Made available in DSpace on 2014-06-12T15:58:28Z (GMT). No. of bitstreams: 2 arquivo4572_1.pdf: 1149011 bytes, checksum: 92aae8f6f9b5145bfcecb94d96dbbc0b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004 / Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco / Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas como sistema de reconhecimento de padrões para narizes artificiais e vêm apresentando resultados promissores. Desde os anos 80, pesquisas para criação de narizes artificiais, que permitam detectar e classificar odores, vapores e gases automaticamente, têm tido avanços significativos. Esses equipamentos podem ser utilizados no monitoramento ambiental para controlar a qualidade do ar, na área de saúde para realizar diagnóstico de doenças e nas indústrias de alimentos para o controle de qualidade e o monitoramento de processos de produção. Esta dissertação investiga a utilização de quatro técnicas diferentes de redes neurais para criação de sistemas de reconhecimento de padrões em narizes artificiais. O trabalho está dividido em quatro partes principais: (1) introdução aos narizes artificiais, (2) redes neurais artificiais para sistema de reconhecimento de padrões, (3) métodos para medir o desempenho de sistemas de reconhecimento de padrões e comparar os resultados e (4) estudo de caso. Os dados utilizados para o estudo de caso, foram obtidos por um protótipo de nariz artificial composto por um arranjo de oito sensores de polímeros condutores, expostos a nove tipos diferentes de aguarrás. Foram adotadas as técnicas Multi-Layer Perceptron (MLP), Radial Base Function (RBF), Probabilistic Neural Network (PNN) e Time Delay Neural Network (TDNN) para criar os sistemas de reconhecimento de padrões. A técnica PNN foi investigada em detalhes, por dois motivos principais: esta técnica é indicada para realização de tarefas de classificação e seu treinamento é feito em apenas um passo, o que torna a etapa de criação dessas redes muito rápida. Os resultados foram comparados através dos valores dos erros médios de classificação utilizando o método estatístico de Teste de Hipóteses. As redes PNN correspondem a uma nova abordagem para criação de sistemas de reconhecimento de padrões de odor. Estas redes tiveram um erro médio de classificação de 1.1574% no conjunto de teste. Este foi o menor erro obtido entre todos os sistemas criados, entretanto mesmo com o menor erro médio de classificação, os testes de hipóteses mostraram que os classificadores criados com PNN não eram melhores do que os classificadores criados com a arquitetura RBF, que obtiveram um erro médio de classificação de 1.3889%. A grande vantagem de criar classificadores com a arquitetura PNN foi o pequeno tempo de treinamento dos mesmos, chegando a ser quase imediato. Porém a quantidade de nodos na camada escondida foi muito grande, o que pode ser um problema, caso o sistema criado deva ser utilizado em equipamentos com poucos recursos computacionais. Outra vantagem de criar classificadores com redes PNN é relativa à quantidade reduzida de parâmetros que devem ser analisados, neste caso apenas o parâmetro relativo à largura da função Gaussiana precisou ser investigado

Este trabalho apresenta um método para o reconhecimento e a detecção automática dos diferentes valores ou graus de deformação plástica em Ensaios Não Destrutivos empregando o Ruído Magnético de Barkhausen. O método é baseado no uso de uma Rede Neural Probabilística que permite o diagnóstico automático dos diferentes valores de deformação plástica, conteúdo de carbono, estas medidas são procedentes das medições das amostras de placas de aço AISI 1006, 1050 e 1070, esta base de dados foi feita pelo grupo de pesquisadores do Laboratório de Dinâmica e Instrumentação LADIN da Escola Politécnica da USP, departamento da Mecânica. Os excelentes resultados da rede neural probabilística de detectar automaticamente os valores de deformação mostram a efetividade do desempenho da rede neural probabilística que tem um desempenho superior aos métodos não destrutivos tradicionais e que realmente esta nova tecnologia é uma excelente solução para o diagnóstico. / This work presents a method for automatic detection and recognition of different levels or degrees of plastic deformation in Non-Destructive Testing using the Magnetic Barkhausen Noise. The method is based on using a Probabilistic Neural Network that allows the automatic diagnosis of the different values of plastic deformation and carbon content. The measurements corresponds to samples of steel plates AISI 1006, 1050 and 1070, this database was made by the group of researchers from the Laboratory of Dynamics and Instrumentation LADIN the Polytechnic School of USP, Department of Mechanical Engineering. The results show the effectiveness of the probabilistic neural network to automatically detect plastic deformation levels as well as carbon content level. This method has a superior performance in comparison to traditional nondestructive methods.

In general humans are said to be social animals. In the huge expanded internet, it's really difficult to detect and find out useful information about a medical illness. In anticipation of more definitive studies of a causal organization between stroke risk and social network, It would be suitable to help social individuals to detect the risk of stroke. In this work, a DRFS methodology is proposed to find out the various symptoms associated with the stroke disease and preventive measures of a stroke disease from the social media content. We have defined an architecture for clustering tweets based on the content using Spectral Clustering an iterative fashion. The class label detection is furnished with the use of highest TF-IDF value words. The resultant clusters obtained as the output of spectral clustering is prearranged as input to the Probability Neural Network (PNN) to get the suitable class labels and their probabilities. Find Frequent word set using support count measure from the group of clusters for identify the risk factors of stroke. We found that the anticipated approach is able to recognize new symptoms and causes that are not listed in the World Health Organization (WHO), Mayo Clinic and National Health Survey (NHS). It is marked that they get associated with precise outcomes portray real statistics. This type of experiments will empower health organization, doctors and Government segments to keep track of stroke diseases. Experimental results shows the causes preventive measures, high and low risk factors of stroke diseases.

"Many current and future medical devices are wearable, using the human body as a conduit for wireless communication, which implies that human body serves as a crucial part of the transmission medium in body area networks (BANs). Implantable medical devices such as Pacemaker and Cardiac Defibrillators are designed to provide patients with timely monitoring and treatment. Endoscopy capsules, pH Monitors and blood pressure sensors are used as clinical diagnostic tools to detect physiological abnormalities and replace traditional wired medical devices. Body-mounted sensors need to be investigated for use in providing a ubiquitous monitoring environment. In order to better design these medical devices, it is important to understand the propagation characteristics of channels for in-body and on- body wireless communication in BANs. The IEEE 802.15.6 Task Group 6 is officially working on the standardization of Body Area Network, including the channel modeling and communication protocol design. This thesis is focused on the propagation characteristics of human body movements. Specifically, standing, walking and jogging motions are measured, evaluated and analyzed using an empirical approach. Using a network analyzer, probabilistic models are derived for the communication links in the medical implant communication service band (MICS), the industrial scientific medical band (ISM) and the ultra- wideband (UWB) band. Statistical distributions of the received signal strength and second order statistics are presented to evaluate the link quality and outage performance for on-body to on- body communications at different antenna separations. The Normal distribution, Gamma distribution, Rayleigh distribution, Weibull distribution, Nakagami-m distribution, and Lognormal distribution are considered as potential models to describe the observed variation of received signal strength. Doppler spread in the frequency domain and coherence time in the time domain from temporal variations is analyzed to characterize the stability of the channels induced by human body movements. The shape of the Doppler spread spectrum is also investigated to describe the relationship of the power and frequency in the frequency domain. All these channel characteristics could be used in the design of communication protocols in BANs, as well as providing features to classify different human body activities. Realistic data extracted from built-in sensors in smart devices were used to assist in modeling and classification of human body movements along with the RF sensors. Variance, energy and frequency domain entropy of the data collected from accelerometer and orientation sensors are pre- processed as features to be used in machine learning algorithms. Activity classifiers with Backpropagation Network, Probabilistic Neural Network, k-Nearest Neighbor algorithm and Support Vector Machine are discussed and evaluated as means to discriminate human body motions. The detection accuracy can be improved with both RF and inertial sensors."

With the deregulation of power industry and the market competition, stable and reliable power supply is a major concern of the independent system operator (ISO). Power-quality (PQ) study has become a more and more important subject lately. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. In recent years, high speed railway (HSR) and massive rapid transit (MRT) system have been rapidly developed, with the applications of widespread semiconductor technologies in the auto-traction system. The harmonic distortion level worsens due to these increased uses of electronic equipment and non-linear loads. To ensure the PQ, power-quality disturbances (PQD) detection becomes important. A detection method with classification capability will be helpful for detecting disturbance locations and types. Electric machinery fault diagnosis is another issue of considerable attentions from utilities and customers. ISO need to provide a high quality service to retain their customers. Fault diagnosis of turbine-generator has a great effect on the benefit of power plants. The generator fault not only damages the generator itself, but also causes outages and loss of profits. With high-temperature, high-pressure and factors such as thermal fatigues, many components may go wrong, which will not only lead to great economic loss, but sometimes a threat to social security. Therefore, it is necessary to detect generator faults and take immediate actions to cut the loss. Besides, induction motor plays a major role in a power system. For saving cost, it is important to run periodical inspections to detect incipient faults inside the motor. Preventive techniques for early detection can find out the incipient faults and avoid outages. This dissertation developed various soft computing (SC) algorithms for detection including power-quality disturbances (PQD), turbine-generator fault diagnosis, and induction motor fault diagnosis. The proposed SC algorithms included support vector machine (SVM), grey clustering analysis (GCA), and probabilistic neural network (PNN). Integrating the proposed diagnostic procedure and existing monitoring instruments, a well-monitored power system will be constructed without extra devices. Finally, all the methods in the dissertation give reasonable and practical estimation method. Compared with conventional method, the test results showed a high accuracy, good robustness, and a faster processing performance.

This dissertation focused in the comprehensive analysis of the capabilities of some non-tested types of Artificial Neural Networks, specifically: the Probabilistic Neural Networks (PNN) and the Multi-Layer Perceptron (MLP) Ensembles. The analysis of the capabilities of these techniques was performed using the native brown trout (Salmo trutta; Linnaeus, 1758), the bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) and the redfin barbel (Barbus haasi; Mertens, 1925) as target species. The analyses focused in the predictive capabilities, the interpretability of the models and the effect of the excess of zeros in the training datasets, which for presence-absence models is directly related to the concept of data prevalence (i.e. proportion of presence instances in the training dataset). Finally, the effect of the spatial scale (i.e. micro-scale or microhabitat scale and meso-scale) in the habitat suitability models and consequently in the e-flow assessment was studied in the last chapter. / Esta tesis se centra en el análisis comprensivo de las capacidades de algunos tipos de Red Neuronal Artificial aún no testados: las Redes Neuronales Probabilísticas (PNN) y los Conjuntos de Perceptrones Multicapa (MLP Ensembles). Los análisis sobre las capacidades de estas técnicas se desarrollaron utilizando la trucha común (Salmo trutta; Linnaeus, 1758), la bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) y el barbo colirrojo (Barbus haasi; Mertens, 1925) como especies nativas objetivo. Los análisis se centraron en la capacidad de predicción, la interpretabilidad de los modelos y el efecto del exceso de ceros en las bases de datos de entrenamiento, la así llamada prevalencia de los datos (i.e. la proporción de casos de presencia sobre el conjunto total). Finalmente, el efecto de la escala (micro-escala o escala de microhábitat y meso-escala) en los modelos de idoneidad del hábitat y consecuentemente en la evaluación de caudales ambientales se estudió en el último capítulo. / Aquesta tesis se centra en l'anàlisi comprensiu de les capacitats d'alguns tipus de Xarxa Neuronal Artificial que encara no han estat testats: les Xarxes Neuronal Probabilístiques (PNN) i els Conjunts de Perceptrons Multicapa (MLP Ensembles). Les anàlisis sobre les capacitats d'aquestes tècniques es varen desenvolupar emprant la truita comuna (Salmo trutta; Linnaeus, 1758), la madrilla roja (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) i el barb cua-roig (Barbus haasi; Mertens, 1925) com a especies objecte d'estudi. Les anàlisi se centraren en la capacitat predictiva, interpretabilitat dels models i en l'efecte de l'excés de zeros a la base de dades d'entrenament, l'anomenada prevalença de les dades (i.e. la proporció de casos de presència sobre el conjunt total). Finalment, l'efecte de la escala (micro-escala o microhàbitat i meso-escala) en els models d'idoneïtat de l'hàbitat i conseqüentment en l'avaluació de cabals ambientals es va estudiar a l'últim capítol. / Muñoz Mas, R. (2016). Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/76168 / TESIS

Tato práce pojednává o technikách behaviorálního modelování pro speciální úlohy v elektromagnetismu, které je možno formulovat jako problém aproximace, klasifikace, odhadu hustoty pravděpodobnosti nebo kombinatorické optimalizace. Zkoumané methody se dotýkají dvou základních problémů ze strojového učení a combinatorické optimalizace: ”bias vs. variance dilema” a NP výpočetní komplexity. Boltzmanův stroj je v práci navržen ke zjednodušování komplexních impedančních sítí. Bayesovský přístup ke strojovému učení je upraven pro regularizaci Parzenova okna se snahou o vytvoření obecného kritéria pro regularizaci pravděpodobnostní a regresní neuronové sítě.