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Opis: V diplomski nalogi je obravnavana optimizacija mehanskih lastnosti FDM-natisnjenih izdelkov z uporabo Taguchijeve metode načrtovanja eksperimentov. V teoretičnem delu so predstavljeni tehnologija FDM, vpliv procesnih parametrov na mehanske lastnosti polimernih izdelkov ter osnove Taguchijeve metode, razmerja signal–šum, analize variance in večodzivne optimizacije. V raziskovalnem delu je najprej didaktično prikazana uporaba ortogonalne matrike L9(34) za analizo vpliva štirih procesnih dejavnikov. Nato je robustni pristop uporabljen na realnem naboru PLA podatkov, kjer je stopnja zapolnjenosti obravnavana kot kontrolni faktor, čas izpostavitve pa kot šumni faktor. Obravnavane so natezna, upogibna in tlačna trdnost. Rezultati so analizirani z razmerjem signal–šum, analizo variance in metodo Grey Relational Analysis. Ugotovljeno je, da ima stopnja zapolnjenosti največji vpliv na mehanske lastnosti, najbolj robustna večodzivna rešitev pa je dosežena pri 100 % zapolnjenosti. Zapolnjenost 80 % predstavlja možno kompromisno rešitev, kadar sta pomembna manjša poraba materiala in krajši čas izdelave. Naloga pokaže, da je Taguchijeva metoda uporabno orodje za pregledno in eksperimentalno ekonomično optimizacijo FDM procesa.
Ključne besede: Aditivna proizvodnja, ANOVA, FDM, mehanske lastnosti, ortogonalne matrike, PLA, signal – šum, Taguchijeva metoda
Objavljeno v ReVIS: 02.07.2026; Ogledov: 282; Prenosov: 7
Celotno besedilo (2,79 MB)

Opis: The modern computer resources and the data analysis methods allow for a biomedical data to be examined in a more detail than ever. The heart rate variability (HRV) is an easily accessible vital signal that offers a range of useful information about the person under a study. One such application regards an automatical determining whether a person is awake or asleep from the HRV data only. This is of an importance not just for medical but also for practical applications, such as a traffic safety or smart homes. In this doctoral work we study the HRV data of 75 healthy individuals of a varying age and sex, recorded with a microsecond precision. We employ the empirical fact that heart and respiration cycles couples differently during a sleep and awake period. Namely, a respiratory modulation of a heart rhythm or a respiratory sinus arrhythmia (RSA) is more pronounced while asleep, as both sleep and RSA are connected to a strong vagal activity. Therefore, the onset of sleep can be recognized or perhaps even predicted by a carefully examining the cardio-respiratory coupling. We show that the above can indeed be used, at least in principle, to design an algorithmic method to automatically determine the state of a person's consciousness from the HRV data only. On the methodological front we rely on quantifying the (self)similarity among the shapelets, the short chunks of the HRV time series, that allow for a consistent comparison among and within the time series. To establish a better benchmark, we also carry out a comprehensive analysis of the overall HRV dynamics depending on age and sex. Our results include: (i) that a distinctive patterns of the HRV dynamics are consistent across age and sex, (ii) that they are not only an indicative of sleep and awake, but allow to pinpoint the change from awake to sleep and vice versa almost immediately, (iii) that the shapelet analysis is a viable tool to examine these data with a great precision. We conclude that a more systematic analysis involving more subjects could lead to a practical method for the prediction of the onset of sleep.
Ključne besede: algoritem, EKG, Holter, vzorčna entropija, razmerje signal-šum, klasifikacija, doktorska disertacija
Objavljeno v ReVIS: 18.09.2020; Ogledov: 5563; Prenosov: 251
Celotno besedilo (5,09 MB)