The paper presents recurrence plot based stability analysis of the horizontal band sawing process of structural steel profiles. The analysis is performed in the parameter space defined by the cutting speed, the distance between the blade supports, and the feed rate. The corresponding stability diagrams have been constructed using the recurrence plot characteristic, the determinism of the sound pressure emitted by the process, which quantifies the process predictability. The topology of the experimentally obtained stability diagrams revealed non-linear non-monotonic dynamic behaviour, which made two different chatter avoidance strategies possible by cutting speed variation.
COBISS.SI-ID: 13966107
In the paper the results of the acoustic emission (AE) based detection and characterization of stress-corrosion cracking (SCC) in stainless steel are presented. As supportive methods for AE interpretation, electrochemical noise, specimen elongation measurements, and digital imaging of the specimen surface were used. Based on the defined qualitative and quantitative time and power spectra characteristics of the AE bursts, a manual and an automatic procedure for the detection of crack related AE bursts were introduced. The results of the analysis of the crack related AE bursts indicate that the AE method is capable of detecting large scale cracks, where, apart from intergranular crack propagation, also some small ductile fractures occur. The sizes of the corresponding ductile fracture areas can be estimated based on a relative comparison of the energies of the detected AE bursts. It has also been shown that AE burst time and power spectra features can be successfully used for the automatic detection of SCC.
COBISS.SI-ID: 14050843
Since it is known that there is a relative shortage of doctors in family medicine, it is important to know how the decision to choose a career in this field is made. Since this decision is closely linked to students’ attitudes towards family medicine, we were interested in identifying those attitudes that predict intended career choice in family medicine. A cross-sectional study was performed among 316 final-year medical students of the Ljubljana Medical Faculty in Slovenia. The students filled out a 164-item questionnaire, developed based on the European definition of family medicine and the EURACT Educational Agenda, using a seven-point Likert scale containing attitudes towards family medicine. The students also recorded their interest in family medicine on a five-point Likert scale. Attitudes were selected using a feature selection procedure with artificial neural networks that best differentiated between students who are likely and students who are unlikely to become family physicians. Thirty-one out of 164 attitudes predict a career in family medicine, with a classification accuracy of at least 85%. Predictors of intended career choice in family medicine are related to three categories: understanding of the discipline, working in a coherent health care system and person-centredness. The most important predictor is an appreciation of a long-term doctor–patient relationship. Students whose intended career choice is family medicine differ from other students in having more positive attitudes towards family physicians’ competences and towards characteristics of family medicine and primary care.
COBISS.SI-ID: 3380965
In this paper, mineral wool fiberization process on a spinner wheel was studied by the means of the nonlinear time series analysis. Melt film velocity time series was calculated using computer-aided visualization of the process images recorded with a high speed camera. The time series were used to reconstruct the state space of the process and were tested for stationarity, determinism, chaos and recurrent properties. Mineral wool fiberization was determined to be a low-dimensional and nonstationary process. The 0-1 chaos test results suggest that the process is chaotic while the determinism test indicates weak determinism.
COBISS.SI-ID: 13404443
Successful operation of a district heating system requires optimal scheduling of heating resources to satisfy heating demands. The optimal operation, therefore, requires accurate short-term forecasts of future heat load. In this paper, short-term forecasting of heat load in a district heating system of Ljubljana is presented. Various linear models and nonlinear neural network-based forecasting models are developed to forecast the future daily heat load with the forecasting horizon one day ahead. The models are evaluated based on generalization error, obtained on an independent test data set. Results demonstrate the importance of outdoor temperature as the most important influential variable. Other influential inputs include solar radiation and extracted features denoting population activities (such as day of the week). Comparison of forecasting models reveals good forecasting performance of a linear stepwise regression model (SR) that utilizes only a subset of the most relevant input variables. The operation of the SR model was improved by using neural network (NN) models, and also NN models with a direct linear link (NNLL). The latter showed the overall best forecasting performance, which suggests that NN or the proposed NNLL structures should be considered as forecasting solutions for applied forecasting in district heating markets.
COBISS.SI-ID: 14183195