A5103788516- Energy Load and Power Forecasting
- Speech Recognition and Synthesis
- Electrocatalysts for Energy Conversion
- Network Packet Processing and Optimization
- Face and Expression Recognition
- Innovative Microfluidic and Catalytic Techniques Innovation
- Water Systems and Optimization
- Water resources management and optimization
- Parallel Computing and Optimization Techniques
- Transportation and Mobility Innovations
- Electric Vehicles and Infrastructure
- Neural Networks and Applications
- Nanopore and Nanochannel Transport Studies
- Blind Source Separation Techniques
- Fuel Cells and Related Materials
- Air Quality Monitoring and Forecasting
- Transition Metal Oxide Nanomaterials
- Grey System Theory Applications
- Building Energy and Comfort Optimization
- Advanced Clustering Algorithms Research
- Surface Chemistry and Catalysis
- Fault Detection and Control Systems
- Advanced battery technologies research
- Gas Sensing Nanomaterials and Sensors
- Advanced Decision-Making Techniques
Tongji University
2023-2025
National University of Singapore
2024-2025
Southeast University
2017
The preactivation of reactants within the cavities carbon nanotubular materials has remained largely unexplored due to scarcity with well-defined sizes and precisely engineered doping sites. Herein, we demonstrate that catalytic activity toward oxygen reduction reaction (ORR) is primarily governed by cavity nanobelt doped sp2-nitrogen atoms. Our results show confinement effect induced size electron-rich chemical environment are crucial for O2 adsorption preactivation, leading enhanced...
Traditional performance analysis aiming at OMC data mainly focuses on two aspects: longitudinal comparison of the same network element in different time periods and horizontal elements time. It's lack association between indexes in-depth mining. In this paper, a approach based correlation regression is presented which focusing solving difficult problem that general diagnosis algorithms can hardly build for CDMA optimization. Based characteristic indexes, statistical method was introduced to...
With the rapidly increasing applications of deep learning, LSTM-RNNs are widely used. Meanwhile, complex data dependence and intensive computation limit performance accelerators. In this paper, we first proposed a hybrid network expansion model to exploit finegrained parallelism. Based on model, implemented Reconfigurable Processing Unit(RPU) using Memory(PIM) units. Our work shows that gates cells in LSTM can be partitioned fundamental operations then recombined mapped into heterogeneous...