Učni načrt predmeta

Predmet:
Senzorji v procesnem vodenju
Course:
Sensors in Process Control
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Senzorske tehnologije, 3. stopnja / 1 1
Sensor Technologies, 3rd cycle / 1 1
Vrsta predmeta / Course type
Izbirni
Univerzitetna koda predmeta / University course code:
ST3-550
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Juš Kocijan
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
Slovenski ali angleški / Slovene or English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisits:
Vsebina:
Content (Syllabus outline):
Temeljna literatura in viri / Readings:
Cilji in kompetence:
Objectives and competences:
Predvideni študijski rezultati:
Intendeded learning outcomes:
Metode poučevanja in učenja:
Learning and teaching methods:
Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga s predstavitvijo in zagovorom rešitve izbranega primera iz študentovega raziskovalnega dela.
50
Seminar work with presentation and defence of the proposed solving of the selected problem from student’s research work.
Pisni del izpita, s katerim se preverjajo teoretična in praktična znanja o senzorjih v procesnem vodenju.
50
Written exam, which assesses knowledge of the theory and the implementation of concepts of sensors in process control.
Reference nosilca / Lecturer's references:
1. KRIVEC, Tadej, KOCIJAN, Juš, PERNE, Matija, GRAŠIČ, Boštjan, BOŽNAR, Marija, MLAKAR, Primož. Data-driven method for the improving forecasts of local weather dynamics. Engineering applications of artificial intelligence, ISSN 0952-1976. [Print ed.], 2021, vol. 105, str. 104423-1-104423-14
2. KRIVEC, Tadej, PAPA, Gregor, KOCIJAN, Juš. Simulation of variational Gaussian process NARX models with GPGPU. ISA transactions, ISSN 0019-0578, 2021, vol. 109, str. 141-151
3. KOCIJAN, Juš, PERNE, Matija, MLAKAR, Primož, GRAŠIČ, Boštjan, BOŽNAR, Marija. Hybrid model of the near-ground temperature profile. Stochastic environmental research and risk assessment, ISSN 1436-3240, 2019, vol. 33, no. 11/12, str. 2019-2032
4. ALEKSOVSKI, Darko, KOCIJAN, Juš, DŽEROSKI, Sašo. Ensembles of fuzzy linear model trees for the identification of multi-output systems. IEEE transactions on fuzzy systems, 2016, vol. 24, no. 4, 916-929.
5. KOCIJAN, Juš. Modelling and control of dynamic systems using Gaussian process models, (Advances in industrial control). Cham [etc.]: Springer, cop. 2016. XVI, 267 str.