Artificial Intelligence based Position Detection for Hydraulic Cylinders using Scattering Parameters

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that mathematically describes the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To overcome this problem, we propose Artificial Intelligence (AI)-based methods to learn the relationship directly data-driven. As a result, all Artificial Neural Network (ANN) models in this paper consistently outperform the physical one by a large margin. Given the success of AI-based models for our task, we further deliberate the choice of models based on domain knowledge and provide in-depth analyses combining model performance with the physical characteristics. Specifically, we use Convolutional Neural Network (CNN)s to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. By combining these three techniques, our best performing model, a complex-valued CNN with Frequency Encoding, manages to significantly reduce the test error to hardly 1/12 of the one given by the traditional physical model.Comment: 16 pages, 10 figure

Structural instabilities during cyclic loading of ultrafine-grained copper studied with micro bending experiments

The cyclic mechanical properties and microstructural stability of severe plastically deformed copper were investigated by means of micro bending experiments. The ultrafine-grained structure of OFHC copper was synthesized utilizing the high pressure torsion (HPT) technique. Micron sized cantilevers were focused-ion-beam milled and subsequently tested within a scanning electron microscope in the low cycle fatigue regime at strain amplitudes in the range of 1.1 − 3.2 ∗ 10−3. It was found that HPT processed ultra-fine grained copper is prone to cyclic softening, which is a consequence of grain coarsening in the absence of shear banding in the micro samples. Novel insights into the grain coarsening mechanism were revealed by quasi in-situ EBSD scans, showing i) continuous migration of high angle grain boundaries, ii) preferential growth of larger grains at the expense of adjacent smaller ones, iii) a reduction of misorientation gradients within larger grains if the grain structure in the neighborhood is altered and iv) no evidence that a favorable crystallographic orientation drives grain growth during homogeneous coarsening at moderate accumulated strains, tested here

Роль и место иностранного языка в профессиональной компетентности преподавателей технических специальностей

Раскрывается состав профессиональной компетентности преподавателей неязыковых вузов для выявления особенностей типологии ключевых компетенций, определяющих содержание иноязычного компонента. Автор предлагает рассматривать иноязычную профессионально-коммуникативную компетенцию как эффективное средство реализации иноязычной переподготовки преподавателей неязыковых вузов

Coatings of different carbon nanotubes on platinum electrodes for neuronal devices: Preparation, cytocompatibility and interaction with spiral ganglion cells

Cochlear and deep brain implants are prominent examples for neuronal prostheses with clinical relevance. Current research focuses on the improvement of the long-term functionality and the size reduction of neural interface electrodes. A promising approach is the application of carbon nanotubes (CNTs), either as pure electrodes but especially as coating material for electrodes. The interaction of CNTs with neuronal cells has shown promising results in various studies, but these appear to depend on the specific type of neurons as well as on the kind of nanotubes. To evaluate a potential application of carbon nanotube coatings for cochlear electrodes, it is necessary to investigate the cytocompatibility of carbon nanotube coatings on platinum for the specific type of neuron in the inner ear, namely spiral ganglion neurons. In this study we have combined the chemical processing of as-delivered CNTs, the fabrication of coatings on platinum, and the characterization of the electrical properties of the coatings as well as a general cytocompatibility testing and the first cell culture investigations of CNTs with spiral ganglion neurons. By applying a modification process to three different as-received CNTs via a reflux treatment with nitric acid, long-term stable aqueous CNT dispersions free of dispersing agents were obtained. These were used to coat platinum substrates by an automated spray-coating process. These coatings enhance the electrical properties of platinum electrodes, decreasing the impedance values and raising the capacitances. Cell culture investigations of the different CNT coatings on platinum with NIH3T3 fibroblasts attest an overall good cytocompatibility of these coatings. For spiral ganglion neurons, this can also be observed but a desired positive effect of the CNTs on the neurons is absent. Furthermore, we found that the well-established DAPI staining assay does not function on the coatings prepared from single-wall nanotubes. © 2016 Burblies et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.DFG/EXC 1077/1 “Hearing4all