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Conventional EEG devices cannot be used in everyday life and
hence, past decade research has been focused on Ear-EEG for mobile,
at-home monitoring for various applications ranging from
emotion detection to sleep monitoring. As the area available for
electrode contact in the ear is limited, the electrode size and location
play a vital role for an Ear-EEG system. In this investigation, we
present a quantitative study of ear-electrodes with two electrode
sizes at different locations in a wet and dry configuration. Electrode
impedance scales inversely with size and ranges from 450 kΩ to
1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz.
For any size, the location in the ear canal with the lowest impedance
is ELE (Left Ear Superior), presumably due to increased contact
pressure caused by the outer-ear anatomy. The results can be used
to optimize signal pickup and SNR for specific applications. We
demonstrate this by recording sleep spindles during sleep onset
with high quality (5.27 μVrms).
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
For now, the Planetary Defense Conference Exercise 2021's incoming fictitious(!), asteroid, 2021 PDC, seems headed for impact on October 20th, 2021, exactly 6 months after its discovery. Today (April 26th, 2021), the impact probability is 5%, in a steep rise from 1 in 2500 upon discovery six days ago. We all know how these things end. Or do we? Unless somebody kicked off another headline-grabbing media scare or wants to keep civil defense very idle very soon, chances are that it will hit (note: this is an exercise!). Taking stock, it is barely 6 months to impact, a steadily rising likelihood that it will actually happen, and a huge uncertainty of possible impact energies: First estimates range from 1.2 MtTNT to 13 GtTNT, and this is not even the worst-worst case: a 700 m diameter massive NiFe asteroid (covered by a thin veneer of Ryugu-black rubble to match size and brightness), would come in at 70 GtTNT. In down to Earth terms, this could be all between smashing fireworks over some remote area of the globe and a 7.5 km crater downtown somewhere. Considering the deliberate and sedate ways of development of interplanetary missions it seems we can only stand and stare until we know well enough where to tell people to pack up all that can be moved at all and save themselves. But then, it could just as well be a smaller bright rock. The best estimate is 120 m diameter from optical observation alone, by 13% standard albedo. NASA's upcoming DART mission to binary asteroid (65803) Didymos is designed to hit such a small target, its moonlet Dimorphos. The Deep Impact mission's impactor in 2005 successfully guided itself to the brightest spot on comet 9P/Tempel 1, a relatively small feature on the 6 km nucleus. And 'space' has changed: By the end of this decade, one satellite communication network plans to have launched over 11000 satellites at a pace of 60 per launch every other week. This level of series production is comparable in numbers to the most prolific commercial airliners. Launch vehicle production has not simply increased correspondingly – they can be reused, although in a trade for performance. Optical and radio astronomy as well as planetary radar have made great strides in the past decade, and so has the design and production capability for everyday 'high-tech' products. 60 years ago, spaceflight was invented from scratch within two years, and there are recent examples of fast-paced space projects as well as a drive towards 'responsive space'. It seems it is not quite yet time to abandon all hope. We present what could be done and what is too close to call once thinking is shoved out of the box by a clear and present danger, to show where a little more preparedness or routine would come in handy – or become decisive. And if we fail, let's stand and stare safely and well instrumented anywhere on Earth together in the greatest adventure of science.
Digital Shadows as the aggregation, linkage and abstraction of data relating to physical objects are a central vision for the future of production. However, the majority of current research takes a technocentric approach, in which the human actors in production play a minor role. Here, the authors present an alternative anthropocentric perspective that highlights the potential and main challenges of extending the concept of Digital Shadows to humans. Following future research methodology, three prospections that illustrate use cases for Human Digital Shadows across organizational and hierarchical levels are developed: human-robot collaboration for manual work, decision support and work organization, as well as human resource management. Potentials and challenges are identified using separate SWOT analyses for the three prospections and common themes are emphasized in a concluding discussion.
Past earthquakes demonstrated the high vulnerability of industrial facilities equipped with complex process technologies leading to serious damage of the process equipment and multiple and simultaneous release of hazardous substances in industrial facilities. Nevertheless, the design of industrial plants is inadequately described in recent codes and guidelines, as they do not consider the dynamic interaction between the structure and the installations and thus the effect of seismic response of the installations on the response of the structure and vice versa. The current code-based approach for the seismic design of industrial facilities is considered not enough for ensure proper safety conditions against exceptional event entailing loss of content and related consequences. Accordingly, SPIF project (Seismic Performance of Multi- Component Systems in Special Risk Industrial Facilities) was proposed within the framework of the European H2020 - SERA funding scheme (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe). The objective of the SPIF project is the investigation of the seismic behavior of a representative industrial structure equipped with complex process technology by means of shaking table tests. The test structure is a three-story moment resisting steel frame with vertical and horizontal vessels and cabinets, arranged on the three levels and connected by pipes. The dynamic behavior of the test structure and installations is investigated with and without base isolation. Furthermore, both firmly anchored and isolated components are taken into account to compare their dynamic behavior and interactions with each other. Artificial and synthetic ground motions are applied to study the seismic response at different PGA levels. After each test, dynamic identification measurements are carried out to characterize the system condition. The contribution presents the numerical simulations to calibrate the tests on the prototype, the experimental setup of the investigated structure and installations, selected measurement data and finally describes preliminary experimental results.
Development of open educational resources for renewable energy and the energy transition process
(2021)
The dissemination of knowledge about renewable energies is understood as a social task with the highest topicality. The transfer of teaching content on renewable energies into digital open educational resources offers the opportunity to significantly accelerate the implementation of the energy transition. Thus, in the here presented project six German universities create open educational resources for the energy transition. These materials are available to the public on the internet under a free license. So far there has been no publicly accessible, editable media that cover entire learning units about renewable energies extensively and in high technical quality. Thus, in this project, the content that remains up-to-date for a longer period is appropriately prepared in terms of media didactics. The materials enable lecturers to provide students with in-depth training about technologies for the energy transition. In a particular way, the created material is also suitable for making the general public knowledgeable about the energy transition with scientifically based material.
A new formulation to calculate the shakedown limit load of Kirchhoff plates under stochastic conditions of strength is developed. Direct structural reliability design by chance con-strained programming is based on the prescribed failure probabilities, which is an effective approach of stochastic programming if it can be formulated as an equivalent deterministic optimization problem. We restrict uncertainty to strength, the loading is still deterministic. A new formulation is derived in case of random strength with lognormal distribution. Upper bound and lower bound shakedown load factors are calculated simultaneously by a dual algorithm.
In positron emission tomography improving time, energy and spatial detector resolutions and using Compton kinematics introduces the possibility to reconstruct a radioactivity distribution image from scatter coincidences, thereby enhancing image quality. The number of single scattered coincidences alone is in the same order of magnitude as true coincidences. In this work, a compact Compton camera module based on monolithic scintillation material is investigated as a detector ring module. The detector interactions are simulated with Monte Carlo package GATE. The scattering angle inside the tissue is derived from the energy of the scattered photon, which results in a set of possible scattering trajectories or broken line of response. The Compton kinematics collimation reduces the number of solutions. Additionally, the time of flight information helps localize the position of the annihilation. One of the questions of this investigation is related to how the energy, spatial and temporal resolutions help confine the possible annihilation volume. A comparison of currently technically feasible detector resolutions (under laboratory conditions) demonstrates the influence on this annihilation volume and shows that energy and coincidence time resolution have a significant impact. An enhancement of the latter from 400 ps to 100 ps leads to a smaller annihilation volume of around 50%, while a change of the energy resolution in the absorber layer from 12% to 4.5% results in a reduction of 60%. The inclusion of single tissue-scattered data has the potential to increase the sensitivity of a scanner by a factor of 2 to 3 times. The concept can be further optimized and extended for multiple scatter coincidences and subsequently validated by a reconstruction algorithm.
For typical cases of non-isolated lightning
protection systems (LPS) the impulse currents are investigated which may flow through a human body directly touching a structural part of the LPS. Based on a basic LPS model with conventional down-conductors especially the cases of external and internal steel columns and metal façades are considered and compared. Numerical simulations of the line quantities voltages and currents in the time domain are performed with an equivalent circuit of the entire LPS.
As a result it can be stated that by increasing the number of conventional down-conductors and external steel columns the threat for a human being can indeed be reduced, but not down to an acceptable limit. In case of internal steel columns used as natural down-conductors the threat can be reduced sufficiently, depending on the low-resistive connection of the steel columns to the lightning equipotential bonding or the earth termination system, resp. If a metal façade is used the threat for human beings touching is usually very low, if the façade is sufficiently interconnected and multiply connected to the lightning equipotential bonding or the earth termination system, resp.
Für die Herstellung von metallischen Bauteilen wird in der heutigen Zeit eine Vielzahl
von Verfahren auf dem Markt angeboten. Dabei stehen die additiven im Wettbewerb
zu den konventionellen Verfahren. Die erreichbaren Oberflächenqualitäten der additiven sind nicht mit denen spanender Verfahren vergleichbar. Für diesen Beitrag wurde analysiert, ob sich ein mittels Selektivem Laserschmelzen (SLM) additiv hergestellter Edelstahl hinsichtlich seiner Oberflächenqualität nach der Zerspanung von einem umgeformten konventionell hergestellten Edelstahl gleicher Sorte unterscheidet.
A new method for improved autoclave loading within the restrictive framework of helicopter manufacturing is proposed. It is derived from experimental and numerical studies of the curing process and aims at optimizing tooling positions in the autoclave for fast and homogeneous heat-up. The mold positioning is based on two sets of information. The thermal properties of the molds, which can be determined via semi-empirical thermal simulation. The second information is a previously determined distribution of heat transfer coefficients inside the autoclave. Finally, an experimental proof of concept is performed to show a cycle time reduction of up to 31% using the proposed methodology.
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere für eine Open-Book Online Prüfung geeignet, da eine faire Prüfungssituation gewährleistet werden kann.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is
required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.