Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shenyinying Tu"'
Autor:
Shyr-Shea Chang, Shenyinying Tu, Kyung In Baek, Andrew Pietersen, Yu-Hsiu Liu, Van M Savage, Sheng-Ping L Hwang, Tzung K Hsiai, Marcus Roper
Publikováno v:
PLoS Computational Biology, Vol 13, Iss 12, p e1005892 (2017)
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:
https://doaj.org/article/d18c0833fb4c471c81521215caebff71
Autor:
Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu, Andreas Wächter, Ermin Wei, Elizabeth Wong
Publikováno v:
Operations Research.
The article “A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF” presents the decomposition algorithm used by Team GO-SNIP for the ARPA-E Grid Optimization (GO) Competition Cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10673331b68badb3cb119a6e9cecbf5e
https://doi.org/10.1287/opre.2023.2453
https://doi.org/10.1287/opre.2023.2453
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::669a64606788c36a51ce37714e190899
http://arxiv.org/abs/2002.08003
http://arxiv.org/abs/2002.08003
Publikováno v:
IEEE Signal Processing Letters. 25:45-49
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f165e3adbd4e651382e4d6d185d61a02
https://doi.org/10.1109/lsp.2017.2770018
https://doi.org/10.1109/lsp.2017.2770018
Publikováno v:
ITA
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6606ace657528032532755c9b519d5cf
http://arxiv.org/abs/1512.09184
http://arxiv.org/abs/1512.09184
Publikováno v:
IEEE Signal Processing Letters; Jan2018, Vol. 25 Issue 1, p45-49, 5p