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Wyświetlanie 1-2 z 2
Tytuł:
Intermittent demand forecasting using data mining techniques
Autorzy:
Kaya, Gamze Ogcu
Turkyilmaz, Ali
Powiązania:
https://bibliotekanauki.pl/articles/117926.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
ANN
support vector regression
Intermittent Demand Forecasting
regresja wektora nośnego
Opis:
Intermittent demand occurs randomly with changing values and a lot of periods having zero demand. Ad hoc intermittent demand forecasting techniques have been developed which take special intermittent demand characteristics into account. Besides traditional techniques and specialized methods, data mining offers a better alternative for intermittent demand forecasting since data mining methods are powerful techniques. This study contributes to the current literature by showing the benefit of using data mining methods for intermittent demand forecasting purpose by comprising mostly used data mining methods.
Źródło:
Applied Computer Science; 2018, 14, 2; 38-47
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft-Sensing in Batch Annealing Based on Finite Differential Method and Support Vector Regression
Autorzy:
Kačur, Ján
Durdán, Milan
Laciak, Marek
Flegner, Patrik
Powiązania:
https://bibliotekanauki.pl/articles/103270.pdf
Data publikacji:
2019
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
annealing
steel coil
temperature measurement
soft-sensing
finite differences method
support vector regression
wyżarzanie
cewka stalowa
pomiar temperatury
wyczuwanie
metoda różnic skończonych
regresja wektora nośnego
Opis:
The temperature of annealed steel coils is a determining variable of the future steel sheets quality. This variable also determines the energy consumption in operation. Unfortunately, the monitoring of coil inner temperature is problematic due to the furnace environment with high temperature, coil structure, and annealing principle. Currently, there are no measuring principles that can measure the temperature inside the heat-treated product in a non-destructive manner. In this paper, the soft sensing of inner temperature based on the theory of non-stationary heat conduction and approach based on Support Vector Regression (SVR) was presented. The results showed that a black-box approach based on the SVR could replace an analytic approach, though with lesser performance. Several annealing experiments were performed to create a training data set and model performance improvement in the estimation of inner coil temperatures. The proposed software based on non-stationary heat conduction can calculate the behavior of inner coil temperature from the measured boundary temperatures that are measured by thermocouples. The soft-sensing principles presented in this paper were verified under laboratory conditions and on the data obtained from a real annealing plant.
Źródło:
Advances in Science and Technology. Research Journal; 2019, 13, 4; 70-86
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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