Refine
Year of publication
- 2021 (6) (remove)
Document Type
- Bachelor Thesis (6) (remove)
Has Fulltext
- yes (6)
Is part of the Bibliography
- no (6)
Ziel dieser Arbeit ist es, eine deutlich definierte Markenidentität für DigiCerts zu konzipieren. Zu diesem Zweck wird das Markensteuerrad von Esch (2018, S.98) angewandt, um in detaillierten Schritten eine nützliche Markenidentität aufzubauen. Mithilfe dieser soll anschließend folgende Fragestellung beantwortet werden: Wie kann sich DigiCerts in der Hochschullandschaft positionieren? Zur Beantwortung der zugrunde liegenden Frage, wird die Positionierungspyramide von Esch(2009, S. 163)genutzt.
Insgesamt soll mithilfe dieser Arbeit eine für DigiCerts anwendbare Identität aufgebaut werden.
This work aims to generate synthetic electromyographic (EMG) signals using Generative Adversarial Network (GAN). GANs are considered as one of the most exciting and promising approaches in deep learning [6], offering the possibility to generate artificial data based on real data. GAN consists of two main parts, a discriminator that attempts to differentiate between the generated data and the original data, and a generator that tries to fool the discriminator by generating data which looks like real data, the GAN works by staging a two-player
minimax game between generator and discriminator networks. To achieve the objective of generating realistic artificial electromyographic signals, two different architectures are considered for the generator and the discriminator networks of the GAN model: Long short-term memory (LSTM), which can avoid the long-term dependency problem and remembers information over a long period of time, and convolutional neural network (CNN), which is a powerful tool at automatic feature extraction. Different combinations of CNN and LSTM including hybrid model are experimented within the GAN using the same training data-set. The results and performances of each combination are compared and reviewed. The generated artificial EMG signals can be used to
simulate real muscle activity situations to for example improve muscle signal controlled prostheses using artificial data that may include conditions that does not exist in real data. This method of artificial data generation is not limited to EMG signals, the network can also be used to generate other synthetic biomedical signals such as electroencephalogram (EEG) or electrocardiogram (ECG) that can be practically used for testing algorithms and classifiers.
Prediction of movement onset and direction based on muscle activity during reaching movements
(2021)
Electromyography as a technology allows one to be able to measure muscle activity, which in turn can be used to detect movement direction and onset. The process for this classification problem often involves a multitude of various extracted features and classification techniques, that differ a lot across different scientific papers. This thesis analyzed different features and classifiers and tackled a center out reaching task to determine a good workflow for classifying
arm movement direction. The data was recorded with 6 sEMG electrodes
placed on the upper arm of 5 healthy participants.
The different experiments show good classification accuracy of over 96 % in a reaching task with 16 targets placed in a circle within reaching distance. The results also show that the classification accuracy did not differ a lot between features. The individual EMG channels also display high correlation, which suggests a possible reduction in necessary electrodes. Classification accuracy
before movement onset also only dropped by 2 % compared to the accuracy of the whole time window of a reaching motion. This seems especially vital to ensure proper support via prosthesis or orthesis for people with heavily impaired movement.
In this document a reliable data streaming mechanism for a TDMA LPWAN application is developed by adapting a link layer solution for power line communication, published at the International Symposium on Power Line Communications and its Applications (ISPLC) 2015. A C++ implementation of the services link layer is provided and demonstrated
working at a packet error rate of 50%.
According to various studies, a strong market penetration of electric mobility is expected in the next few years. On the one hand, electric vehicles can contribute to achieve climate targets, but on the other hand, they can place a heavy burden on the power grid and have serious consequences, such as component overload and voltage instabilities, if they are charged in an uncoordinated manner.
Proper grid integration of electric vehicles with a coordinated charging approach can minimize these negative impacts and brings about positive aspects, such as improving grid quality and integrating larger amounts of renewable energy.
Taking into consideration the legal framework and the different requirements of network operators, vehicle manufacturers and owners, this paper compares different network integration techniques.
It is concluded that a decentralized charging management approach, in which the vehicle owners themselves make the charging decisions, is a good compromise between the different parties and consequently the best alternative for the grid integration of electric vehicles in Ger-many.
One aspect that needs further investigation is which is the best way to motivate vehicle owners to actively participate in a flexible charging management.