Two-Dimensional Finite Element Analysis of Turning Processes

Despite crucial efforts invested into computational methods, explicit dynamics simulation of cutting operations may still be unacceptably expensive. Therefore, in many cases a two-dimensional model is considered. Here an overview of the possibilities of two-dimensional simulations is given. For this, simulation and measurement of a straight turning process on AISI 1045 steel is presented. In the numerical analysis, material behavior and its failure was described by Johnson-Cook law, considering damage evolution. Coupled thermo-mechanical model with mass-scaling and adaptive remeshing was built. The numerically obtained cutting force was compared to the measured data. It was found that the forces obtained with simulation and the measured ones show good agreement. Sensitivity analyses were performed to examine the influence of specific parameters on the reaction force. The effect of these parameters is also shown

Automatic detection of hard and soft exudates from retinal fundus images

According to WHO estimates, 400 million people suffer from diabetes, and this number is likely to double by year 2030. Unfortunately, diabetes can have severe complications like glaucoma or retinopathy, which both can cause blindness. The main goal of our research is to provide an automated procedure that can detect retinopathy-related lesions of the retina from fundus images. This paper focuses on the segmentation of so-called white lesions of the retina that include hard and soft exudates. The established procedure consists of three main phases. The preprocessing step compensates the various luminosity patterns found in retinal images, using background and foreground pixel extraction and a data normalization operator similar to Z-transform. This is followed by a modified SLIC algorithm that provides homogeneous superpixels in the image. The final step is an ANN-based classification of pixels using fifteen features extracted from the neighborhood of the pixels taken from the equalized images and from the properties of the superpixel where the pixel belongs. The proposed methodology was tested using high-resolution fundus images originating from the IDRiD database. Pixelwise accuracy is characterized by a 54% Dice score in average, but the presence of exudates is detected with 94% precision

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability