3633 research outputs found
Sort by
Investigation of common source feedback in SiC power modules regarding performance and short circuit robustness
No full textSiC power modules are crucial in the automotive industry due to their high efficiency, but the change from Si to SiC brings new challenges regarding the short-circuit withstand time (SCWT). This paper investigates the influence of a common source feedback gate topology on short-circuit behavior. Implementing source feedback enhances the short-circuit withstand time but comes at the cost of increased switching losses. A more balanced trade-off between robustness and performance can be achieved by combining a welldefined common source feedback with an increasing gate-source voltage. This article investigates the concept using simulations, followed by characterization tests on a prototype commutation cell
Approach for autonomous control of intralogistics considering deterministic and probabilistic material demand information in flexible production systems
Get PDFIn today's dynamic production landscape, flexible and resilient production systems are essential to meet the constant changes in internal product and production requirements as well as external market and customer demands. To meet these challenges, various flexible and resilient production system approaches offer the necessary structural, process-related and technological flexibility and resilience. However, the intralogistics material provision within these complex production systems is challenging due to increasing degrees of freedom and uncertainties caused by emerging turbulence, which has even risen very sharply in recent years due to global instability. This makes it difficult to coordinate material demands and material provision in the production system precisely regarding location and quantity. This paper presents a comprehensive approach for determining the material requirements and autonomously executing the corresponding material provision processes in a complex production system, which considers both deterministic and probabilistic information about the material demand’s location, quantity, and time. Utilizing autonomous control in intralogistics decentralizes complexity management in flexible production systems by transferring decision-making and process execution tasks to the system elements. For the development and verification of the comprehensive approach, an experimental research study was pursued based on a flexible production system, including simulation and practical experiments. Defining the deterministic and probabilistic material demands is based on the Monte Carlo method for carrying out simulation experiments with parameter variation. The autonomously controlled, target size-optimized execution of material provision to fulfil the determined material demands is based on an agent-based modelling and control approach for manual and automated intralogistics transport resources. The study showed that improved logistics performance (throughput time and adherence to schedules) can be achieved in flexible production systems in the event of turbulences by considering deterministic and probabilistic material demand information together with autonomous control of material provision
Sensorless robust anomaly detection of roller chain systems based on motor driver data and deep weighted KNN
No full textCondition monitoring (CM) is crucial for ensuring equipment reliability. Yet, its practical implementation encounters several challenges. On the one hand, these include hurdles in big data collection/ storage, sensor selection/calibration/ installation. On the other hand, developing effective CM techniques, especially for anomaly detection throughout the degradation process, requires extracting and selecting appropriate Health Indicators (HI) and ensuring accurate anomaly detection. While bearings and gears have been intensively studied in the past, only little attention has been given to roller chain systems. Therefore, this study introduces an innovative approach leveraging a sensorless strategy and Deep Weighted K-Nearest Neighborhood (DWKNN) for detecting the abnormal status in roller chain systems. Firstly, by utilizing readily available motor driver data, the need for expensive sensor selection/installation and data management is eliminated, enhancing cost-effectiveness and applicability across diverse industrial applications. Secondly, leveraging position information from the motor, the raw data is segmented, transformed into the frequency domain, and fused to provide a comprehensive understanding of the system’s behavior, thus improving CM performance. Subsequently, DWKNN entails blind indicator extraction and anomaly detection. Blind indicator extraction utilizes the Deep Sparse Autoencoder (DSAE) method to dig hidden information in the acquired data and represent the degradation process. Meanwhile, anomaly detection is achieved through a weighted KNN, ensuring effectiveness and robustness. Through validation on multiple chains, the developed methodology demonstrates its effectiveness in addressing real-world CM challenges in industrial environments, offering a cost-effective and reliable solution compared to other methods
RESTRuler : towards automatically identifying violations of RESTful design rules in web APIs
No full textRESTful APIs based on HTTP are one of the most important ways to make data and functionality available to applications and software services. However, the quality of the API design strongly impacts API understandability and usability, and many rules have been specified for this. While we have evidence for the effectiveness of many design rules, it is still difficult for practitioners to identify rule violations in their design. We therefore present RESTRuler, a Java-based open-source tool that uses static analysis to detect design rule violations in OpenAPI descriptions. The current prototype supports 14 rules that go beyond simple syntactic checks and partly rely on natural language processing. The modular architecture also makes it easy to implement new rules. To evaluate RESTRuler, we conducted a benchmark with over 2,300 public OpenAPI descriptions and asked 7 API experts to construct 111 complicated rule violations. For robustness, RESTRuler successfully analyzed 99% of the used real-world OpenAPI definitions, with some failing due to excessive size. For performance efficiency, the tool performed well for the majority of files and could analyze 84% in less than 23 seconds with low CPU and RAM usage. Lastly, for effectiveness, RESTRuler achieved a precision of 91% (ranging from 60% to 100% per rule) and recall of 68% (ranging from 46% to 100%). Based on these variations between rule implementations, we identified several opportunities for improvements. While RESTRuler is still a research prototype, the evaluation suggests that the tool is quite robust to errors, resource-efficient for most APIs, and shows good precision and decent recall. Practitioners can use it to improve the quality of their API design
Sharing economy and its potential to achieve SDG 12 : the fashion sharing platform case
No full textThe fast fashion industry is one of the most polluting industries. For this reason, the industry should look into new circular business models in order to reduce its material footprint as well as the amount of waste produced. This article focuses on the question of how the sharing economy, as one possible circular business model, can contribute to achieving Sustainable Development Goal 12 (SDG 12) “Ensuring Sustainable Consumption and Production.” After a brief introduction to SDG 12, a short outline of the current development of the sharing economy in the fast fashion sector is given. To develop consumer buying behavior toward environmental sustainability, it is important to understand their motives. Utilitarian and hedonic motives are examined in order to determine to what extent they can positively influence buying intention and thus the acceptance of fashion sharing platforms. The database gathered through a master thesis is used to investigate the specific influence these motives have on buying intention. To increase the acceptance and thus the use of fashion sharing platforms, recommendations for action are developed in the final step of this chapter throughout the five steps of the buying cycle model. Circular business models will play a key role in the context of sustainable transformation in the future. Therefore, it is particularly important to derive concrete recommendations for action based on research in order to get the ecological footprint of environmentally harmful industries – such as the fast fashion industry – under control
Risks of decentralized finance and their potential negative effects on capital markets : the Terra-Luna case
Get PDFPurpose: This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse.
Design/methodology/approach: A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event.
Findings: While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events.
Practical implications: The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors.
Originality/value: This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space
Rapid detection of cleanliness on direct bonded copper substrate by using UV hyperspectral imaging
Get PDFIn the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R 2 cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes
Evaluation of an app-based mobile triage system for mass casualty incidents: within-subjects experimental study
Get PDFBackground: Digitalization in disaster medicine holds significant potential to accelerate rescue operations and ultimately save lives. Mass casualty incidents demand rapid and accurate information management to coordinate effective responses. Currently, first responders manually record triage results on patient cards, and brief information is communicated to the command post via radio communication. Although this process is widely used in practice, it involves several time-consuming and error-prone tasks. To address these issues, we designed, implemented, and evaluated an app-based mobile triage system. This system allows users to document responder details, triage categories, injury patterns, GPS locations, and other important information, which can then be transmitted automatically to the incident commanders.
Objective: This study aims to design and evaluate an app-based mobile system as a triage and coordination tool for emergency and disaster medicine, comparing its effectiveness with the conventional paper-based system.
Methods: A total of 38 emergency medicine personnel participated in a within-subject experimental study, completing 2 triage sessions with 30 patient cards each: one session using the app-based mobile system and the other using the paper-based tool. The accuracy of the triages and the time taken for each session were measured. Additionally, we implemented the User Experience Questionnaire along with other items to assess participants’ subjective ratings of the 2 triage tools.
Results: Our 2 (triage tool) × 2 (tool order) mixed multivariate analysis of variance revealed a significant main effect for the triage tool (P<.001). Post hoc analyses indicated that participants were significantly faster (P<.001) and more accurate (P=.005) in assigning patients to the correct triage category when using the app-based mobile system compared with the paper-based tool. Additionally, analyses showed significantly better subjective ratings for the app-based mobile system compared with the paper-based tool, in terms of both school grading (P<.001) and across all 6 scales of the User Experience Questionnaire (all P<.001). Of the 38 participants, 36 (95%) preferred the app-based mobile system. There was no significant main effect for tool order (P=.24) or session order (P=.06) in our model.
Conclusions: Our findings demonstrate that the app-based mobile system not only matches the performance of the conventional paper-based tool but may even surpass it in terms of efficiency and usability. This advancement could further enhance the potential of digitalization to optimize processes in disaster medicine, ultimately leading to the possibility of saving more lives
Die Entwicklung eines raman-spektroskopie-basierten Workflows zur Identifikation von Speicheldrüsentumorgewebe und die Diskussion der Hürden der Translation von spektroskopischen Verfahren
No full textDie prä-, intra- und postoperative Entitäts- und Dignitätsbestimmung von Speicheldrüsen-tumoren (ST) allein anhand von histomorphologischen Kriterien ist nicht in allen Fällen zuverlässig möglich. Die Spektren der Raman-Spektroskopie (RS) enthalten Informationen zur molekularen Zusammensetzung des untersuchten Gewebes. Ziel der Arbeit war die Etablierung eines RS-basierten Mess-Setups und eines Workflows zur Differenzierung von Speicheldrüsentumorgewebe und Speicheldrüsengewebe. Zudem werden die Hürden der Translation von RS in der Speicheldrüsendiagnostik diskutiert. Es wurden 10 mm dicke, native Kryo-Gewebeschnitte von Warthin-Tumoren (n=5) und pleomorphen Adenomen (n=4) mit der RS sowohl im Tumorgewebe als auch im gesunden Speicheldrüsengewebe untersucht und die Daten multivariat ausgewertet. Alle Messungen wurden in einem korrespondierenden HE-Schnitt histomorphologisch lokalisiert. Durch eine "Principal component"-Analyse (PCA) der RS-Daten und gekoppelte Diskriminanzanalyse war sowohl eine Unterscheidung von Tumor- und Nicht-Tumorgewebe als auch die Differenzierung der verschiedenen Tumorentitäten (basierend auf der histopathologischen Begutachtung) mit einer hohen Genauigkeit (93%) möglich. Zusammenfassend konnte gezeigt werden, dass anhand der RS-Messungen sicher zwischen ST-gewebe und gesundem Speicheldrüsengewebe unterschieden werden konnte. Ein wichtiges Ergebnis ist ebenfalls, dass die Gewebeaufarbeitung mit pathologischen Standard-Methoden zuverlässig möglich ist. Die hohe Anzahl an verschiedenen ST-Entitäten stellt eine biostatistische Herausforderung dar. Lösungsansätze sind mehrstufige statistische Modelle und die gleichzeitige Korrelation mit histomorphologischen Kriterien
Interdisziplinäre Anwendung des supervised machine learning für nachfragerbezogene Analysen im Marketing
Get PDFDer konzeptionelle Beitrag gibt einen Überblick über den Stand der Forschung zum Einsatz des Supervised Machine Learning im Kontext von nachfragerbezogenen Marketinganalysen. Der Artikel befasst sich dabei mit dem Funktionsprinzip und der Systematisierung dieses Gebiets und identifiziert Praxisanforderungen aus beiden Fachrichtungen. Diskutiert werden etwa marketingspezifische Anwendungsvoraussetzungen für das Supervised Machine Learning, technische Rahmenbedingungen sowie Methodiken zur Erzeugung von Modelltransparenz. Ebenso werden dahingehende Limitationen erörtert, beispielsweise mögliche Verzerrungen in Nachfragerdaten. Die Untersuchungsergebnisse leisten so einen Beitrag für ein differenziertes Verständnis des Anforderungsspektrums im analysierten interdisziplinären Anwendungsgebiet.This conceptual paper synthesizes the state-of-the-art knowledge at the intersection of consumer-related marketing analytics and Supervised Machine Learning. The article discusses the principles and systematization of this area and identifies practical requirements coming from both disciplines. It includes marketing-specific application criteria for Supervised Machine Learning, general technical prerequisites, and strategies for creating model transparency. Also, relevant limitations, such as potential biases in consumer data, are explored. Thus, the findings contribute to a differentiated understanding of key aspects to be considered in this interdisciplinary marketing field