Digital twin as a service : Ressourcenmanagement mit Energiedaten aus cyber-physischen Systemen

Die Energiewende führt zu einer Paradigmenänderung. Der Zeitpunkt der Energieabnahme wird sich zunehmend an dem der Energieerzeugung orientierten. Die Steuerung des Energiebedarfs kann durch energieorientierte Produktionsplanung gesteigert werden. Dies erfordert eine Vorhersage des Energiebedarfs. Hierfür wird ein System entwickelt, das eine Modellierung mittels maschinellen Lernens nutzt. Die Datenbasis wird durch eine Vorgehensweise zur Abstrahierung von Fertigungsmaschinen erzeugt. Das System besteht aus gruppierten Microservices, es berücksichtigt die unterschiedlichen Anforderungen der Modelle an die Infrastruktur. Die Modelle sind in digitalen Zwillingen integriert, die als Dienst genutzt werden. Hierdurch ist eine effiziente Adaption von ˜Äderungen an Fertigungsmaschine oder Modell-Methodik möglich. Eine exemplarische Anwendung der Abstraktionsmethode und der Modellierung mittels neuronalen Netzes demonstrieren die Umsetzbarkeit.The energy transition in Germany leads to a shift of paradigm. Time of energy consumption will increasingly be oriented to that of energy production. Control of the energy demand can be increased by energy-oriented production planning. This requires a prediction of the energy demand. For this purpose, a system is developed which uses modelling by machine learning. A procedure for abstraction of production machines generates the data basis. The models are integrated in digital twins as services, following the microservice architecture. An exemplary application of the abstraction method and modelling by means of neural networks demonstrate the feasibility

Linguistic Driven Feature Selection for Text Classification as Stop Word Replacement

The common corpus optimization method “stop words removal” is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance. Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology. We propose the Weighted Unimportant Part-of-Speech Model (WUP-Model) for token removal in the pre-processing of text corpora. The weighted relevance of a token is determined using classification relevance and classification performance impact. The WUP-Model uses linguistic information (part of speech) as grouping criteria. Analogous to stop word removal, we provide a set of irrelevant part of speech (WUP-Instance) for word removal. In a proof-of-concept we created WUP-Instances for several classification algorithms. The evaluation showed significant advantages compared to classic stop word removal. The tree-based classifier increased runtime by 65% and 25% in performance. The performance of the other classifiers decreased between 0.2% and 2.4%, their runtime improved between −4.4% and −24.7%. These results prove beneficial effects of the proposed WUP-Model

Comparable Machine Learning Efficiency : Balanced Metrics for Natural Language Processing

As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner

Industry Use Cases on Blockchain Technology

Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and trace- able evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross- company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes

Security Audit of a Blockchain-Based Industrial Application Platform

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case

Kommunale Gründach-Strategien Inventarisierung, Potentialanalyse, Praxisbeispiele

Vor dem Hintergrund der aktuellen Klimaentwicklung stehen begrünte Dächer weiter im Fokus der Aufmerksamkeit. Egal, ob es sich dabei um Regenwasserrückhalt, sommerliche Hitzeabwehr oder den Schutz der Dachabdichtung vor Temperaturextremen und Hagelschäden handelt – mit ihren vielfältigen Vorteilen liefert die Umwelttechnik Dachbegrünung ein perfektes Werkzeug, um diese Herausforderungen zu meistern. Viele Städte räumen begrünten Dächern deshalb im Rahmen ihrer Anpassungspläne an den Klimawandel eine besonders hohe Priorität ein. Auf nationaler Ebene findet dieser Ansatz Unterstützung durch das Grünbuch „Stadtgrün“ des Bundesministeriums für Umwelt, Naturschutz, Bau und Reaktorsicherheit (BMUB) und die branchenübergreifende Charta „Zukunft Stadt und Grün“, die vom Bundesverband Garten-, Landschafts- und Sportplatzbau e. V. (BGL) und der Stiftung DIE GRÜNE STADT ins Leben gerufen wurde. Mit der vorliegenden Broschüre „Kommunale Gründach-Strategien – Inventarisierung, Potenzialanalyse, Praxisbeispiele“ möchten wir Ihnen zeigen, wie vielfältig die Einsatz- und Fördermöglichkeiten begrünter Dächer auf kommunaler Ebene sein können. Neben aktuellen Gründach-Initiativen aus Berlin, Hamburg, Hannover, Ludwigsburg und Stuttgart stellen wir Ihnen dabei auch das spannende Forschungsprojekt „Inventarisierung und Potenzialanalyse von Dachbegrünung“ vor. Unter der Projektleitung des Deutschen Dachgärtner Verbandes hat das Deutsche Zentrum für Luft- und Raumfahrt (DLR) ein fernerkundliches Verfahren entwickelt, das auf Grundlage von Luftbildaufnahmen und Gebäudebasisdaten bereits vorhandene Dachbegrünungen identifizieren kann und mögliche Potenzialflächen in der Dachlandschaft ausweist. Jedes Jahr werden in Deutschland mehrere Millionen Quadratmeter Dachbegrünung neu installiert. Gleichzeitig wird aber nach wie vor der größte Teil der Dachflächen ohne Begrünung ausgeführt und leistet dadurch keinen Beitrag zur Verbesserung der Umweltund Lebensqualität in den Städten. Eine Flächenverschwendung, die wir uns in Zeiten des Klimawandels und der zunehmenden Urbanisierung nicht leisten können

Wireless closed-loop optogenetics across the entire dorsoventral spinal cord in mice

Optoelectronic systems can exert precise control over targeted neurons and pathways throughout the brain in untethered animals, but similar technologies for the spinal cord are not well established. In the present study, we describe a system for ultrafast, wireless, closed-loop manipulation of targeted neurons and pathways across the entire dorsoventral spinal cord in untethered mice. We developed a soft stretchable carrier, integrating microscale light-emitting diodes (micro-LEDs), that conforms to the dura mater of the spinal cord. A coating of silicone–phosphor matrix over the micro-LEDs provides mechanical protection and light conversion for compatibility with a large library of opsins. A lightweight, head-mounted, wireless platform powers the micro-LEDs and performs low-latency, on-chip processing of sensed physiological signals to control photostimulation in a closed loop. We use the device to reveal the role of various neuronal subtypes, sensory pathways and supraspinal projections in the control of locomotion in healthy and spinal-cord injured mice.ISSN:1546-1696ISSN:1087-015