MK EN
ФИНКИ

Subject

Smart Manufacturing and Monitoring Systems

1. Course Title Smart Manufacturing and Monitoring Systems
Smart production and monitoring systems
2. Code m23_s_073
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 10 / Summer
7. Number of ECTS credits 6
8. Teacher —
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) Во рамките на овој предмет ќе бидат обработени напредните IT алгоритми и системи за мониторинг и менаџирање производството кои се користат во производството на храна, земјоделството и сточарството. Кандидатите ќе бидат оспособени да дизајнираат и имплементираат IT системи за прецизно земјоделство и сточарство и да вршат напредна анализа и мониторинг на производството со користење на податоци сензори и автономни роботски системи.
11. Course content Интеграција на IT со производството на храна и суровини
Примена на напредни сензори и ефектори во паметното производство
IOT и Cloud парадигмата во производството и прецизното земјоделство
Методи за анализа на податоци во прецизното земјоделство и паметното производство
Примена на вештачка интелигенија и роботиката во во прецизното земјоделство и паметното производство
12. Learning methods Lectures, practical exercises, independent work, project assignments and seminar papers
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 30 + 50 + 40 = 180 часа
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 50 hours
16.2. Independent assignments 30 hours
16.3. Home study 40 hours
17. Assessment method
17.1. Tests 45 points
17.2. Seminar paper / project (presentation: written and oral) 50 points
17.3. Activities and learning 10 points
17.4. Final exam 0 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam completion of activities 15.1 and 15.2
20. Language of instruction Македонски и Англиски
21. Method for monitoring the quality of teaching Интерна самоевалуација
22. Literature
22.1. Required literature
1. Thomas Lillesand,‎ Ralph W. Kiefer,‎ Jonathan Chipman | Remote Sensing and Image Interpretation 7th Edition | Wiley | 2015
2. Antonit Mucherino, Petraq J. Papajorgji, Panos M. Padalos | Data Mining in Agriculture | Springer | 2009
3. Nicolas Baghdadi, Mehrez Zribi | Land Surface Remote Sensing in Agriculture and Forest | Elsevier | 2016
4. Yingfeng Zhang and Fei Tao | Optimization of Manufacturing Systems Using the Internet of Things | Academic Press | 2016
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects