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Spreading dynamics on lithium niobate: An example of an intrinsically charged ferroelectric surface
(2023)
Droplet wetting and manipulation are essential for the efficient functioning of many applications, ranging from microfluidic applications to electronic devices, agriculture, medical diagnosis, etc. As a means of manipulating droplet wetting, the effect of applying an external voltage or surface charge has been extensively exploited and is known as electrowetting. However, there also exist many materials which bear a quasi-permanent surface charge, like electrets, which are widely employed in sensors or energy storage. In addition, other materials in nature can acquire surface charge by the triboelectric effect, like human hair, natural rubber, and polymers. Nevertheless, there do not exist any studies on spreading on this class of charged surfaces. In our work, we for the first time investigate spreading dynamics on lithium niobate (LiNbO3) as an example of a ferroelectric material with strong instantaneous polarization (0.7C/m2). We find a spreading behavior that significantly differs from classic surfaces. Spreading times can be significantly enlarged compared to standard surfaces, up to hundreds of seconds. Furthermore, the classic Tanner’s law does not describe the spreading dynamics. Instead, the evolution of the droplet radius is dominated by an exponential law. Contact angles and spreading dynamics are also polarization-dependent. They are also influenced by adsorption layers, such as they are left behind by cleaning. Overall, all results indicate that adsorption layers play a significant role in the wetting dynamics of lithium niobate and possibly other charged materials, where such processes are very pronounced. Possible mechanisms are discussed. Our findings are essential for the understanding of wetting on charged surfaces like ferroelectric materials in general. The knowledge of surface charge-based wettability difference, surface charge specific adsorption and its impact on wettability can be utilized in applications like, printing, microfluidics, triboelectric nanogenerators, and to develop biocompatible components for tissue engineering.
Insbesondere bei öffentlichen Aufträgen mit einer längeren Ausführungsdauer kommt es in der Praxis häufig vor, dass ein nachträglicher Anpassungsbedarf zur Erreichung des ursprünglichen Beschaffungsziels erforderlich wird. Da der Gesetzgeber die Rechtsfolge einer vergaberechtswidrigen Auftragsänderung stets mit einer Neuausschreibungspflicht knüpft, ist für die Anwender des Vergaberechts – insbesondere für die Vergabestellen – eine gewisse rechtliche Kompetenz zur Erfüllung der ihnen übertragenen Aufgaben unverzichtbar. Mithin trägt die Kenntnis der aktuellen Rechtslage und damit der eigenen Handlungsmöglichkeiten wesentlich für ein schnelles und sicheres Agieren am Markt bei.
Im Rahmen der vorliegenden Masterarbeit werden unterschiedliche Problematiken nachträglicher Vertragsänderungen behandelt. Aufgrund der bis zu der Vergaberechtsreform 2014/2016 fehlenden Kodifizierung konkreter Regelungssätze für Auftragsänderungen während der Vertragslaufzeit wird die erforderliche Analyse zunächst auf die Rechtsprechung des Europäischen Gerichtshofs und die der nationalen Gerichte gestützt. Anschließend wird die Rechtslage nach der erfolgten Reform des europäischen Vergaberechts und der damit einhergehenden Harmonisierung des deutschen Vergaberechts im Ober- und Unterschwellenbereich untersucht. Dabei werden die Bestimmungen der §§ 132 GWB, 47 UVgO als Maßstab für zulässige Änderungen von Liefer- und Dienstleistungsaufträgen herangezogen.
Diese Masterarbeit kann durch öffentliche Auftraggeber von Liefer- und Dienstleistungsaufträgen bei einer beabsichtigten nachträglichen Auftragsänderung zur Entscheidungsfindung herangezogen werden. Sie gibt Hinweise dazu, welche Auftragsänderungen mit einer Ausschreibungspflicht verbunden sind und welche hingegen vergaberechtsfrei umgesetzt werden können. Der Prozess der erforderlichen Entscheidungsfindung wird durch die im Rahmen dieser Arbeit enthaltene Auslegung der einschlägigen Vorschriften des Ober- und Unterschwellenbereichs sowie die dargestellte Prüfungsreihenfolge dieser Vorschriften in Verbindung mit den ausgearbeiteten Ergebnissen der praxisrelevanten Beispiele aus der Rechtsprechung unterstützt. Zu beachten ist jedoch, dass diese Ausarbeitung keinesfalls die Pflicht der Vergabestelle zur Überprüfung des Einzelfalls und gegebenenfalls zur Heranziehung eines juristischen Rats ersetzt.
Model Identification of Power Electronic Systems for Interaction Studies and Small-Signal Analysis
(2023)
The rapid growth in offshore wind brings various challenges to power system research
and industry, such as the development of multi-terminal multi-vendor HVDC grids.
To ensure interoperability in those power converter dominated systems, suitable
models are needed to efficiently perform stability and interaction studies. With
state-space based small-signal methods stability and interaction phenomena can be
assessed globally for a complex system. Yet detailed models are needed. However,
in multi-vendor projects most likely only black-boxed models will be available to
protect the intellectual property, so that identification techniques are necessary to
obtain suitable models. This thesis contributes to the research activities on statespace
model identification of black-boxed power electronic systems.
In the first part of the thesis, a method was developed and tested, where the matrix
elements of linearized state-space models were fitted in dependency of the operating
point, based on input sweeps performed on the model of a grid forming power converter
controlled as a virtual synchronous machine. It was discussed how changes in
multiple inputs can be approximated by the superposition of the individual input
dependencies and a fully operating point dependent state-space model approximation
was created. The results were validated in time and frequency domain analyses.
It was found that the method can provide a good approximation, especially for the
operating range around the default operating point.
In the second part, identification of a power electronic system was performed based
on measurement data which was generated experimentally from a low voltage laboratory
system. A sequence of input perturbations was applied to the laboratory
system and frequency response data was calculated from the corresponding output
perturbations. The data served as basis for model identification with N4SID and a
soon to be published vector fitting method. The identified models were validated by
a visual inspection of the transfer function and by comparison of the calculated step
responses to the step responses measured in the laboratory. It was found that the
treatment of incomplete data sets, the generation of substitute data and the impact
of time delays on the identification might be worth further investigation.
This work provides a valuable contribution to the research of state-space model
identification of black-boxed power electronic systems. It points out challenges and
presents promising approaches to enable state-space based methods for stability
analysis and interaction studies in future multi-terminal multi-vendor HVDC grids.
3D joint angles based human pose is needed for applications like activity recognition, musculoskeletal health, sports biomechanics and ergonomics. The microelectromechanical systems (MEMS) based magnetic-inertial measurement units (MIMUs) can estimate 3D orientation. Due to small size, MIMUs can be attached to the body as wearable sensors for obtaining full 3D human pose and this system is termed as inertial motion capture (i-Mocap). But the MIMUs suffer from sensor errors and disturbances, due to which orientation estimated from individual MIMUs can be erroneous. Accurate sensor calibration is essential and subsequently alignment of these sensors to body segments must also be precisely known, which is called sensor-to-segment calibration. Sensor fusion is employed to address the disturbances and noise in MIMUs. Many state-of-art inertial motion capture approaches ignore the magnetometer and only use IMUs to reduce the error arising from inhomogeneous magnetic field. These algorithms rely on kinematic constraints and assumptions regarding joints and are based on IMUs located on the adjacent body segments. The full body coverage requires 13-17 such units and can be quite obtrusive. The setting up and calibration of so many wearable sensors also take time.
This thesis focuses on 3D human pose estimation from a reduced number of MIMUs and deals with this problem systematically. First we propose an accurate simultaneous calibration of multiple MIMUs, which also learns the uncertainty of individual sensors. We then describe a novel sensor fusion algorithm for robust orientation estimation from an MIMU and for updating sensors calibration online. The residual errors in both sensor calibration and fusion can result in drift error in the joint angles. Therefore, we present anatomical (sensor-to-segment) calibration in which an orientation offset correction term is updated and used for online correction of residual drift in individual joint angles. Subsequently we demonstrate that 3D human joint angle constraints can be learned using a data-driven approach in a high dimensional latent space. Owing to temporal and joint angle constraints, it is possible to use only a reduced set of sensors (as opposed to one sensor per segment) and still obtain 3D human pose. But the spatial and temporal prior learning from data is often limited due to finite set of movement patterns in most datasets. This introduces uncertainty while estimating 3D human pose from sparse MIMU sensors. We propose a magnetometer robust orientation parameterization and a data-driven deep learning framework to predict 3D human pose with associated uncertainty from sparse MIMUs. The model is evaluated on real MIMU data and we show that the uncertainty predicted by the trained model is well-correlated with actual error and ambiguity.
An Efficient Automated Machine Learning Framework for Genomics and Proteomics Sequence Analysis
(2023)
Genomics and Proteomics sequence analyses are the scientific studies of understanding the language of Deoxyribonucleic Acid (DNA), Ribonucleic Acid (RNA) and protein biomolecules with an objective of controlling the production of proteins and understanding their core functionalities. It helps to detect chronic diseases in early stages, root causes of clinical changes, key genetic targets for pharmaceutical development and optimization of therapeutics for various age groups. Most Genomics and Proteomics sequence analysis work is performed using typical wet lab experimental approaches that make use of different genetic diagnostic technologies. However, these approaches are costly, time consuming, skill and labor intensive. Hence, these approaches slow down the process of developing an efficient and economical sequence analysis landscape essential to demystify a variety of cellular processes and functioning of biomolecules in living organisms. To empower manual wet lab experiment driven research, many machine learning based approaches have been developed in recent years. However, these approaches cannot be used in practical environment due to their limited performance. Considering the sensitive and inherently demanding nature of Genomics and Proteomics sequence
analysis which can have very far-reaching as well as serious repercussions on account of misdiagnosis, the main
objective of this research is to develop an efficient automated computational framework for Genomics and Proteomics sequence analysis using the predictive and prescriptive analytical powers of Artificial Intelligence (AI) to significantly improve healthcare operations.
The proposed framework is comprised of 3 main components namely sequence encoding, feature engineering and
discrete or continuous value predictor. The sequence encoding module is equipped with a variety of existing and newly developed sequence encoding algorithms that are capable of generating a rich statistical representation of DNA, RNA and protein raw sequences. The feature engineering module has diverse types of feature selection and dimensionality reduction approaches which can be used to generate the most effective feature space. Furthermore, the discrete and/or continuous value predictor module of the proposed framework contains a wide range of existing machine learning and newly developed deep learning regressors and classifiers. To evaluate the integrity and generalizability of the proposed framework, we have performed a large-scale experimentation over diverse types of Genomics and Proteomics sequence analysis tasks (i.e., DNA, RNA and proteins).
In Genomics analysis, Epigenetic modification detection is one of the key component. It helps clinical researchers and practitioners to distinguish normal cellular activities from malfunctioned ones, which can lead to diverse genetic disorders such as metabolic disorders, cancers, etc. To support this analysis, the proposed framework is used to solve the problem of DNA and Histone modification prediction where it has achieved state-of-the-art performance on 27 publicly available benchmark datasets of 17 different species with best accuracy of 97%. RNA sequence analysis is another vital component of Genomics sequence analysis where the identification of different coding and non-coding RNAs as well as their subcellular localization patterns help to demystify the functions of diverse RNAs, root causes of clinical changes, develop precision medicine and optimize therapeutics. To support this analysis, the proposed framework is utilized for non-coding RNA classification and multi-compartment RNA subcellular localization prediction. Where it achieved state-of-the-art performance on 10 publicly available benchmark datasets of Homo sapiens and Mus Musculus species with best accuracy of 98%.
Proteomics sequence analysis is essential to demystify the virus pathogenesis, host immunity responses, the way
proteins affect or are affected by the cell processes, their structure and core functionalities. To support this analysis, the proposed framework is used for host protein-protein and virus-host protein-protein interaction prediction. It has achieved state-of-the-art performance on 2 publicly available protein protein interaction datasets of Homo Sapiens and Mus Musculus species with best accuracy of 96% and 7 viral host protein protein interaction datasets of multiple hosts and viruses with best accuracy of 94%. Considering the performance and practical significance of proposed framework, we believe proposed framework will help researchers in developing cutting-edge practical applications for diverse Genomic and Proteomic sequence analyses tasks (i.e., DNA, RNA and proteins).
Global temperature rise, and growing consumption of limited resources are global
threats. Therefore, industry and consumers will need to reduce their environmental im-
pacts. For this Product Environmental Declarations (EPD) are used for eco design and
product impact comparison. As EPDs are likely to become mandatory the total number
of products to be assessed will increase tremendously. Therefore, the entire EPD work-
flow will need to be automatized to allow large-scale application of EPDs. The goal of
this thesis is to develop an automated workflow for EPDs (aEPD) by combining Model-
Based-Systems Engineering (MBSE), Digital Twin and Life Cycle Assessment concepts.
While MBSE is used for the multilevel requirements analysis the focus was set on auto-
mating data collection along the supply and value chain using the AAS 4.0 Implementa-
tion of the Digital Twin concept. The applicability of the aEPD workflow is shown in the
prototypical implementation of an aEPD for an electric motor. Even though progress has
been made research should be continued in the development of further AAS Submodel
templates and PCRs to allow standardized data collection and communication on a
global scale.
This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.
Bees are recognized as an indispensable link in the human food chain and general ecological system.
Numerous threats, from pesticides to parasites, endanger bees and frequently lead to hive collapse. The varroa destructor mite is a key threat to bee keeping and the monitoring of hive infestation level is of major concern for effective treatment. Sensors and automation, e.g., as in condition-monitoring and Industry 4.0, with machine
learning offer help. In numerous activities a rich variety of sensors have been applied to apiary/hive
instrumentation and bee monitoring. Quite recent activities try to extract estimates of varroa infestation level by
hive air analysis based on gas sensing and gas sensor systems. In our work in the IndusBee4.0 project [8, 11], an hive-integrated, compact autonomous gas sensing system for varroa infestation level estimation based on low-
cost highly integrated gas sensors was conceived and applied. This paper adds to [11] with the first results of a
mid-term duration investigation from July to September 2020 until formic acid treatment. For the regarded hive more than 79 % of detection probability based on the SGP30 gas sensor readings have been achieved.
The nondestructive testing of multilayered materials is increasingly applied in
both scientific and industrial fields. In particular, developments in millimeter
wave and terahertz technology open up novel measurement applications, which
benefit from the nonionizing properties of this frequency range. One example is
the noncontact inspection of layer thicknesses. Frequently used measuring and
analysis methods lead to a resolution limit that is determined by the bandwidth
of the setup. This thesis analyzes the reliable evaluation of thinner layer thicknesses
using model-based signal processing.
Modern microtechnology has the central task of ensuring technological progress through the miniaturization and reduction of component dimensions. Micro grinding with micro pencil grinding tools (MPGTs) has established itself as a manufacturing process in microtechnology, especially for the machining of hard and brittle materials. The process has been investigated by numerous researchers. Yet, tools with diameters of <100 μm, could not satisfy the needs of the industry. The tool life of MPGTs was insufficient, their feed rates were too slow for a mean-ingful application and both the MPGTs, and their microstructures were not reproducible. Therefore, this dissertation is dedicated to the task of investigating and revising the complete manu-facturing process and application methodology of these tools. New substrate geometries and materials are investigated. Surface treatment methods are investigated to increase adhesion between the abrasive layer and the substrate. In addition, conventional coating processes like electroplating are replaced by an autocatalytic electroless plating process, that has a much higher reproducibility rate of MPGTs with diameters of about 50 μm and less. The micro grinding methodology is optimized by parameter studies, and new coolant supplying methods with new metalworking fluids, which are introduced to achieve the best possible result when machining 16MnCr5.