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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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distributed moving horizon estimation with pre-estimating observer

Distributed moving horizon estimation with pre-estimating ob...

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international conference on System theory, Control, and computing (ICSTCC)

作者： Antonello Venturino Sylvain Bertrand Cristina Stoica Maniu Teodoro Alamo Eduardo F. Camacho CentraleSupélec Laboratoire des signaux et systèmes Université Paris- Saclay Gif-sur-Yvette France Traitement de l’information et systèmes Universite Paris-Saclay Palaiseau France Universidad de Sevilla Sevilla Spain

ISBN: (数字)9781728198095

ISBN: (纸本)9781728198101

this paper proposes a new distributed Moving Horizon Estimation (DMHE) algorithm for state estimation of a discrete-time linear system by a sensor network. the main contribution consists in using a pre-estimating Luenberger observer in the formulation of the local problem to be solved by each sensor. this results in a significant reduction of the computation time while preserving at the same time the accuracy of the estimates. the state estimate computed by each sensor is capable to converge even under weak observability conditions. Comparison with existing approaches is proposed through a simulation example, for both centralized and distributed cases, in terms of estimation accuracy and computation time.

关键词： Sensors Sensor systems Observers Observability Computational modeling Sensor phenomena and characterization Linear systems

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On distributed consensus algorithms and their application to power sharing in DC microgrids

On distributed consensus algorithms and their application to...

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international conference on System theory, Control, and computing (ICSTCC)

作者： Chris Zevenbergen Sergio Grammatico Delft Center for Systems and Control TU Delft The Netherlands

ISBN: (数字)9781728198095

ISBN: (纸本)9781728198101

We consider nonlinear consensus algorithms and their application to proportional power sharing in DC microgrids. We show that a power sharing algorithm is a special case of nonlinear consensus protocol and we establish convergence to state consensus with adaptive gains. the key facts are the presence of an agreement function in the power sharing algorithm and the preservation of a weighted geometric mean over time. these observations allow us to improve the performance of this class of algorithms via the theory of nonlinear consensus.

关键词： Microgrids Consensus protocol Voltage control Energy resources Consensus algorithm Power system stability Lyapunov methods

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Post-Turing computing, Hierarchical Named Networks and a New Class of Edge computing 28

Post-Turing Computing, Hierarchical Named Networks and a New...

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28th IEEE international conference on Enabling Technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Mikkilineni, Rao Morana, Giovanni Golden Gate Univ Edward S Ageno Sch Business San Francisco CA 94105 USA Platina Syst Santa Clara CA USA

ISBN: (纸本)9781728106762

Advances in our understanding of the nature of cognition in its myriad forms (Embodied, Embedded, Extended, and Enactive) displayed in all living beings (cellular organisms, animals, plants, and humans) and new theories of information, info-computation and knowledge are throwing light on how we should build software systems in the digital universe which mimic and interact with intelligent, sentient and resilient beings in the physical universe. Recent attempts to infuse cognition into computing systems to push the boundaries of Church-Turing thesis have led to new computing models that mimic biological systems in encoding knowledge structures using both algorithms executed in stored program control machines and neural networks. this paper presents a new model and implements an application as hierarchical named network composed of microservices to create a managed process workflow by enabling dynamic configuration and reconfiguration of the microservice network. We demonstrate the resiliency, efficiency and scaling of the named microservice network using a novel edge cloud platform by Platina systems. the platform eliminates the need for Virtual Machine overlay and provides high performance and low-latency with L3 based 100 GbE network and SSD support with RDMA and NVMeoE. the hierarchical named microservice network using Kubernetes provisioning stack provides all the cloud features such as elasticity, autoscaling, self-repair and live-migration without reboot. the model is derived from a recent theoretical framework for unification of different models of computation using "Structural Machines." they are shown to simulate Turing machines, inductive Turing machines and also are proved to be more efficient than Turing machines. the structural machine framework with a hierarchy of controllers managing the named service connections provides dynamic reconfiguration of the service network from browsers to database to address rapid fluctuations in the demand for or the avai

关键词： Turing Machine Cloud computing Edge Cloud Hierarchical named networks Kubernetes

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An Event-Driven Multi Agent System for Scalable Traffic Optimization

An Event-Driven Multi Agent System for Scalable Traffic Opti...

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workshops of the 34th international conference on Advanced Information Networking and Applications, WAINA 2020

作者： Horn, Geir Przeźdiȩk, Tomasz Büscher, Monika Venticinque, Salvatore Aversa, Rocco Martino, Beniamino Di Esposito, Antonio Skrzypek, Pawel Leznik, Mark University of Oslo P.O. Box 1080 Blindern Oslo0316 Norway Optimali Świȩtojerska 5/7 Warsaw00-236 Poland Lancaster University Bailrigg LancasterLA1 4YW United Kingdom Università degli Studi della Campania Luigi Vanvitelli Caserta Italy *** Al. Szucha 8 Warsaw00-582 Poland Ulm University Ulm Germany

ISBN: (纸本)9783030440374

Global demand for mobility will grow from 44 trillion to 122 trillion passenger-kilometres by 2050, and freight demand will triple in that time increasing traffic emissions by 60%. With current innovation and policy measures we are ‘on course for a 3.2C temperature rise’, according to the 2019 UN Emissions Gap Report. Nothing short of revolutionary is required to address this emergency. However, there is hope: shared mobility and widespread adoption of autonomous vehicles could cut emissions by 73% and congestion by 24% if managed by appropriate policies. this paper presents a vision and a concept for future distributed management systems for complex multi-modal transport networks that exploit Multi Agent systems (MAS) to support individual actors based on data collected from heterogeneous sources like vehicles, freight items, infrastructures, Global Positioning systems (GPS);and simulations of the behaviour of the many different actors involved in the transport system. Event driven approaches are envisioned to react and respond to real-time events efficiently. the main objective is to identify the best optimization strategies to reduce traffic emissions and maximize the use of the public infrastructures and shared mobility. Motivations, expected impacts, and challenges are also discussed. © 2020, Springer Nature Switzerland AG.

关键词： Multi agent systems

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MAD-C: Multi-stage Approximate distributed Cluster-Combining for Obstacle Detection and Localization 24th

MAD-C: Multi-stage Approximate Distributed Cluster-Combining...

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24th international European conference on Parallel and distributed computing (Euro-Par)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Nikolakopoulos, Yiannis Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783030105495;9783030105488

Efficient distributed multi-sensor monitoring is a key feature of upcoming digitalized infrastructures. We address the problem of obstacle detection, having as input multiple point clouds, from a set of laser-based distance sensors;the latter generate high-rate data and can rapidly exhaust baseline analysis methods, that gather and cluster all the data. We propose MAD-C, a distributed approximate method: it can build on any appropriate clustering, to process disjoint subsets of the data distributedly;MAD-C then distills each resulting cluster into a data-summary. the summaries, computable in a continuous way, in constant time and space, are combined, in an order-insensitive, concurrent fashion, to produce approximate volumetric representations of the objects. MAD-C leads to (i) communication savings proportional to the number of points, (ii) multiplicative decrease in the dominating component of the processing complexity and, at the same time, (iii) high accuracy (with RandIndex > 0.95), in comparison to its baseline counterpart. We also propose MAD-C-ext, building on the MAD-C's output, by further combining the original data-points, to improve the outcome granularity, with the same asymptotic processing savings as MAD-C.

关键词： Point cloud processing Approximations Fog computing

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Accelerating Hyperparameter Optimisation with PyCOMPSs 19

Accelerating Hyperparameter Optimisation with PyCOMPSs

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48th international conference on Parallel Processing (ICPP)

作者： Njoroge Kahira, Albert Bautista Gomez, Leonardo Conejero, Javier Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781450371964

Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. there has been a recent surge in Machine Learning applications in High Performance computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming and resource intensive parts in a Machine Learning Workflow. Numerous algorithms and tools exist to accelerate the process of finding the right parameters for a model. Most of these tools do not utilize the parallelism provided by modern systems and are serial or limited to a single node. the few ones that are offer distributed execution require a serious amount of programming effort. there is, therefore, a need for a tool/scheme that can scale and leverage HPC infrastructures such as supercomputers, with minimum programmers effort and little or no overhead in performance. We present a HPO scheme built on top of PyCOMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures. We show that PyCOMPSs is a powerful framework that can accelerate the process of Hyperparameter Optimisation across multiple devices and computing units. We also show that PyCOMPSs provides easy programmability, seamless distribution and scalability, key features missing in existing tools. Furthermore, we perform a detailed performance analysis showing different configurations to demonstrate the effectiveness our approach.

关键词： Machine Learning Hyperparameter Optimisation distributed computing HPC PyCOMPSs

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Decomposition-based Verification of Global Compliance in Process Choreographies

Decomposition-based Verification of Global Compliance in Pro...

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international conference on Enterprise distributed Object computing (EDOC)

作者： Walid Fdhila Stefanie Rinderle-Ma David Knuplesch Manfred Reichert SBA-Research Vienna Austria Faculty of Computer Science University of Vienna Vienna Austria Ulm University Ulm Germany

ISBN: (数字)9781728164731

ISBN: (纸本)9781728164748

the verification of global compliance rules (GCR) in process choreographies (e.g., partner-spanning quality assurance in supply chains) is crucial and challenging due to the restricted visibility of the private processes of the collaborating partners. this paper provides a novel algorithm that decomposes global compliance rules into assertions that can be verified by the partners in a distributed way without revealing any private process details. the decomposition is based on transitivity properties of the underlying GCR specification. this work uses GCR based on antecedent and occurrence patterns and illustrates the transitivity properties based on their specification in first order predicate logic. It is formally shown that the original GCR can be reconstructed from the assertions, which ensures the viability of the approach. the algorithms are prototypically implemented and applied to several scenarios. the ability of checking global compliance constitutes a fundamental pillar of any approach implementing process choreographies with multiple partners.

关键词： Task analysis Business Supply chains Connectors Safety Pattern matching Information systems

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Modeling and Optimizing Data Transfer in GPU-Accelerated Optical Coherence Tomography 24th

Modeling and Optimizing Data Transfer in GPU-Accelerated Opt...

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24th international European conference on Parallel and distributed computing (Euro-Par)

作者： Schroedter, Tobias Pallasch, David Wienke, Sandra Schmitt, Robert Mueller, Matthias S. Rhein Westfal TH Aachen Aachen Germany Fraunhofer Inst Prod Technol IPT Aachen Germany Rhein Westfal TH Aachen IT Ctr Aachen Germany Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn WZL Aachen Germany

ISBN: (纸本)9783030105495;9783030105488

Signal processing of optical coherence tomography (OCT) has become a bottleneck for using OCT in medical and industrial applications. Recently, GPUs gained more importance as compute device to achieve video frame rate of 25 frames/s. therefore, we develop a CUDA implementation of an OCT signal processing chain: We focus on reformulating the signal processing algorithms in terms of high-performance libraries like CUBLAS and CUFFT. Additionally, we use NVIDIA's stream concept to overlap computations and data transfers. Performance results are presented for two Pascal GPUs and validated with a derived performance model. the model gives an estimate for the overall execution time for the OCT signal processing chain, including compute and transfer times.

关键词： GPU OCT Performance model CUDA

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Assessing Resilience in Enterprise Architecture: A Systematic Review

Assessing Resilience in Enterprise Architecture: A Systemati...

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international conference on Enterprise distributed Object computing (EDOC)

作者： Adina Aldea Egle Vaicekauskaitė Maya Daneva Jean Paul Sebastian Piest University of Twente Enschede Netherlands

ISBN: (数字)9781728164731

ISBN: (纸本)9781728164748

this review paper aims to explore state-of-the-art research and scientific literature about Enterprise Architecture (EA) resilience. Based on a systematic literature review, 850 articles have been subjected to evaluation for relevance. Based on the findings in 58 selected papers, we conclude that the field of EA resilience is still in its infancy. We identified several definitions and classified six types of resilience measures, based on information type (qualitative/quantitative), the source of the disruption (internal/external), and the duration of the resilience (short-term/long-term). Based on the review, we found 19 metrics that are candidates for EA practitioners to consider for the design of measurement and assessment methods for EA resilience. In addition, we identified relevant research from Information systems sub-domains and other sciences that can be incorporated to create a holistic view on EA resilience. Based on published definitions of resilience in the selected papers, we propose a definition of the concept of EA resilience. this definition is validated using expert opinion and creates a starting point for reasoning about EA resilience and future research.

关键词： Resilience Measurement Systematics Information systems Organizations Data mining Bibliographies

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Smart Manufacturing Control with Cloud-embedded Digital Twins

Smart Manufacturing Control with Cloud-embedded Digital Twin...

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international conference on System theory, Control, and computing (ICSTCC)

作者： theodor Borangiu Silviu Răileanu Andrei Silişteanu Silvia Anton Florin Anton University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781728198095

ISBN: (纸本)9781728198101

the paper presents a model for smart control of large scale manufacturing systems, in which pools of shop floor and computing resources are shared in a dual cloud pattern. the proposed architecture uses the holonic manufacturing paradigm by decoupling the decision layer from the control one. the decision layer uses intelligent agents that reconfigure optimally in real time the resource allocation and scheduling of operations on products at batch level; also, the resource health is monitored continuously. Decisions are taken in the high layer of the MES based on real time machine learning algorithms that predict resource performances and QoS influencing usage costs, classify and cluster resource states to predict anomalies in behaviours and prevent resource failures. the distributed control layer keeps reality awareness during production by using digital twins replicated for all resources. Data is collected in real time streams from physical resource and process twins, aggregated in time series and sent to the intelligent agents in the cloud without delaying production. Experiments discuss the forecast of abnormal pick-and-place robot operations.

关键词： Production Optimization Manufacturing Cloud computing Real-time systems Process control Smart manufacturing

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