Temat: DMM - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Novel anchor-selection scheme for distributed mobility management
Autorzy:: Davaasambuu, Battulga
Telmuun, Tumnee
Sasko, Dominik
Keping, Yu
Sodbileg, Shirmen
Powiązania:: https://bibliotekanauki.pl/articles/2097899.pdf
Data publikacji:: 2021
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: cost function
DMM
distributed anchor
handover
mobility management
Opis:: The number of subscribers in mobile networks is growing rapidly, which challenges network management and data delivery. Efficient management and routing are key solutions. One important solution is distributed mobility management (DMM), which handles the mobility of subscribers at the edges of mobile networks and load balancing. Otherwise, mobility anchors are distributed across a network that can manage the handover procedures. In this paper, we propose a novel mobility anchor-selection scheme based on the results of a cost function with three factors to select a suitable cell as well as an anchor for moving subscribers and improving the handover performances of networks. Our results illustrate that the proposed scheme provides significantly enhanced handover performance.
Źródło:: Computer Science; 2021, 22 (1); 143-163
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Data mining models to predict ocean wave energy flux in the absence of wave records
Autorzy:: Mahmoodi, K.
Ghassemi, H.
Nowruzi, H.
Powiązania:: https://bibliotekanauki.pl/articles/135663.pdf
Data publikacji:: 2017
Wydawca:: Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:: ocean wave energy
meteorological parameters
GEP
LDBOD
DMM
modeling
Opis:: Ocean wave energy is known as a renewable energy resource with high power potential and without negative environmental impacts. Wave energy has a direct relationship with the ocean’s meteorological parameters. The aim of the current study is to investigate the dependency between ocean wave energy flux and meteorological parameters by using data mining methods (DMMs). For this purpose, a feed-forward neural network (FFNN), a cascade-forward neural network (CFNN), and gene expression programming (GEP) are implemented as different DMMs. The modeling is based on historical meteorological and wave data taken from the National Data Buoy Center (NDBC). In all models, wind speed, air temperature, and sea temperature are input parameters. In addition, the output is the wave energy flux which is obtained from the classical wave energy flux equation. It is notable that, initially, outliers in the data sets were removed by the local distribution based outlier detector (LDBOD) method to obtain the best and most accurate results. To evaluate the performance and accuracy of the proposed models, two statistical measures, root mean square error (RMSE) and regression coefficient (R), were used. From the results obtained, it was found that, in general, the FFNN and CFNN models gave a more accurate prediction of wave energy from meteorological parameters in the absence of wave records than the GEP method.
Źródło:: Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2017, 49 (121); 119-129
1733-8670
2392-0378
Pojawia się w:: Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:: Biblioteka Nauki

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