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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 2021-2022. Please note that changes may occur.

Facts
Course code

DAT530

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

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.
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.
Required prerequisite knowledge
None
Exam

Written exam and assignment(s)

Form of assessment Weight Duration Marks Aid
Written exam 40/100 4 Hours A - F No printed or written materials are allowed. Approved basic calculator allowed
Project work 60/100 A - F 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's 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