Search Results for author: Shengyuan Xu

Found 8 papers, 1 papers with code

Quantized distributed Nash equilibrium seeking under DoS attacks: A quantized consensus based approach

no code implementations24 Aug 2023 Shuai Feng, Maojiao Ye, Lihua Xie, Shengyuan Xu

To solve the quantizer saturation problem caused by DoS attacks, the quantization mechanism is equipped to have zooming-in and holding capabilities, in which the holding capability is consistent with the results in quantized consensus under DoS.

Quantization

Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor

no code implementations1 Jun 2023 Shuai Feng, Maopeng Ran, Hideaki Ishii, Shengyuan Xu

This paper deals with dynamic quantized consensus of dynamical agents in a general form under packet losses induced by Denial-of-Service (DoS) attacks.

Quantization

Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning

no code implementations27 Sep 2022 Yang Li, Shenglan Yuan, Shengyuan Xu

The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields.

A Two-phase On-line Joint Scheduling for Welfare Maximization of Charging Station

no code implementations22 Aug 2022 Qilong Huang, Qing-Shan Jia, Xiang Wu, Shengyuan Xu, Xiaohong Guan

First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers.

Model Predictive Control Scheduling

Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network

1 code implementation12 Jun 2022 Zongyuan Huang, Shengyuan Xu, Menghan Wang, Hansi Wu, Yanyan Xu, Yaohui Jin

Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods.

Multi-Task Learning

RefineGAN: Universally Generating Waveform Better than Ground Truth with Highly Accurate Pitch and Intensity Responses

no code implementations1 Nov 2021 Shengyuan Xu, Wenxiao Zhao, Jing Guo

To address this problem, we propose RefineGAN, a high-fidelity neural vocoder focused on the robustness, pitch and intensity accuracy, and high-speed full-band audio generation.

Audio Generation Generative Adversarial Network +1

Extracting stochastic dynamical systems with $α$-stable Lévy noise from data

no code implementations30 Sep 2021 Yang Li, Yubin Lu, Shengyuan Xu, Jinqiao Duan

Despite the wide applications of non-Gaussian fluctuations in numerous physical phenomena, the data-driven approaches to extract stochastic dynamical systems with (non-Gaussian) L\'evy noise are relatively few so far.

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