Managing Social Challenges in Cross-Organizational Event-Based Systems

During the last decade the manufacturing industry focused on the realization of industry 4.0 aspects. Besides the implementation of new technologies, existing software structures also need to be reviewed and adapted in this context. To stay competitive in the global market, especially small and medium-sized companies need to emphasize on better cooperation with other organizations. This leads to the implementation of cross-organizational distributed software system structures. The development of distributed systems faces different challenges - technical and code-centric as well as social challenges. This paper focuses on the social challenges that appear in distributed development processes. After defining the main challenges, the paper introduces a development approach that is based on the integration of a Federated Management System (FMS). FMS is a technical approach to minimize social challenges by the generation of system transparency and the provision of a platform for communication and interaction. It facilitates a distributed system development of cross-organizational event-based systems

Transportation Planning Through Mobile Mapping Technology

This report describes the development and testing of the Fix This Tool, a spatial, participatory, active transportation and built environment assessment tool created on an iPhone platform. The goal of this tool development was to create an instrument that could be widely distributed to communities across the country to develop a spatially based assessment of the micro-scaled elements of their local active transportation environment such that public officials and community members could focus energy in making appropriate improvements. The development of this tool emerged out of previous work with such tools built on a GIS platform and a workshop-based format to engage residents in data collection of their walking and biking environments. While this past work proved successful in both data collection tool use and in facilitating community conversations, the technological infrastructure had significant limitations in terms of being able to widely distribute the effort. In a GIS-based approach, GIS technicians must be present and when combined with community training and data processing, the cost of development, collection, and distribution is a significant limit to which communities could adopt such assessment tools for their own use. The Fix This Tool is designed to overcome these distribution and cost barriers by developing a tool that can be easily downloaded by community members using technology they already own. The report that follows outlines the philosophical positioning of a community-based data collection process, describes the tool itself, and provides some reflections between this smart phone based model and the previous GIS-based model of community-engaged active transportation assessment tools

Spezifikation von Ereignis-Nachrichten im unternehmensübergreifenden Industrie 4.0 Umfeld

Der in diesem Aufsatz dargestellte Föderierte Daten-Spezifizierungs-Prozess (FDSP) unterstützt die Entwicklung von unternehmensübergreifenden Softwaresystemen. Der Prozess ist derart ausgelegt, dass unabhängige Personen erforderliche Systeminformation erhalten und mit nur geringen Einstiegbarrieren Systemkomponenten entwickeln können. Mithilfe des FDSP wird durch die Softwareentwickler des Systems ein systeminterner Datenstandard entwickelt, der entsprechend der Systemanforderungen definiert und stetig weiterentwickelt werden kann

Montagegerechte Gestaltungsrichtlinien mittels Deep Learning

Die Anwendung von Deep Learning in der manuellen Montage birgt großes Potenzial, Montagezeiten zu reduzieren und Montagefehler zu vermeiden. Indem der Montageablauf mithilfe einer Kamera erfasst und die aufgezeichneten Bilder durch einen Objekterkennungsalgorithmus analysiert werden, lassen sich Position, Lage und Art der montierten Bauteile bestimmen. Daraus lassen sich wiederum Informationen über Arbeitsschritte, Montagefehler oder den aktuellen Zustand des Produkts ableiten, sodass die Mitarbeiter bei der Montage durch entsprechende Anweisungen unterstützt werden können. Es stellt sich jedoch die Frage, inwieweit gegenwärtige Produkte für den Einsatz von Deep Learning geeignet sind. Nur wenn die zu montierenden Bauteile sicher erkannt werden, ist der Einsatz in der manuellen Montage sinnvoll. Bestehende Gestaltungsrichtlinien adressieren diesen Aspekt bislang nicht. Im Forschungsprojekt wurde daher untersucht, welche Eigenschaften Produkte aufweisen sollten, um eine optimale Objekterkennung zu ermöglichen. Dazu wurden Hypothesen zu positiven und negativen Bauteileigenschaften hinsichtlich der Erkennungsgenauigkeit formuliert und in praktischen Versuchen überprüft. Dabei konnte gezeigt werden, dass alle untersuchten Objekte durch den eingesetzten Objekterkennungsalgorithmus sehr gut detektiert werden. Aus den vorliegenden Forschungsergebnissen lassen sich daher keine Einschränkungen in der Produktgestaltung ableiten

Toward a Digital Platform for Spacecraft Manufacturing

Professionals of many disciplines are involved in a spacecraft mission. They all use different software tools that are tailored to their tasks and they share data in various ways among themselves. These data sharing activities form a network, which, given modern software engineering practices, offers a lot of opportunities for improvement: simplify data source discoverability, automate previously manual data sharing activities, and better make use available data sources. To simplify data source discoverability, we propose a digital platform with a serviceoriented architecture. Such an architecture also helps to better make use of available data sources. Additionally, we present our projects that automate previously manual data sharing activities and that make better use of available data sources. With the development of the digital platform we aim at providing a significant reduction in resource expenditure, especially time expenditure, for spacecraft missions

Broadening the antibacterial spectrum of histidine kinase autophosphorylation inhibitors via the use of epsilon-poly-L-lysine capped mesoporous silica-based nanoparticles

[EN] Two-component systems (TCS) regulate diverse processes such as virulence, stress responses, metabolism and antibiotic resistance in bacteria but are absent in humans, making them promising targets for novel antibacterials. By incorporating recently described TCS histidine kinase autophosphorylation inhibitors (HKAIs) into epsilon-poly-L-lysine capped nanoparticles (NPs) we could overcome the Gram negative (Gr(-)) permeability barrier for the HKAIs. The observed bactericidal activity against Gr(-) bacteria was shown to be due to the enhanced delivery and internalization of the HKAIs and not an inhibitory or synergistic effect of the NPs. The NPs had no adverse effects on mammalian cell viability or the immune function of macrophages in vitro and showed no signs of toxicity to zebrafish larvae in vivo. These results show that HKAIs are promising antibacterials for both Gr(-) and Gr + pathogens and that NPs are a safe drug delivery technology that can enhance the selectivity and efficacy of HKAIs against bacteria. (C) 2016 Elsevier Inc. All rights reserved.This work was funded by FP7 ITN STARS-Scientific Training in Antimicrobial Research Strategies (Contract No. PITN-GA-2009-238490, J.M.W., A.M.), H2020 MSCA IF (AND-659121, N.V.), grant BIO2013-42619-P from the Ministerio de Economia y Competitividad (A.M.), grant from the Spanish Government (Project MAT2015-64139-C4-1-R,N. M., J.R.M, R.M.M.), and a grant from Generalitat Valenciana (Project PROMETEOII/2014/047, N.M.). and Prometeo II/2014/029, A.M.).Velikova, N.; Mas Font, N.; Miguel-Romero, L.; Polo, L.; Stolte, E.; Zaccaria, E.; Cao, R.... (2017). Broadening the antibacterial spectrum of histidine kinase autophosphorylation inhibitors via the use of epsilon-poly-L-lysine capped mesoporous silica-based nanoparticles. Nanomedicine Nanotechnology Biology and Medicine. 13(2):569-581. https://doi.org/10.1016/j.nano.2016.09.011S56958113

Managing Social Challenges in Cross-Organizational Event-Based Systems

During the last decade the manufacturing industry focused on the realization of industry 4.0 aspects. Besides the implementation of new technologies, existing software structures also need to be reviewed and adapted in this context. To stay competitive in the global market, especially small and medium-sized companies need to emphasize on better cooperation with other organizations. This leads to the implementation of cross-organizational distributed software system structures. The development of distributed systems faces different challenges - technical and code-centric as well as social challenges. This paper focuses on the social challenges that appear in distributed development processes. After defining the main challenges, the paper introduces a development approach that is based on the integration of a Federated Management System (FMS). FMS is a technical approach to minimize social challenges by the generation of system transparency and the provision of a platform for communication and interaction. It facilitates a distributed system development of cross-organizational event-based systems

Service-oriented Development of Federated ERP Systems

Abstract. The paper presents a new architecture approach for the distribution of the application logic in ERP systems. The approach proposes the provision of software components which implement specific functionality as Web Services. The paper shows how these Web Services can be developed and provided by independent software vendors. The model advances the reusability of data types and reduces the necessity of data transformation functions in business process descriptions. Furthermore a first prototype implementation (FERPxONE) is presented and an example process for the generation of a simple diagram for the comparison of customers is given

Hand Gesture Recognition of Methods-Time Measurement-1 Motions in Manual Assembly Tasks Using Graph Convolutional Networks

Gesture recognition is gaining popularity in many fields, including gesture control, robotics, or medical applications. However, the technology is barely used in industrial manufacturing processes due to high costs, a time-consuming configuration, and changes in the workflow. This paper proposes a minimally invasive approach to recognize workers' hand motions in manual assembly tasks. The novelty of this approach is the use of only one camera instead of any other sensors and the application of state-of-the-art graph neural networks. The method relies on monocular RGB video data to predict the basic motions of the industry standard motion-time system Methods-Time Measurement-1. Our two-stage neural network composed of hand key point extraction and adaptive graph convolution delivers accurate classification results in real-time. To train and validate the model, we created a dataset containing 22,000 frames of real-world assembly tasks. The data produced by this method in a production line can be used for motion time verification, assembly-line design, or assembly cost estimation. In a use-case study, we show that the proposed approach can generate Methods-Time Measurement analysis tables. These have so far only been accurately created by human experts. Source code: https://github.com/alexriedel1/Hand-Gesture-Recognition-in-manual-assembly-tasks-using-GC