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The use of artificial neural networks for colour prediction in textile printing
Darko Golob, Jure Zupan, Đurđica Parac-Osterman, 2008, objavljeni znanstveni prispevek na konferenci

Opis: An attempt of using artificial neural networks for the prediction of dzes in textile printing paste preparation is presented. An existing collection of printed samples served as the basis for neural network training. It consists of 1340 samples printed using either a single dze or a combination of two dzes. First the proper combination of dzes was determined, because in most cases onlz two dzes are combined in the printing paste. Then the necessarz concentration of each dze was predicted. The reflectance value, and the colourvalues L*, a*, b* serve as input data and the known combination and concentrations of dzes for each sample were the targets. Some variations of neural network were tested, as well as various numbers of neurons in the hidden lazer. In addition, the influence of the training set organisation was examined, together with the number of learning epochs on the learning success.
Ključne besede: artificial neural networks, textile printing, colour recipe prediction
Objavljeno: 31.05.2012; Ogledov: 932; Prenosov: 23
URL Celotno besedilo (0,00 KB)

Tool cutting force modeling in ball-end milling using multilevel perceptron
Uroš Župerl, Franc Čuš, 2004, izvirni znanstveni članek

Opis: This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. A neural network algorithms are developed for use as a direct modeling method, to predict forces for ball-end milling operation. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling process. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and neural network approaches are compared using statistics, which showed that neural network predictions for three cutting force components were for 4% closer to the experimental measurements, compared to 11% using analytical method. Exhaustive experimentation is conduced to develop the model and to validate it. The milling experiments prove that this model can predict accurately the cutting forces in three Cartesian directions.The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system.
Ključne besede: ball end milling, cutting forces, modelling, artificial intelligence, neural networks
Objavljeno: 01.06.2012; Ogledov: 982; Prenosov: 12
URL Celotno besedilo (0,00 KB)

Implementation of massive artificial neural networks with CUDA
Domen Verber, 2012, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

Ključne besede: CUDA, artificial neural networks, implementation
Objavljeno: 10.07.2015; Ogledov: 341; Prenosov: 18
URL Celotno besedilo (0,00 KB)

Prediction of wine sensorial quality by routinely measured chemical properties
Adriána Bednárová, Roman Kranvogl, Darinka Brodnjak-Vončina, Tjaša Jug, 2014, izvirni znanstveni članek

Opis: The determination of the sensorial quality of wines is of great interest for wine consumers and producers since it declares the quality in most of the cases. The sensorial assays carried out by a group of experts are time-consuming and expensive especially when dealing with large batches of wines. Therefore, an attempt was made to assess the possibility of estimating the wine sensorial quality with using routinely measured chemical descriptors as predictors. For this purpose, 131 Slovenian red wine samples of different varieties and years of production were analysed and correlation and principal component analysis were applied to find inter-relations between the studied oenological descriptors. The method of artificial neural networks (ANNs) was utilised as the prediction tool for estimating overall sensorial quality of red wines. Each model was rigorously validated and sensitivity analysis was applied as a method for selecting the most important predictors. Consequently, acceptable results were obtained, when data representing only one year of production were included in the analysis. In this case, the coefficient of determination (R2) associated with training data was 0.95 and that for validation data was 0.90. When estimating sensorial quality in categorical form, 94 % and 85 % of correctly classified samples were achieved for training and validation subset, respectively.
Ključne besede: overall sensorial quality, prediction, Slovenian wine, artificial neural networks, multivariate data analysis
Objavljeno: 03.04.2017; Ogledov: 265; Prenosov: 35
.pdf Celotno besedilo (1,02 MB)

The accuracy of the germination rate of seeds based on image processing and artificial neural networks
Uroš Škrubej, Črtomir Rozman, Denis Stajnko, 2015, izvirni znanstveni članek

Opis: This paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linked by our specially developed code. After object detection, we applied artificial neural networks (ANN), which was able to correctly classify 95.44% of germinated seeds of tomato (Solanum lycopersicum L.).
Ključne besede: image processing, artificial neural networks, seeds, tomato
Objavljeno: 14.11.2017; Ogledov: 285; Prenosov: 25
.pdf Celotno besedilo (353,43 KB)

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