Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Prediction Of Drilling Fluid Lost-Circulation Zone Based On Deep Learning, Yili Kang, Chenglin Ma, Chengyuan Xu, Lijun You, Zhenjiang You
Research outputs 2022 to 2026
Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis …
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Research outputs 2022 to 2026
Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
Research outputs 2022 to 2026
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …