Changan Automobile Applies for Driving Style Recognition Patent: Can Expert Models Revolutionize Future Driving Experience?

Changan Automobile Applies for Driving Style Recognition Patent, Leading a New Trend in Intelligent Driving ...

Changan Automobile Applies for Driving Style Recognition Patent: Can AI See Through Your Driving Habits?

Data shows that over 60% of drivers exhibit irregular behaviors while driving, such as sudden acceleration and frequent lane changes. These behaviors not only affect driving safety but may also ...
IEEE

XChange: An Explainable Dynamic Convolutional Autoencoder for Unsupervised Change Detection

Abstract: Explainable Artificial Intelligence (XAI) approaches started to be studied in the last period to improve the interpretability of increasingly complex Deep Learning (DL) methods for Remote ...
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 ...
Scientific Research Publishing

Ma, J., Zhao, J. and Hou, Y. (2024) Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model. Sensors, 24, Article No. 5502.

ABSTRACT: Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction ...
IEEE

An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing

Abstract: Spectral unmixing is an important technique in remote sensing for analyzing hyperspectral images to identify endmembers and estimate fractional abundance maps. Over the past few decades, ...
Frontiers

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly ...
GitHub

A quantised temporal convolutional autoencoder deployed on the PYNQ-Z2 FPGA using the FINN framework.

The model architecture, training, and validation can be found in the QuantisedAutoEncoder.ipynb Python Jupyter Notebook This part of the project was done using the Google Drive and Google Colab, and ...
Frontiers

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM

Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect ...
Oak Ridge National Lab

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event ...