Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Semin Kwak"'
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks. 7:648-659
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space
Autor:
Nikolas Geroliminis, Semin Kwak
Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cecd4e5f0f8dd39e85a1c99f4cd511d
http://arxiv.org/abs/2009.01016
http://arxiv.org/abs/2009.01016
Publikováno v:
IET Radar, Sonar & Navigation. 11:1251-1258
In this study, the authors present monopulse beam synthesis methods with sparse elements robust to the antenna module failure. The monopulse beam radar generates two beams (sum/difference beams) simultaneously and it requires a large number of antenn
Publikováno v:
IEEE Antennas and Wireless Propagation Letters. 15:1622-1625
A radar with a large number of antennas requires efficient design in its feeding network or transmit/receive modules (TRMs). We propose a method for simplifying the antenna system of a monopulse radar by attaching a single common weight to each anten
A conventional monopulse radar system uses three beams, namely, sum beam, elevation difference beam, and azimuth difference beam, which require different layers of weights to synthesize each beam independently. Since the multilayer structure increase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc41f429ae05c2e4ffe3718819c63fe7
https://infoscience.epfl.ch/record/266841
https://infoscience.epfl.ch/record/266841
Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66fbf9a29a9eeea6c72d9f751d01e948
https://infoscience.epfl.ch/record/302238
https://infoscience.epfl.ch/record/302238