hilbert-qyw Goto Github PK
Name: Yuwen Qin
Type: User
Company: Xi'an Jiaotong University
Bio: I am currently a Ph.D. candidate in the School of Electrical Engineering at Xi'an Jiaotong University.
Location: Xi'an City , Shanxi Province
Name: Yuwen Qin
Type: User
Company: Xi'an Jiaotong University
Bio: I am currently a Ph.D. candidate in the School of Electrical Engineering at Xi'an Jiaotong University.
Location: Xi'an City , Shanxi Province
Tensorflow implementation of Restricted Boltzmann Machine
project test
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, etc.)
An example of time series augmentation methods with Keras
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Everything about Transfer Learning and Domain Adaptation--迁移学习
A Python package for time series augmentation
PyTorch tutorials.
The code is for the paper "Ma et al. A Two-Stage Integrated Method for Early Prediction of Remaining Useful Life of Lithium-ion Batteries"
Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM
Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Increased 3x3 wind farm output by 18.5\% using NREL's FLORIS library in Python with optimum yaw angles to each wind turbine Studied 4 wake models, fatigue life of turbine, optimal power outputs for 3x3 wind farm caused by yaw misalignment of wind turbines
For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.
Analysis of NREL Wind Toolkit dataset using AWS clusters
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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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.