Automotive Computing for Mobility Innovation

• Co-founded by European Union
• Project period: 2024 - 2026
• Solver: doc. Ing. David Hartman Ph.D., doc. Ing. Pavel Mertlík, CSc.
• Cooperating institutions
  • Unicorn University, Škoda Auto Vysoka škola, Žilinská univerzita v Žilině, Seinäjoki University of Applied Sciences, Transilvania University of Brasov
• Annotation
  • Automotive Computing is an important area in the context of the strategic priorities of the European Union. The automotive industry provides a significant proportion of European industrial jobs and is of great importance for global competitive advantage and European competitiveness.

Use of Neural Networks for Real-time Prediction of Variable Baselines in Energy Devices at HV and LV Voltage Levels

• Project Code: TK05020142
• Funding Agency: Technology Agency of the Czech Republic (TA ČR)
• Project Duration: 2023 – 2025
• Principal Investigator: doc. Ing. David Hartman, Ph.D. 
• Collaborating Institution
  • Institute of Computer Science, Czech Academy of Sciences (ICS CAS)
• Related Research Team
  • Utilisation of AI Methods in Complex Data Tasks
• Abstract
  • The project involves the development of a software solution capable of meeting the requirements for variable baseline prediction according to ČEPS standards across a wide range of consumption and production technologies. The software – thanks to an innovative baseline prediction method – will significantly increase the potential for these technologies to participate in the provision of support services. The project will use innovative deep learning methods for neural networks with regularisation (Deep Learning – DL & Deep Neural Networks – DNN). Newly created and implemented DNN models will then be practically validated for baseline prediction for specific flexibility providers.

Optimization of Energy Portfolios to Increase the Utilisation of Renewable Sources

• TS01020123
• Funding Agency: TA ČR
• Project Duration: 2024 – 2026
• Principal Investigator: Ing. Ludmila Petkovová, Ph.D.
• Collaborating Institution
  • ICS CAS
• Related Research Team
  • Utilisation of AI Methods in Complex Data Tasks
• Abstract
  • The project aims to develop a comprehensive tool, Lancelot GDS (Generation Dispatch System), to increase the utilisation of renewable sources for Aggregators of Power Flexibility. The tool will enable efficient management of production and consumption device portfolios and optimisation of operational and commercial production and consumption plans on flexible devices, taking into account market price developments, forecasted renewable energy generation, technical limits of storage (batteries), and operational constraints of production and consumption devices within the aggregation block.

Social and Motivational Factors in Study and Their Impact on Academic Outcomes in Tertiary Education with a Focus on Technical Fields

• TQ01000538
• Funding Agency: TA ČR
• Project Duration: 2023 – 2025
• Principal Investigator: Ing. Ludmila Petkovová, Ph.D.
• Collaborating Institutions
• Související výzkumný tým
  • Institute of Philosophy, CAS
  • ICS CAS
  • Czech Institute of Informatics, Robotics and Cybernetics (CIIRC CTU)
  • University of Chemistry and Technology Prague (UCT Prague)
• Related Research Team: 
  • Student Analytics and Its Improvement Using AI Methods
• Abstract
  • The project aims to create recommended practices for the sensitive and effective handling of social and motivational factors affecting academic outcomes. The project is based on interdisciplinary research with a core in complex sociological analysis, complemented by analysis using machine learning and AI methods. The unique integration of various sources and types of data (quantitative and qualitative) and their analyses, grounded in multiple disciplines, allows the problem to be addressed in its complexity from multiple perspectives, considering both objective indicators of students and study progress, as well as their subjective evaluations and personal experience. The goal is to provide universities with tools to work with students’ needs and potential and to more effectively target interventions, e.g., for students at risk of academic failure.

Evaluation of the Effectiveness of Public Support Programmes in Research and Development

• TB94TACR001
• Funding Agency: TA ČR
• Project Duration: 2014 – 2016
• Principal Investigator: prof. Ing. Jan Čadil, Ph.D. 
• Collaborating Institutions
  • University of Economics in Prague, Faculty of Informatics and Statistics
  • Research Centre BIVŠ, z.ú.
• Related Research Team
  • Economics and Econometric Methods in Sparse and Complex Data 
• Abstract
  • The project aims to create a certified methodology for evaluating R&D support in the Czech Republic based on a counterfactual approach. A sub-goal is to create an independent data source to assess the impact of R&D on supported private entities, combining the ISVAV and SSV (Albertina) databases. Using this dataset, which may have broader applicability, basic statistical analysis will be conducted, and recommendations formulated regarding the data requirements of impact evaluations (CIE), including the adequacy of the data and handling potential data issues. Another sub-goal is the comparison of methodological approaches within CIE, primarily based on analyses of individual approaches and consultations with international experts (Finnish Technology Agency TEKES). The final sub-goal is the application of the selected methodology to the dataset created in the first sub-goal, effectively testing the proposed methodology on real data

• Subprojekt projektu MŠMT – LM 2015058
• Funding Agency: CERN
• Project Duration: 2015 – 2024
• Principal Investigator: Ing. Marek Beránek, Ph.D.
• Collaborating Institution
  • CERN
• Related Research Team
  • Modern Software Development
• Abstract
  • The project concerns the production database for one of the two largest multipurpose particle detectors installed at the Large Hadron Collider (LHC). ATLAS is a general-purpose experiment in particle physics at CERN’s LHC. The ATLAS detector provided the first evidence for the Higgs boson in 2012, confirmed by experimental results in 2013. The detector is now used to collect further data on fundamental particles at increased luminosity and is gradually being upgraded for higher energy performance. The aim of this subproject is the creation of a product database for the ATLAS detector.