Search Results for author: Fucai Ke

Found 5 papers, 0 papers with code

Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data

no code implementations20 Mar 2024 Fucai Ke, Hao Wang

To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data.

energy management Management +2

HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning

no code implementations19 Mar 2024 Fucai Ke, Zhixi Cai, Simindokht Jahangard, Weiqing Wang, Pari Delir Haghighi, Hamid Rezatofighi

Recent advances in visual reasoning (VR), particularly with the aid of Large Vision-Language Models (VLMs), show promise but require access to large-scale datasets and face challenges such as high computational costs and limited generalization capabilities.

Reinforcement Learning (RL) Visual Reasoning

Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving

no code implementations15 Mar 2024 Zheng Fang, Fucai Ke, Jae Young Han, Zhijie Feng, Toby Cai

The study opens new avenues for exploring the application of graph theory and reinforcement learning in social and behavioral sciences, highlighting the potential for empirical validation in future work.

reinforcement-learning

Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection

no code implementations4 Dec 2023 Cameron Martin, Fucai Ke, Hao Wang

Our experimental results demonstrate high-accuracy EV charging detection at the feeder level, achieving an F-Score of 98. 88% in offline detection and 93. 01% in online detection.

Management Non-Intrusive Load Monitoring

HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing

no code implementations23 Dec 2022 Fucai Ke, Weiqing Wang, Weicong Tan, Lan Du, Yuan Jin, Yujin Huang, Hongzhi Yin

Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.

Knowledge Tracing

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