Dependency- and similarity-aware caching for HTTP adaptive streaming

Zhang, Cong; Liu, Jiangchuan; Chen, Fei; Cui, Yong; Ngai, Edith C.-H.; Hu, Yueming

doi:10.1007/s11042-016-4308-z

Cited by 2 publications

(1 citation statement)

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“…The establishment of the 2.5D vision system model is similar to that of 2D, with only one additional Z-axis height monitoring. However, the acquisition of such information is difficult in visual calculations [17][18][19]. In some applications, in order to solve this problem, it is usually only a simple addition of the distance sensor, which only measures the deviation of the Z axis.…”

Section: Composition Of Industrial Robot Vision Guidance Systemmentioning

confidence: 99%

Complex industrial automation data stream mining algorithm based on random Internet of robotic things

Cui

2019

Automatika

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In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness.

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Section: Composition Of Industrial Robot Vision Guidance Systemmentioning

confidence: 99%