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Mit zunehmender Dynamik im Forschungsumfeld – Digitalisierung der Produktentwicklung – steigen neben der Komplexität auch die technischen Anforderungen an die künftigen Entscheidungsprozesse. Die Einführung von neuen IT-Systemen zur Automation von Entscheidungen haben Anpassungen in den derzeitigen Geschäftsprozessen der Unternehmen zur Folge. Für eine erfolgreiche Implementierung neuer IT-Informationstools gilt es im Voraus mögliche Auswirkungen auf die bisherigen Anwendersysteme genauer zu untersuchen. Neue Technologien, KI-Informationssysteme oder auch neues Wissen entstehen in der Wissenschaft oft durch Interpretation und Synthese von bestehendem Wissen. Aus diesem Grund nimmt die Qualität von Literaturanalysen eine immer größere Relevanz in der Ingenieur- und Informatikwissenschaft ein. Neben der Anzahl an Publikationen wächst auch der Aufwand für die strukturierte Literaturrecherche (SLA). Die Autoren stellen in diesem Paper den Rechercheprozess und die Ergebnisse einer SLA vor. Mit dieser Arbeit soll der derzeitige Forschungsstand zur Entscheidungsunterstützung in der Produktentwicklung von Klein- und mittelständischen Unternehmen sowie Großunternehmen in der
Automobilbranche ermittelt und nach Analyse sowie Bewertung mögliche Forschungslücken zu automatisierten Entscheidungsunterstützungssystemen (aEUS) aufgezeigt werden.
Impact of a large distribution network on radiation characteristics of planar spiral antenna arrays
(2023)
Designing antenna arrays with a central feed point has gained ground in the antenna technique. This approach, which is usually applied because of manufacturing costs, is difficult to achieve and leads to a large feeding network. The impact of which is numerically investigated in the present work. Upon comparing three different antennas, it is shown that the enlargement of the feed strongly affects the antenna's overall dimensions and the antenna's radiation characteristics. The antenna with the plug-in solution is not only small in size but also performs better compared to antennas with a central feed point. Considering the high effort in designing the feed network with a central point and the influence of the resulting enlarged network on the dimensions and radiation characteristics of the antenna, the cost saving in production can be put into perspective.
Advancing mental health diagnostics: AI-based method for depression detection in patient interviews
(2023)
In this paper, we present a novel artificial intelligence (AI) application for depression detection, using advanced transformer networks to analyse clinical interviews. By incorporating simulated data to enhance traditional datasets, we overcome limitations in data protection and privacy, consequently improving the model’s performance. Our methodology employs BERT-based models, GPT-3.5, and ChatGPT-4, demonstrating state-of-the-art results in detecting depression from linguistic patterns and contextual information that significantly outperform previous approaches. Utilising the DAIC-WOZ and Extended-DAIC datasets, our study showcases the potential of the proposed application in revolutionising mental health care through early depression detection and intervention. Empirical results from various experiments highlight the efficacy of our approach and its suitability for real-world implementation. Furthermore, we acknowledge the ethical, legal, and social implications of AI in mental health diagnostics. Ultimately, our study underscores the transformative potential of AI in mental health diagnostics, paving the way for innovative solutions that can facilitate early intervention and improve patient outcomes.
Analog integrated circuit sizing still relies heavily on human expert knowledge as previous automation approaches have not found wide-spread acceptance in industry. One strand, the optimization-based automation, is often discarded due to inflated constraining setups, infeasible results or excessive run times. To address these deficits, this work proposes a alternative optimization flow featuring a designer’s intuition for feasible design spaces through integration of expert knowledge based on the gm/ID-method. Moreover, the extensive run times of simulation-based optimization flows are overcome by incorporating computationally efficient machine learning methods. Neural network surrogate models predicting eleven performance parameters increase the evaluation speed by 3 400× on average compared to a simulator. Additionally, they enable the use of optimization algorithms dependent on automatic differentiation, that would otherwise be unavailable in this field. First, an up to 4× more efficient way for sampling training data based on the aforementioned space is detailed. After presenting the architecture and training effort regarding the surrogate models, they are employed as part of the objective function for sizing three operational amplifiers with three different optimization algorithms. Additionally, the benefits of using the gm/ID-method become evident when considering technology migration, as previously found solutions may be reused for other technologies.
Most Question-answering (QA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose TFCSG, an unsupervised similar question retrieval approach that leverages pre-trained language models and multi-task learning. Firstly, topic keywords in question sentences are extracted sequentially based on a latent topic-filtering algorithm to construct unsupervised training corpus data. Then, the multi-task learning method is used to build the question retrieval model. There are three tasks designed. The first is a short sentence contrastive learning task. The second is the question sentence and its corresponding topic sequence similarity judgment task. The third is using question sentences to generate their corresponding topic sequence task. The three tasks are used to train the language model in parallel. Finally, similar questions are obtained by calculating the cosine similarity between sentence vectors. The comparison experiment on public question datasets that TFCSG outperforms the comparative unsupervised baseline method. And there is no need for manual marking, which greatly saves human resources.
The efficient production and utilization of green hydrogen is vital to succeed in the global strive for a sustainable future. To provide the necessary amount of green hydrogen a high number of electrolyzers will be connected as decentralized power consumers to the grid. A large amount of decentralized renewable power sources will provide the energy. In such a system a control method is necessary to dispatch the available power most efficiently. In particular, the shutdown of renewable energy sources due to temporary overproduction must be avoided. This paper presents a decentralized tertiary control algorithm that provides a new decentralized control approach, thus creating a flexible, robust and easily scalable system. The operation of each grid participant within this grid connected microgrid is optimized for maximum financial profit, while minimizing the exchange of power with the mains grid and reducing the shutdown of renewable power sources.
The increase in distributed energy generation, such as photovoltaic systems (PV) or combined heat and power plants (CHP), poses new challenges to almost every distribution network operator (DNO). In the low-voltage (LV) grids, where installed PV capacity approaches the magnitude of household load, reverse power flow occurs at the secondary substa-tions. High PV penetration leads to voltage rise, flicker and loading problems. These problems have been addressed by the application of various techniques amongst which is the deployment of step voltage regulators (SVR). SVR can solve the voltage problem, but do not prevent or reduce reverse power flows. Therefore, the application of SVR in low voltage grids can result in significant power losses upstream. In this paper we present part of a research project investi-gating the application of remote-controlled cable cabinets (CC) with metering units in a low-voltage network as a possible alternative for SVR. A new generation of custom-made remote-control cable cabinets has been deployed and dynamic network reconfigurations (NR) have been realized with the following objectives: (i) reduction of reverse power flow through the secondary substation to the upstream network and therefore a reduction of upstream losses, (ii) reduction of the voltage rise caused by distributed energy resources and (iii) load balancing in the low-voltage grid. Secondary objec-tives are to improve the DNO's insight into the state of the network and to provide further information on future smart grid integration.
Werkzeugmaschinen sind im Bereich des Maschinen- und Anlagenbau die größte Branche, mit denen auch in Unternehmen anderer Bereiche (z. B. Automobilbau, Aerospace) wesentliche Teile der Bruttowertschöpfung stattfinden. (Destatis, 2022) Das dynamische Verhalten von Werkzeugmaschinen beeinflusst in entscheidendem Maße die Produktivität der Produktionsanlage und die Qualität der darauf erzeugten Werkstücke. Sowohl fremderregte Schwingungen (z. B. Unwucht, Pulsation, periodisch schwankende Prozesskräfte) als auch selbsterregte Schwingungen (z. B. Rattern) führen zu schlechter Qualität der gefertigten Bauteile. Das dynamische Verhalten vonWerkzeugmaschinen wird durch die Masse, Dämpfung und Steifigkeit der einzelnen Komponenten (z. B. Maschinenbett, Ständer, Schlitten) als auch der im Kraftfluss liegenden Fügestellen (z. B. Führungen, Antriebe) beeinflusst. In diesem Beitrag werden die Auswirkungen von konstruktiven Modifikationen der Dämpfung in Gestellbauteilen bezüglich des dynamischen Verhaltens an der Zerspanstelle näher beleuchtet.
As fuel prices climb and the global automotive sector migrates to more sustainable vehicle technologies, the future of South Africa’s minibus taxis is in flux. The authors’ previous research has found that battery electric technology struggles to meet all the mobility requirements of minibus taxis. They investigate the technical feasibility of powering taxis with hydrogen fuel cells instead. The following results are projected using a custom-built simulator, and tracking data of taxis based in Stellenbosch, South Africa. Each taxi requires around 12 kg of hydrogen gas per day to travel an average distance of 360 km. 465 kWh of electricity, or 860 m2 of solar panels, would electrolyse the required green hydrogen. An economic analysis was conducted on the capital and operational expenses of a system of ten hydrogen taxis and an electrolysis plant. Such a pilot project requires a minimum investment of € 3.8 million (R 75 million), for a 20 year period. Although such a small scale roll-out is technically feasible and would meet taxis’ performance requirements, the investment cost is too high, making it financially unfeasible. They conclude that a large scale solution would need to be investigated to improve financial feasibility; however, South Africa’s limited electrical generation capacity poses a threat to its technical feasibility. The simulator is uploaded at: https://gitlab.com/eputs/ev-fleet-sim-fcv-model.
Modern wide bandgap power devices promise higher power conversion performance if the device can be operated reliably. As switching speed increases, the effects of parasitic ringing become more prominent, causing potentially damaging overvoltages during device turn-off. Estimating the expected additional voltage caused by such ringing enables more reliable designs. In this paper, we present an analytical expression to calculate the expected overvoltage caused by parasitic ringing based on parasitic element values and operating point parameters. Simulations and measurements confirm that the expression can be used to find the smallest rise time of the switches’ drain-source voltage for minimum overvoltage. The given expression also allows the prediction of the trade off overvoltage amplitude in case of faster required rise times.