Zobrazeno 1 - 10
of 1 662
pro vyhledávání: '"Shi Yuanyuan"'
Publikováno v:
智慧农业, Vol 6, Iss 4, Pp 76-90 (2024)
ObjectiveNowadays most no contact body size measurement studies are based on point cloud segmentation method, they use a trained point cloud segmentation neural network to segment point cloud of pigs, then locate measurement points based on them. But
Externí odkaz:
https://doaj.org/article/25924140a4c44731a8e8c3a8b41647ff
Predictor feedback designs are critical for delay-compensating controllers in nonlinear systems. However, these designs are limited in practical applications as predictors cannot be directly implemented, but require numerical approximation schemes. T
Externí odkaz:
http://arxiv.org/abs/2411.18964
Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation. Previous work tackles this challenge by training on the source domain with modified reward
Externí odkaz:
http://arxiv.org/abs/2411.09891
Effective control of real-world systems necessitates the development of controllers that are not only performant but also interpretable. To this end, the field has seen a surge in model-based control policies, which first leverage historical data to
Externí odkaz:
http://arxiv.org/abs/2411.07484
In this work, we introduce a planning neural operator (PNO) for predicting the value function of a motion planning problem. We recast value function approximation as learning a single operator from the cost function space to the value function space,
Externí odkaz:
http://arxiv.org/abs/2410.17547
The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solution to alleviate performance degradation is using power electronics in
Externí odkaz:
http://arxiv.org/abs/2408.15436
Neural operator approximations of the gain kernels in PDE backstepping has emerged as a viable method for implementing controllers in real time. With such an approach, one approximates the gain kernel, which maps the plant coefficient into the soluti
Externí odkaz:
http://arxiv.org/abs/2407.01745
We propose CoNSAL (Combining Neural networks and Symbolic regression for Analytical Lyapunov function) to construct analytical Lyapunov functions for nonlinear dynamic systems. This framework contains a neural Lyapunov function and a symbolic regress
Externí odkaz:
http://arxiv.org/abs/2406.15675
This paper develops a risk-aware net demand forecasting product for virtual power plants, which helps reduce the risk of high operation costs. At the training phase, a bilevel program for parameter estimation is formulated, where the upper level opti
Externí odkaz:
http://arxiv.org/abs/2406.10434
Autor:
Feng, Jie, Muralidharan, Manasa, Henriquez-Auba, Rodrigo, Hidalgo-Gonzalez, Patricia, Shi, Yuanyuan
The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the pow
Externí odkaz:
http://arxiv.org/abs/2405.20489