A5101861070- Advanced materials and composites
- Advanced ceramic materials synthesis
- Text and Document Classification Technologies
- Diamond and Carbon-based Materials Research
- Rough Sets and Fuzzy Logic
- Dental materials and restorations
- Advanced Sensor and Energy Harvesting Materials
- Metal and Thin Film Mechanics
- Topic Modeling
- Innovative Energy Harvesting Technologies
- Powder Metallurgy Techniques and Materials
- Orthopaedic implants and arthroplasty
- Injection Molding Process and Properties
- Nanoparticles: synthesis and applications
- Bone Tissue Engineering Materials
- Wireless Power Transfer Systems
Huazhong University of Science and Technology
2015-2022
Wuhan National Laboratory for Optoelectronics
2022
Sichuan University of Science and Engineering
2019-2020
Sichuan University
2006-2016
Abstract Implantable ultrasonic energy harvesters that scavenge wireless mechanic from ultrasound own remarkable potential in advanced medical protocols for neuroprosthetics, power, biosensor, etc. The main challenge this kind of device is to achieve high‐efficiency conversion a weak pressure field. Here, multilayered piezoelectret with strain enhanced piezoelectricity by introducing parallel‐connected air hole array an interdielectric layer sandwiched between pair electrets efficient...
In this paper a series of silver ions-substituted hydroxyapatites (HA) were prepared. The antibacterial activities these materials on textiles against bacteria have been investigated. Titania (TiO2) was selectively added into the to decrease silver-ions concentration get same active antimicrobial effects. microstructure, shape and size, silver, groups composite characterized using transmission electron microscopy (TEM), infrared spectroscopy (IR), Atomic absorption (AAS), X-ray diffraction...
Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as frequency of each class typically different. The performance current mainstream learning algorithms suffers when training are highly imbalanced. problem can get worse categories with fewer severely undersampled to extent that variation within category not fully captured given data. At present, there few studies on which put...