Area of research
Computer Vision and Pattern Recognition · Artificial Intelligence
Research interest
Research interests include Computer science, Artificial intelligence, Electromagnetic shielding, Materials science, Pattern recognition (psychology), and Physics.
BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration
Can GNNs Learn Link Heuristics? a Concise Review and Evaluation of Link Prediction Methods
Protective CaSO <sub>4</sub> ‐Rich Interphase in Dual‐Intercalated Vanadium‐Based Cathodes for Zinc‐Ion Battery Performance Enhancement
Analysis of Factors Influencing the Shielding Effectiveness of Multi-layer Cavity Based on BLT Equation
The prediction of shielding effectiveness of the rectangular enclosure with complex apertures based on the BLT equation
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
A systematic spatial-variably volumetric error model and machining optimization method based on continuous moving support variation of machine tool
A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification
Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval
Conditional mixture modeling and model-based clustering
Structural Design and Simulation of a High Performance Electromagnetic Shielding Cabinet Door
Design of a Shielding Box for Interference of Switching Power Supply at L-Band
Structure design and Simulation of a high performance double-layer shielding box
Failure Prediction for Large-scale Water Pipe Networks Using GNN and Temporal Failure Series
Joint Input and Output Space Learning for Multi-Label Image Classification
Deep Degradation Prior for Low-Quality Image Classification
Statistical tolerance allocation design considering form errors based on rigid assembly simulation and deep Q-network
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
Smart pothole detection system using vehicle-mounted sensors and machine learning
Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference
Selecting high-quality negative samples for effectively predicting protein-RNA interactions
Solid-state synthesis of Y-doped ZnO nanoparticles with selective-detection gas-sensing performance
Solid-state chemical synthesis of mesoporous α-Fe2O3 nanostructures with enhanced xylene-sensing properties
GSR and Blink Features for Cognitive Load Classification