Ladislav Havaš

Information about the paper titled "DATA MODELING - LEARNING OUTCOMES AS THE FINAL METHOD OF EVALUATING THE ACQUIRED KNOWLEDGE AT THE UNIVERSITY NORTH" at IATED Digital Library

Information about the paper titled "NETWORK TESTING ENVIRONMENT BASED ON VIRTUALIZATION TECHNOLOGY - LAB SOLUTION" at IATED Digital Library

Information about the paper titled "TESLA COIL – CONSTRUCTION PROJECT – EDUCATIONAL APPLICATION" at IATED Digital Library

Information about the paper titled "APPLICATION OF SIMULATION TOOLS COMBINED WITH PRACTICAL EXERCISES IN STEM TEACHING" at IATED Digital Library

Disruptions in the global market are influencing value and supply chains reminding businesses and industries that variability and diversity of supply chains may be essential for surviving and sustainability. Operations management of any business has to address these challenges in order to avoid any serious interruptions in supply of materials in production industries by seeking substitute inputs. At the same time, the technological development offers new materials with similar quality properties, making thereby the substitute material search more difficult in terms of selecting appropriate materials with a level of quality which is similar enough. Another aspect in shifting can be found in more social-related reasons addressing changes in the value chains like traceability, low carbonization, and a more customer-oriented approach, because of moving towards green digital business. In this sense the intention of this work was to propose an algorithm for customizing the process of identifying appropriate materials in production by relying on existing algorithms i.e., the Ashby mapping, big data, applying algorithms of data analysis based on exclusion criteria embracing transformation paradigms, for enabling customization of the material selection process. The proposed algorithm was applied on two case examples, demonstrating that diversity of materials plays an important role in addressing customization requests from customers. Consequently, understanding and implementing a customer-centric approach in various phases of the product life cycle contributes to a better response by businesses faced with issuing customized offerings.

Monitoring, protection and control of the electrical power system require the design and implementation of specific applications that are based on analytical methods for the processing of synchronized measurements. Therefore, it is necessary to select the adequate type of mathematical analysis that best suits the requirements of a particular application. This paper describes analytical methods used for the processing of synchronized measured electrical quantities for detection and analysis of the variety of oscillations. The oscillatory phenomena of active power and frequency as a case study of one disturbance in the power system are analyzed. The results of processing the actual synchronized measurements for that case study are presented afterwards. Different data processing methods (spectral analysis methods) are compared, and finally, a recommendation for appropriate methods for processing synchronized measurements in application for recognition, processing and alarming of oscillations of active power is given.

Interest in sports predictions as well as the public availability of large amounts of structured and unstructured data are increasing every day. As sporting events are not completely independent events, but characterized by the influence of the human factor, the adequate selection of the analysis process is very important. In this paper, seven different classification machine learning algorithms are used and validated with two validation methods: Train&Test and cross-validation. Validation methods were analyzed and critically reviewed. The obtained results are analyzed and compared. Analyzing the results of the used machine learning algorithms, the best average prediction results were obtained by using the nearest neighbors algorithm and the worst prediction results were obtained by using decision trees. The cross-validation method obtained better results than the Train&Test validation method. The prediction results of the Train&Test validation method by using disjoint datasets and up-to-date data were also compared. Better results were obtained by using up-to-date data. In addition, directions for future research are also explained.

This paper proposes an approach to schedule and allocate regulating reserve in systems with a large share of generation from wind power plants (WPPs). The method for scheduling regulating power is based on the correction of day-ahead schedules planned by the known variations of the load- and wind-power from the previous hour. The correction term incorporates the tendency of changes for previous hours. In this way, more of the regulating power can be reserved during sudden changes in the intensity of the wind. Hence, available existing operational data are required, instead of historical data. The method for allocation of regulating power to regulating units is based on minimization of the transmission losses with optional correction according to economic criteria. The qualitative analysis of the relevant economic features is introduced, as well as the possibilities for applying advanced information and communications technology. The methods discussed were tested for the standard test network RTS 96 and for the Croatian transmission network. Test results showed considerable reduction of the unnecessary positive and negative regulating reserve with increased safety compared to the classic method. Furthermore, optimal allocation of regulating reserve reduced transmission losses for all the cases discussed. Reductions were more significant when the layouts of WPPs and regulating capacities were more geographically dispersed.

This paper analyses how the type of winding affects the current distribution and power losses in armature winding. Using finite element method, four different types of winding used in large asynchronous motors are analyzed: form-wound...