[HTML][HTML] Tool condition monitoring for high-performance machining systems—A review
In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems
have gained considerable interest in high-value manufacturing industries to cope with the …
have gained considerable interest in high-value manufacturing industries to cope with the …
[HTML][HTML] Deep electrical resistivity tomography for geophysical investigations: the state of the art and future directions
Electrical Resistivity Tomography (ERT) is a robust and well-consolidated method largely
applied in near-surface geophysics. Nevertheless, the mapping of the spatial resistivity …
applied in near-surface geophysics. Nevertheless, the mapping of the spatial resistivity …
Large-scale chemical process causal discovery from big data with transformer-based deep learning
X Bi, D Wu, D Xie, H Ye, J Zhao - Process Safety and Environmental …, 2023 - Elsevier
Fault diagnosis is critical for ensuring safe and stable chemical production. Correct
identification of causal relationships among variables in large-scale chemical processes is a …
identification of causal relationships among variables in large-scale chemical processes is a …
Photoplethysmography signal processing and synthesis
This chapter presents the fundamental signal processing techniques used to analyze the
PPG signal. The chapter starts by providing an overview of the PPG signal, covering its …
PPG signal. The chapter starts by providing an overview of the PPG signal, covering its …
SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals
Sleep apnea is a common symptomatic disease affecting nearly 1 billion people around the
world. The gold standard approach for determining the severity of sleep apnea is full-night …
world. The gold standard approach for determining the severity of sleep apnea is full-night …
End-effects mitigation in empirical mode decomposition using a new correlation-based expansion model
The real-time analysis of time-varying data has become extremely significant in mechanical
systems. The Hilbert–Huang transform (HHT) is a recent development in signal processing …
systems. The Hilbert–Huang transform (HHT) is a recent development in signal processing …
[HTML][HTML] Magnetospheric–ionospheric–lithospheric coupling model. 1: Observations during the 5 August 2018 Bayan Earthquake
The short-term prediction of earthquakes is an essential issue connected with human life
protection and related social and economic matters. Recent papers have provided some …
protection and related social and economic matters. Recent papers have provided some …
基于时序分解与深度学习的堆石坝变形预测
冷天培， 马刚， 向正林， 梅江洲， 关少恒， 周伟， 高宇 - 水力发电学报, 2021 - slfdxb.cn
堆石坝变形监测数据是一种时间序列数据, 可以用时序预测模型挖掘其规律并进行预测.
本文利用时序预测模型提出一种堆石坝变形预测方法, 该方法首先采用时间序列分解(seasonal …
本文利用时序预测模型提出一种堆石坝变形预测方法, 该方法首先采用时间序列分解(seasonal …
[HTML][HTML] A hybrid Framework Using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis
K Srijiranon, Y Lertratanakham, T Tanantong - Applied Sciences, 2022 - mdpi.com
The aim of investors is to obtain the maximum return when buying or selling stocks in the
market. However, stock price shows non-linearity and non-stationarity and is difficult to …
market. However, stock price shows non-linearity and non-stationarity and is difficult to …
A dagging‐based deep learning framework for transmission line flexibility assessment
A Morteza, M Sadipour, RS Fard… - IET Renewable …, 2023 - Wiley Online Library
Uncertainty in renewable energy generation, energy consumption, and electricity prices, as
well as transmission congestion, pose a number of problems in modern power grids …
well as transmission congestion, pose a number of problems in modern power grids …