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Are the long–short term memory and convolution neural networks really based on biological systems?
David Balderas Silva,Pedro Ponce Cruz,Arturo Molina Gutierrez 한국통신학회 2018 ICT Express Vol.4 No.2
In general, it is not a simple task to predict sequences or classify images, and it is even more problematic when both are combined. Nevertheless, biological systems can easily predict sequences and are good at image recognition. For these reasons Long–Short Term Memory and Convolutional Neural Networks were created and were based on the memory and visual systems. These algorithms have shown great properties and shown certain resemblance, yet they are still not the same as their biological counterpart. This article reviews the biological bases and compares them.
Tribological performance of TiN and TiCN coatings on a working tool steel
M. T. Hernández-Sierra,L. D. Aguilera-Camacho,Arturo Ponce,J. S. García-Miranda,K. J. Moreno 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.8
Although the applications of TiN and TiCN coatings are extensive, their mechanical and tribological properties are influenced by the substrates in which they are deposited. The present work is focused on the tribological performance of TiN and TiCN coatings on a working tool steel. Besides, adhesion and microhardness tests were carried out. The adhesion performance of both coatings resulted in class 1, according to CEN/TS 1071-8 standard, which allows observing the quality of adhesion. The composite microhardness was investigated by the analysis of relative indentation (β). Pin-on-disk tests were performed in dry and lubricated condition at 100 °C against tungsten carbide (WC). Low friction coefficients of µ k = 0.08 for TiN and µ k = 0.03 for TiCN were obtained in lubricated conditions. Wear mechanisms were analyzed by scanning electron microscopy (SEM). Abrasive wear was observed as the principal wear mechanism in dry condition, while in lubricated conditions wear signals seem to be scarcely noticeable.