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A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control applica-tion (MPC) with a flexible objective function and on-line tunable weights, which runs on a In-dustrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dy-namics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the re-ceiver was developed. By parallelising the model solver, the estimation of service-life con-sumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and archi-tecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies.
Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual’s eyes. It allows for an evaluation of the individual’s gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness.
Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.
Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery.
Suffizienz als Ausweg?!
(2024)
Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur
Sollen Forschungsleistungen gemessen und bewertet werden, stellen Publikationen eines der wichtigsten Kriterien dar. Die Bibliometrie liefert eine Vielzahl von (absoluten, relativen, normalisierten) Indikatoren, die Wissenschaftsmanager*innen für Leistungsträgeranalysen oder Forschungsevaluationen heranziehen können. Um valide Aussagen über wissenschaftliche Leistungen vornehmen zu können, müssen bibliometrische Kennzahlen mit anderen Leistungskriterien in Bezug gesetzt werden und es sind viele Faktoren sowie Umstände zu berücksichtigen. Auf diese wird in der vorliegenden Handreichung eingegangen. Sie sollen Wissenschaftsmanager*innen das Rüstzeug an die Hand geben, um bibliometrische Untersuchungen und deren Ergebnisse in dem jeweiligen Untersuchungsfall zielführend einordnen zu können.
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.
Hintergrund: Durch die Digitalisierung hat sich die Art des Publizierens verändert. Es ergeben sich neue Publikationswege und -formen. Eine Reflexion der traditionellen Ansichten über Publikationen und die Sicherung der Qualität ist erforderlich. Damit ergibt sich die Intension der Arbeit: Gegenwärtige Qualitätskriterien zu erfassen, zu analysieren und neuartige Möglichkeiten aufzuzeigen.
Methoden: Die theoretische Basis für den Stand der Forschung erfolgt durch eine repräsentative Literaturrecherche. Die Extraktion der Qualitätskriterien basiert auf einer selektiven Quellenauswahl. Ergänzend werden sechs Expert:innen anhand eines halbstrukturiertem Interviewleitfadens befragt. Die extrahierten Erkenntnisse aus der Analyse fließen in einen Wissensgraph und liefern Ansätze für eine Qualitätssicherung. Die Modellierung einer Anwendung wird auf Basis des Standards BPMN 2.0 umgesetzt.
Ergebnisse: Es wird ein Qualitätskriterien-Index erstellt, der zur Ausweisung von Qualitätsmerkmalen für Publikationen führt. Diese bilden die Wissensbasis für eine Web-Anwendung. Der darin enthaltene prototypische Workflow leitet die:den Autor:in durch Kriterien. Als Ergebnis werden die Publikationen mit Qualitätsmerkmalen angereichert. Diese Arbeit liefert das theoretische Konzept für die Anwendung. Es beinhaltet ein Mockup und eine erste prototypische Umsetzung. Dies bietet die Basis für eine bedarfsorientierte, reale Entwicklung.
Schlussfolgerungen: Eine reale, technische Umsetzung ist mit entsprechenden Ressourcen realisierbar. Vorab wird empfohlen, die ermittelten Qualitätskriterien mit einer
Nutzer:innengruppe zu reflektieren bspw. in Form einer qualitativen Erhebung. Diese liefert Hypothesen und fördert die Akzeptanz für die Ausweisung von Qualität. Das Ergebnis behandelt keine fachdisziplin-spezifischen Aspekte. Erfolgt dies, wird es zu einer Erweiterung der Anwendung führen. Ebenfalls sollte die Usability der Anwendung getestet werden, bevor die reale Umsetzung erfolgt. Das erstellte Konzept, ein Mockup und der Prototyp liefern die Ausgangslage für die Entwicklung.
Traineeprogramme
(2022)
A numerical study is carried out meticulously to scrutinize the impact of different shapes of chimneys like circular (outer dia, dc), convergent (outer dia, 0.5dc), divergent (outer dia, 1.5dc), sudden contraction (outer dia, 0.5dc), and sudden expansion (outer dia, 1.5dc) on the performance of an SCPP. Furthermore, the parametric impact with different chimney divergence angles (CDA, ϕ), and ground absorber slope angle (GSA, γ) on the SCPP performance is also scrutinized. Optimum divergence angle (ϕ=+0.75◦) enhances the power generation up to ~ 47% (76 kW) with a horizontal ground absorber surface. An increase or decrease in CDA lessens the power generation. With a sloped ground absorber angle γ=0.6◦, the gain in power generation is 60% (82 kW). The study of combination of ground sloped absorber (γ=0.6◦) and divergent chimney (ϕ=+0.75◦) shows enhancement of the power generation upto 80% (92 kW) more than the classical Manzaranes plant.
Nachhaltigkeit als transdisziplinäre Herausforderung für Nonprofit-Organisationen – Eine Einführung
(2023)
Despite the higher uptake of digital media also in older age groups, there are still challenges for people of high ages and with cognitive or motoric problems, which prevent them from learning and using devices like smartphones or tablets. Tangible user interfaces provide an opportunity to connect these people to social media, thereby increasing their digital participation in society and reducing loneliness. In this paper, we discuss the challenges of intergenerational communication and research the opportunities affiliated with tangible technologies to overcome technology barriers for adults of high ages. We present a hybrid system for social media interactions between young smartphone users and their old relatives, who do not use mobile technologies. The system consists of a tangible user interface designed specifically for old adults that connects to a standard mobile messenger service. TAMI was designed in a user-centered process to fulfill the needs of older adults and tested in a longer-term field study with pairs of young and old relatives.
User-centered and Participatory Design approaches have become widespread within HCI, also when designing digital media for older adults. These methodologies are seen as crucial to adapt technology design to older people's needs and increase their acceptance of digital technology. Nevertheless, it is often difficult to reach a diverse group of older people, for instance, people with limited technology skills, limited mobility, or cognitive issues. In addition, gerontologists criticized lately how older people's participation unfolds in technology design projects, e.g. that people are pushed into the role of users. Little is known about the motivations and needs of older adults regarding their participation in HCI research. To close this gap, we conducted an online survey (N=46) and semi-structured interviews (N=8) focusing on motivations, willingness and experiences regarding participation. Results show that people are generally willing to participate due to interest in the topic, learning new things and social bonding. However, despite the conceptions within HCI that long-term engagement is crucial within participatory design, older people in the study preferred less commitment and more open formats.