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The simultaneous assessment of the status of the glottis opening and the position of the larynx can be beneficial for the diagnosis of disorders of voice production and swallowing. The method presented here makes use of a time-multiplex algorithm for the measurement of space-resolved transfer impedances through the larynx. The fast sequence of measurements allows a quasi simultaneous assessment of both larynx position and EGG signal in 32 channels. First results indicate a high potential of the method for use as a non-invasive tool in the diagnosis of voice dysfunction, ventricular fold phonation and swallowing disorders.
This paper introduces three novel approaches to size geothermal energy piles in a MILP, offering fresh perspectives and potential solutions. The research overlooks MILP models that incorporate the sizing of a geothermal borefield. Therefore, this paper presents a new model utilizing a g-function model to regulate the power limits. Geothermal energy is an essential renewable source, particularly for heating and cooling. Complex energy systems, with their diverse sources of heating and cooling and intricate interactions, are crucial for a climate-neutral energy system. This work significantly contributes to the integration of geothermal energy as a vital energy source into the modelling of such complex systems. Borehole heat exchangers help generate heat in low-temperature energy systems. However, optimizing these exchangers using mixed-integer-linear programming (MILP), which only allows for linear equations, is complex. The current research only uses R-C, reservoir, or g-function models for pre-sized borefields. As a result, borehole heat exchangers are often represented by linear factors such as 50 W/m for extraction or injection limits. A breakthrough in the accuracy of borehole heat exchanger sizing has been achieved with the development of a new model, which has been rigorously compared to two simpler models. The geothermal system was configured for three energy systems with varying ground and bore field parameters. The results were then compared with existing geothermal system tools. The new model provides more accurate depth sizing with an error of less than 5 % compared to simpler models with an error higher than 50 %, although it requires more calculation time. The new model can lead to more accurate borefield sizing in MILP applications to optimize energy systems. This new model is especially beneficial for large-scale projects that are highly dependent on borefield size.
Obstacle encounter probability dependent local path planner for UAV operation in urban environments
(2024)
Unmanned aerial vehicles (UAVs) are well-suited for various short-distance missions in urban environments. However, the path planner of such UAV is constantly challenged with the choice between avoiding obstacles horizontally or vertically. If the path planner relies on sensor information only, i.e. the path planner is a local planner, usually predefined manoeuvres or preferences are used to find a possible way. However, this method is stiff and inflexible. This work proposes a probabilistic decision-maker to set the control parameters of a classic local path planner during a flight mission. The decision-maker defines whether performing horizontal or vertical avoidance is preferable based on the probability of encountering a given number of obstacles. Here, the decision-maker considers predictions of possible future avoidance manoeuvres. It also defines an ideal flight altitude based on the probability of encountering obstacles. This work analyses the building height of all European capital cities and the probability of encountering obstacles at different altitudes to feed the decision-maker. We tested the feasibility of the proposed decision-maker with the 3DVFH*, a commonly used local path planner, in multiple simulations. The proposed probabilistic decision-maker allows the local path planner to reach the goal point significantly more often than the standard version of the 3DVFH*.
Interoperability at the field level is dependent on the specific technology implementation and its semantics. Integrating field devices with different communication protocols is not a simple process, as there is no direct semantic mapping between them. In recent years, standards such as the OPC UA Field eXchange and the Asset Administration Shell have proposed neutral data models to reduce the heterogeneity of field device semantics. However, to integrate different field device standards, a formal mapping between the semantic terms of these standards and the neutral data models must still be defined. A research topic that remains open is how different standards can be automatically mapped to a neutral interface independent of their implementation. In this paper we present a novel approach that generalizes the semantics of field devices at the communication level, enabling the use of inference rules that are independent of specific standards. Our method, based on the Industry 4.0 Field Device ontology, identifies the generic type of any field device and provides an interoperable capability description adaptable to various protocols. The framework includes a semantic broker that automates the creation of device instances, executes inference requests, and generates a generic semantic model for field devices. The objective of this work is to simplify the integration of field device semantics, with a generalization of the application layer and facilitate their mapping to other higher-level data models.
Comparative performance analysis of active learning strategies for the entity recognition task
(2024)
Supervised learning requires a lot of annotated data, which makes the annotation process time-consuming and expensive. Active Learning (AL) offers a promising solution by reducing the number of labeled data needed while maintaining model performance. This work focuses on the application of supervised learning and AL for (named) entity recognition, which is a subdiscipline of Natural Language Processing (NLP). Despite the potential of AL in this area, there is still a limited understanding of the performance of different approaches. We address this gap by conducting a comparative performance analysis with diverse, carefully selected corpora and AL strategies. Thereby, we establish a standardized evaluation setting to ensure reproducibility and consistency across experiments. With our analysis, we discover scenarios where AL provides performance improvements and others where its benefits are limited. In particular, we find that strategies including historical information from the learn ing process and maximizing entity information yield the most significant improvements. Our findings can guide researchers and practitioners in optimizing their annotation efforts.
Ende 2022 erreichte das Amt für Denkmalpflege im Rheinland eine Anfrage zur Beratung hinsichtlich historischer Farbbefunde in der Trauerhalle des jüdischen Friedhofs an der Lütticher Straße 39 in Aachen. So waren durch Abplatzungen des Anstrichs an den Wänden blaue Farbreste zum Vorschein gekommen, die im Rahmen einer historischen Bauforschung zum jüdischen Friedhof durch die FH Aachen einer farbigen Gestaltung des gesamten Raumes durch den Maler Max Lazarus zugeordnet werden konnten. Sie führten zu der Frage, ob an der Decke ebenfalls noch Farbreste erhalten sind. Eine Farbuntersuchung des von Max Lazarus ausgemalten Raumes ergab schließlich erstaunliche Ergebnisse.
