Practical control problems are subject to dealing with instrumentation noise and inaccurate models. These can be modelled as measurement and state noise, respectively. Nonlinear state estimators, for example a particle filter, can be used to mitigate these effects. However, they are usually computationally expensive which makes them impractical for industrial use. This text investigates using General Purpose Graphics Processing Units (GPGPU) to improve the performance particle and Gaussian sum filters by parallelizing their prediction, update and resampling steps. GPGPU accelerated filters are found to outperform non-accelerated filters as the number of particle increases. GPGPU acceleration also allows particle filters with 2^19.5 particles to be used on systems with dynamic time constants on the order of 0.1 second and for Gaussian sum filters with 2^18.5 particles to be used with time constants on the order of 1 second. The filters are applied to a bioreactor system containing R. Oryzae, where MPC control is applied to the production phase fumaric acid and glucose concentrations. The bioreactor is modelled using results from Iplik (2017) and Swart (2019). It is found that the GPGPU filters improved run times allow for more particles to be used which provides increased filter accuracy and thus better performance. This improved performance comes at the cost of consuming more energy. Thus, it is believed that the GPGPU implementations should be used for applications with complex dynamics/noise that require large numbers of particles and/or high sampling rates. / Dissertation (MEng (Control Engineering))--University of Pretoria, 2021. / Chemical Engineering / MEng (Control Engineering) / Unrestricted

In this project, an algorithm has been implemented in Python to estimate the parameters describing the production and decay of a spin 1/2 baryon - antibaryon pair. This decay can give clues about a fundamental asymmetry between matter and antimatter. A model-independent formalism developed by the Uppsala hadron physics group and previously implemented in C++, has been shown to be a promising tool in the search for physics beyond the Standard Model (SM) of particle physics. The program developed in this work provides a more user-friendly alternative, and is intended to motivate further use of the formalism through a more maintainable, customizable and readable implementation. The hope is that this will expedite future research in the area of charge parity (CP)-violation and eventually lead to answers to questions such as why the universe consists of matter. A Monte-Carlo integrator is used for normalization and a Python library for function minimization. The program returns an estimation of the physics parameters including error estimation. Tests of statistical properties of the estimator, such as consistency and bias, have been performed. To speed up the implementation, the Just-In-Time compiler Numba has been employed which resulted in a speed increase of a factor 400 compared to plain Python code.

This study aims to examine how simulation models can be performance optimized in Python. Optimized in the sense of executing faster and enabling upscaling with more data. To meet this aim, two models simulating the Greenland ice sheet are studied. The simulation of ice sheets is an important part of glaciology and climate change research. By following an iterative spiral model of software development and evolution with focus on the bottlenecks, it is possible to optimize the most time-consuming code sections. Several iterations of implementing tools and techniques suitable for Python code are performed, such as implementing libraries, changing data structures, and improving code hygiene. Once the models are optimized, the upscaling with a new dataset, called CARRA, created from observations and modelled outcomes combined, is studied. The results indicate that the most effective approach of performance optimizing is to implement the Numba library to compile critical code sections to machine code and to parallelize the simulations using Joblib. Depending on the data used and the size and granularity of the simulations, simulations between 1.5 and 3.2 times the speed are gained. When simulating CARRA data, the optimized code still results in faster simulations. However, the outcome demonstrates that differences exist between the ice sheets simulated by the dataset initially used and CARRA data. Even though the CARRA dataset yields a different glaciological result, the overall changes in the ice sheet are similar to the changes shown in the initial dataset simulations. The CARRA dataset could possibly be used for getting an overview of what is happening to the ice sheet, but not for making detailed analyses, where exact numbers are needed.

This diploma thesis deals with the use of Cartesian Genetic Programming (CGP) for combinational circuits design. The work addresses the issue of optimizaion of selected logic circuts, arithmetic adders and multipliers, using Cartesian Genetic Programming. The implementation of the CPG is performed in the Python programming language with the aid of NumPy, Numba and Pandas libraries. The method was tested on selected examples and the results were discussed.