Výsledky vyhledávání

Akademický článek

Research on Low Voltage Ride Through Technology of DFIG Based on Deep Neural Networks

Autor: YU Xinmei, CHEN Haojun, WANG Xinghua

Publikováno v: 南方能源建设, Vol 8, Iss 3, Pp 122-130 (2021)

[Introduction] During the power grid fault, the low voltage ride through (LVRT) performance of the doubly-fed induction generator (DFIG) depends on the control parameters. At present, the optimization of control parameters is basically in the offline

Externí odkaz: https://doaj.org/article/ceab3cd38ad545ca8ba5d46b0ce32bc3

Zobrazit plný text záznamu

Report

SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters

Autor: Lin, Shengsheng, Lin, Weiwei, Wu, Wentai, Chen, Haojun, Yang, Junjie

This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resourc

Externí odkaz: http://arxiv.org/abs/2405.00946

Zobrazit plný text záznamu

Report

Panacea: Pareto Alignment via Preference Adaptation for LLMs

Autor: Zhong, Yifan, Ma, Chengdong, Zhang, Xiaoyuan, Yang, Ziran, Chen, Haojun, Zhang, Qingfu, Qi, Siyuan, Yang, Yaodong

Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogeneous nature of human preferences, leading to reduced expressivity and e

Externí odkaz: http://arxiv.org/abs/2402.02030

Zobrazit plný text záznamu

Report

Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks

Autor: Wang, Jun, Chen, Haojun, Sun, Zihe, Kantaros, Yiannis

Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captured by Linear Temporal Logic (LTL). Due to the sample-inefficiency of

Externí odkaz: http://arxiv.org/abs/2311.10863

Zobrazit plný text záznamu

Report

MANSA: Learning Fast and Slow in Multi-Agent Systems

Autor: Mguni, David, Chen, Haojun, Jafferjee, Taher, Wang, Jianhong, Fei, Long, Feng, Xidong, McAleer, Stephen, Tong, Feifei, Wang, Jun, Yang, Yaodong

In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be inefficient and runs the risk of failing to successfully train, particularly

Externí odkaz: http://arxiv.org/abs/2302.05910

Zobrazit plný text záznamu