912 resultados para algoritmo, localizzazione, sonar


Relevância:

20.00% 20.00%

Publicador:

Resumo:

A utilização de equipamentos de climatização é cada vez mais frequente, e surgem novas tecnologias para aumentar a eficiência do processo, e neste caso, a opção da instalação de um sistema de Unidade de Tratamento de Ar com Economizador é a fundamental temática deste trabalho de dissertação. O “Free-Cooling” baseia-se na utilização total ou parcial do ar exterior para proceder à climatização de um espaço, quando se verificam as condições ótimas para o processo, e quando o sistema apresenta um controlador que permita gerir a abertura dos registos face à temperatura exterior e interior medida. A análise das condições exteriores e interiores é fundamental para dimensionar um Economizador. É necessário determinar o tipo de clima do local para fazer a seleção do tipo de controlo do processo, e recolher também, o perfil de temperaturas exterior para justificar a utilização de “Free-Cooling” no local. A determinação das condições interiores como a quantificação da utilização da iluminação, ocupação e equipamentos, é necessária para determinar a potência das baterias de arrefecimento ou aquecimento, e no caso de ser utilizado “Free-Cooling”, determinar o caudal de ar exterior a insuflar. O balanço térmico das instalações explicita todas as cargas influentes no edifício, e permite quantificar a potência necessária para climatização. Depois, adicionando o Economizador no sistema e comparando os dois sistemas, verifica-se a redução dos custos de utilização da bateria de arrefecimento. O desenvolvimento de um algoritmo de controlo é fundamental para garantir a eficiência do Economizador, onde o controlo dos registos de admissão e retorno de ar é obrigatoriamente relacionado com a leitura dos sensores de temperatura exterior e interior. A quantidade de ar novo insuflado no espaço depende, por fim, da relação entre a carga sensível do local e a diferença de temperatura lida entre os dois sensores.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Pós-graduação em Ciência da Computação - IBILCE

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Maestría en Ciencias de la Administración con Especialidad en Sistemas) U.A.N.L.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis ( Maestro en Ciencias de la Administración con Especialidad en Sistemas) U.A.N.L.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Maestro en Ciencias de la Ingeniería Eléctrica con Especialidad en Potencia) U.A.N.L.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Maestría en Ciencias en Ingeniería de Sistemas) UANL, 2012.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Maestría en Ciencias en Ingeniería de Sistemas) UANL, 2014.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Doctor en Filosofía con Especialidad en Administración) U.A.N.L.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Doctor en Filosofía con Especialidad en Administración) UANL, 2004.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Tesis (Doctor en Ingeniería Eléctrica) UANL, 2013.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Systems which employ underwater acoustic energy for observation or communication are called sonar systems. The active and passive sonars are the two types of systems used for the detection and localisation of targets in underwater. Active sonar involves the transmission of an acoustic signal which, when reflected from a target, provides the sonar receiver with a basis for the detection and estimation. Passive sonar bases its detection and estimation on sounds which emanate from the target itself--Machinery noise, flow noise, transmission from its own active sonar etc.Electroacoustic transducers are used in sonar systems for the transmission and detection of acoustic energy. The transducer which is used for the transmission of acoustic energy is called projector and the one used for reception is called hydrophone. Since a single transducer is not sufficient enough for long range and directional transmission, a properly distributed array of transducers are to be used [9-11].The need and requirement for spatial processing to generate the most favourable directivity patterns for transducer systems used in underwater applications have already been analysed by several investigators [12-21].The desired directivity pattern can be either generated by the use of suitable focussing techniques or by an array of non-directional sensor elements, whose arrangements, spacing and the mode of excitation provide the required radiation pattern or by the combination of these.While computing that the directivity pattern, it is assumed strength of the elements are unaffected by the the source acoustic pressure at each source. However, in closely packed a r r a y s , the acoustic interaction effects experienced among the elements will modify the behaviour of individual elements and in turn will reduce the acoust ic source leve 1 wi t h respect to the maximum t heoret i cal va 1ue a s well as degrade the beam pa t tern. Th i s ef fect shou 1d be reduced in systems that are intended to generate high acoustic power output and unperturbed beam patterns [2,22-31].The work herein presented includes an approach for designing efficient and well behaved underwater transd~cer arrays, taking into account the acoustic interaction effect experienced among the closely packed multielement arrays.Architectural modifications reducing the interaction effect different radiating apertures.