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Course of studies
In this paper, the influence of current sensors of a NILM system is investigated. The current sensors of a classical inductive current transformer and a Rogowski coil are compared. To evaluate the actual influence on the NILM, measurements are performed with two measuring systems with different current sensors. With these measuring systems, 20 different consumers with 50 switch-on and switch-off cycles are measured in parallel. Besides, the influence of the sampling rate on the results of the NILM classification is evaluated. The classification is carried out with features normalized to the performance and without phase information, so only the signal waveform is used to differentiate the devices.
Thermofluorimetric, magnetic and lateral flow aptamer based assays for point of care applications
(2020)
The assessment of views on ageing: a review of self-report measures and innovative extensions
(2020)
Werkzeugmaschine zur Laserkonditionierung von Schleifwerkzeugen unabhängig von deren Spezifikation
(2020)
Machine learning applications, like machine condition monitoring, predictive maintenance, and others, become a state of the art in Industry 4.0. One of many machine learning algorithms are decision trees for the decision-making process. A new approach for creating distributed decision trees, called node based parallelization, is presented. It allows data to be classified through a network of industrial devices. Each industrial device is responsible for a single classification rule. Also, nodes that react incorrectly, for example, due to an attack, are taken into account using a variety of methods to remain the decision-making process correct and robust.
Senatsausschuss Mobilität
(2020)
Der editierte Mensch. Künstliche Intelligenz als Kurator von Erinnerung. Ein postdisziplinärer Essay
(2020)
Michael Burawoy: "For Public Sociology" als Referenzdokument der Debatte um öffentliche Soziologie
(2020)
Lifelogging
(2020)
Notions of "coronavirus" from the perspective of a clinical immunologist and medical historian
(2020)
Adding evidence of the effects of treatments into relevant Wikipedia pages: a randomised trial
(2020)
Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review
(2020)
Prostate segmentation is an essential part of brachytherapy treatment planning, in order to perform the procedure with required accuracy. Nowadays, segmentation of the prostate is still carried out manually during the planning steps, therefore it is a process that can be tedious, time-consuming and prone to inter-observer error. Much effort has been made in development of an computer-based algorithm that can perform prostate segmentation automatically, but only with appearance of deep learning methods, more promising algorithms emerged. So far, convolutional neural networks demonstrated excellent results in fully automatic prostate segmentation. Development of such an algorithm and training an efficient deep learning model is a challenging task, and requires a lot of optimizations. The objective of this study is development and evaluation of an algorithm for image processing based on deep learning methods that can perform fully automatic segmentation of the prostate gland in transrectal ultrasound images. Additionally, we made an overview of the development process, along with challenges and their solutions and demonstrated an algorithm implemented using Python and Tensorflow library, consisted of preprocessing, augmentation, training and validation, postprocessing and validation steps, which is able to successfully carry out fully automatic prostate segmentation with expert level of accuracy. Finally, we presented our implementation of fully convolutional neural network model and results that are encouraging to continue with model improvements and potential clinical application.