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Investigation of magnetic effects on austenitic stainless steels after low temperature carburization
(2018)
This work aims at investigating the magnetic effects of austenitc stainless steels which can occur after a low temperature carburisation depending on the alloy. Samples were prepared of different alloys and subjected to a multiple low temperature carburisation to obtain different treatment conditions for each alloy. The layer characterisation was carried out by light microscope and also by hardening profiles and shows that the layer develops with each additional treatment cycle. A lattice expansion could be detected in all treated samples by X-ray diffraction. Magnetisability was measured using Feritscope and SQUID measurements. Not all alloys showed magnetisability after treatment. In addition to MFM measurements, experiments with Ferrofluid were also used to visualize the magnetic areas. These studies show that only about half of the formed layer becomes magnetisable and has a domain-like structure.
Knot placement for curve approximation is a well known and yet open problem in geometric modeling. Selecting knot values that yield good approximations is a challenging task, based largely on heuristics and user experience. More advanced approaches range from parametric averaging to genetic algorithms.
In this paper, we propose to use Support Vector Machines (SVMs) to determine suitable knot vectors for B-spline curve approximation. The SVMs are trained to identify locations in a sequential point cloud where knot placement will improve the approximation error. After the training phase, the SVM can assign, to each point set location, a so-called score. This score is based on geometric and differential geometric features of points. It measures the quality of each location to be used as knots in the subsequent approximation. From these scores, the final knot vector can be constructed exploring the topography of the score-vector without the need for iteration or optimization in the approximation process. Knot vectors computed with our approach outperform state of the art methods and yield tighter approximations.
Der Begriff "Integrität" nimmt das Verhältnis zwischen individuellem Handeln und der Einhaltung von Regeln und Werten in den Blick. Grüninger/Wanzek betonen, dass integres Handeln nicht blinde Regelbefolgung, sondern die Erfüllung der zugrundeliegenden Werte erfordert. Von Integrität wird gesprochen, wenn die ethischen Werte im individuellen Denken und Tun sowie auf persönlicher und organisationaler Ebene übereinstimmen.
In Anlehnung an das Tempcore-Verfahren wurde an wärmebehandeltem Stabstahl das Zugverfestigungsverhalten des Kernes, der Außenhaut sowie dem gesamten Stab experimentell und numerisch ermittelt. Es zeigte sich, dass die Dehnungen am Kern und am äußeren Rand gleich sind und der Einfluss des Kerngefüges entscheidend für den Beginn der Einschnürung in der Außenhaut ist. Eine Verbesserung der Eigenschaften des Kerngefüges kann somit die Bruchempfindlichkeit des gesamten Stabes reduzieren.
The overall goal of this work is to detect and analyze a person's movement, breathing and heart rate during sleep in a common bed overnight without any additional physical contact. The measurement is performed with the help of
sensors placed between the mattress and the frame. A two-stage pattern classification algorithm based has been implemented that applies statistics analysis to recognize the position of patients. The system is implemented in a sensors-network, hosting several nodes and communication end-points to support quick and efficient classification. The overall tests show convincing results for the position recognition and a reasonable overlap in matching.
Know when you don't know
(2018)
Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experiments, it is often impossible to know all phenotypes in advance. Moreover, novel phenotype discovery itself can be an HCS outcome of interest. This aspect of HCS is not yet covered by classical deep learning approaches. When presenting an image with a novel phenotype to a trained network, it fails to indicate a novelty discovery but assigns the image to a wrong phenotype. To tackle this problem and address the need for novelty detection, we use a recently developed Bayesian approach for deep neural networks called Monte Carlo (MC) dropout to define different uncertainty measures for each phenotype prediction. With real HCS data, we show that these uncertainty measures allow us to identify novel or unclear phenotypes. In addition, we also found that the MC dropout method results in a significant improvement of classification accuracy. The proposed procedure used in our HCS case study can be easily transferred to any existing network architecture and will be beneficial in terms of accuracy and novelty detection.
Although the Hospice Foundation in Constance knew they had a personnel
problem, they were unsure how to begin to fix it. In addition to difficulties in
finding and keeping employees, the Hospice Foundation’s employees were
often on sick leave, adding pressure on remaining staff. Twelve communication
design students in the masters program at the University of Applied
Sciences in Constance (HTWG Konstanz) conducted a study aimed at
identifying the causes for these problems and, more generally, understanding
how the employees work and feel. Even though the methods in this
study are well known, it presents an important prototype for designers and
design researchers because of its success in finding useful insights. It also
serves as a pre-design project briefing for both management and designers.
It demonstrates the usefulness of qualitative methods in providing a deeper
understanding of a complex situation and its usefulness as a strategic tool
and for defining a project’s focus and scope. Ideally, it also provides insights
into health care for the elderly.
A constructive nonlinear observer design for self-sensing of digital (ON/OFF) single coil electromagnetic actuators is studied. Self-sensing in this context means that solely the available energizing signals, i.e., coil current and driving voltage are used to estimate the position and velocity trajectories of the moving plunger. A nonlinear sliding mode observer is considered, where the stability of the reduced error dynamics is analyzed by the equivalent control method. No simplifications are made regarding magnetic saturation and eddy currents in the underlying dynamical model. The observer gains are constructed by taking into account some generic properties of the systems nonlinearities. Two possible choices of the observer gains are discussed. Furthermore, an observer-based tracking control scheme to achieve sensorless soft landing is considered and its closed-loop stability is studied. Experimental results for observer-based soft landing of a fast-switching solenoid valve under dry conditions are presented to demonstrate the usefulness of the approach.