14 research outputs found

    Digitization of the work environment for sustainable production

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    Global pandemics, devastating wars and natural disasters with increasing frequency and impact are disrupting previously carefully balanced manufacturing networks. All industrial companies are required to examine their operations and adjust accordingly. The increasing cost of resources require enterprises to re-design their value creation processes to be more sustainable, to optimize the supplier network to become more resilient and to accelerate digitizing of operations to enhance operational effectiveness. This year's WGAB research seminar is themed around Digitization of the work environment for sustainable production and seeks to contribute solutions to the current challenges. The scientific discourse aims to advance the sustainable and data-based organization of value creation processes. Exemplary efforts for the sustainable production of 3D printed footwear and the circular supply chain of energy production will be discussed. With advances in sensory data collection in cyber-physical production systems (CPPS), there are new opportunities for sensing the status of manufacturing systems, which enable advanced data analytics to contribute to a sustainable production. Intelligent processes enable sustainable value creation and bi-directional knowledge exchange between humans and machines. With people at the centre of the CPPS, production systems shall be both adaptive and personalized for every worker. People need to be involved in the technological and organizational changes. Simulating the migration from a linear economy to a circular economy supports the trend of regionalized production networks. Digital assistance systems are tested to back up resilient manufacturing. We would like to thank all authors for their efforts in preparing the contributions, which are valuable inputs to the discourse to solve the current challenges

    Adaptive Cognitive Interaction Systems

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    Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthÀlt zahlreiche BeitrÀge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

    Factories of the Future

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    Engineering; Industrial engineering; Production engineerin

    Multi-Agent Systems

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    This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

    Factories of the Future

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    Engineering; Industrial engineering; Production engineerin

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Recent Developments in Smart Healthcare

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    Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine
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