About us

Ole Magnus Brastein, M.Sc


Brastein started BEDKO AS in 2011. Since then, the company has offered consulting services in software engineering and electronics. He has 15+ years of experience as a programmer and software architect.

Brasteins experience range from the fields of machine learning (ML) and system identification (SID) to software engineering. Combining a solid theoretical understanding of cybernetics, control theory, machine learning and system identification, with hands on experience in software development has proven to be a valuable combination for our customers.

 

Brasteins interests are software design and engineering, machine learning, system identification, model predictive control, empirical system modelling and analysis, stochastic and dynamic systems.

In addition to hands on consulting/work on projects within the bespoke fields, Brastein is available to conduct seminars within his fields of interest.

List of technologies and fields of interest


Software development:

  1. C# (WinForms, UWP)
  2. C (for embedded uC / RISC)
  3. C++ (incl. MFC for legacy projects)
  4. Python
  5. MATLAB
  6. UML and its use in Object Oriented Analysis and Design

Machine learning

  1. Artificial neural networks
  2. Deep learning
  3. Decision trees
  4. Evolutionary algorithms
  5. Ensemble methods (boosting, bagging, Random Forrest, etc)

Cybernetics and algorithms

  1. Kalman Filters (incl. non-linear variants such as EKF, UKF, EnKF)
  2. System identification (PEM, SSID, etc)
  3. Stochastic models and parameter estimation
  4. Model Predictive Control

Applied mathematics

  1. Numerical optimization
  2. Statistics and probability

Software and tools

  1. Visual Studio (v6.0 – 2017)
  2. MATLAB (incl SID and ML tools)
  3. Python
  4. Eagle PCB layout / CAD
  5. Microchip tools for embedded software
  6. LyX (a LaTeX based editor)

Bibliography


[1] O. Brastein, D. Perera, C. Pfeifer, and N.-O. Skeie, Parameter estimation
for grey-box models of building thermal behaviour, Energy and Buildings,
vol. 169, pp. 58  68, 2018.
[2] O. Brastein, R. Olsson, N.-O. Skeie, and T. Lindblad, Human activity recognition
by machine learning methods, Norsk Informatikkonferanse, 2017.
[3] O. M. Brastein, Grey-box models for estimation of heating times for buidings,
 Master’s thesis, Høgskolen i Sørøst-Norge, 2016.