Pinned Repositories
Classification-of-oil-wells-rock-formation-
Classifying rock formation using well logs data
Coconut-oil-adulteraion-code
MATLAB Code for the conference paper - Transmittance Multispectral Imaging for Edible Oil Quality Assessment
code_papers
I post here the code that is used in my papers
DistFlow
This function solves the distribution power flow equations for a generic, radial, single-phase distribution network based on Baran's (1989) paper.
Drilling-fluid-lost-circulation
The project evaluates causal effects in the prediction of mud loss during drilling activities. Model agnostic metrics, PFI and Shapley, are used to analyse each feature to understand their global implications in predicting mud loss. Several supervised machine learning models are used to predict the mud loss using these selected features.
E2Metrix-Membrane-Fouling-Model
Simulation to mathematically determine the fouling mechanism on MF/UF membranes during operation with electrocoagulated thickener overflow water.
Fair-K-Means-Clustering
Code of the paper Fair k-Means Clustering
FRACkR
MATLAB code for modelling the time-dependent evolution of permeability in fractured volcanic systems
General-Advanced-Deep-Learning-Trainings
Contents, •Neural networks – Perceptron, Adaline, BP neural networks, unsupervised learning neural networks, RBF neural networks, etc. •Optimization methods – Genetic algorithms, swarm intelligence, etc. •Training deep neural networks – Parameter and structure tuning, etc. •Deep learning neural network models – Convolutional Neural Networks (CNN), autoencoders
Group3_Fouling_Factor_Prediction_in_Heat_Exchanger
Fouling
momar210's Repositories
momar210/Classification-of-oil-wells-rock-formation-
Classifying rock formation using well logs data
momar210/Coconut-oil-adulteraion-code
MATLAB Code for the conference paper - Transmittance Multispectral Imaging for Edible Oil Quality Assessment
momar210/code_papers
I post here the code that is used in my papers
momar210/DistFlow
This function solves the distribution power flow equations for a generic, radial, single-phase distribution network based on Baran's (1989) paper.
momar210/Drilling-fluid-lost-circulation
The project evaluates causal effects in the prediction of mud loss during drilling activities. Model agnostic metrics, PFI and Shapley, are used to analyse each feature to understand their global implications in predicting mud loss. Several supervised machine learning models are used to predict the mud loss using these selected features.
momar210/E2Metrix-Membrane-Fouling-Model
Simulation to mathematically determine the fouling mechanism on MF/UF membranes during operation with electrocoagulated thickener overflow water.
momar210/Fair-K-Means-Clustering
Code of the paper Fair k-Means Clustering
momar210/FRACkR
MATLAB code for modelling the time-dependent evolution of permeability in fractured volcanic systems
momar210/General-Advanced-Deep-Learning-Trainings
Contents, •Neural networks – Perceptron, Adaline, BP neural networks, unsupervised learning neural networks, RBF neural networks, etc. •Optimization methods – Genetic algorithms, swarm intelligence, etc. •Training deep neural networks – Parameter and structure tuning, etc. •Deep learning neural network models – Convolutional Neural Networks (CNN), autoencoders
momar210/Group3_Fouling_Factor_Prediction_in_Heat_Exchanger
Fouling
momar210/kinetic-modelling
Code related to the paper on regularization and concave loss functions for estimation of chemical kinetic models
momar210/lstm_anomaly_thesis
Anomaly detection for temporal data using LSTMs
momar210/MathWorks-Excellence-in-Innovation
Capstone and senior design project ideas for undergraduate and graduate students to gain practical experience and insight into technology trends and industry directions.
momar210/microfluidics-inertial-lift
Tools for simulating particle migration, focusing, and separation in inertial microfluidic devices with ANSYS Fluent
momar210/RBF-Network
Radial Basis Function Network Implementation.
momar210/rbf_keras
RBF layer for Keras
momar210/Supplementary-Data-Codes
This folder contains the source codes and data for the metallic glass paper
momar210/ThesisUESTC
ThesisUESTC-电子科技大学毕业论文模板
momar210/Underwater-Acoustic-Target-Classification-Based-on-Dense-Convolutional-Neural-Network
In oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. Expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly re-use all former feature maps to optimize classification rate under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing original audio signal in time domain as the network input data. Based on the experimental results evaluated on the real-world dataset of passive sonar, our classification model achieves the overall accuracy of 98.85$\%$ at 0 dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.