Information Support for the Process of Multiphase Flows Transportation Based on the Introduction of a Radioisotope Non-Separation Hydrocarbon Measuring System

Koptev, Vladimir; Kopteva, Alexandra V.; Starshaya, V.V.

doi:10.1109/eiconrus49466.2020.9039314

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Due to the particularity of oil transportation operations, the use of GPS satellites for PIG localization is risky, the cost of using distributed optical fibers for localization is high, and the localization is not sufficiently accurate, which is prone to false signals and missing reports [3,4]. Current PIG tracking and localization methods mainly include the radioisotope method [5], mechanical method [6], acoustic method [7], pressure method and magnetic field method [7,8]. However, the above methods require considerable manpower and material resources.…”

Section: Introductionmentioning

confidence: 99%

Research on pipeline inspection gauges localization based on residual denoising autoencoder

Guo,

Song,

Zeng

et al. 2023

Eng. Res. Express

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To improve pipeline transportation efficiency, long-distance pipelines need to be cleaned regularly. Pipeline inspection gauges (PIGs) are widely used in pigging operations. The traditional PIG localization method generally has a large error and cannot achieve continuous tracking. To solve the problem of PIG real-time tracking and localization, this paper proposes a method of PIG localization for oil pipelines based on a denoising autoencoder(DAE). The attention deconvolution residual block (ADR) is introduced in the decoder part of the DAE, so that this model can learn and use global information to selectively emphasize key features, suppress useless features, and improve the noise removal performance of the model. After denoising, the negative pressure wave(NPW) signal is normalized, the time delay is extracted, and the PIG is located using the NPW localization formula. Experiments show that this method can effectively achieve real-time PIG localization. Compared with the EMD and DAE positioning methods, the relative positioning accuracy is increased by 3.5 times and 8.4 times, respectively.

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Section: Introductionmentioning

confidence: 99%

Research on pipeline inspection gauges localization based on residual denoising autoencoder

Guo,

Song,

Zeng

et al. 2023

Eng. Res. Express

View full text Add to dashboard Cite

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Artificial intelligence technologies in automation of corporate risk management

Alchinov

Avetisyan

Druzhinina

2020

J. Phys.: Conf. Ser.

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This research focuses on the application of BD technologies and artificial intelligence, implemented in the convergence of neural network bases and open libraries (BigData). The proposed solution in the form of a distributed analysis system and automated corporate risk management is created on the basis of deep learning algorithms. It is effectively integrated into management processes. In addition, it improves the quality and time of decisions made at all levels.

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Enhancing cryptographic stability of applications based on the principles of dynamic polymorphism

Avetisyan¹,

Похомчикова

Daudov

2020

J. Phys.: Conf. Ser.

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Today, encryption reflects a high level of reliability and confidentiality when working with data, both within local networks and in the global Internet network. At present, data processing has become an integral part of society. It penetrates practically the whole sphere of human activity. There are negative consequences of the development of this area. Very achievements in the field of high-tech technologies are used everywhere and are very new.

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Information Support for the Process of Multiphase Flows Transportation Based on the Introduction of a Radioisotope Non-Separation Hydrocarbon Measuring System

Cited by 6 publications

References 9 publications

Research on pipeline inspection gauges localization based on residual denoising autoencoder

Research on pipeline inspection gauges localization based on residual denoising autoencoder

Artificial intelligence technologies in automation of corporate risk management

Enhancing cryptographic stability of applications based on the principles of dynamic polymorphism

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