Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Tu, Shenyinying"'
Autor:
Huang, Kaixuan, Wu, Yu, Zhang, Xuezhou, Tu, Shenyinying, Wu, Qingyun, Wang, Mengdi, Wang, Huazheng
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies followed a non-adaptive setting, where the seed nodes are selected before th
Externí odkaz:
http://arxiv.org/abs/2206.14846
Autor:
Aravena, Ignacio, Molzahn, Daniel K., Zhang, Shixuan, Petra, Cosmin G., Curtis, Frank E., Tu, Shenyinying, Wächter, Andreas, Wei, Ermin, Wong, Elizabeth, Gholami, Amin, Sun, Kaizhao, Sun, Xu Andy, Elbert, Stephen T., Holzer, Jesse T., Veeramany, Arun
The optimal power flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum cost operating point for an electric power grid while satisfying both engineering requirements and physical la
Externí odkaz:
http://arxiv.org/abs/2206.07843
Autor:
Curtis, Frank E., Molzahn, Daniel K., Tu, Shenyinying, Wächter, Andreas, Wei, Ermin, Wong, Elizabeth
A decomposition algorithm for solving large-scale security-constrained AC optimal power flow problems is presented. The formulation considered is the one used in the ARPA-E Grid Optimization (GO) Competition, Challenge 1, held from November 2018 thro
Externí odkaz:
http://arxiv.org/abs/2110.01737
The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. This paper proposes a scalable decomposition approach in w
Externí odkaz:
http://arxiv.org/abs/2002.08003
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gained popularity due to their eff
Externí odkaz:
http://arxiv.org/abs/1610.00040
This letter is focused on quantized Compressed Sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we provide a framework to optimize the quantization function and show that the recovered signal converges to the actual
Externí odkaz:
http://arxiv.org/abs/1606.03055
In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Qu
Externí odkaz:
http://arxiv.org/abs/1512.09184
Autor:
Chang, Shyr-Shea, Tu, Shenyinying, Baek, Kyung In, Pietersen, Andrew, Liu, Yu-Hsiu, Savage, Van, Hwang, Sheng-Ping L., Hsiai, Tzung K., Roper, Marcus
Publikováno v:
Chang, S. S., Tu, S., Baek, K. I., Pietersen, A., Liu, Y. H., Savage, V. M., ... & Roper, M. (2017). Optimal occlusion uniformly partitions red blood cells fluxes within a microvascular network. PLoS computational biology, 13(12), e1005892
In animals, gas exchange between blood and tissues occurs in narrow vessels, whose diameter is comparable to that of a red blood cell. Red blood cells must deform to squeeze through these narrow vessels, transiently blocking or occluding the vessels
Externí odkaz:
http://arxiv.org/abs/1512.04184
Compressive sensing (CS) is a new technology which allows the acquisition of signals directly in compressed form, using far fewer measurements than traditional theory dictates. Recently, many so-called signal space methods have been developed to exte
Externí odkaz:
http://arxiv.org/abs/1511.03763
Autor:
Garnatz, Chris, Gu, Xiaoyi, Kingman, Alison, LaManna, James, Needell, Deanna, Tu, Shenyinying
Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most of the literature focuses on signals sparse in a fixed orthonormal basi
Externí odkaz:
http://arxiv.org/abs/1409.1527