scispace - formally typeset
Search or ask a question
Journal ArticleDOI

A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

TL;DR: A new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis is introduced and a proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics.
About: This article is published in Journal of Manufacturing Systems.The article was published on 2017-04-01. It has received 223 citations till now. The article focuses on the topics: Prognostics & Wireless sensor network.
Citations
More filters
Journal ArticleDOI
TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.

1,025 citations

Journal ArticleDOI
TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.

937 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0.

568 citations

Journal ArticleDOI
TL;DR: 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin, and the frequently-used enabling technologies and tools for digital twin are investigated and summarized to provide Technologies and tools references for the applications of digital twin in the future.

541 citations

Journal ArticleDOI
TL;DR: A systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyzing the previous is presented, providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field.

411 citations

References
More filters
Journal ArticleDOI
01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

79,257 citations

ReportDOI
28 Sep 2011
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.

15,145 citations

Journal ArticleDOI
TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Abstract: Clearing the clouds away from the true potential and obstacles posed by this computing capability.

9,282 citations

Book
01 Oct 1998
TL;DR: The Globus Toolkit as discussed by the authors is a toolkit for high-throughput resource management for distributed supercomputing applications, focusing on real-time wide-distributed instrumentation systems.
Abstract: Preface Foreword 1. Grids in Context 2. Computational Grids I Applications 3 Distributed Supercomputing Applications 4 Real-Time Widely Distributed Instrumentation Systems 5 Data-Intensive Computing 6 Teleimmersion II Programming Tools 7 Application-Specific Tools 8 Compilers, Languages, and Libraries 9 Object-Based Approaches 10 High-Performance Commodity Computing III Services 11 The Globus Toolkit 12 High-Performance Schedulers 13 High-Throughput Resource Management 14 Instrumentation and Measurement 15 Performance Analysis and Visualization 16 Security, Accounting, and Assurance IV Infrastructure 17 Computing Platforms 18 Network Protocols 19 Network Quality of Service 20 Operating Systems and Network Interfaces 21 Network Infrastructure 22 Testbed Bridges from Research to Infrastructure Glossary Bibliography Contributor Biographies

7,569 citations

Proceedings ArticleDOI
17 Aug 2012
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Abstract: Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).

4,440 citations