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Eingeladener Vortrag
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We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach.
This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest.
The application of implants e.g. for osteosynthesis or substitution of worn out joints is common practice since decades. Successes in surgery and orthopedics are highly dependent on the use of artificial parts for implanting into the human body. Over the years the number of surgical procedures and the number of different implants were growing rapidly. Implants, however, are exposed to complex mechanical, corrosive and tribological loads along with restriction in geometric dimension. While being essentially successful, the sheer number of cases leads to a rising number of implant failures. Such failures are painful for the affected patients and are very costly.
The failure cases presented in this paper occurred from 1981 to 2016. The predominant failure mechanism of all kinds of implants is fatigue fracture. Fatigue cracks are initiated due to a whole bunch of reasons: Material defects and fabrication defects are rare events. The main factors are cyclic overload due to improper fitting with delayed bone healing, corrosion and unauthorized modification of the implant during surgery
A shell test bench was developed at BAM 5.3 which allows for static and fatigue testing of curved fiber-reinforced plastic (FRP) structures, during which in-situ the damage state can be non-destructively inspected by thermography and strain-field measurement techniques. Sandwich shell specimens with typical wind turbine blade manufacturing defects were designed and tested. The tested imperfections show a fairly significant reduction (up to 90%) of the shell test specimens‘ lifetime, depending on the type of imperfection. Using the in-situ NDT methods incorporated in the shell test bench, the location and cycle time of the initial defects and the damage evolution was investigated.
Mycotoxins are toxic secondary metabolites produced by filamentous fungi. The contamination of mycotoxins in food and feed products has been recognized to be an important safety issue for human and animal health. In the context of food safety, medical herbs nowadays are also considered as potential source of mycotoxins. Considering that mycotoxins are generally stable compounds and could not be destroyed completely during food-processing operations, prevention of contamination is identified as main issue.
Currently, biochemical, and immunochemical methods based on the use of antibodies as a specific recognition bioreagents are increasingly found to be applied in screening. Along with good common enzyme linked immunosorbent assay (ELISA), more and more widely used method is fluorescence polarization immunoassay (FPIA). These methods help to monitor of many toxic substances in large number of samples and carried out quickly, easily, cheaply, and give good results in quantifying the one or more substances. That is why the use of immunochemical methods of analysis, such as ELISA and FPIA, is the most promising for solving this problem.
Thus, the aim of our work is to optimize the determination of mycotoxins by ELISA and FPIA in medical herbs. We selected antibodies and a tracer for the FPIA, then we constructed obtained a calibration curve and determined the sensitivity of this method for the samples in aqueous media. Then, the optimal sample preparation was elaborated: for spiked nuts samples were selected and prepared and a calibration curve was obtained with respect to the matrix effect. After that, the analysis of spiked nuts samples was performed by ELISA and FPIA and the sensitivity of both methods was compared.
The main drawback in radiometric imaging is the degradation of the spatial resolution with increasing depth, which results in blurred images for deeper lying structures. We circumvent this degradation with blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. We demonstrate this by imaging a line pattern and a star-shaped structure through a metal sheet with a resolution four times better than the width of the thermal point-spread-function. The ground-breaking concept of super-resolution can be transferred from optics to diffusive imaging by defining a thermal point-spread-function similar to the Abbe limit for a certain optical wavelength.