Lehrveranstaltungen in Stud.IP

aktuelles 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
voriges 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

Lehrveranstaltungen

Informationen zu den Lehrveranstaltungen und Modulen entnehmen Sie bitte dem aktuellen Vorlesungsverzeichnis und dem Modulhandbuch Ihres Studienganges.

Modul / Lehrveranstaltung Zeitraum ECTS Leistungspunkte
Modul: Elektrische Energiesysteme I: Einführung in elektrische Energiesysteme WiSe 6
Modul: Elektrische Energiesysteme II: Betrieb und Informationssysteme elektrischer Energienetze WiSe 6
Modul: Elektrische Energiesysteme III: Dynamik und Stabilität elektrischer Energiesysteme SoSe 6
Modul: Elektrotechnik II: Wechselstromnetzwerke und grundlegende Bauelemente SoSe 6
Modul: Elektrotechnisches Projektpraktikum SoSe 6
Modul: Prozessmesstechnik SoSe 4
Modul: Smart-Grid-Technologien WiSe, SoSe 6

Lehrveranstaltung: Seminar zu Elektromagnetischer Verträglichkeit und Elektrischer Energiesystemtechnik

weitere Information

WiSe, SoSe 2

SoSe: Sommersemester
WiSe: Wintersemester