SSFs dataset: Self-supervised features dataset of the ultrasonic signals of two-phase flow in an S-shaped riser

Date

2023-03-09 15:15

Supervisor/s

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Publisher

Cranfield University

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Dataset

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Format

Free to read from

Citation

Kuang, Boyu (2023). SSFs dataset: Self-supervised features dataset of the ultrasonic signals of two-phase flow in an S-shaped riser. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.22241530

Abstract

Title: SSFs dataset: Self-supervised features dataset of the ultrasonic signals of two-phase flow in an S-shaped riser Author: Boyu Kuang, neiil.kuang@cranfiel.ac.uk Time: 09th March 2023 Description: The source data of the proposed SSFs dataset comes from: https://doi.org/10.17862/cranfield.rd.11369379.v1 The Self-supervised features (SSFs) dataset is opened to the community along with our latest journal paper entitled: "Self-supervised learning-based two-phase flow regimen identification using ultrasonic sensors in an S-shape riser". NOTE: the journal DOI will be provided after the acceptance. This dataset is produced using the settings in TABLE I. Here are some details, and please contact me if you got any issues with using the dataset: SSFs_dataset: "the root directory of the dataset" | | -- ex: "the SSFs from the experiment group (ex)" | | | | | -- train: "the training set (70%)" | | | | | -- test: "the testing set (15%)" | | | | | -- valid: "the validation set (the rest)" | | -- ctr-A: "the SSFs from the control group (ctr-A)" | | | | | -- train: "the training set (70%)" | | | | | -- test: "the testing set (15%)" | | | |

Description

Software Description

Software Language

Github

Keywords

two-phase flow', 'Self-supervised learning', 'feature extraction', 'dataset'

DOI

10.17862/cranfield.rd.22241530

Rights

CC BY 4.0

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