Robot serial number:20225300304

Passwords:

• safety: 0000

Supervisor: Vedant Dave, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 15th August 2023

Theoretical difficulty: Mid
Practical difficulty: High

Abstract

The aim of this Thesis is to predict the electricity price for the Hydrogen plants from open-sourced Energy data provided by the European Network of Transmission System Operators (ENTSO-E) [1]. We explore multiple machine learning techniques to achieve this aim. At the end, a standalone GUI is provided, that can be used in the industry with ease. This work was done in collaboration HyCenta Research GmbH.

Further, this thesis seeks to address the following research questions:

• How do different determinants such as the electricity mix (the proportion of energy from various generation sources), in-house generation, and gas prices, influence the cost of electricity?
• Which machine learning approaches/algorithms are most suitable for accurately predicting future electricity price trends, particularly in Austria or other European countries? 
• To what extent does the sensitivity of our model to inputs, like solar and wind energy, affect its overall accuracy and reliability in predicting electricity prices?

Thesis

Deep Neural Energy Price Forecasting for the Hydrogen Industry

Tentative Work Plan

To achieve the objectives, the following concrete tasks will be focused on:

• Literature review
• Evaluation of SOTA methods
• Designing network and hyperparameter tuning
• Evaluation on unseen country’s data
• Development of Standalone GUI

Related Work

[1]  Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. “The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform,” Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.

Supervisor: Fotios Lygerakis and Prof. Elmar Rueckert

Start Date: 1st March 2023

Theoretical difficulty: low
Practical difficulty: mid

Abstract

As the interaction with robots becomes an integral part of our daily lives, there is an escalating need for more human-like communication methods with these machines. This surge in robotic integration demands innovative approaches to ensure seamless and intuitive communication. Incorporating sign language, a powerful and unique form of communication predominantly used by the deaf and hard-of-hearing community, can be a pivotal step in this direction. 

By doing so, we not only provide an inclusive and accessible mode of interaction but also establish a non-verbal and non-intrusive way for everyone to engage with robots. This evolution in human-robot interaction will undoubtedly pave the way for more holistic and natural engagements in the future.

Thesis

ROS2-based Human-Robot Interaction Framework with Sign Language

Project Description

The implementation of sign language in human-robot interaction will not only improve the user experience but will also advance the field of robotics and artificial intelligence.

This project will encompass 4 crucial elements.

1. Human Gesture Recognition with CNNs and/or Transformers – Recognizing human gestures in sign language through the development of deep learning methods utilizing a camera.
   • Letter-level
   • Word/Gloss-level
2. Chat Agent with Large Language Models (LLMs) – Developing a gloss chat agent.
3. Finger Spelling/Gloss gesture with Robot Hand/Arm-Hand –
   
   • Human Gesture Imitation
   • Behavior Cloning
   • Offline Reinforcement Learning
4. Software Engineering – Create a seamless human-robot interaction framework using sign language.
   • Develop a ROS-2 framework
   • Develop a robot digital twin on simulation
5. Human-Robot Interaction Evaluation – Evaluate and adopt the more human-like methods for more human-like interaction with a robotic signer.

Quelle: Obersteirische Rundschau (www.rundschau-medien.at)

Dear CPS team, thank you very much for this great and successful year. We all enjoyed our Christmas party at the kitchen. 

New Checklists from SSC

As of 20th of January 2024, the Vice Rector for studies, digitalization and international affairs, Thomas Prohaska, sent out new general guidelines for Mastertheses on the Montanuniversity of Leoben.

English Versions

• Guideline Supervision of Mastertheses
• Checklist Supervision of Masthertheses

German Versions

• Leitfaden Betreuung von Masterarbeiten für Betreuende
• Checkliste Betreuung von Masterarbeiten für Betreuende

German Version of the Checklists – CPS

• Checkliste Betreuer
• Checkliste Student

English Version of the Checklists – CPS

• Checklist Supervisor
• Checklist Student

FFG Projekt 01/03/2024-28/02/2027

Aushubmaterialien machen mit rund 42 Mio. t/a fast 60 % des österreichischen Abfallaufkommens aus, von denen 73 % deponiert und nur 8 % in Behandlungsanlagen eingebracht (BMK, 2021) und deren Outputströme größtenteils einer geringwertigen Verwendung, z.B. Untergrundverfüllung, zugeführt werden. Gleichzeitig werden in Österreich 55 Mio. t/a grundeigene mineralischer Rohstoffe abgebaut (Statista, 2022). Ursache für diese Diskrepanz sind die Herausforderungen bei der Materialdisposition, aber auch die günstige (ALSAG-freie) Deponierung von nicht kontaminierten Aushubmaterialien. Somit stellt die Verwendung von Aushubmaterialien einen ungenutzten Beitrag zur Kreislaufwirtschaft dar, welcher sich vor allem in der Schonung heimischer Ressourcen und in der Minimierung des CO₂ Ausstoß von Tiefbauprojekten bemerkbar macht (Galler, 2015).

Projektziele

• Erörterung von nachhaltigen Verwertungswegen aufgrund der geotechnischen, mineralogischen, petrographischen, geochemischen und hydrogeologischen Ergebnisse aus der geologischen Vorerkundung von geplanten und im Projekt bearbeiteten Tief- und Tunnelbaustellen
• Sensorbasierte Stoffstromcharakterisierung mittels online-Analyse von Wert- bzw Störstoffen durch LIBS und HyperSpecs sowie mineralogische Zusammensetzung durch Raman Spektroskopie am Förderband unter realen Bedingungen in der Tunnelforschungsanlage „Zentrum am Berg“ der MUL
• Entwicklung eines Qualitätssicherungssystems durch Erstellung eines Klassifikationsmodells, welches das Material durch KI-gestützten Abgleich der Ergebnisse der online Analyse mit gesetzlichen Anforderungen verschiedenen Recyclingpfaden bzw. Deponieklassen zuführt
• Baustofftechnische Überprüfung der aus dem Aushubmaterial hergestellten Produkte

Projekt Consortium

• Montanuniversität Leoben
  • Lehrstuhl für Subsurface Engineering (Koordinator)
  • Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft
  • Lehrstuhl für Cyber-Physical-Systems
• AGIR Austria GmbH
• AiDEXA GmbH
• LSA Laser Analytical Systems & Automations GmbH
• Austin Powder GmbH
• Master Builder Solutions
• Edaphos Engineering
• Daxner & Merl
• Universität Innsbruck, Materialtechnologie Innsbruck (MTI)
• ÖBB-Infrastruktur AG

Fördergeber

• Österreichische Forschungsförderungsgesellschaft mbH (FFG)

Project Information

In the NNATT project model research and experimental work is conducted to identify tunnel- and excavated material with sensor based classification and deep learning. Representative tunnel- and excavation material from Austria is sampled, mineralogically, chemically and geotechnically characterized, sensor based measured in preliminary tests and finally applied in a conceptual pilot plant for material classification at the Zentrum am Berg in Eisenerz. Additionally, the project focuses on opportunities for application in alternative building materials, resulting in saving of primary raw materials and the associated reduction of CO2 emissions.

In 2021, excavated materials, such as tunnel excavation, accounted for around 46.1 million tons, or 60% of Austria’s total waste. Our project proposes an innovative solution for the recycling of excavated materials from tunneling and construction projects by using spectral imaging technology data in deep neural networks to predict rocks and their recycling potentials. By implementing real-time material identification on a conveyor belt based on deep neural networks, we aim to feed the excavated material into a recycling chain. This cutting-edge technology enables us to identify the resources potential of the material, facilitating efficient processing and sorting both on-site and off-site.

The objectives of our project are fourfold: to conserve valuable resources in Austria by maximizing material reuse, to reduce the burden on landfills by minimizing waste disposal, to shorten transportation routes and decrease CO2 emissions associated with material transport, and to promote sustainable practices within the construction industry.

Through the integration of advanced technology and a commitment to sustainability, our project represents a significant step towards creating a future in which excavated material is considered a valuable resource that contributes to a circular economy.

Poster

• Abfallwirtschaftstagung: Click here for poster
• Exzellenzcluster – Recycling: Click here for poster

Other

Website AVAW: https://www.avaw-unileoben.at/de/forschung/projekte/nnatt

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 30th October 2023

Theoretical difficulty: mid
Practical difficulty: High

Abstract

This thesis explores machine learning techniques for analysing onboard recordings from the TU Graz Racing Team, a prominent Formula Student team. The main goal is to design and train an end-to-end machine learning model to autonomously discern race courses based on sensor observations.

Further, this thesis seeks to address the following research questions:

• Can track markers (cones) be reliably detected and segmented from onboard recordings?
• Does the delineated racing track provide an adequate level of accuracy to support autonomous driving, minimizing the risk of accidents?
• How well does a neural network trained on simulated data adapt to real-world situations?
• Can the neural network ensure real-time processing in high-speed scenarios surpassing 100 km/h?

Tentative Work Plan

To achieve the objectives, the following concrete tasks will be focused on:

• Thesis initialisation and literature review:
  • Define the scope and boundaries of your work.
  • Study the existing project in [1]  and [2] to identify gaps and methodologies.
  •  
• Setup and familiarize with the simulation environment
  • Build the car model (URDF) for the simulation (optional if you wish to use the existing one)
  • Setup the ROS framework for the simulation (Gazebo, Rviz)
  • Recommended programming tools: C++, Python, Matlab
  •  
• Data acquisition and preprocessing (3D Lidar and RGB-D data)
  • Collect onboard recordings and sensor data from the TU Graz Racing track.
  • Augment the data with additional simulated recordings using ROS, if necessary.
  • Preprocess and label the data for machine learning (ML). This includes segmenting tracks, markers, and other relevant features.
  •  
• Intermediate presentation:
  • Present the results of the literature study or what has been done so far
  • Detailed planning of the next steps
  •  
• ML Model Development:
  •  Design the initial neural network architecture.
  • Train the model using the preprocessed data.
  • Evaluate model performance using metrics like accuracy, precision, recall, etc.
  • Iteratively refine the model based on the evaluation results.
  •  
• Real-world Testing (If Possible):
  • Implement the trained model on a real vehicle’s onboard computer.
  • Test the vehicle in a controlled environment, ensuring safety measures are in place.
  • Analyze and compare the model’s performance in real-world scenarios versus simulations.
  •  
• Optimization for Speed and Efficiency (Optional):
  • Validate the model to identify bottlenecks.
  • Optimize the neural network for real-time performance, especially for high-speed scenarios
  •  
• Documentation and B.Sc. thesis writing:
  • Document the entire process, methodologies, and tools used.
  • Analyze and interpret the results.
  • Draft the thesis, ensuring that at least two of the research questions are addressed.
  •  
• Research paper writing (optional)
  •  

Related Work

[1]   Autonomous Racing Graz, “Enhanced localisation for autonomous racing with high-resolution lidar“, Article by Tom Grey, Visited 30.10.2023.

[2]   Autonomous RC car racing ETH Zürich, “The ORCA (Optimal RC Racing) Project“, Article by Alex Liniger, Visited 30.10.2023.

[3]   P. Cai, H. Wang, H. Huang, Y. Liu and M. Liu, “Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning,” in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7262-7269, Oct. 2021, doi: 10.1109/LRA.2021.3097345.

[4]   Z. Lu, C. Zhang, H. Zhang, Z. Wang, C. Huang and Y. Ji, “Deep Reinforcement Learning Based Autonomous Racing Car Control With Priori Knowledge,” 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 2241-2246, doi: 10.1109/CAC53003.2021.9728289.

[5]   J. Kabzan, L. Hewing, A. Liniger and M. N. Zeilinger, “Learning-Based Model Predictive Control for Autonomous Racing,” in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3363-3370, Oct. 2019, doi: 10.1109/LRA.2019.2926677.

FFG K1-MET Project 07/2023-06/2026

This project aims at employing advanced data analyses and methodology in order to investigate process data from different processes in the steelmaking chain, generating process understanding and knowledge on correlations and causations in operation, as well as develop recommendation or warning systems for the operator in order to adjust and improve operation. Topics range from questions on operation and stability of the blast furnace (BF), to the production of ultra-clean steels with Ruhrstahl-Heraeus (RH) treatment and the optimization of the continuous casting (CC) process.

Work Packages

• WP1-4: Temperature irregularities in BF bottom/ hearth, mass balance of zinc and alkali elements, investigations of BF charging models/ charging profile, raceway monitoring analyses
• WP5: Image analysis and state classification at the RH plant
• WP6: Hybrid Mold – Data evaluations around the CC process

Expected Results

• (WP1-4) Blast furnace: Prediction of temperature irregularities, mass balances in BF operation, charging models and development of optimized charging strategies, analyses of raceway blockages and possible correlations with process parameters and image material, predictive maintenance for tuyeres
• (WP5) RH plant: Comprehensive benchmark case for machine learning algorithms, setup of an advisory algorithm for the operator to be warned of irregular states of the RH plant
• (WP6) Continuous Casting: Modelling of heat transfers in the mold based on a hybrid approach, combining data from sensors in the CC mold with physical/ metallurgical-based process models

Project Consortium

• Joanneum Research GmbH – Institute DIGITAL

• Johannes Kepler University Linz – Department of Particulate Flow

• Linz Center of Mechatronics – Area SENS

• Montanuniversität Leoben

  •  Chair of Cyberphysical Systems

  • Chair of Ferrous Metallurgy

• Primetals Technologies Austria GmbH

• voestalpine Stahl GmbH

• voestalpine Stahl Donawitz GmbH

Links

Details on the research project can be found on the project webpage.

Funding Agency

• Österreichische Forschungsförderungsgesellschaft mbH (FFG)

FFG, BMVIT Leitprojekt 07/2023-06/2026

Die metallverarbeitende Industrie ist bei ihrer Produktion auf hochwertigen Metallschrott angewiesen. Derzeit muss dieser nach Österreich importiert werden. Mit Juli startet nun ein FFG-Leitprojekt, das mit Hilfe von Künstlicher Intelligenz das Recycling von Metallverbundabfällen verbessern will.

Vor dem Hintergrund des „Europäischen Green Deals“ und des Kreislaufwirtschaftspaketes müssen Ressourcenverbrauch (minus 25 Prozent) und CO2-Emissionen (minus 55 Prozent) bis 2030 drastisch reduziert und gleichzeitig die Ressourceneffizienz massiv gesteigert werden. Bei Metallen ist der Ökologische Fußabdruck durch den Rohstoffeinsatz besonders hoch, gleichzeitig sind sie ideale Kandidaten fürs Recycling. Genau hier setzt das neue FFG- Leitprojekt an, und will mit Künstlicher Intelligenz die Qualität der metallischen Abfälle steigern.

Haushaltsschrotte und Schrotte aus Altfahrzeugen sowie Elektro-Altgeräten zeichnen sich durch einen hohen Metallgehalt aus und haben daher großes Potenzial zum Recycling. Leider fallen diese Metalle nicht sortenrein an, sondern in Form von Kunststoffmetallverbunden oder Legierungsmischungen. „Derzeit werden die Metalle geschreddert und aufgrund der minderen Qualität ins Ausland exportiert,“ erklärt Dr. Alexia Tischberger-Aldrian, Projektverantwortliche seitens des Lehrstuhls für Abfallverwertungstechnik und Abfallwirtschaft. Gleichzeitig importiert Österreich höherwertigen Schrott, der für die Metallproduktion sehr wichtig ist.

Projektziele

• Entwicklung einer KI unterstützten Sortierstraße zur Bereitstellung von definierten Metallfraktionen
• Entwicklung einer Prozess Modellierungs- und Optimierungsumgebung zur Erstellung von digitalen Zwillingen zur Prozesssimulation und Ableitung optimaler Handlungsempfehlungen
• Etablierung eines Datenflusses für recyclingrelevante Daten der im Projekt betrachteten Abfallströme
• Entwicklung und Bereitstellung einer intelligenten Redyclingplattform zur übergeordneten Prozessseuerung und Vernetzung von Stakeholdern
• Anwendung der entwickelten KI basierten Lösungen in relevanten Use Cases

Projekt Consortium

• Montanuniversität Leoben (Koordinator)
  • Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft
  • Lehrstuhl für Cyber-Physical-Systems
• 7lytix gmbh
• Fabasoft R&D GmbH
• Bernegger GmbH
• voestalpine High Performance Metals GmbH
• Breitenfeld Edelstahl Aktiengesellschaft
• METTOP GmbH
• ETA Umweltmanagement GmbH
• Nekonata XR Technologies GmbH
• Mayer Recycling GmbH
• BT-Wolfgang Binder GmbH
• O.Ö. Landes-Abfallverwertungsunternehmen GmbH
• K1-MET GmbH
• Scholz Austria GmbH
• Andritz AG
• Software Competence Center Hagenberg GmbH
• voestalpine Stahl GmbH
• PROFACTOR GmbH
• Salzburg Research Forschungsgesellschaft m.b.H.

Fördergeber

• Österreichische Forschungsförderungsgesellschaft mbH (FFG)

• Bundesministerium für Verkehr, Innovation und Technologie (BMVIT)