58 citations · 77 across the 10 of their papers we have counts for
10 papers
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport
Songpengcheng Xia, Lei Chu, Ling Pei +3
Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted…
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Songpengcheng Xia, Lei Chu, Ling Pei +2
Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR…
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors
Dongrui Liu, Chuanchuan Chen, Changqing Xu +2
As a fundamental yet challenging problem in intelligent transportation systems, point cloud registration attracts vast attention and has been attained with various deep learning-ba…
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model
Ling Pei, Songpengcheng Xia, Lei Chu +4
Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technolog…
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation
Dongrui Liu, Chuanchuan Chen, Changqing Xu +4
Point cloud recognition is an essential task in industrial robotics and autonomous driving. Recently, several point cloud processing models have achieved state-of-the-art performan…
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
Fanyi Xiao, Ling Pei, Lei Chu +4
Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement un…
Improving Power System State Estimation Based on Matrix-Level Cleaning
Haosen Yang, Robert C. Qiu, Lei Chu +3
Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Trad…
Eigen-Inference Precoding for Coarsely Quantized Massive MU-MIMO System with Imperfect CSI
Lei Chu, Robert Qiu, Fei Wen
This work considers the precoding problem in massive multiuser multiple-input multiple-output (MU-MIMO) systems equipped with low-resolution digital-to-analog converters (DACs). In…
Early Anomaly Detection and Location in Distribution Network: A Data-Driven Approach
Xin Shi, Robert Qiu, Xing He +3
The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network e…
Invisible Units Detection and Estimation Based on Random Matrix Theory
Xing He, Lei Chu, Robert C. Qiu +3
Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does sig…