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This paper presents initial findings from aeroelastic studies conducted on a wing-propeller model, aimed at evaluating the impact of aerodynamic interactions on wing flutter mechanisms and overall aeroelastic performance. Utilizing a frequency domain method, the flutter onset within a specified flight speed range is assessed. Mid-fidelity tools with a time domain approach are then used to account for the complex aerodynamic interaction between the propeller and the wing. Specifically, open-source software DUST and MBDyn are leveraged for this purpose. This investigation covers both windmilling and thrusting conditions of the wing-propeller model. During the trim process, adjustments to the collective pitch of the blades are made to ensure consistency across operational points. Time histories are then analyzed to pinpoint flutter onset, and corresponding frequencies and damping ratios are meticulously identified. The results reveal a marginal destabilizing effect of aerodynamic interaction on flutter speed, approximately 5%. Notably, the thrusting condition demonstrates a greater destabilizing influence compared to windmilling. These comprehensive findings enhance the understanding of the aerodynamic behavior of such systems and offer valuable insights for early design predictions and the development of streamlined models for future endeavors.
This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy.
The Inverted Rotary Pendulum: Facilitating Practical Teaching in Advanced Control Engineering
(2024)
This paper outlines a practical approach to teach control engineering principles, with an inverted rotary pendulum, serving as an illustrative example. It shows how the pendulum is embedded in an advanced course of control engineering. This approach is incorporated into a flipped-classroom concept, as well as classical teaching concepts, offering students practical experience in control engineering. In addition, the design of the pendulum is shown, using a Raspberry Pi as the target platform for Matlab Simulink. This pendulum can be used in the classroom to evaluate the controller design mentioned above. It is analysed if the use of the pendulum generates a deeper understanding of the learning contents.
Sexism in online media comments is a pervasive challenge that often manifests subtly, complicating moderation efforts as interpretations of what constitutes sexism can vary among individuals. We study monolingual and multilingual open-source text embeddings to reliably detect sexism and misogyny in Germanlanguage online comments from an Austrian newspaper. We observed classifiers trained on text embeddings to mimic closely the individual judgements of human annotators. Our method showed robust performance in the GermEval 2024 GerMS-Detect Subtask 1 challenge, achieving an average macro F1 score of 0.597 (4th place, as reported on Codabench). It also accurately predicted the distribution of human annotations in GerMS-Detect Subtask 2, with an average Jensen-Shannon distance of 0.301 (2nd place). The computational efficiency of our approach suggests potential for scalable applications across various languages and linguistic contexts.
The use of industrial robots allows the precise manipulation of all components necessary for setting up a large-scale particle image velocimetry (PIV) system. The known internal calibration matrix of the cameras in combination with the actual pose of the industrial robots and the calculated transform from the fiducial markers to camera coordinates allow the precise positioning of the individual PIV components according to the measurement demands. In addition, the complete calibration procedure for generating the external camera matrix and the mapping functions for e.g. dewarping the stereo images can be automatically determined without further user interaction and thus the degree of automation can be extended to nearly 100%. This increased degree of automation expands the applications range of PIV systems, in particular for measurement tasks with severe time constraints.
In recent years, more and more digital startups have been founded and many of them work remotely by applying enterprise collaboration systems (ECS). The study investigates the functional affordances of ECS, particularly Slack, and examines its potential as a virtual office environment for cultural development in digital startups. Through a case study and based on affordance theoretical considerations, the paper explores how ECS facilitates remote collaboration, communication, and socialization within digital startups. The findings comprise material properties of ECS (synchrony and asynchrony communication), functional affordances (virtual office and culture development affordances) as well as its realization (through communication practices, openness, and inter-company accessibility) and are conceptualized as a model for ECS affordances in digital startups.
Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt.
Industrial field devices exchange information through standardized communication interfaces and data models,
encompassing process data, communication properties, and vendor details. Despite enhancing interoperability within a specific
protocol, integrating these devices with diverse systems poses challenges due to data model fragmentation and custom
interfaces. The absence of a universal semantic model for categorizing field device process data independently of standards
necessitates engineers to repetitively devise custom exchange data models for different sensors and actuators, relying on
standards like OPC-UA. In response, this work proposes an ontology-based architecture to tackle information data model
fragmentation, aiming for seamless data interoperability across a universal interface. By focusing on two open-access field
device standards, IO-Link and CANOpen, we compare their information data models, identify existing limitations, and put
forth a semantic information model. The objective is to offer an interoperable interface for Industry 4.0 applications,
showcasing the potential of an ontology-based approach in streamlining data exchange and reducing heterogeneity among
field devices.
Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.
The complex questions of today for a world of tomorrow are characterized by their global impact. Solutions must therefore not only be sustainable in the sense of the three pillars of sustainability (economic, environmental, and social) but must also function globally. This goes hand in hand with the need for intercultural acceptance of developed services and products. To achieve this, engineers, as the problem solvers of the future, must be able to work in intercultural teams on appropriate solutions, and be sensitive to intercultural perspectives. To equip the engineers of the future with the so-called future skills, teaching concepts are needed in which students can acquire these methods and competencies in application-oriented formats. The presented course "Applying Design Thinking - Sustainability, Innovation and Interculturality" was developed to teach future skills from the competency areas Digital Key Competencies, Classical Competencies and Transformative Competencies. The CDIO Standard 3.0, in particular the standards 5, 6, 7 and 8, was used as a guideline. The course aims to prepare engineering students from different disciplines and cultures for their future work in an international environment by combining a digital teaching format with an interdisciplinary, transdisciplinary and intercultural setting for solving sustainability challenges. The innovative moment lies in the digital application of design thinking and the inclusion of intercultural as well as trans- and interdisciplinary perspectives in innovation development processes. In this paper, the concept of the course will be presented in detail and the particularities of a digital implementation of design thinking will be addressed. Subsequently, the potentials and challenges will be reflected and practical advice for integrating design thinking in engineering education will be given.
Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core.
In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars.
In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany.
The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.
Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.
Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.
Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.
Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.