Courses in Stud.IP

current semester
link to course in Stud.IP Studip_icon
Machine Learning in Electromagnetic Compatibility (EMC) Engineering (VL)
Subtitle:
This course is part of the module: Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3006_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Dr. Cheng Yang
Description:

Electromagnetic Compatibility (EMC) Engineering dealswith design, simulation, measurement, and certification of electronic andelectric components and systems in such a way that their operation is safe,reliable, and efficient in any possible application. Safety is herebyunderstood as safe with respect to parasitic effects of electromagnetic fieldson humans as well as on the operation of other components and systems nearby.Examples for components and systems range from the wiring in aircraft and shipsto high-speed interconnects in server systems and wirless interfaces for brainimplants. In this part of the course we will give an introduction to thephysical basics of EMC engineering and then show how methods of MachineLearning (ML) can be applied to expand todays physcis-based approaches in EMCEngineering.

Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS credit points:
1
Stud.IP informationen about this course:
Home institute: Institut für Theoretische Elektrotechnik (E-18)
Registered participants in Stud.IP: 2
former semester
link to course in Stud.IP Studip_icon
Machine Learning in Electromagnetic Compatibility (EMC) Engineering (VL)
Subtitle:
This course is part of the module: Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3006_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Dr. Cheng Yang
Description:

Electromagnetic Compatibility (EMC) Engineering dealswith design, simulation, measurement, and certification of electronic andelectric components and systems in such a way that their operation is safe,reliable, and efficient in any possible application. Safety is herebyunderstood as safe with respect to parasitic effects of electromagnetic fieldson humans as well as on the operation of other components and systems nearby.Examples for components and systems range from the wiring in aircraft and shipsto high-speed interconnects in server systems and wirless interfaces for brainimplants. In this part of the course we will give an introduction to thephysical basics of EMC engineering and then show how methods of MachineLearning (ML) can be applied to expand todays physcis-based approaches in EMCEngineering.

Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS credit points:
1
Stud.IP informationen about this course:
Home institute: Institut für Theoretische Elektrotechnik (E-18)
Registered participants in Stud.IP: 2

Courses

For information on courses and modules, please refer to the current course catalogue and module manual of your degree programme.

Module / Course Period ECTS Credit Points
Module: Electrical Power Systems I: Introduction to Electrical Power Systems WiSe 6
Module: Electrical Power Systems II: Operation and Information Systems of Electrical Power Grids WiSe 6
Module: Electrical Power Systems III: Dynamics and Stability of Electrical Power Systems SuSe 6
Module: Electrical Engineering II: Alternating Current Networks and Basic Devices SuSe 6
Module: Electrical Engineering Project Laboratory SuSe 6
Module: Process Measurement Engineering SuSe 4
Module: Smart Grid Technologies WiSe, SuSe 6

Course: Seminar on Electromagnetic Compatibility and Electrical Power Systems

Further Information

WiSe, SuSe 2

SuSe: Summer Semester
WiSe: Winter Semester