Pinned Repositories
JavaWork
It's best for myself
NTU-Machine-learning
台湾大学李宏毅老师机器学习
coursera-gan-specialization
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
Human-Activity-Recognition
Multimodal human activity recognition using wrist-worn wearable sensors.
Human-Activity-Recognition-using-LSTM
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. Description of experiment The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. 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. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
ideaTaotao
使用dubbo+springboot改造淘淘商城（宜立方商城）
MY-Blogs
Make a little progress Everyday
opencode
Selective-Kernel-Convolution
This repository provides the codes and data used in our paper "Deep neural networks for sensor based human activity recognition using selective kernel convolution".
duanfurong's Repositories
duanfurong/unified-activity-segmentation-and-recognition
Source code for the AIJ-21 paper "Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions".
duanfurong/Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
duanfurong/Selective-Kernel-Convolution
This repository provides the codes and data used in our paper "Deep neural networks for sensor based human activity recognition using selective kernel convolution".
duanfurong/coursera-gan-specialization
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
duanfurong/Human-Activity-Recognition
Multimodal human activity recognition using wrist-worn wearable sensors.
duanfurong/opencode
duanfurong/spring-boot-examples
about learning Spring Boot via examples. Spring Boot 教程、技术栈示例代码，快速简单上手教程。
duanfurong/Human-Activity-Recognition-using-LSTM
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. Description of experiment The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. 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. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
duanfurong/NTU-Machine-learning
**大学李宏毅老师机器学习
duanfurong/MY-Blogs
Make a little progress Everyday
duanfurong/JavaWork
It's best for myself
duanfurong/ideaTaotao
使用dubbo+springboot改造淘淘商城（宜立方商城）
duanfurong/singular-spectrum-transformation
fast implementation of singular spectrum transformation (change point detection algorithm)