Automatisierung von Kabelverteilern (Teil 4) : Simulation der Ergebnisse

Eine Flexibilisierung der elektrischen Netze, insbesondere der Niederspannungsnetze, beschäftigt immer mehr Verteilnetzbetreiber rund um die Welt. Ein wesentlicher Grund hierfür ist die schnelle Integration von dezentralen elektrischen Erzeugungsanlagen, um die Energiewende zu ermöglichen. Dieser Teil der Publikationsreihe zu Forschungen am Reutlinger Energiezentrum REZ hinsichtlich der Automatisierung von Kabelverteilern und der Trennstellenverlagerung beschäftigt sich mit den Ergebnissen der verschiedenen Simulationen

Global versus regional carbon taxation: exploring a natural experiment

The reduction of global greenhouse gas emissions has priority due to climate change. Scientists across disciplines promote carbon taxation or carbon pricing as an instrument to mitigate the negative externality of fossil energies. We exhibit two insufficiencies of today’s regulatory policy by utilizing a novel natural experiment. First, carbon taxation is only effective if it is implemented on a global scale because fossil energy markets and emissions are cross-border and global. Second, carbon taxation is based on an extrinsic mechanism and does not alter intrinsic behaviour sustainably. Our applied theory and modelling approach is corroborating these findings. The present regulatory approach is doomed to fail due to overambitious European countries and unambitious (partly realistic) rest of the world. Our interdisciplinary analysis unravels a new agenda to achieve the essential aim. What is needed is a global climate club, as proposed by Nobel Laureate William Nordhaus, with a least but global greenhouse gas reduction

Anomaly detection in hobbing tool images: using an unsupervised deep learning approach in manufacturing industry

This study explores the application of the PatchCore algorithm for anomaly classification in hobbing tools, an area of keen interest in industrial artificial intelligence application. Despite utilizing limited training images, the algorithm demonstrates capability in recognizing a variety of anomalies, promising to reduce the time-intensive labeling process traditionally undertaken by domain experts. The algorithm demonstrated an accuracy of 92%, precision of 84%, recall of 100%, and a balanced F1 score of 91%, showcasing its proficiency in identifying anomalies. However, the investigation also highlights that while the algorithm effectively identifies anomalies, it doesn't primarily recognize domain-specific wear issues. Thus, the presented approach is used only for pre-classification, with domain experts subsequently segmenting the images indicating significant wear. The intention is to employ a supervised learning procedure to identify actual wear. This premise will be further investigated in future research studies

re:search : das Forschungsmagazin, Ausgabe 6, September 2024

Eine Katze als Titel für ein Magazin zu Künstlicher Intelligenz? Und dann ausgerechnet mit diesen unnatürlichen Glubschaugen! Die Reaktionen unserer Hochschulmitglieder auf den Titel der neuesten Ausgabe von re:search - das Forschungsmagazin waren selten so kontrovers. Lassen Sie uns also ein paar Worte dazu sagen: Die Katze - mit KI erstellt und hier als Sinnbild für KI - wirkt klein und unschuldig. Doch in ihren Augen spiegelt sich auch eine Bedrohung, die noch nicht ganz greifbar ist. Wozu wird sie heranwachsen - zu einer Technologie, die uns hilft und unterstützt, oder zu einer Übermacht, die uns Menschen obsolet macht? Wir können das beeinflussen. Der erste Schritt dazu: Verständnis. Der zweite: Kritisches Denken. Also: Forschung. Die Professoren Notholt und Thomas zeigen uns zum Beispiel, wie wir mit Hilfe von KI die Energiewende schaffen können (Seite 20), ein Team des Hermann-Hollerith-Zentrums forscht mit dem Deutschen Zentrum für Luft- und Raumfahrt an zuverlässiger Software für sichere Raumfahrtmissionen (Seite 48) und Professor Himpel nutzt Methoden der Künstlichen Intelligenz, um die allgemeingültigste Sprache der Menschen zu verstehen: Die Musik (Seite 24). In diesem Heft zeigen wir Ihnen die vielfältigen Blickwinkel auf das Thema KI an der Hochschule Reutlingen

Heart rate estimation based on in-bed accelerometer sensor measurement

Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders

Oral health phenotype of postmenopausal women using AI

Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic

Development of an IALF overarching learning module for circular economy

The increasing importance of sustainable business models due to the scarcity of resources and resulting political regulations leads many companies and research institutions towards the concept of Circular Economy. Subsequently, the learning factory community is facing the challenge of expanding existing concepts to include Circular Economy in their learning factories. Therefore, a learning factory- and competency-based module for Circular Economy is developed by eight members of the International Association of Learning Factories to define a common ground. The module covers the basics of Circular Economy in the production environment and make it tangible through concrete use cases. To achieve these goals, extensive research on existing concepts has been conducted by IALF experts to analyze learning objectives and requirements in detail and derive required competencies. Based on that, the learning module is developed

Governance of interconnected value creation systems: rules of engagement for effective cooperation among companies in a competitive context

In the context of the digital transformation of the 21st century, this paper explores the evolution from traditional, hierarchically organized value chains to dynamic, networked multi-actor constellations. The focus is on identifying and analyzing suitable governance mechanisms that are crucial for the success of both individual partners and the value creation system as a whole. Through a combination of comprehensive literature research and qualitative interviews with various leading experts from business, science, and politics the relationships between value creation, networking, and governance were analyzed as part of this study. This emphasizes the importance of transparency, motivation, and trust in these networked systems and discusses the use of technology to ensure effective governance. Technologies can be used to create data security through the traceability and immutability of data. The literature shows that there are no universal solutions, but that individual, situation-specific approaches are required that combine legal expertise and the use of technology. The results of the study indicate that customers are increasingly becoming integral parts of networked value creation systems, with data protection, laws and guidelines, exit plans, exploitation rights, and the challenges of international contract negotiations playing key roles. The work provides important insights into the need for continuous adaptability to constantly changing framework conditions

Echtzeitnahe Dokumentation von Treibhausgasemissionen auf Basis der Verwaltungsschale

Dieser Beitrag stellt eine Methodik zur echtzeitnahen Erfassung und Dokumentation von Treibhausgasemissionen in komplexen Wertschöpfungsnetzwerken vor. Die Methodik orientiert sich konzeptionell an relevanten Normen und Ansätzen im Kontext der Treibhausgasbilanzierung. Sie gestattet eine systematische produktionssynchrone, (teil-)autonome Erfassung der Treibhausgasemissionen von Produktions- und Logistikprozessen auf Basis produktbezogener Verwaltungsschalen.This article presents a methodology for the near-real-time recording and documentation of greenhouse gas emissions in complex value creation networks. The methodology is conceptually based on relevant standards and approaches in the context of greenhouse gas accounting. It allows a systematic, production-synchronous, (partially) autonomous recording of greenhouse gas emissions from production and logistics processes based on product-related administrative shells

Robot-based 6D bioprinting for soft tissue biomedical applications

Within this interdisciplinary study, we demonstrate the applicability of a 6D printer for soft tissue engineering models. For this purpose, a special plant was constructed, combining the technical requirements for 6D printing with the biological necessities, especially for soft tissue. Therefore, a commercial 6D robot arm was combined with a sterilizable housing (including a high-efficiency particulate air (HEPA) filter and ultraviolet radiation (UVC) lamps) and a custom-made printhead and printbed. Both components allow cooling and heating, which is desirable for working with viable cells. In addition, a spraying unit was installed that allows the distribution of fine droplets of a liquid. Advanced geometries on uneven or angled surfaces can be created with the use of all six axes. Based on often used bioinks in the field of soft tissue engineering (gellan gum, collagen, and gelatin methacryloyl) with very different material properties, we could demonstrate the flexibility of the printing system. Furthermore, cell-containing constructs using primary human adipose-derived stem cells (ASCs) could be produced in an automated manner. In addition to cell survival, the ability to differentiate along the adipogenic lineage could also be demonstrated as a representative of soft tissue engineering