GitHub

Autoencoder-based anomaly detection for sensor data using MATLAB

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how ...
IEEE

Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers

Abstract: The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these ...
Nature

Enhancing cybersecurity in virtual power plants by detecting network based cyber attacks using an unsupervised autoencoder approach

The increasing adoption of the Internet of Things (IoT) in energy systems has brought significant advancements but also heightened cyber security risks. Virtual Power Plants (VPPs), which aggregate ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
EDN

Applying AI to RF design

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...
Frontiers

Autoencoder-based myoelectric controller for prosthetic hands

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, ...
IEEE

Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Neural Networks on the DIII-D Tokamak

Abstract: Magnetic diagnostics in tokamaks are key to plasma equilibrium control (plasma current, plasma shape, and position) and amelioration of plasma instabilities. Thus, real-time identification ...
Frontiers

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy ...
Nature

Autoencoder based blind source separation for photoacoustic resolution enhancement

Photoacoustic (PA) tomography is a multiscale imaging method, utilizing both minimal scattering of acoustic waves and excellent contrast of optical absorption. The lateral resolution in photoacoustics ...
C&EN

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon ...