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Future Internet
Special Issues
Manufacturing Systems and Internet of Thing
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Manufacturing Systems and Internet of Thing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (10 September 2019) | Viewed by 4731

Special Issue Editors

Prof. Li-Hua Li

E-Mail Website
Guest Editor
Chaoyang University of Technology, Department of Information Management, Taichung, Taiwan
Interests: neural networks; deep learning; decision support systems; internet applications
Prof. Dr. Cheng-Ying Yang

E-Mail Website
Guest Editor
Department of Computer Science, University of Taipei, Taipei 10066, Taiwan
Interests: communication system; signal processing; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the realization of cyber–physical systems introduced in the fourth stage of industrialization, industry 4.0 is leading the manufacturing process into the smart factory era. To help with smart manufacturing, IT-based integrations are being studied and implemented such as the horizontal integration of inter-corporation value networks, vertical integration inside factories and the end-to-end integration of the engineering value chain.

To allow machines connected as a collaborative community and to produce products based on analyzed feedback data, we rely on the IoT and cloud technologies to build smart factories. With the pervasive trends of collecting big data from IoT sensors and of analyzing data using machine learning or AI technologies, come novel methods to control or change the way physical goods are produced in all industrial sectors.

This includes important issues for smart factories, including how to smartly balance and compensate the work load and stress for each machine according to its individual health condition, how to produce quality products and maintain excellent machine performance, how to integrate various networks. This Special Issue therefore will therefore cover industry 4.0, industrial IoT, smart manufacturing, predictive maintenance, and cloud solutions for smart factory. The topics of interest include, but are not limited to:

  • Internet architecture, protocols and application
  • Internet operation and management for smart factories
  • Industrial IoT, robotics and AI applications
    Big data, machine learning and AI for industry 4.0
  • Preventive/predictive maintenance for smart factories
  • Cloud solutions and edge computing for smart factories
  • Secure and scalable SCADA systems
  • Data security and privacy in smart factories
  • Cyber–physical systems (CPS)
  • Conditional monitoring and control for intelligent manufacturing
  • Wireless sensor and actuator networks in manufacturing
  • Secure protocols for industrial applications
Prof. Li-Hua Li
Prof. Cheng-Ying Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Industry 4.0
  • Artificial intelligence (AI)
  • Smart factory
  • Sensor networks
  • Internet applications

Published Papers (1 paper)

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Research

25 pages, 5713 KiB  
Article
MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
by Baoxi Yuan, Yang Li, Fan Jiang, Xiaojie Xu, Yingxia Guo, Jianhua Zhao, Deyue Zhang, Jianxin Guo and Xiaoli Shen
Future Internet 2019, 11(9), 197; https://doi.org/10.3390/fi11090197 - 13 Sep 2019
Cited by 15 | Viewed by 4409
Abstract
In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation [...] Read more.
In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high- and low-quality images, and thus more suitable for Industry 4.0. Full article
(This article belongs to the Special Issue Manufacturing Systems and Internet of Thing)
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