A dual-branch local-global graph convolutional network for tool wear prediction

MSCALNet: a multiscale convolutional attention LSTM network for IMU-based human activity recognition

This work proposes MSCALNet, a Multi-Scale Convolutional Attention LSTM Network, a Multi-Scale Convolutional Attention LSTM Network that employs a multi-branch differential encoding strategy to fuse heterogeneous sensor information, and efficiently models multi-timescale dynamics through a dilated convolutional pyramid...

A spatial multi-scale and adaptive filtering attention framework for industrial soft sensor

Soft sensors are vital for industrial monitoring but face severe challenges arising from complex spatiotemporal multi-scale dynamics and prevalent information redundancy. Existing deep learning methods often struggle to capture multi-scale dependencies and suffer from weak or redundant interactions introduced by fully...

SAT-GCN: Spatial-Aware Transformer-Enhanced Graph Convolutional Network for Skeleton-Based Action Recognition

Spatio-temporal local graph convolutional network for skeleton-based action recognition

A novel approach called Spatio-Temporal Local Graph Convolutional Network, which consists of a Partitioning Module (PM), a Spatio-Temporal Graph Convolution (ST-GC) module and a Joint Prototype Loss (JPL) function, which facilitates the capture of local relationships between joints.

Multi-Dimensional Feature Interaction Enabling Density-Driven Contrastive Learning for Skeleton-Based Action Recognition

MAC‐MLSTM: A Dual‐Branch Spatiotemporal Model With Adaptive Fusion and Uncertainty Quantification for RUL Prediction

Remaining useful life (RUL) prediction is a key component in ensuring equipment reliability and optimizing maintenance strategies. To address the limitations of existing models that overlook the dynamic correlations and heterogeneous contributions of spatiotemporal features in multi‐sensor data, as well as the lack o...