Zobrazeno 1 - 10
of 1 182
pro vyhledávání: '"Wu Chenxi"'
Publikováno v:
Zhejiang dianli, Vol 41, Iss 10, Pp 34-41 (2022)
To improve the transmission line status evaluation accuracy， the paper proposes a transmission line status portrait and assessment model based on clustering and later regression. Firstly， the self-organizing neural network （SONN） is designed
Externí odkaz:
https://doaj.org/article/cff4e924181b47ddac49b2161d3f08c9
Publikováno v:
E3S Web of Conferences, Vol 522, p 01014 (2024)
The advancement of unmanned aerial vehicle (UAV) and remote sensing technologies has fueled interest in automatic UAV inspection path planning based on inspection tasks. However, traditional methods suffer from limitations such as manual operation, i
Externí odkaz:
https://doaj.org/article/81b15002debd47b7bba5fe996f957ff1
We prove that the principal minors of the distance matrix of a tree satisfy a combinatorial expression involving counts of rooted spanning forests of the underlying tree. This generalizes a result of Graham and Pollak. We also give such an expression
Externí odkaz:
http://arxiv.org/abs/2411.11488
Autor:
Toscano, Juan Diego, Oommen, Vivek, Varghese, Alan John, Zou, Zongren, Daryakenari, Nazanin Ahmadi, Wu, Chenxi, Karniadakis, George Em
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the pa
Externí odkaz:
http://arxiv.org/abs/2410.13228
Autor:
He, Yan Mary, Wu, Chenxi
We study endperiodic maps of an infinite graph with finitely many ends. We prove that any such map is homotopic to an endperiodic relative train track map. Moreover, we show that the (largest) Perron-Frobenius eigenvalue of the transition matrix is a
Externí odkaz:
http://arxiv.org/abs/2408.13401
Polynomial neural networks have been implemented in a range of applications and present an advantageous framework for theoretical machine learning. A polynomial neural network of fixed architecture and activation degree gives an algebraic map from th
Externí odkaz:
http://arxiv.org/abs/2408.04569
Every pseudo-Anosov flow $\phi$ in a closed $3$-manifold $M$ gives rise to an action of $\pi_1(M)$ on a circle $S^{1}_{\infty}(\phi)$ from infinity \cite{Fen12}, with a pair of invariant \emph{almost} laminations. From certain actions on $S^{1}$ with
Externí odkaz:
http://arxiv.org/abs/2407.07634
The new polymath Large Language Models (LLMs) can speed-up greatly scientific reviews, possibly using more unbiased quantitative metrics, facilitating cross-disciplinary connections, and identifying emerging trends and research gaps by analyzing larg
Externí odkaz:
http://arxiv.org/abs/2312.03769
Autor:
Richter, Ole, Wu, Chenxi, Whatley, Adrian M., Köstinger, German, Nielsen, Carsten, Qiao, Ning, Indiveri, Giacomo
Publikováno v:
Neuromorph. Comput. Eng. 4 (2024)
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract r
Externí odkaz:
http://arxiv.org/abs/2310.00564
Autor:
Zhang, Qian, Wu, Chenxi, Kahana, Adar, Kim, Youngeun, Li, Yuhang, Karniadakis, George Em, Panda, Priyadarshini
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural
Externí odkaz:
http://arxiv.org/abs/2308.16372