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61.
62.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, original scientific article

Abstract: 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.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Published: 01.06.2012; Views: 1232; Downloads: 72
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63.
Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms
Mirko Ficko, Miran Brezočnik, Jože Balič, 2004, original scientific article

Abstract: 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.
Keywords: flexible manufacturing systems, facility layout, optimization, genetic algorithms
Published: 01.06.2012; Views: 1252; Downloads: 75
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64.
Prediction of surface roughness with genetic programming
Miran Brezočnik, Miha Kovačič, Mirko Ficko, 2004, original scientific article

Abstract: In this paper we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate,depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.
Keywords: end milling, surface roughness, prediction of surface roughness, genetic programming
Published: 01.06.2012; Views: 1264; Downloads: 100
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65.
Evolutionary approach for cutting forces prediction in milling
Miha Kovačič, Jože Balič, Miran Brezočnik, 2004, original scientific article

Abstract: Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.
Keywords: milling, simulation, milling cutting forces prediction, genetic programming
Published: 01.06.2012; Views: 922; Downloads: 62
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66.
Predicting stress distribution in cold-formed material with genetic programming
Miran Brezočnik, Leo Gusel, 2004, original scientific article

Abstract: In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.
Keywords: metal forming, stress distribution, prediction, genetic programming, modelling
Published: 01.06.2012; Views: 1377; Downloads: 77
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67.
Genetic programming approach for the material flow curve determination of copper alloy - CuCrZr
Leo Gusel, Miran Brezočnik, Rebeka Rudolf, Ivan Anžel, Z. Lazarević, Nebojša Romčević, 2010, original scientific article

Abstract: For the control of the forming process it is necessary to know as precisely as possible the flow curve of the formed material. The paper presents the determination of the equation for the flow curve of cooper alloy (CuCrZr) with artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimization technique based on the Darwinist principles of the evolution of species and the survival of the fittest organisms. The main characteristic of GP is its non- deterministic way of computing. It is probably the most general approach out of evolutionary computation methods. The comparison between the experimental results, analytical solution and the solution obtained genetically clearly shows that the genetic programming method is a very promising approach.
Keywords: forming, flow curve, copper alloys, genetic programming
Published: 01.06.2012; Views: 1063; Downloads: 15
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68.
Evolutionary programming of CNC machines
Miha Kovačič, Miran Brezočnik, Ivo Pahole, Jože Balič, Borut Kecelj, 2005, original scientific article

Abstract: The paper proposes a new concept for programming of CNC machines. The concept based on genetic algorithms assures evolutionary generation and optimization of NC programs on the basis of CAD models of manufacturing environment. The structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the machine which performs simple elementary motions. During the evolution the machine movement becomes more and more complex and intelligent solutions emerge gradually as a result of the interaction between machine movements and manufacturing environment. The examples of evolutionary programming of CNC lathe and CNC milling machine tool for different complexities of the blanks and products are presented. The proposed concept showed a high degree of universality, efficiency, and reliability and it can be also simply adopted to other CNC machines.
Keywords: manufacturing systems, NC-programming, CNC lathes, simulated evolution, genetic algorithms
Published: 01.06.2012; Views: 1172; Downloads: 88
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69.
Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling
Petra Žohar, Miha Kovačič, Miran Brezočnik, Matej Podbregar, 2005, original scientific article

Abstract: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by"genetic" programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48 h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension,mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance ofsinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratiosof the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4%) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6%) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or anexpanded form.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction, atrial fibrillation, electrical cardioversion, prediction
Published: 01.06.2012; Views: 1535; Downloads: 62
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70.
Prediction of total manufacturing costs for stamping tool on the basis of CAD-model of finished product
Mirko Ficko, Igor Drstvenšek, Miran Brezočnik, Jože Balič, Boštjan Vaupotič, 2005, original scientific article

Abstract: One of the orientations of the tool-making industry is towards shortening the time from enquiry to the supply of tools. The tool-making shops must prepare within the shortest possible time an offer for the manufacturer of the tool based on the enquiry in the form of the CAD-model of the final product. For preparation of a proper offer, the values of certain technological features occurring in the manufacture of the tool are needed. Most frequently the tool manufacturer is interested in total cost for manufacture of the tool. Because of lack of time for making a detailed analysis the total costs of tool manufacture are predicted by the expert on the basis of the experience gathered during several years of work in this area. In our work, we conceived an intelligent system for predicting of total cost of the tool manufacture. We limited ourselves to tools for manufacture of sheet metal products by stamping; the system is based on the concept of case-based reasoning. On the basis of target and source cases, the system prepares the prediction of costs.The target case is the CAD-model in whose costs we are interested, whereas the source cases are the CAD-model of products, for which the tools had already been made, and the relevant total costs are known. The system first abstracts from CAD-models the geometrical features, and then it calculates the similarities between the source cases and target case. Then the most similar cases are used for preparation of prediction by genetic programming method. The genetic programming method provides the model connecting the individual geometrical features with total costs searched for. In the experimental work, we made a system adapted for predicting of tool costs used for tool manufacture on the basis of a theoretic model. The results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert.
Keywords: intelligent systems, prediction of costs, tool-making, stamping, CAD model, costs, genetic programming
Published: 01.06.2012; Views: 1346; Downloads: 71
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