Anas Abdelrazeq

RWTH Aachen University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

VRW 2020 Index

2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (2020)

Anas Abdelrazeq Haley Adams Ahmed Aiyaz Sun Joo Ileri Akinnola

10.1109/vrw50115.2020.00284

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Requirements and Learning Modules for Implementing a Hologram Table in University Lectures

European Conference on Games Based Learning (2023)

Bruna Pereira de Souza Johannes Zysk Samira Khodaei Maximilian Weber Anas Abdelrazeq

Holograms are relatively new technologies that can be used in university lectures. They enhance the learning experience by adding highly detailed three-dimensional (3D) visualization of objects and environments, which leads to more engagement of students and teachers. Particularly in mining engineering education, holograms have great potential to achieve learning goals, where students often struggle with understanding complex 3D concepts. When implementing new technologies in lectures, there is a must to consider the perception of students and teachers, as well as the didactical and technical requirements of courses. The consideration of these aspects ensures an effective learning environment. This paper presents the concept of three holo-modules with gamification features designed with the main goal of supporting the learning and understanding of mining engineering content. The different conceptual modules have been developed based on different learning objectives extracted from teaching requirements and technical aspects of the lectures. The results show a requirements analysis performed and the prototypes designed to integrate a hologram table in different teaching setups, such as seminars, group work, and frontal lectures.

Table (database)

10.34190/ecgbl.17.1.1569

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Human-Centric Machine Learning for Temporal Knowledge Graphs: Towards Understanding the European Alternative Fuels Market

Robert Jungnickel Aymen Gannouni Anas Abdelrazeq Ingrid Isenhardt

Machines' ability to learn the behavior of complex real world systems has been the main research focus in temporal knowledge graphs (TKG). However, combining the human's input - as part of a real-world TKG - into the modeling process has not yet been investigated. To fill this gap, we propose a novel human-centric machine learning (HCML) framework for TKG link prediction. The main goal is to demonstrate the value of a human-machine online optimization coupled with the self-attention mechanism. We argue that the joint development of a human-machine TKG model can detect low-signal information about the evolution of the graph that can have a significant impact on the dynamics. Finally, our proposed HCML framework is discussed on the basis of the European alternative fuels market as an exemplary use case with the outlook of the approach.

Knowledge graph

10.1109/vl/hcc53370.2022.9833109

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A blueprint description of production scheduling models using asset administration shells

Production & Manufacturing Research (2024)

Aymen Gannouni Patrick Sapel Anas Abdelrazeq Christian Hopmann Robert Schmitt

Production Planning and Control involves combinatorial optimization problems subject to domain-related constraints. Hence, decision-support systems are required to support operators in finding suitable production scheduling models that match the conditions on the real shopfloor. Digital Shadows were introduced to solve this task by automatically gathering suitable models and the required input data to provide decision-support. Therefore, a standard definition of production scheduling models and their grouping within a model catalog is required in the virtual world. In this contribution, we introduce an Asset Administration Shell submodel that defines production scheduling models based on Graham's three-field notation, which is independent of the underlying production process. We consider metadata, which ensures those models are FAIR, i.e. findable, accessible, interoperable, and reusable. In addition, we build a model catalog for production scheduling models in the injection molding domain as the foundation for implementing a decision-support system based on the Digital Shadows.

Blueprint

10.1080/21693277.2024.2324339

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LEARNING GOALS IN 360° VIRTUAL EXCURSION - MEDIA CREATION GUIDELINE FOR MIXED-REALITY-BASED CLASSES

ICERI proceedings (2020)

Samira Khodaei Johannes Sieger Anas Abdelrazeq Ingrid Isenhardt

S. Khodaei1, J. Sieger2, A. Abdelrazeq1, I. Isenhardt1 1Information Management in Mechanical Engineering (IMA) - RWTH Aachen (GERMANY) 2Institute of Mineral Resources Engineering (MRE)- RWTH Aachen (GERMANY)

Excursion

10.21125/iceri.2020.0600

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Automated Anomaly Detection in Concentrated Solar Power Plants with Linear Least Squares Approximation

2022 IEEE International Conference on Industrial Technology (ICIT) (2024)

Robert Jungnickel Sylwia Olbrych Michael Zeng Thomas Kraft Fabian Behrens

Concentrated Solar Power (CSP) plants generate extensive multivariate time series data due to their operational complexity. In these plants, heat transfer fluids in parabolic trough systems are crucial in collecting and transferring solar heat to the power generation system. However, detecting anomalies such as vacuum heat losses in parabolic trough systems is challenging. This is further exacerbated by the daily fluctuations in solar radiation, which make accurate measurements difficult. To address these challenges, we propose a new automated anomaly detection algorithm using least linear squares approximation. The application of our approach to time series data from a commercial CSP plant in Spain provides promising preliminary results. Future applications of this methodology can improve the reliability and efficiency of parabolic trough systems in CSP plants, which represents a key aspect of our future work.

Least-squares function approximation

10.1109/icit58233.2024.10540932

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What do children need to learn for an MRI-scan? Gamification of learning goals into the cosmo@home app

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2023)

Sam Geuens Sanne Nauts Anas Abdelrazeq Michaël Aertsen Gunnar Buyse

Undergoing an awake MRI-scan can be very stressful for young children and bears the risk that image quality is poor due to motion artefacts. Behavioral training by an experienced trainer has shown success in preparing children before a scan, however, this approach is costly and trainer dependent. We have designed a mobile app to prepare children for an upcoming MRI-scan at home. This app was tested by 52 children in four different hospitals. First data show that children and parents appreciate the app very much and that learning goals can be reached with a digital application at home.

Trainer

Mobile apps

Smartphone app

Mri scan

10.58530/2022/0747

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Designing an LLM-Based Copilot for Manufacturing Equipment Selection

arXiv (Cornell University) (2024)

Jonas Werheid Oleksandr Melnychuk Hans Aoyang Zhou Meike Huber Christoph Rippe

Effective decision-making in automation equipment selection is critical for reducing ramp-up time and maintaining production quality, especially in the face of increasing product variation and market demands. However, limited expertise and resource constraints often result in inefficiencies during the ramp-up phase when new products are integrated into production lines. Existing methods often lack structured and tailored solutions to support automation engineers in reducing ramp-up time, leading to compromises in quality. This research investigates whether large-language models (LLMs), combined with Retrieval-Augmented Generation (RAG), can assist in streamlining equipment selection in ramp-up planning. We propose a factual-driven copilot integrating LLMs with structured and semi-structured knowledge retrieval for three component types (robots, feeders and vision systems), providing a guided and traceable state-machine process for decision-making in automation equipment selection. The system was demonstrated to an industrial partner, who tested it on three internal use-cases. Their feedback affirmed its capability to provide logical and actionable recommendations for automation equipment. More specifically, among 22 equipment prompts analyzed, 19 involved selecting the correct equipment while considering most requirements, and in 6 cases, all requirements were fully met.

10.48550/arxiv.2412.13774

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A BUSINESS INTELLIGENCE FRAMEWORK FOR LOCAL GROCERIES IN THE CITY OF AACHEN: TOWARDS UNDERSTANDING COMPLEX MARKET DYNAMICS WITH BUSINESS INTELLIGENCE TOOLS

ICERI proceedings (2017)

Albert Bou Sleiman Anas Abdelrazeq Stefan Schröder Anja Richert Sabina Jeschke

A BUSINESS INTELLIGENCE FRAMEWORK FOR LOCAL GROCERIES IN THE CITY OF AACHEN: TOWARDS UNDERSTANDING COMPLEX MARKET DYNAMICS WITH BUSINESS INTELLIGENCE TOOLS

Business Intelligence

Dynamics

Business activity monitoring

Competitive Intelligence

Market Intelligence

10.21125/iceri.2017.1305

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Virtual reality mine: A vision for digitalised mining engineering education

CRC Press eBooks (2019)

Rudolf Suppes Yannick Feldmann Anas Abdelrazeq Lea M. Daling

Virtual Reality (VR) is becoming increasingly popular in domains such as tertiary education and industrial training. For prospective mining engineers, a comprehensive understanding of complex 3D processes is crucial but is difficult to convey in traditional ways. The benefits of VR are the possible direct 3D immersion into locations that are remote, too costly to visit and/or unsafe. To improve future graduate education, an informative and interactive underground VR environment is being developed by RWTH Aachen University and TalTech University, called the VR-Mine. It is based on the concepts of blended learning, gamification and flipped classroom, and focusses on the topics health and safety, and principles of underground mining. Furthermore, it could help increasing the industry's health and safety standards with specialised training in safe environments. The vision of the project is to create a comprehensive VR mining environment where all process-related aspects of a mine's life can be explored.

10.1201/9780429320774-3

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Citations (6)