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  • Glösekötter, Peter (27)
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Vorhabensbezeichnung: IngenieurNachwuchs2014: Ein neues Entwicklungswerkzeug für die Optimierung von Rührsystemen in Biogas-Fermentern unter Einsatz der Wireless Sensor Networks (WSN)-Technik in Kombination mit CFD : Schlussbericht : Berichtszeitraum: 01.11.2014 bis 31.12.2018 (2019)
Annas, Sven ; Jantzen, Hans-Arno ; Scholz, Jürgen ; Glösekötter, Peter ; Wetter, Christof
GNSS based Automatic Anchor Positioning in Real Time Localization Systems (2019)
Heller, Andreas ; Horsthemke, Ludwig ; Gebing, Marcel ; Kappen, Götz ; Glösekötter, Peter
This work describes the setup of an ultrawideband (UWB) realtime localization system (RTLS) in which the determination of anchor coordinates is automated by assistance of global navigation satellite systems (GNSS).
All optical readout scheme for photoluminescence based magnetic field sensors (2020)
Horsthemke, Ludwig ; Bischoff, Christian ; Glösekötter, Peter ; Burchard, Bernd ; Staacke, Robert ; Meijer, Jan
An improvement on a concept for all optical mag- netometry using nitrogen vacancies in diamond is presented. The concept is based on the fluorescence attenuation of optically pumped nitrogen vacancies by magnetic fields up to ≈ 50 mT. The attenuation is registered by modulating the pumping power to generate a constant signal at a photodetector. A sensitivity of 2.6μT/√Hz at a sampling frequency of 500 Hz is achieved.
Development of a platform for the exchange of biodatasets with integrated opportunities for artificial intelligence using MatLab (2015)
Wansing, Christian ; Banos, Oresti ; Pomares, Hector ; Glösekötter, Peter ; Rojas Ruiz, Ignacio
This paper deals with the issue of automating the process of machine learning and analyzing bio-datasets. For this a user-friendly website has been developed for the interaction with the researchers. On this website it is possible to upload datasets and to share them, if desired, with other scientists. The uploaded data can also be analyzed by various methods and functions. The signals inside these datasets can also be visualized. Furthermore several algorithms have been implemented to create machine learning models with the uploaded data. Based on these generated models new data can be classified or calculated. For all these applications the simplest possible handling was implemented to make the website available to all interested researchers.
Analysis of switching electronics for metal-air batterie (2022)
Löchte, Andre ; Thranow, Jan-Ole ; Winters, Felix ; Glösekötter, Peter
The subject of this paper is the analysis of various switching electronics for batteries with separate electrodes for charging and discharging. The aim is to find a switching method that is energy-efficient on the one hand, but also economically viable on the other. Both relays and MOSFETs are suitable for switching between the electrodes. Both variants have advantages and disadvantages. The results show that a solution with MOSFETs is generally more energy-efficient, but requires a large number of cycles to be economically viable compared to the relay.
Development and implementation of a neural network-based abnormal state prediction system for a piston pump (2021)
Gómez Zuluaga, Mauricio Andrés ; Ordikhani, Ahmad ; Bauer, Christoph ; Glösekötter, Peter
Piston pumps play a key role in factory automation and their availability is very critical for the smooth running of production processes. Modern installations, such as production plants and machines, are becoming increasingly complex. Therefore, the probability of a complete system failure due to a single critical component also increases. Maintenance processes with intelligent devices are therefore very important to achieve maximum economic efficiency and safety. Periodic or continuous monitoring of system components provides key information about the current physical state of the system, enabling early detection of emerging failures. Knowledge of future failures makes it possible to move from the concept of preventive maintenance to intelligent predictive maintenance. In this way, consequential damage and complete system failure can be avoided, maximizing system availability and safety. This paper reflects the development and implementation of a neural network system for abnormal state prediction of piston pumps. After a short introduction into piston pumps and their potential abnormal states, statistical and periodical analysis are presented. Then the design and implementation of suitable neural networks are discussed. Finally, a conclusion is drawn and the observed accuracies as well as potential next steps are discussed.
Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond (2023)
Homrighausen, Jonas ; Horsthemke, Ludwig ; Pogorzelski, Jens ; Trinschek, Sarah ; Glösekötter, Peter ; Gregor, Markus
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
NeoBio: Neue Entwicklungswerkzeuge zur Optimierung der Mischregime in Bioreaktoren (2023)
Wetter, Christof ; Brügging, Elmar ; Häner, Jurek ; Jantze, H.-A. ; Annas, Sven ; Glösekötter, Peter ; Horsthemke, L. ; Heller, A. ; Scholz, Dieter ; Reineck, S. ; Baumkötter, Daniel ; Grüner, Victoria ; Budelmann, J.
Highly Sensitive Compact Room Temperature Quantum Scalar Magnetormeter (2020)
Horsthemke, Ludwig ; Staake, Robert ; Burchard, Bernd ; Meijer, Jan ; Bischoff, Christian ; Glösekötter, Peter
Magnetometry with nitrogen–vacancy (NV) defects in diamond has been extensively stud-ied in the past [1]. While most approaches in-clude the use of microwaves (MW) for the de-tection of electron spin resonance, only few investigate the sensitivity of the photolumines-cence (PL) from NV centers to an external magnetic field without MW [2, 3, 4]. This work aims to utilize this effect to build a highly sensi-tive and compact room temperature magne-tometer. The avoidance of MW serves the re-duction of production costs and allows a com-mercialization at the current patent situation.
Isotropic Scalar Quantum Sensing of Magnetic Fields for Industrial Application (2020)
Staacke, Robert ; John, Roger ; Wunderlich, Ralf ; Horsthemke, Ludwig ; Knolle, Wolfgang ; Laube, Christian ; Glösekötter, Peter ; Burchard, Bernd ; Abel, Bernd ; Meijer, Jan
Magnetic field sensors based on quantum mechanic effects are often susceptible to misalignments of the magnetic field or need advanced procedures to compensate for these. Also, the record breaking sensitivities reported for superconducting quantum interference devices and alkali vapor magnetometers come along with large and complex experimental setups. The nitrogen vacancy center in diamond can be used to design a simple, small, and robust sensor without employing microwave radiation. By using compressed nanodiamond particles, it is possible to eliminate the need of an alignment of the magnetic field and still obtain the absolute magnetic flux density in a single measurement. In order to demonstrate the capabilities of this approach, a centimeter-sized modified automotive demo board is employed as a complete sensor with a sensitivity of 78 µT/Wurzel Hz.
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