1. Statistical modeling and optimization of the drawing process of bioderived polylactide/poly(dodecylene furanoate) wet-spun fibersDaniele Rigotti, Giulia Fredi, Davide Perin, Dimitrios Bikiaris, Alessandro Pegoretti, Andrea Dorigato, 2022, izvirni znanstveni članek Opis: Drawing is a well-established method to improve the mechanical properties of wet-spun
fibers, as it orients the polymer chains, increases the chain density, and homogenizes the microstructure. This work aims to investigate how drawing variables, such as the draw ratio, drawing speed,
and temperature affect the elastic modulus (E) and the strain at break (εB) of biobased wet-spun fibers
constituted by neat polylactic acid (PLA) and a PLA/poly(dodecamethylene 2,5-furandicarboxylate)
(PDoF) (80/20 wt/wt) blend. Drawing experiments were conducted with a design of experiment
(DOE) approach following a 24
full factorial design. The results of the quasi-static tensile tests on
the drawn fibers, analyzed by the analysis of variance (ANOVA) and modeled through the response
surface methodology (RSM), highlight that the presence of PDoF significantly lowers E, which instead
is maximized if the temperature and draw ratio are both low. On the other hand, εB is enhanced
when the drawing is performed at a high temperature. Finally, a genetic algorithm was implemented
to find the optimal combination of drawing parameters that maximize both E and εB. The resulting
Pareto curve highlights that the temperature influences the mechanical results only for neat PLA
fibers, as the stiffness increases by drawing at lower temperatures, while optimal Pareto points for
PLA/PDoF fibers are mainly determined by the draw ratio and the draw rate.
Ključne besede: fibers, poly(lactic acid), furanoate polyesters, drawing, response surface methodology, genetic algorithms
Objavljeno v DKUM: 24.03.2025; Ogledov: 0; Prenosov: 2
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2. Study of environmental impacts on overhead transmission lines using genetic algorithmsKristijan Šket, Mirko Ficko, Nenad Gubeljak, Miran Brezočnik, 2023, izvirni znanstveni članek Opis: In our study, we explored the complexities of overhead transmission line (OTL) engineering, specifically focusing on their responses to varying atmospheric conditions (ambient temperature, ambient humidity, solar irradiance, ambient pressure, wind speed, wind direction), and electric current usage. Our goal was to comprehend how these independent variables impact critical responses (dependent variables) such as conductor temperature, conductor sag, tower leg stress, and vibrations – parameters crucial for electric distribution. We modelled the target output variable as a polynomial of a certain degree of the input variables. The precise forms of the polynomial were determined using the genetic algorithms (GA). Developed models are essential for quantifying the influence of each input parameter, enriching our understanding of essential system elements. They provide long-term predictions for assessing transmission line lifespan and structural stability, with particularly high precision in forecasting temperature and sag angle. It is important to note that certain engineering parameters, such as material properties and load considerations, were not included in our research, potentially influencing accuracy.
Ključne besede: Overhead Transmission Lines (OTL), machine learning, modelling, optimization, genetic algorithms (GA)
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 3
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3. Optimization methods for a direct current electric motor design : magistrsko deloVid Černec, 2022, magistrsko delo Opis: In the modern world, the need for planning in advance has become increasingly important. The companies want to know the cost, the dimensions of the elements, and the properties of the final product in advance. Therefore, we can find teams working on the analysis and development of new and different products in larger companies. More experienced workers can determine the given results and see what would be acceptable by simply looking at the situation. Although, the help of technology makes the process easier and faster, and even provides the same result. When creating a specific model, we must focus on the equations describing our result. Then we begin with optimization, which means we define the variables, which will be our subject of research in the project, and define some fixed parameters until the desired goals are reached.
Ključne besede: design, DC electric motor, optimization, optimization algorithms, genetic algorithms
Objavljeno v DKUM: 21.10.2022; Ogledov: 685; Prenosov: 55
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4. Web application for hierarchical organizational structure optimization : human resource management case studyDavorin Kofjač, Blaž Bavec, Andrej Škraba, 2015, izvirni znanstveni članek Opis: Background and Purpose: In a complex strictly hierarchical organizational structure, undesired oscillations may occur, which have not yet been adequately addressed. Therefore, parameter values, which define fluctuations and transitions from one state to another, need to be optimized to prevent oscillations and to keep parameter values between lower and upper bounds. The objective was to develop a simulation model of hierarchical organizational structure as a web application to help in solving the aforementioned problem.
Design/Methodology/Approach: The hierarchical structure was modeled according to the principles of System Dynamics. The problem of the undesired oscillatory behavior was addressed with deterministic finite automata, while the flow parameter values were optimized with genetic algorithms. These principles were implemented as a web application with JavaScript/ECMAScript.
Results: Genetic algorithms were tested against well-known instances of problems for which the optimal analytical values were found. Deterministic finite automata was verified and validated via a three-state hierarchical organizational model, successfully preventing the oscillatory behavior of the structure.
Conclusion: The results indicate that the hierarchical organizational model, genetic algorithms and deterministic finite automata have been successfully implemented with JavaScript as a web application that can be used on mobile devices. The objective of the paper was to optimize the flow parameter values in the hierarchical organizational model with genetic algorithms and finite automata. The web application was successfully used on a three-state hierarchical organizational structure, where the optimal flow parameter values were determined and undesired oscillatory behavior was prevented. Therefore, we have provided a decision support system for determination of quality restructuring strategies.
Ključne besede: hierarchical organizational structure, genetic algorithms, deterministic finite automata, system dynamics, optimization, human resources
Objavljeno v DKUM: 04.04.2017; Ogledov: 1917; Prenosov: 161
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5. A numerical simulation of metal injection mouldingBoštjan Berginc, Miran Brezočnik, Zlatko Kampuš, Borivoj Šuštaršič, 2009, izvirni znanstveni članek Opis: Metal injection moulding (MIM) is already a well-established and promising technology for the mass production of small, complex, near-net-shape products. The dimensions and mechanical properties of MIM products are influenced by the feedstock characteristics, the process parameters of the injection moulding, as well as the debinding and the sintering. Numerical simulations are a very important feature of the beginning of any product or technology development. In the article two different techniques for measuring the rheological properties of MIM feedstocks are presented and compared. It was established that capillary rheometers are more appropriate for MIM feed stocks, while on the other hand, parallel-plate rheometers are only suitable for shear rates lower than 10 s[sup]{-1}. Later on we used genetic algorithms to determine the model coefficients for some numerical simulation software. The results of the simulation of the filling phase and a comparison with the experimental results are presented in the article.
Ključne besede: metal injection moulding, numerical simulation, genetic algorithms
Objavljeno v DKUM: 14.03.2017; Ogledov: 1311; Prenosov: 170
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6. Development of a web application for dynamic production scheduling in small and medium enterprisesDavorin Kofjač, Andrej Knaflič, Miroljub Kljajić, 2010, izvirni znanstveni članek Opis: This article describes the development of a web-based dynamic job-shop scheduling system for small and medium enterprises. In large enterprises, scheduling is mainly performed with appropriate technology by human experts; many small and medium enterprises lack the resources to implement such a task. The main objective was to develop a cost-effective, efficient solution for job-shop scheduling in small and medium enterprises with an emphasis on accessibility, platform independence and ease of use. For these reasons, we decided to develop a web-based solution with the main emphasis on the development of an intelligent and dynamic user interface. The solution is built upon modular programming principles and enables dynamic scheduling on the basis of artificial intelligence, i.e. genetic algorithms. The solution has been developed as a standalone information system, which allows the management of virtually all scheduling activities through an administration panel. In addition, the solution covers the five main functionalities that completely support the scheduling process, i.e. making an inventory of resources available in the company, using it in the process of production planning, collecting data on production activities, distribution of up-to-date information and insight over events in the system.
Ključne besede: dynamic job-shop scheduling, genetic algorithms, web application development
Objavljeno v DKUM: 10.07.2015; Ogledov: 2022; Prenosov: 422
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7. AGRA: analysis of gene ranking algorithmsSimon Kocbek, Rune Saetre, Gregor Štiglic, Jin-Dong Kim, Igor Pernek, Yoshimasa Tsuruoka, Peter Kokol, Sophia Ananiadou, Jun-ichi Tsujii, 2011, drugi znanstveni članki Opis: Often, the most informative genes have to be selected from different gene setsand several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the Analysis of Gene Ranking Algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system FACTA (Tsuruoka et al., 2008), AGRA defines what we call Biomedical Concept Space (BCS) for each gene list and offers comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where specific biomedical concept can be entered. AGRA searches for this concept in each genelistsć BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it.
Ključne besede: genetic algorithms, genetski algoritmi
Objavljeno v DKUM: 05.06.2012; Ogledov: 2291; Prenosov: 152
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8. Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrestMatej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, izvirni znanstveni članek Opis: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.
Ključne besede: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Objavljeno v DKUM: 01.06.2012; Ogledov: 2046; Prenosov: 107
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9. A model of data flow in lower CIM levelsIgor Drstvenšek, Ivo Pahole, Jože Balič, 2004, izvirni znanstveni članek Opis: After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.
Ključne besede: computer integrated manufacturing, flexible manufacturing systems, evolutionary optimisation techniques, production automation, CIM hierarchy, technological databases, production optimisation, genetic algorithms, genetic programming
Objavljeno v DKUM: 01.06.2012; Ogledov: 2248; Prenosov: 106
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10. Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithmsMirko Ficko, Miran Brezočnik, Jože Balič, 2004, izvirni znanstveni članek Opis: The paper presents a model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.
Ključne besede: flexible manufacturing systems, facility layout, optimization, genetic algorithms
Objavljeno v DKUM: 01.06.2012; Ogledov: 2046; Prenosov: 105
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