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In this work, a comparison between different brushless harmonic-excited wound-rotor synchronous machines is performed. The general idea of all topologies is the elimination of the slip rings and auxiliary windings by using the already existing stator and rotor winding for field excitation. This is achieved by injecting a harmonic airgap field with the help of power electronics. This harmonic field does not interact with the fundamental field, it just transfers the excitation power across the airgap. Alternative methods with varying number of phases, different pole-pair combinations, and winding layouts are covered and compared with a detailed Finite-Element-parameterized model. Parasitic effects due to saturation and coupling between the harmonic and main windings are considered.
Deep learning-based EEG detection of mental alertness states from drivers under ethical aspects
(2021)
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.
Hearing contact lens (HCL) is a new type of hearing aid devices. One of its main components is a piezo-electric actuator (PEA). In order to evaluate and maximizethe HCL´s performance, a model of the HCL coupled to the middle ear was developed using finite element (FE)approach. To validate the model, vibrational measurements on the HCL and temporal bones were performed using a Laser-Doppler-Vibrometer (LDV). The model was validated step by step starting with HCL only. Then a silicone cap was fitted onto the HCL to provide an interface between the HCL and the tympanic membrane. The HCL was placed on the tympanic membrane and additional measurements were performed to validate the coupled model. The model was used to evaluate the sensitivity of geometrical and material parameters with respect to performance measures of the HCL. Moreover, deeper insight was gained into the feedback behavior, which causes whistling sounds, and the contact between the HCL and tympanic membrane.
This paper presents a permanent magnet tubular linear generator system for powering passive sensors using vertical vibration harvesting energy. The system consists of a permanent magnet tubular linear vibration generator and electric circuits. By using the design of mechanical resonant movers, the generator is capable of converting low frequencies small amplitude vertical vibration energy into more regular sinusoidal electrical energy. The distribution of the magnetic field and electromotive force are calculated by Finite Element Analysis. The characteristics of the linear vibration generator system are observed. The experimental results show the generator can produce about 0.4W~1.6W electrical power when the vibration source's amplitude is fixed on 2mm and the frequencies are between 13Hz and 22Hz.
This paper presents a modular and scalable power electronics concept for motor control with continuous output voltage. In contrast to multilevel concepts, modules with continuous output voltage are connected in series. The continuous output voltage of each module is obtained by using gallium nitride (GaN) high electron motility transistor (HEMT)s as switches inside the modules with a switching frequency in the range between 500 kHz and 1 MHz. Due to this high switching frequency a LC filter is integrated into the module resulting in a continuous output voltage. A main topic of the paper is the active damping of this LC output filter for each module and the analysis of the series connection of the damping behaviour. The results are illustrated with simulations and measurements.
Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diag- nosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model con- structs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to vali- date the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of skews in the data must be identified. Development of training data and AI algorithms that are robust against biased information is a new challenge for scientists. As aesthetic judgement usually is biased, we want to take it one step further and propose an Unbiased Convolutional Neural Network for FBP. While it is possible to create network models that can rate attractiveness of faces on a high level, from an ethical point of view, it is equally important to make sure the model is unbiased. In this work, we introduce AestheticNet, a state-of-the-art attractiveness prediction network, which significantly outperforms competitors with a Pearson Correlation of 0.9601. Additionally, we propose a new approach for generating a bias-free CNN to improve fairness in machine learning.
Um den Übergang von der Schule zur Hochschule zu erleichtern, brauchen Studierende technischer Fächer häufig eine Auffrischung ihrer Kenntnisse in Mathematik und Physik. Ein Online-Lernsystem für Physik kann Studierende bei der Beschäftigung mit physikalischen Inhalten unterstützen. Zudem kann ein Physik-Wissenstest Lücken im individuellen Wissensstand aufzeigen und zum Lernen der fehlenden Themen motivieren. Die Arbeitsgruppe "eLearning in der Physik" der Hochschulföderation Süd-West (HfSW) bestehend aus den baden-württembergischen Hochschulen Aalen, Esslingen, Heilbronn, Mannheim und Reutlingen hat einen Aufgabenpool von über 200 Physikaufgaben für Erstsemester erarbeitet. Sie stehen den Studierenden mit Lösungen in Lernmanagementsystemen zum Selbststudium und jetzt auch im "Zentralen Open Educational Resources Repositorium der Hochschulen in Baden-Württemberg" (ZOERR) zur Verfügung. In diesem Beitrag wird über den Einsatz der Online-Übungsaufgaben in 2020/2021 berichtet, über die Ergebnisse der Wissenstests und über die in der Corona-Zeit neu eingerichteten eTutorien.
Business opportunities for energy providers to utilize flexible industrial demand are platform-based, connecting small and medium-sized enterprises (SMEs) to a virtual power plant (VPP) in complex ecosystems. Unlike in other VPPs, the focus is on participation, data, and control sovereignty for the SMEs. An exemplary application for an existing cement mill demonstrates positive margins. Viable VPP business models for small and medium-sized utilities include the “orchestrator,” i.e., adding value by linking services of specialized providers, the “integrator,” i.e., incorporating internal and external processes and resources, as well as the “white label user,” i.e., using a turn-key VPP from an exclusive cooperation partner.
Electronic design automation approaches can roughly be divided into optimizers and procedures. While the former have enabled highly automated synthesis flows for digital integrated circuits, the latter play a vital (but mostly underestimated role) in the analog domain. This paper describes both automation strategies in comparison, identifying two fundamentally different automation paradigms that reflect the two basic design practices known as “top-down” and “bottom-up”. Then, with a focus on the latter, the history of procedural approaches is traced from their
early beginnings until today’s evolvements and future prospects to underline their practical importance and to accentuate their scientific value, both in itself and in the overall context of EDA.