Evaluating computing performance of deep neural network models with different backbones on IoT-based edge and cloud platforms

Wang, Xiaoxuan; Zhao, Feiyu; Lin, Ping; Chen, Yongming

doi:10.1016/j.iot.2022.100609

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Moreover, the traditional production line is incapable to support data interaction, and can trace historical data in real time according to product production data, or deploy the technical problems and defects of work order execution information, product processing and quality information in the production line. [7][8][9] It is thus a pressing need to develop a method, system and platform for processing the product production data used for flexible production lines.…”

Section: Introductionmentioning

confidence: 99%

Flexible production line product research and development and manufacturing cloud platform based on intelligent data collaboration

Xie¹,

Yang

et al. 2023

Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022)

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This paper focuses on how to ensure the availability and effectiveness of massive cloud data for industrial robots in the flexible production line, address the technical challenge in building a massive data cloud platform for industrial robots, and resolve the engineering problem of cloud based industrial robot cloud service application. To achieve this purpose, research is conducted on industrial robot hybrid cloud platform architecture, network technology, industrial robot big data system, autonomous learning cloud data processing and other technologies, which provides support for cloud service applications. It is suggested to combine knowledge atlas, digital twins, deep neural network, migration learning and other artificial intelligence technologies, which is conducive to remote monitoring and fault diagnosis cloud service applications. This has been verified and promoted in the handling, polishing, stacking, welding, assembly and other robots in 3C, mold, household appliances, automobile, furniture, electronic equipment manufacturing and other industries.

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Section: Introductionmentioning

confidence: 99%

Flexible production line product research and development and manufacturing cloud platform based on intelligent data collaboration

Xie¹,

Yang

et al. 2023

Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022)

View full text Add to dashboard Cite

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“…Moreover, in the production environment for projects involving the Internet of Things approach, computation resources could be a constraint for fast inference, because of the need to perform predictions with models implemented on edge devices [38,39]. The feature selection process will help to speed up both the training and prediction process.…”

mentioning

confidence: 99%

Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump

Fic,

Czornik,

Rosikowski

2023

Applied Sciences

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Measuring vibration velocity is one of the most common techniques to estimate the condition of industrial machines. At a constant operating point, as the vibration velocity value increases, the machine’s condition worsens. However, there are no precise thresholds that indicate the condition of a machine at different operating points. Also, the axial piston pump, which is the subject of the article, is a device that generates stronger vibrations by design and cannot be enclosed in general vibration norms. Due to different use cases and work regimes of axial piston pumps, the need to determine whether the device is working correctly for a broad spectra of operating points emerges. This article aims to present and compare different methods for vibration velocity prediction for axial piston pumps with use of neural networks including dense networks, variants of recurrent neural networks, and ensemble methods. The result of this research consists of models that have performance metrics that clearly indicate whether the monitored pump has malfunctioned or not across a wide variety of operating points, working conditions, and in case of reassembling. A detailed analysis of the influence of available measured variables on the performance of models is also provided. The conclusion is that the application of commercial implementation of developed models is reasonable in the context of both performance quality and costs of sensors needed to provide the necessary data.

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Energy consumption of on-device machine learning models for IoT intrusion detection

Tekin¹,

Acar²,

Arış³

et al. 2023

Internet of Things

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Evaluating computing performance of deep neural network models with different backbones on IoT-based edge and cloud platforms

Cited by 7 publications

References 10 publications

Flexible production line product research and development and manufacturing cloud platform based on intelligent data collaboration

Flexible production line product research and development and manufacturing cloud platform based on intelligent data collaboration

Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump

Energy consumption of on-device machine learning models for IoT intrusion detection

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