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Discrete Simulation and Performance Analysis DAT530

This course first introduces Petri net theory; then, the theory is used for modeling, simulation and performance analysis of discrete event systems. 


Course description for study year 2022-2023

Facts
Course code

DAT530

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Content
Introduction to quantitative methods for construction, dimensioning, and analysis of distributed systems; performance analysis for systems development and maintenance; basic concepts, measurement techniques, and tools for performance analysis; Petri net theory; discrete event based models with GPenSIM; applied statistical techniques (like factor analysis, ANOVA, hypothesis testing) for filtering and analysis of data.
Learning outcome
After completing the course, the student shall:
  • Understand what performance evaluation and simulation means when many computers are connected in a system
  • Make models of realistic systems and run simulations on the models.
  • Apply Petri nets for advanced mathematical modeling of discrete event systems.
  • Use Applied Statistical techniques for filtering and analysis of data.
Required prerequisite knowledge
None
Exam

Written exam and assignment(s)

Form of assessment Weight Duration Marks Aid
Written exam 40/100 4 Hours Letter grades No printed or written materials are allowed. Approved basic calculator allowed
Project work 60/100 Letter grades All

The course has a continous assessment.Project counts 60% of total grade. The project can be done individually or in groups of 2-3 students.If a student fails to deliver the project on time, then the student will be awarded a fail mark. If valid documentation is provided (e.g., medical certificate), then extended time can be given to complete the project.If a student fails the project, the student may take a new project with a new title the next time the course goes as usual. However, under exceptional circumstances, the course coordinator may allow the student to start a new project in the following semester (no need to wait for the next course start).Both project and exam must be passed to get a final grade in the course.

Course teacher(s)
Course coordinator: Reggie Davidrajuh
Head of Department: Tom Ryen
Method of work
4 hours lectures and 2 hours exercises.
Open for
Computer Science, Master of Science Degree Programme Industrial Automation and Signal Processing - Master's Degree Programme - 5 year Robot Technology and Signal Processing - Master's Degree Programme Exchange programme at Faculty of Science and Technology
Course assessment
Form and/or discussion.
Overlapping courses
Course Reduction (SP)
Discrete Simulation and Performance Analysis (MID280) 10
Literature
The syllabus can be found in Leganto