no code implementations • 3 Sep 2024 • Xingjian Wu, Xiaoting Wang, Xiaozhe Wang, Peter E. Caines, Jingyu Liu
Transient stability and critical clearing time (CCT) are important concepts in power system protection and control.
no code implementations • 7 Aug 2024 • Xiaoting Wang, Yuzhuo Li, Yunwei Li, Gregory Kish
This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations.
no code implementations • 26 Jul 2024 • Zihao Cheng, Jiangbo Si, Zan Li, Pengpeng Liu, Xiaoting Wang, Naofal Al-Dhahir
In particular, when the frequency constraint is strict, MFDA can further increase the covert rate by adjusting the positions of antennas instead of the frequencies.
no code implementations • 19 Jan 2024 • Xiaoting Wang, Jingyu Liu, Xiaozhe Wang
This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation.
1 code implementation • 4 Dec 2023 • Yingli Yang, Zongkang Zhang, Anbang Wang, Xiaosi Xu, Xiaoting Wang, Ying Li
This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost, in terms of the sampling cost of quantum circuits.
no code implementations • 13 Jul 2023 • Xiaoting Wang, Rong-Peng Liu, Xiaozhe Wang, François Bouffard
In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis.
1 code implementation • 20 Apr 2023 • Chiaming Hsu, Changtong Zan, Liang Ding, Longyue Wang, Xiaoting Wang, Weifeng Liu, Fu Lin, Wenbin Hu
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
no code implementations • 20 Mar 2023 • Xiaoting Wang, Yanxiang Zhang
Spiking neural networks (SNNs) present a promising energy efficient alternative to traditional Artificial Neural Networks (ANNs) due to their multiplication-free operations enabled by binarized intermediate activations.
1 code implementation • 19 Aug 2022 • Yaosen Min, Ye Wei, Peizhuo Wang, Xiaoting Wang, Han Li, Nian Wu, Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Ji Wu, Dan Zhao, Jianyang Zeng
Here, an MD dataset containing 3, 218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories.
1 code implementation • 6 Jul 2022 • Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
no code implementations • 9 Jun 2022 • Jingyu Liu, Xiaoting Wang, Xiaozhe Wang
This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis.
no code implementations • 16 Sep 2021 • Xiaoting Wang, Rong-Peng Liu, Xiaozhe Wang, Yunhe Hou, François Bouffard
This letter proposes a data-driven sparse polynomial chaos expansion-based surrogate model for the stochastic economic dispatch problem considering uncertainty from wind power.
no code implementations • 19 Dec 2020 • Shaojun Wu, Shan Jin, Dingding Wen, Donghong Han, Xiaoting Wang
As an application, our method can solve the quantum state-generation problem in a single shot: it only requires a one-shot optimization to generate a model that outputs the desired control sequence for arbitrary target state.
no code implementations • 27 Oct 2020 • Xiaoting Wang, Xiaozhe Wang, Hao Sheng, Xi Lin
The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC).