The article describes technology of measurement of size and size-distribution of aggregates formed during agitation using the digital camera. This method may be used in laboratory batch reactors, pilot plants as well as in full-scale water treatment plants. and Článek popisuje technologii digitálního záznamu a následného měření velikostních charakteristik vločkovitých agregátů tvořených při úpravě vody. Použitá metoda je vhodná ke studiu agregace jak v laboratorních podmínkách při použití vsádkového reaktoru, tak i v provozních podmínkách úpraven vody.
In this paper, we describe the application of a combined neocognitron
type of the neural network classifier in a generic Car License Plate Recognition (CLPR) system. The suggested system contains an image processor, a segment processor and five conpled neocognitron network classifiers that act as a character recognizer. The presented model of the system depends neither on the specific license plate image features nor on the license plates character style and size. Combining neocognitron classifiers were motivated by the fact that manually tuning a training set for a large neocognitron network is tedious. It is shown how the training set tuning for a large neocognitron network can be avoided. By connecting srnall neocognitrons specifically trained on ambiguous character classes, the performance of the recognizer in our CLPR was improved easily. The use of a neocognitron recognizer contributes significantly to the generality of a CLPR systém. Besides, character recognition rates of 94% are realized using the proposed neocognitron.
Applications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to deter- mine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R² = 0:9837-0:9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.
This paper concerns with the finite volume scheme for nonlinear tensor diffusion in image processing. First we provide some basic information on this type of diffusion including a construction of its diffusion tensor. Then we derive a semi-implicit scheme with the help of so-called diamond-cell method (see \cite{Coirier1} and \cite{Coirier2}). Further, we prove existence and uniqueness of a discrete solution given by our scheme. The proof is based on a gradient bound in the tangential direction by a gradient in normal direction. Moreover, the proofs of L2(Ω) - a priori estimates for our discrete solution are given. Finally we present our computational results.