Polycyclic aromatic compounds in curing smoke

Summary The curing smoke, generated by the smouldering of alder chips, from a commercial food smoking plant was investigated with respect to polynuclear aromatic compounds (PAC). Samples were taken from inside the ovens and in the working zone immediately outside them. Analysis showed that considerable amounts of methyl phenanthrenes were present in the smoke as a major part of the PAC content.

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Polycyclische aromatische Verbindungen im Rauch einer Räucherei

Zusammenfassung Rauch aus einer kommerziellen Räucherei von schwelenden Erlenspänen wurde auf polycyclische aromatische Verbindungen (PAC) untersucht. Proben wurden innerhalb der Öfen und in dem Arbeitsbereich außerhalb der Öfen gezogen. Erhebliche Mengen an Methylphenanthren wurden als Hauptbestandteil des PAC-Inhalts im Rauch nachgewiesen.

     

Magnetic traveling fields for metallurgical processes

Alternating magnetic fields lead to electrodynamic forces, which can be used industrially for stirring melts. These magnetic traveling fields, produced by straight or cylindrical stirrers, have become a well-established tool for the metallurgist. Since the size of melts is constantly growing, the stirrers are becoming increasingly valuable for achieving homogenization, temperature equalization, rapid and complete chemical reactions, and effective degassing.     

Approximate calculation of homogenization of liquid steel in a ladle with induction stirring

Derivation of formulae for the calculation of the homogenization in a stirred melt after addition of alloying elements, based partly on the diffusion equation and partly on the tank model. Comparison between calculated and experimental results. Derivation also of a formula for the calculation of the temperature differences in a stirred melt during local heating.     

Optimised deep learning features for improved melanoma detection

In this article, we are addressing the question of effective usage of the feature set extracted from deep learning models pre-trained on ImageNet. Exploring this option will offer very fast and attractive alternative to transfer learning strategies. The traditional task of skin lesion recognition consists of several stages, where the automated system is typically trained on preprocessed images with known diagnosis, which allows classification of new samples to predefined categories. For this task, we are proposing here an improved melanoma detection method based on the combination of linear discriminant analysis (LDA) and the features extracted from the deep learning approach. We are examining the usage of the LDA approach on activation of the fully-connected layer of deep learning in order to increase the classification accuracy and at the same time to reduce the feature space dimensionality. We tested our method on five different classifiers and evaluated results using various metrics. The presented comparison demonstrates the very high effectiveness of the suggested feature reduction, which leads not only to the significant lowering of employed features but also to the increasing performance of all tested classifiers in almost all measured characteristics.

     

Measuring perceptual contrast in digital images

In this paper we present a novel method to measure perceptual contrast in digital images. We start from a previous measure of contrast developed by Rizzi et al. [26], which presents a multilevel analysis. In the first part of the work the study is aimed mainly at investigating the contribution of the chromatic channels and whether a more complex neighborhood calculation can improve this previous measure of contrast. Following this, we analyze in detail the contribution of each level developing a weighted multilevel framework. Finally, we perform an investigation of Regions-of-Interest in combination with our measure of contrast. In order to evaluate the performance of our approach, we have carried out a psychophysical experiment in a controlled environment and performed extensive statistical tests. Results show an improvement in correlation between measured contrast and observers perceived contrast when the variance of the three color channels separately is used as weighting parameters for local contrast maps. Using Regions-of-Interest as weighting maps does not improve the ability of contrast measures to predict perceived contrast in digital images. This suggests that Regions-of-Interest cannot be used to improve contrast measures, as contrast is an intrinsic factor and it is judged by the global impression of the image. This indicates that further work on contrast measures should account for the global impression of the image while preserving the local information.