Temat: clock - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Precision spectroscopy of cold strontium atoms, towards optical atomic clock
Autorzy:: Bober, M.
Zachorowski, J.
Gawlik, W.
Morzyński, P.
Zawada, M.
Lisak, D.
Cygan, A.
Bielska, K.
Piwiński, M.
Trawiński, R.
Ciuryło, R.
Ozimek, F.
Radzewicz, C.
Powiązania:: https://bibliotekanauki.pl/articles/201653.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: laser spectroscopy
cold atoms
optical atomic clock
Opis:: This report concerns the experiment of precision spectroscopy of cold strontium atoms in the Polish National Laboratory of Atomic, Molecular and Optical Physics in Toruń. The system is composed of a Zeeman slower and magneto-optical traps (at 461 nm and 689 nm), a frequency comb, and a narrow-band laser locked to an ultra-stable optical cavity. All parts of the experiment are prepared and the first measurements of the absolute frequency of the 1S0-3P1, 689 nm optical transition in 88Sr atoms are performed.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 4; 707-710
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks
Autorzy:: Luzar, M.
Sobolewski, Ł.
Miczulski, W.
Korbicz, J.
Powiązania:: https://bibliotekanauki.pl/articles/202125.pdf
Data publikacji:: 2013
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: neural network
prediction methods
national timescale
atomic clock
Opis:: In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 589-594
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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