Search Results for author: Meng Yue

Found 8 papers, 0 papers with code

On Modeling Adequacy and Stability Analysis of IBR-related Subsynchronous Oscillations in Multimachine Systems

no code implementations12 Mar 2024 Lilan Karunaratne, Nilanjan Ray Chaudhuri, Amirthagunaraj Yogarathnam, Meng Yue

Further, we consider the quasistationary phasor calculus (QPC) framework that neglects transmission line, load, and SG stator dynamics to show its adequacy in SSO modeling and analysis.

Benchmarking

Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control

no code implementations20 Oct 2023 Ying Zhang, Meng Yue

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies.

reinforcement-learning

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

no code implementations13 Feb 2023 Fei Kong, Xiyue Wang, Jinxi Xiang, Sen yang, Xinran Wang, Meng Yue, Jun Zhang, Junhan Zhao, Xiao Han, Yuhan Dong, Biyue Zhu, Fang Wang, Yueping Liu

We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.

Contrastive Learning Ethics +2

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems

no code implementations21 Sep 2022 Yixuan Sun, Christian Moya, Guang Lin, Meng Yue

This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e. g. the power grid or traffic) with an underlying sub-graph structure.

Zero-Shot Learning

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction

no code implementations22 Jan 2022 Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin

Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers.

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

no code implementations25 Feb 2021 Ying Zhang, Meng Yue, Jianhui Wang

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies.

reinforcement-learning Reinforcement Learning (RL)

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