Indoor Augmented Reality Using Deep Learning for Industry 4.0 Smart Factories

Subakti, Hanas; Jiang, Jehn-Ruey

doi:10.1109/compsac.2018.10204

Cited by 39 publications

(23 citation statements)

References 11 publications

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“…Thereby, Subakti and Jiang propose the design, development, and implementation of an augmented reality system for machines in smart factories. A deep learning image detection module identifies different machines and IoT allows the machines to report machine settings and machine states to a cloud-based server [36]. Susto et al describe a methodology to derive the health factor of machines by applying a Monte Carlo approach based on particle filtering techniques to a real industrial predictive maintenance problem in the semiconductor industry [37].…”

Section: Predictive Maintenancementioning

confidence: 99%

“…Thereby, a multitude of studies suggests the application of recent AL, ML, and DL approaches for the continuous reporting of machine settings, machine states, and quality parameter settings. Based on real-time data, enhanced knowledge can be used for further predictive analysis regarding a strategic and pro-active plant maintenance strategy for production and logistics processes [36][37][38][39][40][41].…”

Section: Framework For the Application Ai ML And Dl In Smart Logisticsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

196

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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Section: Predictive Maintenancementioning

confidence: 99%

Section: Framework For the Application Ai ML And Dl In Smart Logisticsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

196

View full text Add to dashboard Cite

show abstract

“…AR technology allows supplementing the real world view with additional generated content, like images, and sensor data [6]. Such content supplement is based on visual markers, GPS location or analysis of the images of the place.…”

Section: Related Workmentioning

confidence: 99%

“…As we show in this paper, development and deployment of IoT platform, is a nontrivial task, which can be implemented in various ways. In addition, AR applications for campus navigation and visualization of machine operation in industries, have already been implemented and tested [4]- [6]. These papers studied different aspects of transmitting sensor data with diverse underlying IoT technologies.…”

Section: Introductionmentioning

confidence: 99%

Combining IoT Deployment and Data Visualization: experiences within campus maintenance use-case

Yasmin

Salminen

Gilman

et al. 2018

2018 9th International Conference on the Network of the Future (NOF)

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The internet of things (IoT) application domain is evolving continuously and is expected to bring digitalization making everything smart. This paper presents reference architecture for IoT system. Moreover, full implementation of such architecture is demonstrated with the IoT testbed deployed at the University of Oulu. Leveraging MQTT protocol, LoRaWAN is utilized as an IoT enabler technology for our platform, comprising a cloud server as well. In addition, a hybrid server is added to the platform to enable enhanced performance and functionality. To showcase the potential impacts of augmented reality (AR) based applications in an IoT infrastructure, we implemented and tested the maintenance support application. The implemented application visualizes environmental conditions and the sensor node itself as an augmented object on a mobile phone. A user study is carried out to discuss and showcase the potential impacts of using such a visualization approach.

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“…The implementation of registration technology is mainly based on sensors or hardware registration technology and machine vision-based registration technology [46]- [48]. Sensors or hardware-based registration technology uses some sensing devices or position tracking devices to capture the user's current location information, including magnetic field tracking registration technology, mechanical tracking registration technology, acoustic tracking registration technology, optical tracking registration technology, and GPS tracking registration technology [49]- [51]. The machine vision-based registration technology uses image processing technology and computer vision technology to register, and then can be divided into manual identification point method and natural identification point method according to different identification points.…”

Section: Introductionmentioning

confidence: 99%

Registration Technology of Augmented Reality in Oral Medicine: A Review

et al. 2019

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Augmented reality (AR) technology, as a computer simulation technology, combines various technologies such as virtual reality, computer vision, computer network, and human-computer interaction. AR has been widely used in medicine. The introduction of AR can effectively help the doctors to complete preoperative planning, intraoperative guidance, postoperative evaluation, and medical training. Oral medicine is a major branch of modern medicine. AR can enhance the doctor's visual system, making the internal structure of the oral clearer and effectively reducing the difficulty of oral repair/surgery. Real-time tracking, registration, display, and interactive technologies for AR will play an important role in oral medicine. Among them, registration technology has become an important indicator for evaluating the AR system, and it is also the main bottleneck restricting the stability and applicability of the current AR system. Therefore, we reviewed the registration technology of AR in oral medicine. First, we conducted a hot spot analysis of AR keywords based on Citespace. And then, the registration technology is divided into static registration and real-time registration according to the actual clinical application, among which static registration is divided into rigid registration and non-rigid registration. We discussed problems and limitations of static registration and real-time registration in oral applications at this stage. Finally, the future direction of AR registration technology in oral medicine is proposed.

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Indoor Augmented Reality Using Deep Learning for Industry 4.0 Smart Factories

Cited by 39 publications

References 11 publications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Combining IoT Deployment and Data Visualization: experiences within campus maintenance use-case

Registration Technology of Augmented Reality in Oral Medicine: A Review

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