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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.
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.
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.
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.
This paper presents an improvement in usability and integrity of simulation-based analog circuit sizing. Instead of using geometrical sizing parameters (width, length), a transformed design-space, consisting exclusively of electrical parameters (branch currents, efficiencies and speed) is utilized. This design-space is explored more efficiently by optimizers. Moreover, this design-space can be reduced without affecting the quality of the result. The method is illustrated on two application examples, a symmetrical and a miller operational amplifier. Sizing the circuits using the transformed design-space showed significant reduction in required circuit simulations (up to 11x faster), better convergence, without loss in quality.
Already more than 75 countries pledged to become climate neutral by 2050 and the share of global emissions falling into an emission pricing scheme has steeply increased over the past two years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious carbon reduction goals.
This article sheds light on what the pressure points are, what the subtle triggers and what the underlying considerations, as well as hoped-for benefits of industrial companies to achieve decarbonisation. The observations and ideas presented in this paper are derived from quantitative and qualitative data. The quantitative data was collected within the framework of Energy Efficiency Index of German Industry (EEI). The qualitative data has been collected from interviews in industrial organisations and media documents as well as from professional practice.
Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the business more resilient and profitable. Those companies that do not move towards decarbonisation on the other hand may face a costly late mover disadvantage.
This piece uncovers subtle interconnections helping stakeholders from industry and beyond to grasp opportunities and challenges ahead. Taking account of these calls for rethinking economic viability calculations and investment decision making. Doing so may subsequently lead to on-site carbon reduction measures being prioritised to decarbonise effectively.