Discover xuwkk/E2E-AT Open Source project by Wangkun Xu (@xuwkk)

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Python
Created 12 months ago
Updated 9 months ago

xuwkk

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This is the official repo for the paper E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning, to be appeared in AAAI-24.

Visual-Power-Grid

Power System simulation tool to visualize the steady state power system operation, such as optimal power flow and state estimation. The functions also include generating false data injection (FDI) attacks and detect by using Moving Target Defence (MTD).

Python

DDET-MTD

This repo contains all the codes and data for 'Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach'

Jupyter Notebook

gen_pandapower_pv

Network generation for paper Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks.

HTML

Robust_MTD

This repo contains code and visualisation for "Robust moving target defence against false data injection attacks in power grids"

Python

steady-state-power-system

A Python-based steady-state power system operation tool box, inherited from PyPower.

Jupyter Notebook

task_aware_machine_unlearning

This repo contains data and code for Task-Aware Machine Unlearning with Application to Load Forecasting.

Python

AAA_Load_Forecast

Python

adversarial_training_cifar

Implement the PGD-AT and TRADES for robust ResNet on CIFAR-10

Python

Anomaly-Detection-And-Forecasting

frequency_sample_ad

Python

power_system_operation

This repo contains some basic power system operations written in Python and formulated by cvxpy.

Python

blog_code

Python

blog

xuwkk/E2E-AT

xuwkk

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