momar210,github

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

$frackr icon$ 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

kinetic-modelling

Code related to the paper on regularization and concave loss functions for estimation of chemical kinetic models

lstm_anomaly_thesis

Anomaly detection for temporal data using LSTMs

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.

microfluidics-inertial-lift

Tools for simulating particle migration, focusing, and separation in inertial microfluidic devices with ANSYS Fluent

rbf-network

Radial Basis Function Network Implementation.

rbf_keras

RBF layer for Keras

supplementary-data-codes

This folder contains the source codes and data for the metallic glass paper

thesisuestc

ThesisUESTC-电子科技大学毕业论文模板

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.

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