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Wyszukujesz frazę "system wnioskowania" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
A hierarchical inferential method for indoor scene classification
Autorzy:
Jiang, J.
Liu, P.
Ye, Z.
Zhao, W.
Tang, X.
Powiązania:
https://bibliotekanauki.pl/articles/330842.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
indoor scene classification
semantic hierarchical structure
rule based inference
Markov logic network
struktura hierarchiczna
regułowy system wnioskowania
sieć logiczna Markova
Opis:
Indoor scene classification forms a basis for scene interaction for service robots. The task is challenging because the layout and decoration of a scene vary considerably. Previous studies on knowledge-based methods commonly ignore the importance of visual attributes when constructing the knowledge base. These shortcomings restrict the performance of classification. The structure of a semantic hierarchy was proposed to describe similarities of different parts of scenes in a fine-grained way. Besides the commonly used semantic features, visual attributes were also introduced to construct the knowledge base. Inspired by the processes of human cognition and the characteristics of indoor scenes, we proposed an inferential framework based on the Markov logic network. The framework is evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 839-852
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS
Autorzy:
Kumar, D. T.
Soleimani, H.
Kannan, G.
Powiązania:
https://bibliotekanauki.pl/articles/329809.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
artificial neural network
adaptive network based fuzzy
inference system
closed loop supply chain
forecasting methods
fuzzy neural network
sztuczna sieć neuronowa
system wnioskowania
metoda prognozowania
sieć neuronowa rozmyta
Opis:
Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 669-682
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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