Selection of the area of effective and non-efficient work of absorption drying apparatus for input parameters of the saturated diethylene glycol and methanol

The fuzzy analyzing process consists of different steps. In this paper, the author considers only the method for formulation of the membership function of fuzzy loads acting on the structure. Based on the membership function of fuzzy loads, the combinations of deterministic of the regression analyzing process will be determined. The membership function of fuzzy loads is selected by the triangular membership function. It is in conformity with the concept on selection of loads in the design standards. The combination of inputs for the analyzing process will be determined, based on the number of present times of the value of input parameters (including the deterministic parameters, fuzzy parameters and the random ones) in the schema of analysis. The number of present times of input parameters is either proportional to value of the corresponding membership function or to the value of the probabilistic density function. A method for determining the appropriate combination of deterministic inputs so that each input parameter will present only one time in each combination is proposed. To illustrate the proposed method, an example on the determination of input combinations of tornado's velocity in Vietnam is presented.

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D NUMBERS – FUCOM – FUZZY RAFSI MODEL FOR SELECTING THE GROUP OF CONSTRUCTION MACHINES FOR ENABLING MOBILITY

Facta Universitatis Series Mechanical Engineering ◽

10.22190/fume210318047b ◽

2021 ◽

Vol 19 (3) ◽

pp. 447

Author(s):

Darko Božanić ◽

Aleksandar Milić ◽

Duško Tešić ◽

Wojciech Salabun ◽

Dragan Pamučar

Keyword(s):

Decision Making ◽

Hybrid Model ◽

Input Parameters ◽

D Numbers ◽

Weight Coefficients ◽

Selection Of ◽

Decision Making Support

The paper presents a hybrid model for decision-making support based on D numbers, the FUCOM method and fuzzified RAFSI method, used for solving the selection of the group of construction machines for enabling mobility. By applying D numbers, the input parameters for the calculation of the weight coefficients of the criteria were provided. The calculation of the weight coefficients of the criteria was performed using the FUCOM method. The best alternative was selected using the fuzzified method, which was conditioned by the specificity of the issue so that in this case, the selection of the best alternative was made using the fuzzified RAFSI method.

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Initial guidelines for the selection of input parameters for cohesion-weakening-friction-strengthening (CWFS) analysis of excavations in brittle rock

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2018.11.019 ◽

2019 ◽

Vol 84 ◽

pp. 189-200 ◽

Cited By ~ 2

Author(s):

G. Walton

Keyword(s):

Brittle Rock ◽

Input Parameters ◽

Selection Of

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Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

Journal of Metallurgy ◽

10.1155/2014/813234 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11

Author(s):

A. Fotovati ◽

J. Kadkhodapour ◽

S. Schmauder

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Volume Fraction ◽

Nanoindentation Test ◽

Dual Phase ◽

Test Results ◽

Indentation Force ◽

Input Parameters ◽

Artificial Neural ◽

Selection Of

Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case.

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