4 ECTS credits
110 u studietijd

Aanbieding 1 met studiegidsnummer 4019801ENR voor alle studenten in het 2e semester met een verdiepend master niveau.

Semester
2e semester
Inschrijving onder examencontract
Niet mogelijk
Beoordelingsvoet
Beoordeling (0 tot 20)
2e zittijd mogelijk
Ja
Onderwijstaal
Engels
Onder samenwerkingsakkoord
Onder interuniversitair akkoord mbt. opleiding
Faculteit
Faculteit Ingenieurswetenschappen
Verantwoordelijke vakgroep
Elektriciteit
Onderwijsteam
John Lataire (titularis)
Onderdelen en contacturen
24 contacturen Hoorcollege
24 contacturen Werkvormen en Praktische Oef.
Inhoud

General goal of the course

Engineers and scientists build models to understand, describe, predict and control the behaviour of complex dynamical processes. In order to construct these models it is necessary to extract the unknown parameters of these mathematical models from (noisy) measurements. In this course we first learn how to measure the characteristics of a dynamic system (Spectral analysis, frequency response function measurements). Next, we explain how experimental data can be turned into good mathematical models. Each concept is illustrated by a simple example. Besides a sound theoretical basis, we will also provide the students with hands-on experience in the labs.

Outline:

Transfer function measurements:

  • The Fourier based spectrum analyser
    • The front-end of an acquisition channel: Signal conditioning, the anti-alias filter, discretisation in time/amplitude
    • Using the DFT for spectral analysis (Perfect reconstruction, leakage, definition of the bin), and the use of windows to reduce leakage
  • DFT-based network analysis
    • Properties of Linear Time-Invariant systems (Transfer function, Response to periodic excitations)
    • Transfer function estimation using periodic and non-periodic excitation signals, pros's and cons of different excitation signals
    • Design of periodic excitation signals (multisines) for a specified frequency band and frequency resolution
    • Working with non-steady-state measurements: the appearance of the transient term, and transient suppression via non-rectangular windows
    • Noise influence on the estimated transfer function
      • Bias, variance, and asymptotic error of the estimates
      • Reducing the variability of a measured transfer function via different averaging approaches

Data-driven modelling:

  • Basic tools for analysing estimators
    • Stochastic convergence, law of large numbers, central limit theorem, Cramér-Rao lower bound
    • Stochastic properties: consistency, (asymptotic) bias, (asymptotic) covariance, (asymptotic) efficiency, (asymptotic) normality, robustness
  • Linear least squares (LLS)
    • Differences between the cases of noiseless and noisy regression matrix, and related stochastic properties,
    • Noisy regression matrix: how to make the LLS consistent (e.g. compensated least squares, instrumental variables method)
  • Nonlinear least squares and maximum likelihood methods
  • Above estimators in the context of LTI parametric transfer function estimation
  • Neural networks
    • Neural network models: feed forward, recurrent, and convolutional
    • Universal approximation theorem
    • Training via back propagation -- stochastic gradient descent
    • Early stopping
  • Tuning the model complexity: cross-validation techniques and use of penalty terms

Hands-on-experience

  • Five labs will illustrate the different parts of the course with hands-on experiments and Matlab exercises.
Studiemateriaal
Cursustekst (Vereist) : Measurement and Data driven modelling, Pintelon, VUB, 2220170010366, 2024
Bijkomende info

Prior knowledge

Basic skills are expected in:

  • statistics (sample statistics, distributions, covariance matrix)
  • system theory (impulse response, frequency response, stability)
  • digital signal processing (FFT)
  • measurement techniques (voltages, currents and impedances)

Course Material

  • The study material and lab notes are available on the online study platform (Canvas)
  • Printed lecture notes are available at the student shop of the university
Leerresultaten

General Competences

General expectation

An independent problem solving and critical attitude are main skills for an engineer and are therefore mandatory for any item treated in this course.

Detailed outcomes Measurement Techniques

To successfully complete the course, the student is expected to

  1. know and understand the Fourier transform, the discrete Fourier transform (DFT), the normalised frequency (bin), the relationship between the measurement length and the frequency resolution of spectral measurements, and the concept of a Frequency Response Function (FRF) of a Linear Time Invariant System
  2. know and be able to explain the importance of the hardware conditions and setup, specifically the generation and acquisition channels, and the influence of noise, for spectral and FRF measurements
  3. understand and apply leakage suppressing techniques in spectral measurements,
  4. estimate the FRF of an LTI system from measured (or simulated) data, in different experimental conditions
  5. be able to design an experimental setup and excitation signal based on the (well-defined) constraints of a given case study, and implement this design to an LTI system on the hardware platform provided in the labs

Detailed outcomes Data-driven modelling

  1. be able to analyse an estimator and determine its stochastic properties
  2. understand and apply the concepts of linear least squares, nonlinear least squares, maximum likelihood, neural network modeling
  3. implement simple estimators of models of linear time invariant dynamic systems
  4. understand and apply the different techniques for tuning the model complexity: validation data, penalty terms, regularization, early stopping

Program learning outcomes

This course contributes to the following programme outcomes of the Master in Electronics and Information Technology Engineering:

The Master in Engineering Sciences has in-depth knowledge and understanding of

3. the advanced methods and theories to schematize and model complex problems or processes

The Master in Engineering Sciences can

4. reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. correctly report on research or design results in the form of a technical report or in the form of a scientific paper
9. work in an industrial environment with attention to safety, quality assurance, communication and reporting
11. think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information

The Master in Engineering Sciences has

12. a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
13. a critical attitude towards one's own results and those of others
16. an attitude of life-long learning as needed for the future development of his/her career

The Master in Electronics and Information Technology Engineering:

17. Has an active knowledge of the theory and applications of electronics, information and communication technology, from component up to system level.
19. Has a broad overview of the role of electronics, informatics and telecommunications in industry, business and society.
20. Can analyze, specify, design, implement, test and evaluate individual electronic devices, components and algorithms, for signal-processing, communication and complex systems.
21. Is able to model, simulate, measure and control electronic components and physical phenomena.

Beoordelingsinformatie

De beoordeling bestaat uit volgende opdrachtcategorieën:
Examen Mondeling bepaalt 50% van het eindcijfer

Examen Praktijk bepaalt 50% van het eindcijfer

Binnen de categorie Examen Mondeling dient men volgende opdrachten af te werken:

  • Exam met een wegingsfactor 10 en aldus 50% van het totale eindcijfer.

Binnen de categorie Examen Praktijk dient men volgende opdrachten af te werken:

  • Exam on the labs met een wegingsfactor 10 en aldus 50% van het totale eindcijfer.

Aanvullende info mbt evaluatie

The grade in first session of one or more of the exam parts (Theory measurements, Theory Data-driven modelling, Exam on the labs) may be transferred to second session if all following conditions are met:

  • the grade of the part is at least 12/20
  • the student explicitly requests this transfer before the start of the second session exam to the titular of this course.
Toegestane onvoldoende
Kijk in het aanvullend OER van je faculteit na of een toegestane onvoldoende mogelijk is voor dit opleidingsonderdeel.

Academische context

Deze aanbieding maakt deel uit van de volgende studieplannen:
Master of Electrical Engineering: Standaard traject BRUFACE J (enkel aangeboden in het Engels)