zhen001 Goto Github PK

copytranslator

Foreign language reading and translation assistant based on copy and translate.

daily_activity_prediction

Pattern Recognition using sensor data and Machine Learning.

datafault_detection

Data fault detection algorithms in MATLAB.

dazhangshm

Data Processing for Dazhang Bridge

deep-learning-based-structural-damage-detection

Structural damage detection using convolutional neural networks

deep-learning-for-signal-analysis

To show how can we build a model using transfer learning techniques to classify signal data.

deep-neural-network-based-approaches-in-wind-time-series-forecasting

This respiratory contains the implementation codes of wind-speed prediction in energy forecasting. This study focused on deep neural network based approaches, like the nonlinear autoregressive exogenous inputs (NARX), nonlinear input-output (NIO), and nonlinear autoregressive (NAR) neural network models, in time-series forecasting applications. The idea was to propose NARX neural network based prediction models in wind-speed forecasting for the short-term scheme. The meteorological parameters related to wind time-series (e.g., temperature, pressure, wind speed, wind direction) were analyzed and used to evaluate the proposed models' performance.

design-of-smart-material-systems-nc-state

The objective of this project is to reduce vibrations of a cantilever beam by optimal placement of collocated piezoelectric sensor/actuator pairs.

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.

digitaltwinfordamagedetection

My undergraduate final paper

distributed-displacement-monitoring-using-smartphones

Smartphone based Structural Health Monitoring (SHM)

dream

Markov Chain Monte Carlo acceleration by Differential Evolution

eigenvalue-identification

This folder contains codes for eigenvalue identification from time-domain signals.

eigenvalue-sensitivity

ekfukf

Time-varying frequency Estimation of narrow-band signals via Extended Kalman Filter and Unscented Kalman Filter. The two methods are compared and a thorough study on the influence of the parameters is performed, along with numerical considerations. Assignment for the Model Identification and Adaptive Systems course @Polimi, 2017-2018

em-for-bayoma

Bayesian operational modal analysis based on the expectation-maximization algorithm.

end_to_end_machine_learning_framework

A scalable machine learning system that handles on-going data cleaning, model training, model updating, and prediction for large-scale structured data.

engineeringpatternrecognition

Code to reproduce paper results (or as close as possible, depending on data-availability). Each publication has a Jupyter notebook. Mostly probabilistic/Bayesian ML for engineering applications, particularly performance and health monitoring.

engineeringtransferlearning

era-software

Software developed for ERA Group (TUM)

fade_bms

Bayesian model selection for the FADE Paradigm

fanqiang

翻墙-科学上网

fbg-simulation

Adapted work from user "GilmarPereira": Fiber Bragg Grating Simulation Tool for Finite Element Method Models (Updated Version to include Temperature Dependency)

fditools

Frequency Domain Identification Matlab Toolbox

fe_model_updating_for_solid_beam

In short, this script is setting up, running, and processing the results of an ABAQUS simulation, and then comparing these results to experimental data. The script is designed to be used in an optimization loop, where the `parameter` input would be updated in each iteration to try and minimize the output of the `fitness` function

fluctuationanalysis

Matlab implementation of DFA with (Bayesian) model selection

fourstoryshearbuilding

Modal Analysis of a 4 Story Shear Building

frequency-domain-em-sensitivity

google-access-helper

谷歌访问助手破解版、谷歌翻墙、谷歌梯子、谷歌梯子扩展工具、谷歌商店访问、Chrome翻墙

grey-relational-analysis