Pinned Repositories
ATSPM
ATSPM Data Visulization
ATSPM-Data-Process
ATSPM-Data-Process
AttentionConvLSTM
"Attention in Convolutional LSTM for Gesture Recognition" in NIPS 2018
Copy-Files-across-Folders
CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++
DeepST
Deep Learning for Spatio-Temporal Data
DMVST-Net
DMVST-Net for AAAI 2018
Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables
The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
opencv
Open Source Computer Vision Library
yuanjinghui's Repositories
yuanjinghui/ATSPM
ATSPM Data Visulization
yuanjinghui/ATSPM-Data-Process
ATSPM-Data-Process
yuanjinghui/AttentionConvLSTM
"Attention in Convolutional LSTM for Gesture Recognition" in NIPS 2018
yuanjinghui/Copy-Files-across-Folders
yuanjinghui/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++
yuanjinghui/DeepST
Deep Learning for Spatio-Temporal Data
yuanjinghui/DMVST-Net
DMVST-Net for AAAI 2018
yuanjinghui/Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables
The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
yuanjinghui/opencv
Open Source Computer Vision Library
yuanjinghui/Smart-City-K-means-Decision-Tree-GB
yuanjinghui/Smart-City-KNN
yuanjinghui/Smart-City-SVR-ANN
yuanjinghui/Statistical-Computing-HW1
Homework1
yuanjinghui/Statistical-Computing-Project-2
Project 2
yuanjinghui/Statistical-Computing-Project1
Project and assignment for Statistical Computing
yuanjinghui/STDN
Code for our Spatiotemporal Dynamic Network
yuanjinghui/STGCN_IJCAI-18
Spatio-Temporal Graph Convolutional Networks
yuanjinghui/VDOTSmartRoadDataDownloader
yuanjinghui/yuanjinghui.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
yuanjinghui/yuanjinghui1.github.io
:sparkles: Build a beautiful and simple website in literally minutes. Demo at http://deanattali.com/beautiful-jekyll