'Gaussian Processes for 3D Measurements' (29/05/2024)

Ivan De Boi

• woensdag 29 mei 2024
• 15.30 uur
• ​Campus Middelheim- lokaal m.A.143 (aula Patrice Lumumba)
• Promotoren: prof. dr. Rudi Penne & dr. Pieter Jorissen
• Faculteit Toegepaste Ingenieurswetenschappen

Abstract

On the usage of probabilistic machine learning methods for calibrating 3D measuring devices based on straight lines.

'Development of electrochemical steps for glucose electrooxidation to value-added products' (22/04/2024)

Giulia Moggia

• Maandag 22 april 2024
• 17.00 uur
• ​Campus Drie Eiken- lokaal d.O.06
• Promotor: prof. dr. Tom Breugelmans​
• Faculteit Toegepaste Ingenieurswetenschappen

Abstract

Carbohydrates are renewable, inexpensive and available organic raw materials. Only 3–5% of them have industrial use, the rest decays and recycles along natural pathways. One interesting finding in this field has been the recognition that acids derived from sugars have potential uses in fine chemistry. The biggest challenge in the use of carbohydrates as raw materials in fine chemistry is to achieve their direct and region-selective oxidation in aqueous media, which is difficult by classical chemical methods without a preliminary protection strategy. Electroorganic approaches have currently fascinated academicians and industrial researchers because of their high potential prospects for industrial ventures. Electrocatalytic organic synthesis provides a powerful tool to control the reaction rate and selectivity through electrode potential and current, and represents a promising alternative to the traditional industrial methods. In fact, electrosynthesis is naturally suited to obey the principles of Green Chemistry, owning to several environmentally favorable features: i.e., reduced energy consumption, use of renewable raw materials, decreased emission of pollutants or toxic raw materials.
Despite its sustainable nature and its potential to electrify the industry, as such replacing traditional, non-sustainable production processes of a broad range of fine chemicals, electrochemical synthesis methods are still very underdeveloped as compared to their traditional alternatives. More research is needed to better understand electrochemical processes and address the main challenges that prevent their application at industrial scale: i.e., the still unsatisfactory selectivity and/or productivity, the electrodes’ limited lifetime and the insufficient know-how on up-scaling towards industrial scale.
This PhD thesis is specifically dedicated to the study of electrocatalytic routes for the selective oxidation of glucose to gluconic and glucaric acid (both of which are commercially relevant carbohydrates). The aim here is thus to investigate the factors that determine the selectivity of the reaction towards the two products of interest, including the choice of the catalyst and the reaction conditions, and, as such, unravel the reaction mechanism beyond it. To this end, a combination of electrochemical and analytical techniques is used where microscopical surface analysis, used for the morphological characterization, is linked to its electrocatalytic performance.

'Optimizing Simulated-assisted Verification of Safety Properties of Cyber-Physical Systems' (17/01/2024)

Mehrdad Moradi

• Woensdag 17 januari 2024
• 16.00 uur
• Campus Middelheim - lokaal m.G.010
• Promotoren: prof. dr. Joachim Denil​
• Faculteit Toegepaste Ingenieurswetenschappen

Abstract

The validation of the safety properties of Cyber-Physical Systems (CPS) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety validation of CPS is Fault Injection (FI). Fault injection is a testing technique that aids in understanding how the system behavioral when stressed in an unusual way. The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost, as fault space is enormous and multidimensional. Therefore, traditional fault injection methods are not effective in terms of number of found faults and severity of them.
In this thesis, we utilize simulation-based fault injection in the system models, which enables the test engineer to identify the fault in the early phase of system development. We first performed a systematic literature review to categorize the existing methods, fault models, metrics for system models. Then, we propose a fault injection method to inject faults into the MATLAB/Simulink model as white-box models using model transformation. We also worked on the fault injection in black-box models, which is based on Functional Mock-up Interface (FMI). Next, we investigated multiple methods to increase the efficiency (in terms of total number of critical faults and run time execution) of fault injection using sensitivity analysis, reinforcement learning (RL), and the Generative Adversarial Network (GAN). These methods utilize high-level domain knowledge of the model under test to set up the fault injection simulation. The proposed methods automatically configure faults in the model under test and find catastrophic faults that can violate the safety properties of the model in the early stage of system development.
We compared the proposed method (RL-based and GAN-based) with random-based fault injection, and our proposed method outperformed random-based fault injection in terms of the severity or number of faults found.
We also demonstrated our method in Hazard Analysis and Risk Assessment (HARA), specified in ISO 26262 (functional safety standard in automotive), identifies malfunctions that could lead to hazards, and rates their risks.