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[HTML][HTML] An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge
Electrical energy is essential in today's society. Accurate electrical load forecasting is
beneficial for better scheduling of electricity generation and saving electrical energy. In this …
beneficial for better scheduling of electricity generation and saving electrical energy. In this …
[HTML][HTML] Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory
Electricity constitutes an indispensable source of secondary energy in modern society.
Accurate and robust short-term electrical load forecasting is essential for more effective …
Accurate and robust short-term electrical load forecasting is essential for more effective …
Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism
Well logs are employed for analyzing lithology, determining formation parameters, and
evaluating oil and gas reservoirs. However, in practice, well logs are often incomplete or …
evaluating oil and gas reservoirs. However, in practice, well logs are often incomplete or …
S-wave velocity inversion and prediction using a deep hybrid neural network
The S-wave velocity is a critical petrophysical parameter in reservoir description, prestack
seismic inversion, and geomechanical analysis. However, obtaining the S-wave velocity …
seismic inversion, and geomechanical analysis. However, obtaining the S-wave velocity …
An expert system for insect pest population dynamics prediction
Avocado (Persea americana) production is increasing in Kenya, with both small and
largeholder farming for domestic and export markets. However, one of main challenges that …
largeholder farming for domestic and export markets. However, one of main challenges that …
Missing sonic logs generation for gas hydrate-bearing sediments via hybrid networks combining deep learning with rock physics modeling
Logging-while-drilling (LWD) sonic data are critical for marine gas hydrate reservoir
evaluation and production prediction. However, acquiring complete acoustic logs …
evaluation and production prediction. However, acquiring complete acoustic logs …
A vector-to-sequence based multilayer recurrent network surrogate model for history matching of large-scale reservoir
History matching can estimate the parameter of spatially varying geological properties and
provide reliable numerical models for reservoir development and management. However, in …
provide reliable numerical models for reservoir development and management. However, in …
A method for well log data generation based on a spatio-temporal neural network
Well logging helps geologists find hidden oil, natural gas and other resources. However,
well log data are systematically insufficient because they can only be obtained by drilling …
well log data are systematically insufficient because they can only be obtained by drilling …
Eeg daydreaming, a machine learning approach to detect daydreaming activities
In this paper, we propose a new method to detect noise hindrances in
Electroencephalographic (EEG) signals caused by mental distractions, which we named …
Electroencephalographic (EEG) signals caused by mental distractions, which we named …
Well logs reconstruction of petroleum energy exploration based on bidirectional Long Short-term memory networks with a PSO optimization algorithm
H Zhang, W Wu, Z Chen, J **g - Geoenergy Science and Engineering, 2024 - Elsevier
During petroleum energy exploration, estimating missing well logs from existing logging
data is very meaningful. Due to a highly nonlinear relationship between various logging data …
data is very meaningful. Due to a highly nonlinear relationship between various logging data …