1. Fuzzy model for estimating the risk of infection by Covid-19Janez Usenik, 2020, izvirni znanstveni članek Opis: The present paper presents a fuzzy model for predicting the risk of a community (country) to being infected by the coronavirus Covid-19. The research is not the medical field, where favourable news about vaccines against this disease is just emerging from the research community. Instead, it presents a relatively simple mathematical model based on the use of fuzzy logic. The model is created as a fuzzy system, in which the basic postulates of fuzzy logic and fuzzy inference are used. The presented model is, of course, only one possibility for describing and predicting the threat to the population due to the Covid-19 disease.
Ključne besede: Covid-19, fuzzy variable, fuzzy inference, risk of infections
Objavljeno v DKUM: 01.12.2023; Ogledov: 415; Prenosov: 8
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2. Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718Matija Hriberšek, Lucijano Berus, Franci Pušavec, Simon Klančnik, 2020, izvirni znanstveni članek Opis: This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts.
Ključne besede: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Objavljeno v DKUM: 14.07.2023; Ogledov: 564; Prenosov: 40
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4. Fuzzy-sets decision-support system for geotechnical site soundingsDjemalddine M. Boumezerane, Smaïn Belkacemi, Bojan Žlender, 2011, izvirni znanstveni članek Opis: A geotechnical site investigation is an important and complex task that is generally carried out in two steps. The first step, consisting of preliminary soundings, guides the subsequent site characterization. The number of soundings required to adequately characterize a site is set on the basis of an engineering judgement following the preliminary investigation, this is affected by the geological context, the area topography, the project type, and the knowledge of the neighbouring areas. A fuzzy-sets decision-support system, considering parameters that affect the number of soundings required to adequately characterize a site, is proposed. Parameter uncertainties and a lack of information are also considered. On the basis of the available qualitative and quantitative information, the proposed fuzzy system makes it possible to estimate, for a common project, the number of site soundings required to adequately characterize the site. The cases presented show that a Fuzzy Inference System can be used as a systematic decision-support tool for engineers dealing with site characterizations.
Ključne besede: geotechnical investigation, soundings number, fuzzy sets, fuzzy inference, uncertainties
Objavljeno v DKUM: 13.06.2018; Ogledov: 1042; Prenosov: 226
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