Autor: Bing, Y. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Signal analysis on soil stress from vibrating compaction based on wavelet transform
Autorzy:: Qing-Zhe, Z.
Bing, Y.
Jing-Liang, D.
Bao-Gui, Y.
Powiązania:: https://bibliotekanauki.pl/articles/231144.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: grunt
zagęszczanie wibracyjne
naprężenia ściskające
analiza sygnałów
transformata falkowa
wykrywanie osobliwości
soil
vibrating compaction
compressive stress
signal analysis
wavelet transform
singularity detection
Opis:: The paper presented the wavelet transform method for de-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.
Źródło:: Archives of Civil Engineering; 2014, 60, 2; 257-268
1230-2945
Pojawia się w:: Archives of Civil Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Calculation and measurement of tide height for the navigation of ship at high tide using artificial neural network
Autorzy:: Li, Q.
Bing-Dong, Y.
Bi-Guang, H.
Powiązania:: https://bibliotekanauki.pl/articles/258480.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: deep-draft ships
neural network
intelligent navigation
multi-observation stations
Opis:: Accurate tide height is crucial for the safe navigation of large deep-draft ships when they enter and leave the port. We have proposed an accurate forecasting method for the tide heights from the observation data and neural networks, which can easily calculate the tidal window period of large deep-draft ships’ navigation through long channels at high tide. Moreover, an artificial neural network is established for the tide height from the observation of tide heights before their current time node. For an ideal forecast, the neural network was optimized for one year with the tide height data of Huanghua Port. In case of large ships, their tidal characteristics of channels for are complex. A new method is proposed for the observation of multiple stations and artificial neural networks of each observation station. When ships are navigating through the port, the tide height is predicted from the observed data and forecast tide heights of multiple observation stations. Thus, a valid tidal window period is secured when the ships enter the port. Comparative analysis of the ship’s tidal window period with that of the measured one can lead us to conclude that the forecasted data has a strong correlation with the measurement. So, our proposed algorithm can accurately predict the tide height and calculate the node timing when the ship enters and depart the port. Finally, these results can be applied for the safe navigation of large deep-draft ships when the port is at high tide.
Źródło:: Polish Maritime Research; 2018, S 3; 99-110
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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