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A technique which employs a laser-excited optical resonator has been developed to measure the electron density distribution, both temporal and spatial, in the collapse stage of a fast Z-pinch discharge. The resonator device has an experimentally determined time resolution of better than 0.05 µsec and is suitable for measuring electron densities in excess of 5 • 10¹⁶ /Lʎ cm⁻³, where L is the length of the plasma in cm and ʎ is the wavelength of the laser in microns. A novel feature of this instrument is the use of an unstable optical resonator. The technique is applied to a discharge in argon at filling pressures of 100 and 1000 µHg. The temporal and radial electron density distributions obtained exhibit several interesting features which are discussed from the point of view of the collapse process. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

published_or_final_version / Electrical Engineering / Master / Master of Philosophy

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 2015 / During a cloud to ground lightning event a charge centre within the storm cloud is discharged. The discharge of a charge centre within the cloud leads to a change in the electric eld radiated by the charge centre. It is theoretically possible to infer the lightning current from the derivative of the electric eld. It is only possible to infer the lightning current from the electric eld data where the noise is comparatively much smaller than the electric eld data. The changing electric elds for a lightning event that occurred on the 3rd January 2015 13:15:13 were recorded by a at plate electric eld sensor with a passive integrator. The oscilloscope used to capture the electric eld data has a relatively large measurement noise and a low resolution. A low pass digital lter was applied to the recorded electric eld data to reduce the e ects from the high frequency noise. The lightning strokes were recorded by the South African Lightning Detection Network. The Lightning Detection Network data is used to obtain the distance of the lightning event from the sensor, to scale the inferred lightning current. The Lightning Detection Network also provides a lightning peak current measurement to compare to the peak current inferred from the electric eld data. The lightning stroke current was inferred from the electric eld recording for various bandwidths of the low pass lter. Inconsistent changes to the inferred lightning stroke current as the lter bandwidth is changed shows that the frequency components for each stroke di ers. The peak stroke current was not constant for any lter bandwidth range implying that the measurement noise is relatively too large. The case study presented demonstrates that with a relatively large noise magnitude (3 to 4 discrete steps of the digital recording) compared to the electric eld signal (21 discrete steps) it is di cult to accurately infer the lightning current from the electric elds recorded. / MT2017

Thesis. 1976. Ph.D. cn--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / Vita. / Includes bibliographical references. / by Peter W. Huber. / Ph.D.cn

by Raymon, Wai-man Chan. / "March 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese. / by Raymon, Wai-man Chan.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 122-126. / by Peter Michael Hirsch. / M.S.

Thesis (M. Phil.)--University of Hong Kong, 1974. / Mimeographed.

Thesis (M.S.)--Rochester Institute of Technology, 2004. / Typescript. Includes bibliographical references (leaf 122).

Made available in DSpace on 2014-12-02T11:16:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-09-06Bitstream added on 2014-12-02T11:20:52Z : No. of bitstreams: 1 000800318.pdf: 1782561 bytes, checksum: f15b6161ad101bbd18fe9254ffc7771d (MD5) / Com o aumento na demanda por energia elétrica o planejamento de geração, transmissão e distribuição bem como a operação são importantes para uma prestação de serviços de forma eficiente, econômica e confiável. Uma das ferramentas para gestão desses recursos são os modelos de previsão de séries temporais. Há diversos modelos na literatura, como os modelos de regressão, modelos estatísticos, dentre outros. Outro modelo que vem se destacando na literatura é a previsão utilizando as redes neurais artificiais, pela sua capacidade de aprendizado. As redes neurais possuem várias arquiteturas, e uma em particular, que é considerada padrão na literatura, é a rede perceptron multicamadas com o algoritmo backpropagation. O presente trabalho propõe uma rede neural híbrida composta pelo método de regressão linear com seleção de variáveis stepwise juntamente com a rede neural artificial perceptron multicamadas com o algoritmo backpropagation. O objetivo é obter um método simples e eficaz para redução de variáveis sem perda de qualidade de previsão. O modelo de regressão linear com o método de seleção de variáveis stepwise tem a função de selecionar as variáveis mais relevantes para compor o conjunto de dados de entrada para treinamento/diagnóstico da rede neural perceptron multicamadas com o algoritmo backpropagation que, consequentemente, é a responsável em realizar a previsão de carga elétrica. Com esta proposta busca-se uma metodologia que seja capaz de reduzir a quantidade de variáveis de entrada da rede neural e obter resultados satisfatórios, ou seja, boas previsões. Para corroborar a metodologia proposta são apresentados os resultados da previsão de carga elétrica a curto prazo em um período de 24 e 48 horas a frente, considerando-se os dados históricos de uma companhia do setor elétrico / With the increase in electric energy demand the planning of generation, transmission and distribution as well as the operation are important to provide services efficiently, economically and reliably. One of the tools to manage those resources are time series model forecasting. There are several models in the literature, as the regression models, statistical models, among others. Other model that has been highlighted in the literature is the forecasting using artificial neural network, due to the capacity of learning. Neural networks have several architectures, and one in particular, that is considered standard in the literature is the multilayer perceptron network with the backpropagation algorithm. The present work proposes a hybrid neural network composed by the linear regression method with stepwise variable selection with the multilayer perceptron artificial neural network with the backpropagation algorithm. The aim is to provide a simple and effective method to reduce the variables without losing the forecasting quality. The function of the linear regression model with stepwise variable selection is to select the more relevant variables to compose the input data set to training/ diagnostic of the multilayer perceptron neural network with the backpropagation algorithm that, consequently, is the responsible to realize the electric load forecasting. The aim of this proposal is to find a methodology that reduces the amount of input variables of the neural network and obtain satisfactory results. To verify the proposed methodology results are presented for electric short-term load forecasting in a period of 24 and 48 hours ahead, considering the historical data obtained from a company pertaining to the electrical sector