Glösekötter, Peter
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Faculty
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.
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.
State of the art classifiers split Alzheimer’s disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer’s disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32nT/Hz−−−√ and a theoretical shot noise limited sensitivity of 2.87 nT/Hz−−−√ is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) mm3 combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around 0.1W, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.
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.
Die wachsenden Anteile fluktuierender regenerativer Energien in der Energieversorgung (bis 2020 sollen 30 % und 2050 sogar So % des Stroms aus regenerativen Energiequellen stammen) sowie die Steigerung der Elektromobilität machen deutlich: Das Thema der Zwischenspeicherung elektrischer Energie ist von höchster gesellschaftlicher Relevanz und verlangt zwingend nach einer Lösung. Neue Technologien, die umweltfreundlich, sicher, leistungsfähig und bezahlbar zugleich sind, müssen deshalb entwickelt werden.
Oxygen consumption of zinc-air batteries and theirperformance at low oxygen concentration levels
(2018)
Already existing primary Zinc-air batteries providea high energy density. Due to new secondary cells, its tech-nology can become an alternative for energy storage. Sincethese applications require a big amount of storable energy, theoxygen consumption has to be taken into account. This articledetermines the oxygen consumption of zinc-air batteries duringdischarging. Furthermore the performance of zinc-air batteries atlow oxygen concentrations is analyzed. Both aspects are validatedby practical experiments.
Metal air batteries provide a high energy density as the ca-thodic reaction uses the surrounding air. Different metals can be usedbut zinc is very promising due to its disposability and nontoxic behav-ior. State estimation is quite complicated as the voltage characteristicof the battery is rather flat. Especially estimating the state of chargeis important as a secondary electrolysis process during overcharging canlead to an unsafe state. Another technique for state estimation is theelectrochemical impedance spectroscopy. Therefore, this paper describesthe process of setup and measuring a time series of impedance spectraat known states of charge. Then these spectra are used to derive anequivalent circuit. Finally the development of the circuit’s parameter areanalyzed to extract most important parameters.
State of Charge estimation of zinc air batteries using electrochemical impedance spectroscopy
(2018)
The main task of battery management systems is to keep the working area of the battery in a safe state. Estimation of the state of charge and the state of health is therefore essential. The traditional way uses the voltage level of a battery to determine those values. Modern metal air batteries provide a flat voltage characteristic which necessitates new approaches. One promising technique is the electrochemical impedance spectroscopy, which measures the AC resistance for a set of different frequencies. Previous approaches match the measured impedances with a nonlinear equivalent circuit, which needs a lot of time to solve a nonlinear least-squares problem. This paper combines the electrochemical impedance spectroscopy with neural networks to speed up the state estimation using the example of zinc air batteries. Moreover, these networks are trained with different subsets of the spectra as input data in order to determine the required number of frequencies.
When developing new battery technologies, fundamental research means assembling new batteries by hand since a production line is not worthwhile for building and testing individual cells. This causes high production tolerances to occur because manual manufacturing is not as precise as machine-made. When putting these prototypes into operation, problems can arise due to the varying parameters. One of the most important exercise is finding a criterion of a full battery. This can be challenging when parameters like the capacity or the end of charge voltage are not precisely known due to the tolerances. Furthermore, new battery types do not necessarily rely on the same stopping criteria. For example zinc-air secondary batteries do not offer an end of charging voltage. Its charging current is not going to decrease when the battery is full and the charging voltage is held at a fixed value. But instead of de-oxidising zinc oxide, hydrogen is produced. In the majority of cases overcharging should be avoided as it harms the battery. Another even more dangerous consequence is the possibility of an explosion. Especially lithium based batteries are known for their need of compatible ambient and charging parameters. This paper proposes a new criterion for detecting the end of charge that is based on the rate of change of electrochemical impedance spectra of the examined batteries. Device parameter fluctuations influence every measurement. Therefore, using the rate of change offers the possibility to not depend on these fluctuations.
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.
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.
This work describes the setup of an ultrawideband (UWB)
realtime localization system (RTLS) for tracking of particles.We describe
how the RTLS obtains distances and positions through radio waves and
the setup and evaluation of a real world system is stated in detail. In
the proposed system the particles track a subtrates surface
ow inside a
biogas plant for verication of agitation processes.
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.
Die Beiträge, der Konferenz:
• Nanotechnologie –Von Wunder-Materialien und solchen die es werden wollen
• Mikrotechnik –to improve the qualityof live
• Quantum Sensing for Industrial Applications
• Geordnete Defekte in Graphit: Ein Fahrplan in Richtung Raumtemperatur Supraleitung
• Funktionale mikro-und nanostrukturierte Folien als Bestandteil hochintegrierter Systeme
Wireless Sensor Networks
(2013)
Embedded Systems
(2013)
Wireless Sensor
(2013)