A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data

no code implementations • 8 Mar 2024 • Yifan Wu, Yang Liu, Yue Yang, Michael S. Yao, Wenli Yang, Xuehui Shi, Lihong Yang, Dongjun Li, Yueming Liu, James C. Gee, Xuan Yang, Wenbin Wei, Shi Gu

Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification.

Interpretable Machine Learning

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances

no code implementations • 31 Jan 2024 • Ke Lu, Dongjun Li, Qun Wang, Kaidi Yang, Lin Zhao, Ziyou Song

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations.

Reinforcement Learning (RL) Safe Reinforcement Learning

Optimal battery thermal management for electric vehicles with battery degradation minimization

no code implementations • 6 Aug 2023 • Yue Wu, Zhiwu Huang, Dongjun Li, Heng Li, Jun Peng, Daniel Stroe, Ziyou Song

A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures.

Management

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

no code implementations • 2 Jun 2023 • Dongjun Li, Kaixiang Zhang, Haoxuan Dong, Qun Wang, Zhaojian Li, Ziyou Song

In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers.