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Foreign language reading and translation assistant based on copy and translate.
Pattern Recognition using sensor data and Machine Learning.
Data fault detection algorithms in MATLAB.
Data Processing for Dazhang Bridge
Structural damage detection using convolutional neural networks
To show how can we build a model using transfer learning techniques to classify signal data.
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.
The objective of this project is to reduce vibrations of a cantilever beam by optimal placement of collocated piezoelectric sensor/actuator pairs.
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.
My undergraduate final paper
Smartphone based Structural Health Monitoring (SHM)
Markov Chain Monte Carlo acceleration by Differential Evolution
This folder contains codes for eigenvalue identification from time-domain signals.
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
Bayesian operational modal analysis based on the expectation-maximization algorithm.
A scalable machine learning system that handles on-going data cleaning, model training, model updating, and prediction for large-scale structured data.
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.
Software developed for ERA Group (TUM)
Bayesian model selection for the FADE Paradigm
翻墙-科学上网
Adapted work from user "GilmarPereira": Fiber Bragg Grating Simulation Tool for Finite Element Method Models (Updated Version to include Temperature Dependency)
Frequency Domain Identification Matlab Toolbox
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
Matlab implementation of DFA with (Bayesian) model selection
Modal Analysis of a 4 Story Shear Building
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
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.