Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl Kusz A, MArciniAK A, sKwArcz J. implementation of computation process in a bayesian network on the example of unit operating costs determination. Eksploatacja i niezawodnosc -Maintenance and reliability 2015; 17 (2): 266-272, http://dx.doi.org/10.17531/ein.2015.2.14.
Andrzej Kusz Andrzej MArciniAK Jacek sKwArczImplementatIon of computatIon process In a bayesIan network on the example of unIt operatIng costs determInatIon Implementacja procedury oblIczenIowej w sIecI bayesowskIej na przykładzIe wyznaczanIa jednostkowych kosztów eksploatacjI*
In technical systems understood in terms of Agile Systems, the important elements are information flows between all phases of an object existence. Among these information streams computation processes play an important role and can be done automatically and also in a natural way should include consideration of uncertainty. This article presents a model of such a process implemented in a Bayesian network technology. The model allows the prediction of the unit costs of operation of a combine harvester based on the monitoring of dependent variables. The values of the decision variables representing the parameters of the machine's operation and the intensity and the conditions for its operation, are known to an accuracy, which is defined by a probability distribution.The study shows, using inference mechanisms built into the network, how cost simulation studies of various situational options can be carried out.
Keywords: agricultural machinery operation, computing processes, unit operating costs, Bayesian networks. W systemach technicznych rozumianych w kategoriach Agile Systems istotnym elementem są przepływy informacyjne pomiędzy wszystkimi fazami istnienia obiektu. Pośród tych strumieni informacyjnych istotną rolę odgrywają procesy obliczeniowe, które mogą być realizowane automatycznie a ponadto w naturalny sposób powinny umożliwiać uwzględnienie niepewności. W artykule przedstawiono przykład takiego procesu realizowanego w technologii sieci bayesowskiej. Model umożliwia predykcję jednostkowych kosztów eksploatacji kombajnu zbożowego na podstawie monitorowania wielkości zmiennych od których one zależą. Wartości zmiennych decyzyjnych reprezentujących parametry pracy maszyny oraz intensywność i warunki jej eksploatacji są znane z dokładnością do rozkładu prawdopodobieństwa. W pracy pokazano w jaki sposób wykorzystując mechanizmy wnioskowania wbudowane w sieci można prowadzić symulacyjne badania kosztów w różnych wariantach sytuacyjnych.Słowa kluczowe: eksploatacja maszyn rolniczych, procesy obliczeniowe, jednostkowe koszty eksploatacji, sieci bayesowskie.
IntroductionThe technical systems of today can be understood in terms of Agile Systems. This means that their existence is not a series of separate phases: design, manufacturing, operation and recycling. The "agile paradigm" assumes the simultaneous presence of all these phases. For examp...
A significant part of cereal production is intended for agri-food processing, which implies a necessity to search for and implement modern storage systems for this product. Stored grain is exposed to many unfavorable factors, particularly caryopsis macro-damage caused mainly by grain weevil (Sitophilus granarius L.). This triggers a substantial decrease in the value of the stored material, thus resulting in serious economic losses. Due to this fact, it is necessary to take steps to effectively detect this pest’s presence when grain is delivered to storage facilities. The purpose of this work was to identify the representative physical characteristics of wheat caryopsis affected by grain weevil. An automated visual system was developed to ease the detection of damaged kernels and adult weevils. In order to obtain the empirical data, a decision was made to take advance of SKCS 4100 (the Perten Single Kernel Characterization System). The measurements obtained were used to build the training sets necessary in the process of ANN (artificial neural network) learning with digital neural classifiers. Next, a set of identifying neural models was created and verified, and then the optimal topology was selected. The utilitarian goal of the research was to support the decision-making process taking place during grain storage.
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