com as diferentes matérias-primas, apresentando baixos valores de erros relativos (parâmetros preditos vs determinados). Quanto ao comportamento de cristalização, observou-se que as formulações que continham óleos de palma e/ou palmíste apresentaram menores valores de SFC máximo a 25°C. Adicionalmente, também verificou-se que para valores similares de SFC máximo, foram obtidos valores de T3 bastante variados, o que confirma a necessidade do conhecimento dos parâmetros de cristalização. Portanto, as RNAs demonstraram ser uma ferramenta útil na previsão dos parâmetros de cristalização, podendo ser utilizada na indústria para um melhor monitoramento das características dos blends formulados / Abstract: Oils and fats are submitted to the blending process to achieve specific characteristics for their application at various products. Artificial neural networks (ANN) have been used to optimize the process of fat formulation based on the solid fat content (SFC). In addition to the SFC, the crystallization kinetics of fats or blends influences directly the processing conditions, as well as the characteristics and quality of manufactured food products. In this context, the objective was to build and train ANNs that are able to predict the crystallization parameters of fat blends. Two ANNs were trained using blends containing soybean interesterified fats, soybean, palm and palm kernel oils as raw materials. At training, in addition to the SFC data were used the parameters of crystallization induction time (T1), medium time (T2), end time (T3) and maximum SFC (%), obtained by isothermal crystallization at 25 °C. Besides that, as an evaluation, it was verified the ANN ability to predict the crystallization parameters for a biscuit filling and general use formulations. As results, the ANNs showed ability to predict the crystallization parameters for the blends prepared with different raw materials, presenting low relative errors (predicted vs determined parameters). Regarding the crystallization behavior, it was observed that formulations containing palm and /or palm kernel oil showed lower values of maximum SFC at 25 ° C. In addition, it was also noted that for similar maximum SFC, various T3 values were obtained, confirming the need for knowledge of the crystallization parameters of fats. Therefore, ANNs proved to be a useful tool for predicting the crystallization parameters and can be used in food industry for better monitoring of characteristics of formulated blends / Doutorado / Tecnologia de Alimentos / Doutora em Tecnologia de Alimentos

Orientador: Pablo Siqueira Meirelles / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-07-25T06:06:38Z (GMT). No. of bitstreams: 1 Picado_RicardoMigueis_M.pdf: 5102567 bytes, checksum: cc783eef53f8e17d6dcb5ce204188210 (MD5) Previous issue date: 1998 / Resumo: Neste trabalho, será feito um estudo dos principais tipos de suspensões semi-ativas propostas até o presente. A viabilidade (econômica) de um sistema de suspensão semi-ativa depende da rapidez do algorítmo de controle, da capacidade de processamento do hardware disponível e dos custos para instalação e manutenção da suspensão. Para mostrar como estes fatores influenciam na concepção de um sistema de suspensão automotiva, foram reunidos vários algoritmos de controle e um método alternativo de controle semi-ativo baseado em redes neurais artificias foi proposto / Abstract: In this thesis, it was developed a study of the principal sorts of semi-active car suspensions. The possibility of a semi-active suspension system depends on the costs envolved, the hardware available, and the control algorithm. To show how this subjects influence the conception process of a car suspension, we compiled some algorithms and proposed an altemative one based in artificial neural networks / Mestrado / Mestre em Engenharia Mecânica

>Magister Scientiae - MSc / There are five fundamental parameters that characterize any sign language gesture. They are hand shape, orientation, motion and location, and facial expressions. The SASL group at the University of the Western Cape has created systems to recognize each of these parameters in an input video stream. Most of these systems make use of the Support Vector Machine technique for the classification of data due to its high accuracy. It is, however, unknown how other machine learning techniques compare to Support Vector Machines in the recognition of each of these parameters. This research lays the foundation for the process of determining optimum machine learning techniques for each parameter by comparing Support Vector Machines to Artificial Neural Networks and Random Forests in the context of South African Sign Language hand shape recognition. Li, a previous researcher at the SASL group, created a state-of-the-art hand shape recognition system that uses Support Vector Machines to classify hand shapes. This research re-implements Li’s feature extraction procedure but investigates the use of Artificial Neural Networks and Random Forests in the place of Support Vector Machines as a comparison. The machine learning techniques are optimized and trained to recognize ten SASL hand shapes and compared in terms of classification accuracy, training time, optimization time and classification time.

A polymorphic substance is capable of forming a number of different crystalline phases that are referred to as its polymorphs. The critical process that determines the outcome of a crystallization process in a polymorphic system is thought to be the nucleation state, which is the self-assembled stage just prior to the formation of crystals with long-range order. While nucleation is well known to be influenced by macroscopically measurable parameters such as temperature, supersaturation and solvent choice our understanding of the underlying molecular self-assembly processes is very limited. The research described in this thesis explores a new approach to extending our knowledge in this area by the use of a combination of medium throughput crystallisation experiments together with the computation of a range of molecular and solute/solvent descriptors of the system under study.The main objective of the work was to develop a protocol for relating experimental and computational data via artificial neural network (ANN) analysis, to identify significant links between experimental polymorphic outcomes and molecular properties. By creating a model that can predict the polymorphic form in a given experiment it is anticipated that our understanding of links between nucleation and crystallisation will be enhanced through the determining the pivotal properties of a molecule that cause it to form one polymorph over another. The ANN method was developed in the context of the carbamazepine system, applying several statistical techniques to the results of 88 crystallisation experiments, featuring 13 solvents, 3 evaporation rates and 4 temperatures. The results show that this approach allows the formulation of further research hypotheses through examination of the physical meaning of the set of descriptors identified by the ANN approach. Crucially, principal component analysis (PCA) was found to be able to efficiently narrow down large sets of computationally derived descriptors to a manageable set by removing redundancy through strongly cross-correlated parameters. The best ANN model generated in this research was capable of predicting the major polymorphic form in 89 % of cross-validation experiments.The optimised set of descriptors included both solute and solvent properties, which predominantly described the intermolecular interactions in solution. The physical meanings of the descriptors and their impact on the molecular processes during nucleation has been considered and their cross correlation has been examined. Initial results from further experimentation with the tolbutamide and ROY systems indicate that the methodology is also transferable to other polymorphic systems.

GIS has been used in Veterinary Science for a couple of year and the application thereof has been growing rapidly. A number of GIS models have been developed to predict the occurrences of certain types of insect species including the Culicoides species (spp), the insect vectors responsible for the transmission of the African horse sickness (AHS) virus. AHS is endemic to sub-Saharan Africa and is carried by two midges called Culicoides Imicola and Culicoides Bolitinos. The disease causes severe illness in horses and has significant economic impact if not dealt with timeously. Although these models had some success in the prediction of possible abundance of the Culicoides spp. the complicated nature and high number of variables influencing the abundance of Culicoides spp. posed some challenges to these GIS models. This informs the need for models that can accurately predict potential abundance of Culicoides spp to prevent unnecessary horse deaths. This lead the study to the use of a combination of a GIS and an artificial neural networks (ANN) to develop a model that can predict the abundance of C. Imicola and C. Bolitinos. ANNs are models designed to imitate the human brain and have the ability to learn through examples. ANNs can therefore model extremely complex features. In addition, using GIS maps to visualise the predictions will make the models more accessible to a wider range of practitioners. / Dissertation (MSc)--University of Pretoria, 2010. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

Some of the largest United States bankruptcies of publicly-traded non-financial firms have occurred within the last decade. The continuing need to improve bankruptcy prediction has generated numerous research studies utilizing various prediction models. The purpose of this study is to test the usefulness of the multiple discriminant, probit, and artificial neural network (ANN) models in predicting bankruptcy in the United States textile-related industry. Financial data is examined for 47 bankrupt and 104 non-bankrupt publicly-traded firms in the textile-related industry during the time period 1998-2004, which includes the events of the Asian currency crisis and increased competition from China. Models developed by Altman (1968), Altman (1983), Zmijewski (1984) are compared to ANNs based upon each of these models. A comparison to an ANN including all of the ratios of the previous models and variables for firm size and domestic sales is also made. The Altman (1968) model and ANN 68 model are found to have the higher predictive power for one and two years prior to bankruptcy, respectively, for bankrupt firms. The ANN 84 model and the ANN 83 model have the highest correct classification results for nonbankrupt firms for the entire time period. Solvency and leverage variables appear to have the most impact on the bankruptcy prediction of textile-related firms. The additional variables of firm size and domestic sales are not found to improve the predictive accuracy. This study supports the continued use of the original Altman (1968) model for predicting bankruptcy in a manufacturing industry. Simultaneous utilization of the ANN 83 model to predict nonbankrupt firms is also suggested since the majority of the Altman (1968) variables can be used and the higher potential for improved predictability. This study may be extended to years after 2004 with consideration given to quarterly information, NAICs codes, and leverage variable alternatives.

Artificial neural networks are demanding to computational power of a computer. Increasing their learning speed could mean new posibilities for research or aplication of the algorithm. And that is a purpose of this thesis. The usage of graphics processing units for neural networks learning is one way how to achieve above mentioned goals. This thesis is offering a survey of theoretical background and consequently implementation of a software library for neural networks learning with a Backpropagation algorithm with a support of acceleration on graphics processing unit.

No description available.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The aim of this article is to use artificial neural networks (ANN) to perform the structural design of confined masonry buildings. ANN is easy to operate and allows to reduce the time and cost of seismic designs. To generate the artificial neural network, training models (traditional confined masonry designs) are used to identify the input and output parameters. From this, the final architecture and activation functions are defined for each layer of the ANN. Finally, ANN training is carried out using the backpropagation algorithm to obtain the matrix of weights and thresholds that allow the network to operate and provide preliminary structural designs with a 10% margin of error, with respect to the traditional design, in the dimensions and reinforcements of the structural elements.

Internal combustion engines, used for light duty transportation, represent a major role in mobility, contributing 28.6% to CO$_2$ emissions worldwide. To mitigate environmental impact and ease the transition to clean technologies, the search for more efficient, less polluting engines has been demanded, and unique tools are necessary to meet the constantly upgraded policies. Hence, data-driven approaches that emulate current vehicles represent a valuable contribution to the improvement of engine performance. Dynamometer tests of commercial engines are open-data, and a dependable source for understanding on-road behavior of several vehicle variables. Artificial neural network (ANN) algorithms, a subset of machine learning, have received considerable attention recently given their wide number of applications and the possibility to provide accurate data-driven approximations. This work describes a methodology for applying ANN’s to predict emissions, efficiency, and fuel consumption in combustion engines using dynamometer test data, and to extrapolate its use in new technologies. The procedure is also applied to a hybrid vehicle case study. The proposed methodology accurately generates ANN’s for the prediction of brake thermal efficiency (BTE), brake specific fuel consumption (BSFC) and emissions in conventional engines with 𝑅$^2$>0.91 and mean absolute errors (MAE) of less than five percent. Using the same approach, the hybrid vehicle state of charge (SOC), and the fuel scale state, are predicted, showing good agreement 𝑅$^2$>0.96 and confirming the versatility of the proposed algorithm. Finally, an initial approach for dealing with missing data in the databases is introduced. Using various simple and iterative imputation methods, it was possible to obtain 𝑅$^2$>0.80 for predicting the BTE and BSFC with five percent of the data missing from the input values.