whigg Goto Github PK
Type: User
Company: IGG,CAS
Type: User
Company: IGG,CAS
A Jupyter notebook demo of the latest GMT/Python. Runs on mybinder.org.
Small repo answering intro questions to project
GNSS Time Series Coordinates Analysis Packages.
GNSS Time Series Coordinates Analysis Packages.
TSG-QC is a software for interactive analysis and validation of underway SST / SSS (Sea Surface Temperature and Sea Surface Salinity) measurements from a SeaBird Thermosalinograph (TSG). It has been developed under Matlab.
R package that estimates water level time series from satellite altimetry data
Branch and block, dynamic programming, GA, ACO, SOM for TSP(Traveling Salesman Problem) problem
Traveling Santa Problem Formulated as QUBO for D-Wave Quantum Annealer
Traveling Salesman Problem solved using Genetic Algorithms (GA)
A Solution to the Travelling Salesman Problem using Genetic Algorithms
tsp using GA algorithm in matlab
TSP using Population Base SA algorithm in Matlab
TSP using SA algorithm in Matlab
TSP using Taboo algorithm in Matlab
Implementation of TSP and VRP algorithms using a Genetic Algorithm
TSP算法全复现:遗传(GA)、粒子群(PSO)、模拟退火(SA)、禁忌搜索(ST)、蚁群算法(ACO)、自自组织神经网络(SOM)
Python code for visualizations of algorithms that provide approximate solutions to TSP along with two lower bound approximations
Another traveling salesman problem
Geokinematic interpretation of Slovakia based on permanent GNSS stations
A Python package to store time series data in HDF5 files using PyTables
Visualize time series obtained from Google Earth Engine and run pyccd on them.
Geohackweek Tutorial Contents
sandbox for developing tutorial codes for 2019 hackweek
Generating terrestrial water storage estimates from GPS displacement data.
Fupeng Li's code for reconstructing total water storage content
C# code for predicting oceanic tides
# typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA. ## Requirements * BeautifulSoup * PIL * Pytorch ## Usage 1. Run `download.py`, download the satellite images of typhoons to folder `tys_raw`. 2. Run `create_samples.py`, convert raw data into the legal samples for our CNN, create two new forlder `train_set` and `test_set`. 3. Train CNN using `train_net.py`, the trained CNN will be saved as a disk file `net_relu.pt`. 4. Run `test_net.py`, analysis the test set. After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set. ![](https://raw.githubusercontent.com/melissa135/deep_typhoon/master/loss_sequence.png) Here is what this CNN thinks of the top 20 typhoons sorted by max wind. ``` 1 ('197920', 130.27679443359375) 2 ('200914', 127.7662582397461) 3 ('199019', 122.92172241210938) 4 ('200918', 122.84004211425781) 5 ('201614', 122.66597747802734) 6 ('201601', 122.03250885009766) 7 ('201513', 121.75947570800781) 8 ('200922', 121.35771942138672) 9 ('201013', 120.0194091796875) 10 ('201330', 118.92587280273438) 11 ('201419', 117.6025390625) 12 ('198305', 117.10270690917969) 13 ('201422', 116.77259063720703) 14 ('198522', 116.46116638183594) 15 ('201327', 116.42304992675781) 16 ('201216', 116.36921691894531) 17 ('198221', 116.18096923828125) 18 ('199230', 115.96656799316406) 19 ('198210', 115.96611022949219) 20 ('201328', 115.57132720947266) ``` ## Tips * Memory should be at least 1.5G . * This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training. * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).
u-blox GNSS receiver library light weight for low power tracking application
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.