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Real-time Cinematic Design Of Visual Aspects In Computer-generated ImagesObert, Juraj 01 January 2010 (has links)
Creation of visually-pleasing images has always been one of the main goals of computer graphics. Two important components are necessary to achieve this goal --- artists who design visual aspects of an image (such as materials or lighting) and sophisticated algorithms that render the image. Traditionally, rendering has been of greater interest to researchers, while the design part has always been deemed as secondary. This has led to many inefficiencies, as artists, in order to create a stunning image, are often forced to resort to the traditional, creativity-baring, pipelines consisting of repeated rendering and parameter tweaking. Our work shifts the attention away from the rendering problem and focuses on the design. We propose to combine non-physical editing with real-time feedback and provide artists with efficient ways of designing complex visual aspects such as global illumination or all-frequency shadows. We conform to existing pipelines by inserting our editing components into existing stages, hereby making editing of visual aspects an inherent part of the design process. Many of the examples showed in this work have been, until now, extremely hard to achieve. The non-physical aspect of our work enables artists to express themselves in more creative ways, not limited by the physical parameters of current renderers. Real-time feedback allows artists to immediately see the effects of applied modifications and compatibility with existing workflows enables easy integration of our algorithms into production pipelines.
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Emprego de redes neurais artificiais supervisionadas e n?o supervisionadas no estudo de par?metros reol?gicos de excipientes farmac?uticos s?lidosNavarro, Marco Vin?cius Monteiro 05 February 2014 (has links)
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Previous issue date: 2014-02-05 / In this paper artificial neural network (ANN) based on supervised and unsupervised
algorithms were investigated for use in the study of rheological parameters of solid
pharmaceutical excipients, in order to develop computational tools for manufacturing solid
dosage forms. Among four supervised neural networks investigated, the best learning
performance was achieved by a feedfoward multilayer perceptron whose architectures was
composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one
neuron in the output layer. Learning and predictive performance relative to repose angle was
poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good
fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the
next stage of development of supervised ANNs. Clustering capacity was evaluated for five
unsupervised strategies. Network based on purely unsupervised competitive strategies, classic
"Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize
Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform
clustering from database, however this classification was very poor, showing severe
classification errors by grouping data with conflicting properties into the same cluster or even
the same neuron. On the other hand it could not be established what was the criteria adopted
by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas
(NG) networks showed better clustering capacity. Both have recognized the two major
groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM
showed some errors in classify data from minority excipients, magnesium stearate (EMG) ,
talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent
classification of data and solve the misclassification of SOM, being the most appropriate
network for classifying data of the study. The use of NG network in pharmaceutical
technology was still unpublished. NG therefore has great potential for use in the development
of software for use in automated classification systems of pharmaceutical powders and as a
new tool for mining and clustering data in drug development / Neste trabalho foram estudadas redes neurais artificiais (RNAs) baseadas em algoritmos
supervisionados e n?o supervisionados para emprego no estudo de par?metros reol?gicos de
excipientes farmac?uticos s?lidos, visando desenvolver ferramentas computacionais para o
desenvolvimento de formas farmac?uticas s?lidas. Foram estudadas quatro redes neurais artificiais
supervisionadas e cinco n?o supervisionadas. Todas as RNAs supervisionadas foram baseadas em
arquitetura de rede perceptron multicamada alimentada ? frente (feedfoward MLP). Das cinco RNAs
n?o supervisionadas, tr?s foram baseadas em estrat?gias puramente competitivas, "Winner-Take-
All" cl?ssica, "Frequency-Sensitive Competitive Learning" e "Rival-Penalize Competitive Learning"
(WTA, FSCL e RPCL, respectivamente). As outras duas redes n?o supervisionadas, Self-
Organizing Map e Neural Gas (SOM e NG) foram baseadas estrat?gias competitivo-cooperativas.
O emprego da rede NG em tecnologia farmac?utica ? ainda in?dito e pretende-se avaliar seu
potencial de emprego como nova ferramenta de minera??o e classifica??o de dados no
desenvolvimento de medicamentos. Entre os prot?tipos de RNAs supervisionadas o melhor
desempenho foi conseguido com uma rede de arquitetura composta por 8 neur?nios de entrada, 16
neur?nios escondidos e 1 neur?nio de sa?da. O aprendizado de rede e a capacidade preditiva em
rela??o ao ?ngulo de repouso (α) foi deficiente, e muito boa para o ?ndice de Carr e fator de Hausner
(IC, FH). Por esse motivo IC e FH foram considerados bons descritores para uma pr?xima etapa de
desenvolvimento das RNAs supervisionadas. As redes, WTA, RPCL e FSCL, foram capazes de
estabelecer agrupamentos dentro da massa de dados, por?m apresentaram erros grosseiros de
classifica??o caracterizados pelo agrupamento de dados com propriedades conflitantes, e tamb?m
n?o foi poss?vel estabelecer qual o crit?rio de classifica??o adotado. Tais resultados demonstraram
a inviabilidade pr?tica dessas redes para os sistemas estudados sob nossas condi??es experimentais.
As redes SOM e NG mostraram uma capacidade de classifica??o muito superior ?s RNAs puramente
competitivas. Ambas as redes reconheceram os dois agrupamentos principais de dados
correspondentes ? lactose (LAC) e celulose (CEL). Entretanto a rede som demonstrou defici?ncia
na classifica??o de dados relativos aos excipientes minorit?rios, estearato de magn?sio (EMG), talco
(TLC) e atapulgita (ATP). A rede NG, por sua vez, estabeleceu uma classifica??o muito consistente
dos dados e resolveu o erro de classifica??o apresentados pela rede SOM, mostrando-se a rede mais
adequada para a classifica??o dos dado do presente estudo. A rede Neural Gas, portanto, mostrou-
se promissora para o desenvolvimento de softwares para uso na classifica??o automatizada de
sistemas pulverulentos farmac?uticos
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