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Handbuch China-Kompetenzen
(2022)
Angesichts des rasanten wirtschaftlichen und wissenschaftlichen Aufstrebens Chinas offenbart sich an deutschen Hochschulen ein deutlicher Mangel an China-Kompetenzen auf allen Ebenen. Wie sind chinesische Kooperationspartner*innen einzuschätzen? Wie sollten Studierende ausgebildet werden, damit sie in Zukunft informiert und (selbst-)bewusst zusammenarbeiten können? Wie kann erreicht werden, dass chinesische Studierende ihre Zeit in Deutschland als akademisch und persönlich bereichernd empfinden? Best practice-Beispiele von elf deutschen Hochschulen geben Anregungen, die sich auch übergreifend auf verschiedene Bildungseinrichtungen und Partnerländer übertragen lassen.
Compliance ist als Integritätsmanagement heute ein wesentlicher Aspekt erfolgreichen unternehmerischen Handelns. Unter den Bedingungen der digitalen Transformation, die neue Geschäftsmodelle ermöglicht, die Welt verbindet und vielseitige ungekannte Risiken birgt, ist ein robustes Compliance-Management gefragt, das sich nicht nur mit klassischer Korruptionsbekämpfung auseinandersetzt – auch Themen der Cyber- und Privacy-, Produkt- und Technik-Compliance, Geldwäsche und die Einhaltung von Menschen- und Arbeitnehmerrechten in internationalen Wertschöpfungsketten stehen immer mehr im Fokus. Die Neubearbeitung dieses Handbuchs spiegelt diese beispiellose Dynamik der letzten Jahren praxisgerecht wider: Mit internationalem Blick (Sie finden Länderstudien u.a. zu China, Lateinamerika, Russland, Afrika) und über 40 neuen Beiträgen zu den aktuell wichtigsten Compliance-Topics.
With the high resolution of modern sensors such as multilayer LiDARs, estimating the 3D shape in an extended object tracking procedure is possible. In recent years, 3D shapes have been estimated in spherical coordinates using Gaussian processes, spherical double Fourier series or spherical harmonics. However, observations have shown that in many scenarios only a few measurements are obtained from top or bottom surfaces, leading to error-prone estimates in spherical coordinates. Therefore, in this paper we propose to estimate the shape in cylindrical coordinates instead, applying harmonic functions. Specifically, we derive an expansion for 3D shapes in cylindrical coordinates by solving a boundary value problem for the Laplace equation. This shape representation is then integrated in a plain greedy association model and compared to shape estimation procedures in spherical coordinates. Since the shape representation is only integrated in a basic estimator, the results are preliminary and a detailed discussion for future work is presented at the end of the paper.
Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist-rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection). To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run-to-failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear-out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.
In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
Sleep is an important part of our life that significantly influences our health and well-being. The monitoring of sleep can provide data based on which sleep quality could be improved. This paper presents a system for heart rate detection during sleep. The data is collected from sensors underneath the test subjects. Though the data contains noise, it needs to be filtered to remove it. Due to the low strength of the signals, they need to be amplified after filtering. At some points of the signal, particular heartbeats may not be tracked by sensors due to the failure of a sensor or other reasons, which should be considered. The heart rate is detected in intervals of 15 s. A tool is implemented that detects the heart rate and visualizes it. The preprocessing of the data is performed with several filters: a highpass filter, a band-reject filter, a lowpass filter, and a motion detector. After the preprocessing of the data, the quality of the signal is significantly increased, and detection is possible.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders.