Fog-computing concept usage as means to enhance information and control system reliability

Melnik, E. V.; Klimenko, Anna; Ivanov, Donat

doi:10.1088/1742-6596/1015/3/032175

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Product lifecycle management currently uses industrial Internet of things [3], [4] technologies, big data collection technologies [5], cloud technologies [6], [7], fog computing [8]- [10], artificial intelligence technologies for analyzing big data [11]. Actively conducted research in the field of product data management [12], product information modeling [13], product information tracking [14], integration structure [15], [16], knowledge management [17], matching demand and supply product [18], product assembly [19], anyway, some tasks are still not solved.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin of the Oil Well, Based on Data Mining Technologies

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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Section: Introductionmentioning

confidence: 99%

Digital Twin of the Oil Well, Based on Data Mining Technologies

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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“…In this regard, the entire "information shaft" data on the processes occurring in the oil field, it is necessary to process quickly, namely in a time adequate to the adoption of a decisive control action. In this connection, modern approaches and methods of organizing information management systems are widely used [10][11][12][13][14][15]. At the same time, the oil industry has a certain specificity [16], therefore it is necessary to ensure:…”

Section: Introductionmentioning

confidence: 99%

Oilfield Equipment Control Distributed Automated System

Kosenkov¹,

Turchaninov²,

Korovin³

et al. 2019

dtcse

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The article proposes a neural network technique for the operational selection of optimal industrial activities. This technique is a sequential process, most of which is a separate algorithm (training of a neural network) or several algorithms (preliminary data processing). The difference between the methods described in this article is that, firstly, the use of hybrid neural network analysis technology, which uses in the simultaneous application, firstly, a multitude of neural trained neural networks with a threshold activation function in order to simultaneously offer several options solutions (this situation is typical only for the oilfield, when several types of problems arise simultaneously at the facility or there are two or more options for recommended measures for clustering, secondly, in the application of an untrained neural network for better selection. The developed methods are focused on processing oilfield data rather than any other data and, in fact, their implementation is dictated by the specifics of the information being analyzed, as well as the need for its speed processing.

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Fog-computing concept usage as means to enhance information and control system reliability

Cited by 2 publications

References 11 publications

Digital Twin of the Oil Well, Based on Data Mining Technologies

Digital Twin of the Oil Well, Based on Data Mining Technologies

Oilfield Equipment Control Distributed Automated System

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