Temat: adaptive sampling - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Identification of the Oscillation Period of Chemical Reactors by Chaotic Sampling of the Conversion Degree
Autorzy:: Lawnik, M.
Berezowski, M.
Powiązania:: https://bibliotekanauki.pl/articles/185723.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: reaktor chemiczny
adaptacyjne próbkowania
identyfikacja sygnału
chemical reactor
adaptive sampling
signal identification
Opis:: To stabilise the periodic operation of a chemical reactor the oscillation period should be determined precisely in real time. The method discussed in the paper is based on adaptive sampling of the state variable with the use of chaotic mapping to itself. It enables precise determination of the oscillation period in real time and could be used for a proper control system, that can successfully control the process of chemical reaction and maintain the oscillation period at a set level. The method was applied to a tank reactor and tubular reactor with recycle.
Źródło:: Chemical and Process Engineering; 2014, 35, 3; 387-393
0208-6425
2300-1925
Pojawia się w:: Chemical and Process Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
Autorzy:: Goay, Chan Hong
Cheong, Zheng Quan
Low, Chen En
Ahmad, Nur Syazreen
Goh, Patrick
Powiązania:: https://bibliotekanauki.pl/articles/2200709.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: adaptive sampling
deep neural networks
deep learning
power-ground plane
Z-parameters
Opis:: This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the nonnormalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a squareshaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models.
Źródło:: International Journal of Electronics and Telecommunications; 2022, 68, 4; 793--798
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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