w1074098501's repositories
TrafficGPT1
This repository contains the codes of the publication "TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic Classification".
Domain-generalization-fault-diagnosis-benchmark
This is a benckmark for domain generalization-based fault diagnosis （基于领域泛化的相关代码）
Awsome-Multi-modal-based-PHM
Awsome-Multi-modal-based PHM （基于多模态的故障诊断和预测，持续更新）
DG-PHM
This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测，持续更新)
grok-1
Grok open release
HUSTbearing-dataset
This reposotory release a bearing failure dataset, which can support intelliegnt fault diagnosis research（实验室自采轴承开源数据集）
transfer-learning-forecasting
The repository for load forecasting through Transfer Learning techniques
pymc
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
HNUIDG-Fault-Diagnosis-
The intelligent fault diagnosis of HNU IDG
awesome-trustworthy-deep-learning
A curated list of trustworthy deep learning papers. Daily updating...
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
tlbook-code
Code for Transfer Learning book--《迁移学习导论》配套代码
strath_internship22
This is all the work done for the summer internship: Digital Twinning Through Physics Informed Machine Learning. A Vibrating Bearing Case Study
pinn
Physics-informed neural networks package
RMT4ML
Matlab Notebook for visualizing random matrix theory results and their applications to machine learning
deep-transfer-learning
A collection of implementations of deep domain adaptation algorithms
Multi-Objective-Optimization-Under-Uncertainty-of-Part-Quality-in-Fused-Filament-Fabrication
Multi-objective optimization
Reliability-Resilience-Assessment
Reliability
Physics-Informed-and-Hybrid-Machine-Learning-in-Additive-Manufacturing
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
Process-Optimization-Under-Uncertainty-for-Improving-the-Bond-Quality-of-Polymer-Filaments-in-Fused-
This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabrication (FFF). A one-dimensional heat transfer analysis providing an estimate of the temperature profile of the filaments is coupled with a sintering neck growth model to assess the bond quality that occurs at the interfaces between adjacent filaments. Predicting the variability in the FFF process is essential for achieving proactive quality control of the manufactured part; however, the models used to predict the variability are affected by assumptions and approximations. This paper systematically quantifies the uncertainty in the bond quality model prediction due to various sources of uncertainty, both aleatory and epistemic, and includes the uncertainty in the process parameter optimization. Variance-based sensitivity analysis based on Sobol' indices is used to quantify the relative contributions of the different uncertainty sources to the uncertainty in the bond quality. A Gaussian process (GP) surrogate model is constructed to compute and include the model error within the optimization. Physical experiments are conducted to show that the proposed formulation for process parameter optimization under uncertainty results in high bond quality between adjoining filaments of the FFF product.
Digital-twin-approach-for-damage-tolerant-mission-planning-under-uncertainty
The digital twin paradigm that integrates the information obtained from sensor data, physics models, as well as operational and inspection/maintenance/repair history of a system (or a component) of interest, can potentially be used to optimize operational parameters of the system in order to achieve a desired performance or reliability goal. In this article, we develop a methodology for intelligent mission planning using the digital twin approach, with the objective of performing the required work while meeting the damage tolerance requirement. The proposed approach has three components: damage diagnosis, damage prognosis, and mission optimization. All three components are affected by uncertainty regarding system properties, operational parameters, loading and environment, as well as uncertainties in sensor data and prediction models. Therefore the proposed methodology includes the quantification of the uncertainty in diagnosis, prognosis, and optimization, considering both aleatory and epistemic uncertainty sources. We discuss an illustrative fatigue crack growth experiment to demonstrate the methodology for a simple mechanical component, and build a digital twin for the component. Using a laboratory experiment that utilizes the digital twin, we show how the trio of probabilistic diagnosis, prognosis, and mission planning can be used in conjunction with the digital twin of the component of interest to optimize the crack growth over single or multiple missions of fatigue loading, thus optimizing the interval between successive inspection, maintenance, and repair actions.
keras
Deep Learning for humans
Spatial-Temporal-Attention-Network-for-POI-Recommendation
Codes for a WWW'21 Paper. A state-of-the-art recommender system for location/trajectory prediction.
Deep_Learning_Weather_Forecasting
Deep Learning for Weather Forecasting, accepted applied data science of KDD 2019