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Wyszukujesz frazę "learning curves" wg kryterium: Wszystkie pola


Wyświetlanie 1-2 z 2
Tytuł:
Selected elements of unconventional natural gas economics on the example of North American Energy market experience
Autorzy:
Kaliski, M.
Krupa, M.
Sikora, A.P.
Szurlej, A.
Powiązania:
https://bibliotekanauki.pl/articles/298972.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
shale gas
unconventional gas resources
natural gas production
gas prices
learning curves
Opis:
There is a several year experience concerning exploration and production of shale resources in the USA. First production wells started in 1996 (the history of hydraulic fracturing is even 40-50 years older), and last few years one can observe a huge impact on American economy and decreasing level of natural gas import. One can assume that the development will grow significantly and the USA will stay self-sufficient and can start exportation of hydrocarbons - especially LNG. The economy will decide about the share of the natural gas in energy mix - energy balance. In the paper there is a detailed discussion concerning economy of the shale exploration and production (i.e. the costs of drillings, services, geological conditions versus timings and schedule of production). Based on analyzed scope one can predict a stable progress in cost reduction (learning curves) linked with production of shale hydrocarbons.
Źródło:
AGH Drilling, Oil, Gas; 2013, 30, 1; 97-108
2299-4157
2300-7052
Pojawia się w:
AGH Drilling, Oil, Gas
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning novelty detection outside a class of random curves with application to COVID-19 growth
Autorzy:
Rafajłowicz, Wojciech
Powiązania:
https://bibliotekanauki.pl/articles/2031122.pdf
Data publikacji:
2021
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
classification
learning
novelty detection
functional data
Opis:
Let a class of proper curves is specified by positive examples only. We aim to propose a learning novelty detection algorithm that decides whether a new curve is outside this class or not. In opposite to the majority of the literature, two sources of a curve variability are present, namely, the one inherent to curves from the proper class and observations errors’. Therefore, firstly a decision function is trained on historical data, and then, descriptors of each curve to be classified are learned from noisy observations.When the intrinsic variability is Gaussian, a decision threshold can be established from T2 Hotelling distribution and tuned to more general cases. Expansion coefficients in a selected orthogonal series are taken as descriptors and an algorithm for their learning is proposed that follows nonparametric curve fitting approaches. Its fast version is derived for descriptors that are based on the cosine series. Additionally, the asymptotic normality of learned descriptors and the bound for the probability of their large deviations are proved. The influence of this bound on the decision threshold is also discussed.The proposed approach covers curves described as functional data projected onto a finite-dimensional subspace of a Hilbert space as well a shape sensitive description of curves, known as square-root velocity (SRV). It was tested both on synthetic data and on real-life observations of the COVID-19 growth curves.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 195-215
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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