Výsledky vyhledávání - "Zhang, Xuewen"

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Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning

Autor: Zhang, Xuewen, Zhang, Kaixiang, Li, Zhaojian, Yin, Xunyuan

Data-enabled predictive control (DeePC) is a data-driven control algorithm that utilizes data matrices to form a non-parametric representation of the underlying system, predicting future behaviors and generating optimal control actions. DeePC typical

Externí odkaz: http://arxiv.org/abs/2408.16338

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Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes

Autor: Li, Xiaojie, Bo, Song, Zhang, Xuewen, Qin, Yan, Yin, Xunyuan

In this work, we consider a state estimation problem for large-scale nonlinear processes in the absence of first-principles process models. By exploiting process operation data, both process modeling and state estimation design are addressed within a

Externí odkaz: http://arxiv.org/abs/2404.06746

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Reduced-order Koopman modeling and predictive control of nonlinear processes

Autor: Zhang, Xuewen, Han, Minghao, Yin, Xunyuan

Publikováno v: Computers & Chemical Engineering, 2023, 179, p.108440

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting

Externí odkaz: http://arxiv.org/abs/2404.00553

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M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset

Autor: Lu, Yawen, Huang, Yunhan, Sun, Su, Zhang, Tansi, Zhang, Xuewen, Fei, Songlin, Chen, Yingjie

Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark

Externí odkaz: http://arxiv.org/abs/2402.04534

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