4,223 research outputs found
Runtime-guided mitigation of manufacturing variability in power-constrained multi-socket NUMA nodes
This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), by
the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund
programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). This work was also partially performed
under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-689878).
Finally, the authors are grateful to the reviewers for their valuable comments, to the RoMoL team, to Xavier Teruel and Kallia Chronaki from the Programming Models group
of BSC and the Computation Department of LLNL for their technical support and useful feedback.Peer ReviewedPostprint (published version
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Cyber-physical production systems (CPPS) integrate physical and computational
resources due to increasingly available sensors and processing power. This
enables the usage of data, to create additional benefit, such as condition
monitoring or optimization. These capabilities can lead to cognition, such that
the system is able to adapt independently to changing circumstances by learning
from additional sensors information. Developing a reference architecture for
the design of CPPS and standardization of machines and software interfaces is
crucial to enable compatibility of data usage between different machine models
and vendors. This paper analysis existing reference architecture regarding
their cognitive abilities, based on requirements that are derived from three
different use cases. The results from the evaluation of the reference
architectures, which include two instances that stem from the field of
cognitive science, reveal a gap in the applicability of the architectures
regarding the generalizability and the level of abstraction. While reference
architectures from the field of automation are suitable to address use case
specific requirements, and do not address the general requirements, especially
w.r.t. adaptability, the examples from the field of cognitive science are well
usable to reach a high level of adaption and cognition. It is desirable to
merge advantages of both classes of architectures to address challenges in the
field of CPPS in Industrie 4.0
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
Chapter Operationalizing Heterogeneous Data-Driven Process Models for Various Industrial Sectors through Microservice-Oriented Cloud-Based Architecture
Industrial performance optimization increasingly makes the use of various analytical data-driven models. In this context, modern machine learning capabilities to predict future production quality outcomes, model predictive control to better account for complex multivariable environments of process industry, Bayesian Networks enabling improved decision support systems for diagnostics and fault detection are some of the main examples to be named. The key challenge is to integrate these highly heterogeneous models in a holistic system, which would also be suitable for applications from the most different industries. Core elements of the underlying solution architecture constitute highly decoupled model microservices, ensuring the creation of largely customizable model runtime environments. Deployment of isolated user-space instances, called containers, further extends the overall possibilities to integrate heterogeneous models. Strong requirements on high availability, scalability, and security are satisfied through the application of cloud-based services. Tieto successfully applied the outlined approach during the participation in FUture DIrections for Process industry Optimization (FUDIPO), a project funded by the European Commission under the H2020 program, SPIRE-02-2016
Agent-based middleware architecture for reconfigurable manufacturing systems
Modern manufacturing systems are expected to be flexible and efficient in order to cope with challenging market demands. Thus, they must be flexible enough as to meet changing requirements such as changes in production, energy efficiency, performance optimization, fault tolerance to process or controller faults, among others. Demanding requirements can be defined as a set of quality of service (QoS) requirements to be met. This paper proposes a generic and customizable multi-agent architecture that, making use of distributed agents, monitors QoS, triggering, if needed, a reconfiguration of the control system to recover QoS. As a proof of concept, the architecture has been implemented to provide availability of the control system understood as service continuity. The prototype has been tested in a case study consisting of an assembly cell where assessment of the approach has been conducted.This work was financed by the MINECO/FEDER under project DPI2015-68602-R
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