TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Gamified Virtual Reality Training Environment for the Manufacturing Industry T2 - Proceedings of the 2020 19th International Conference on Mechatronics – Mechatronika (ME) N2 - Industry 4.0 imposes many challenges for manufacturing companies and their employees. Innovative and effective training strategies are required to cope with fast-changing production environments and new manufacturing technologies. Virtual Reality (VR) offers new ways of on-the-job, on-demand, and off-premise training. A novel concept and evaluation system combining Gamification and VR practice for flexible assembly tasks is proposed in this paper and compared to existing works. It is based on directed acyclic graphs and a leveling system. The concept enables a learning speed which is adjustable to the users’ pace and dynamics, while the evaluation system facilitates adaptive work sequences and allows employee-specific task fulfillment. The concept was implemented and analyzed in the Industry 4.0 model factory at FH Aachen for mechanical assembly jobs. Y1 - 2020 U6 - https://doi.org/10.1109/ME49197.2020.9286661 N1 - 2020 19th International Conference on Mechatronics – Mechatronika (ME), Prague, Czech Republic, December 2–4, 2020 SP - 1 EP - 6 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Schuba, Marko A1 - Höfken, Hans-Wilhelm A1 - Linzbach, Sophie T1 - An ICS Honeynet for Detecting and Analyzing Cyberattacks in Industrial Plants T2 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) N2 - Cybersecurity of Industrial Control Systems (ICS) is an important issue, as ICS incidents may have a direct impact on safety of people or the environment. At the same time the awareness and knowledge about cybersecurity, particularly in the context of ICS, is alarmingly low. Industrial honeypots offer a cheap and easy to implement way to raise cybersecurity awareness and to educate ICS staff about typical attack patterns. When integrated in a productive network, industrial honeypots may not only reveal attackers early but may also distract them from the actual important systems of the network. Implementing multiple honeypots as a honeynet, the systems can be used to emulate or simulate a whole Industrial Control System. This paper describes a network of honeypots emulating HTTP, SNMP, S7communication and the Modbus protocol using Conpot, IMUNES and SNAP7. The nodes mimic SIMATIC S7 programmable logic controllers (PLCs) which are widely used across the globe. The deployed honeypots' features will be compared with the features of real SIMATIC S7 PLCs. Furthermore, the honeynet has been made publicly available for ten days and occurring cyberattacks have been analyzed KW - Conpot KW - honeypot KW - honeynet KW - ICS KW - cybersecurity Y1 - 2022 SN - 978-1-6654-4231-2 SN - 978-1-6654-4232-9 U6 - https://doi.org/10.1109/ICECET52533.2021.9698746 N1 - 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). 09-10 December 2021. Cape Town, South Africa. PB - IEEE ER - TY - CHAP A1 - Christian, Esser A1 - Montag, Tim A1 - Schuba, Marko A1 - Allhof, Manuel T1 - Future critical infrastructure and security - cyberattacks on charging stations T2 - 31st International Electric Vehicle Symposium & Exhibition and International Electric Vehicle Technology Conference (EVS31 & EVTeC 2018) Y1 - 2018 SN - 978-1-5108-9157-9 SP - 665 EP - 671 PB - Society of Automotive Engineers of Japan (JSAE) CY - Tokyo ER - TY - CHAP A1 - Chavez Bermudez, Victor Francisco A1 - Wollert, Jörg T1 - 10BASE-T1L industry 4.0 smart switch for field devices based on IO-Link T2 - 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS) N2 - The recent amendment to the Ethernet physical layer known as the IEEE 802.3cg specification, allows to connect devices up to a distance of one kilometer and delivers a maximum of 60 watts of power over a twisted pair of wires. This new standard, also known as 10BASE-TIL, promises to overcome the limits of current physical layers used for field devices and bring them a step closer to Ethernet-based applications. The main advantage of 10BASE- TIL is that it can deliver power and data over the same line over a long distance, where traditional solutions (e.g., CAN, IO-Link, HART) fall short and cannot match its 10 Mbps bandwidth. Due to its recentness, IOBASE- TIL is still not integrated into field devices and it has been less than two years since silicon manufacturers released the first Ethernet-PHY chips. In this paper, we present a design proposal on how field devices could be integrated into a IOBASE-TIL smart switch that allows plug-and-play connectivity for sensors and actuators and is compliant with the Industry 4.0 vision. Instead of presenting a new field-level protocol for this work, we have decided to adopt the IO-Link specification which already includes a plug-and-play approach with features such as diagnosis and device configuration. The main objective of this work is to explore how field devices could be integrated into 10BASE-TIL Ethernet, its adaption with a well-known protocol, and its integration with Industry 4.0 technologies. KW - 10BASE-T1L KW - Ethernet KW - Field device KW - Sensors KW - IO-Link Y1 - 2022 SN - 978-1-6654-1086-1 SN - 978-1-6654-1087-8 U6 - https://doi.org/10.1109/WFCS53837.2022.9779176 N1 - 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS), 27-29 April 2022, Pavia, Italy PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Usage of digital twins for gamification applications in manufacturing T2 - Procedia CIRP Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022 N2 - Gamification applications are on the rise in the manufacturing sector to customize working scenarios, offer user-specific feedback, and provide personalized learning offerings. Commonly, different sensors are integrated into work environments to track workers’ actions. Game elements are selected according to the work task and users’ preferences. However, implementing gamified workplaces remains challenging as different data sources must be established, evaluated, and connected. Developers often require information from several areas of the companies to offer meaningful gamification strategies for their employees. Moreover, work environments and the associated support systems are usually not flexible enough to adapt to personal needs. Digital twins are one primary possibility to create a uniform data approach that can provide semantic information to gamification applications. Frequently, several digital twins have to interact with each other to provide information about the workplace, the manufacturing process, and the knowledge of the employees. This research aims to create an overview of existing digital twin approaches for digital support systems and presents a concept to use digital twins for gamified support and training systems. The concept is based upon the Reference Architecture Industry 4.0 (RAMI 4.0) and includes information about the whole life cycle of the assets. It is applied to an existing gamified training system and evaluated in the Industry 4.0 model factory by an example of a handle mounting. KW - Gamification KW - Digital Twin KW - Support System Y1 - 2022 U6 - https://doi.org/10.1016/j.procir.2022.05.044 SN - 2212-8271 N1 - 55th CIRP Conference on Manufacturing Systems, Jun 29, 2022 - Jul 01, 2022, Lugano, Switzerland VL - 107 SP - 675 EP - 680 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Dannen, Tammo A1 - Schindele, Benedikt A1 - Prümmer, Marcel A1 - Arntz, Kristian A1 - Bergs, Thomas T1 - Methodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking T2 - Procedia CIRP Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022 N2 - Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures. The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality. KW - Additive manufacturing KW - Laser-Powder Bed Fusion KW - L-PBF KW - Binder Jetting KW - Directed Energy Deposition Y1 - 2022 U6 - https://doi.org/10.1016/j.procir.2022.05.188 SN - 2212-8271 N1 - 55th CIRP Conference on Manufacturing Systems, Jun 29, 2022 - Jul 01, 2022, Lugano, Switzerland VL - 107 SP - 1539 EP - 1544 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Nierle, Elisabeth A1 - Pieper, Martin T1 - Measuring social impacts in engineering education to improve sustainability skills T2 - European Society for Engineering Education (SEFI) N2 - In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars. In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany. The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison. KW - Social impact measurement KW - Key competences KW - Sustainable engineering education KW - Future skills Y1 - 2023 U6 - https://doi.org/10.21427/QPR4-0T22 N1 - 51st Annual Conference of the European Society for Engineering Education, Technological University Dublin, 10th-14th September, 2023 N1 - Corresponding Author: Elisabeth Nierle ER - TY - CHAP A1 - Groß, Rolf Fritz T1 - Möglichkeiten und Grenzen für Forschung an Fachhochschulen T2 - Smart Building Convention und BIMconvention in Aachen im September Y1 - 2018 N1 - 10. und 11. September 2018, Aachen ER - TY - CHAP A1 - Alhaskir, Mohamed A1 - Tschesche, Matteo A1 - Linke, Florian A1 - Schriewer, Elisabeth A1 - Weber, Yvonne A1 - Wolking, Stefan A1 - Röhrig, Rainer A1 - Koch, Henner A1 - Kutafina, Ekaterina ED - Röhrig, Rainer ED - Grabe, Niels ED - Haag, Martin ED - Hübner, Ursula ED - Sax, Ulrich ED - Schmidt, Carsten Oliver ED - Sedlmayr, Martin ED - Zapf, Antonia T1 - ECG matching: an approach to synchronize ECG datasets for data quality comparisons T2 - Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023 N2 - Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data. KW - Electrocardiography KW - Wearable electronic device KW - Sensors comparison KW - Time-series synchronization Y1 - 2023 SN - 978-1-64368-428-4 (Print) SN - 978-1-64368-429-1 (Online) U6 - https://doi.org/10.3233/SHTI230718 N1 - Proceedings of the 68th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023, 17. - 21.9.23, Heilbronn, Germany Part of the series: Studies in Health Technology and Informatics VL - 307 SP - 225 EP - 232 PB - IOS Press ER - TY - CHAP A1 - Tischbein, Franziska A1 - Kean, Kilian A1 - Vertgewall, Chris Martin A1 - Ulbig, Andreas A1 - Altherr, Lena T1 - Determination of the topology of low-voltage distribution grids using cluster methods T2 - 27th International Conference on Electricity Distribution (CIRED 2023) N2 - Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis. Y1 - 2023 SN - 978-1-83953-855-1 U6 - https://doi.org/10.1049/icp.2023.0478 N1 - 27th International Conference on Electricity Distribution (CIRED 2023), 12-15 June 2023, Rome, Italy. SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Schulte-Tigges, Joschua A1 - Matheis, Dominik A1 - Reke, Michael A1 - Walter, Thomas A1 - Kaszner, Daniel ED - Krömker, Heidi T1 - Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain T2 - HCII 2023: HCI in Mobility, Transport, and Automotive Systems N2 - Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany. KW - V2X KW - Transiton of Control KW - Minimum Risk Manoeuvre KW - Operational Design Domain KW - Connected Automated Vehicle Y1 - 2023 SN - 978-3-031-35677-3 (Print) SN - 978-3-031-35678-0 (Online) U6 - https://doi.org/10.1007/978-3-031-35678-0_12 N1 - 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023. SP - 200 EP - 210 PB - Springer CY - Cham ER - TY - CHAP A1 - Galdi, Chiara A1 - Hartung, Frank A1 - Dugelay, Jean-Luc T1 - Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones T2 - Electronic Imaging N2 - Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos. KW - Image Forensics KW - Mobile Phones KW - Image Database Y1 - 2017 U6 - https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-331 SN - 2470-1173 N1 - IS&T International Symposium on Electronic Imaging 2017 Media Watermarking, Security, and Forensics 2017 SP - 100 EP - 103 PB - Society for Imaging Science and Technology CY - Springfield, Virginia ER - TY - CHAP A1 - Büsgen, André A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Cuzzocrea, Alfredo ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Quix, Christoph T1 - From cracked accounts to fake IDs: user profiling on German telegram black market channels T2 - Data Management Technologies and Applications N2 - Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling. KW - Clustering KW - Natural language processing KW - Information extraction KW - Profile extraction KW - Text mining Y1 - 2023 SN - 978-3-031-37889-8 (Print) SN - 978-3-031-37890-4 (Online) U6 - https://doi.org/10.1007/978-3-031-37890-4_9 N1 - 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022 SP - 176 EP - 202 PB - Springer CY - Cham ER - TY - CHAP A1 - Kohl, Philipp A1 - Freyer, Nils A1 - Krämer, Yoka A1 - Werth, Henri A1 - Wolf, Steffen A1 - Kraft, Bodo A1 - Meinecke, Matthias A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP T2 - Deep Learning Theory and Applications N2 - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework. KW - Active learning KW - Query learning KW - Natural language processing KW - Deep learning KW - Reproducible research Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - https://doi.org/10.1007/978-3-031-39059-3_16 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 235 EP - 253 PB - Springer CY - Cham ER - TY - CHAP A1 - Chavez Bermudez, Victor Francisco A1 - Cruz Castanon, Victor Fernando A1 - Ruchay, Marco A1 - Wollert, Jörg ED - Leipzig, Hochschule für Technik, Wirtschaft und Kultur T1 - Rapid prototyping framework for automation applications based on IO-Link T2 - Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel N2 - The development of protype applications with sensors and actuators in the automation industry requires tools that are independent of manufacturer, and are flexible enough to be modified or extended for any specific requirements. Currently, developing prototypes with industrial sensors and actuators is not straightforward. First of all, the exchange of information depends on the industrial protocol that these devices have. Second, a specific configuration and installation is done based on the hardware that is used, such as automation controllers or industrial gateways. This means that the development for a specific industrial protocol, highly depends on the hardware and the software that vendors provide. In this work we propose a rapid-prototyping framework based on Arduino to solve this problem. For this project we have focused to work with the IO-Link protocol. The framework consists of an Arduino shield that acts as the physical layer, and a software that implements the IO-Link Master protocol. The main advantage of such framework is that an application with industrial devices can be rapid-prototyped with ease as its vendor independent, open-source and can be ported easily to other Arduino compatible boards. In comparison, a typical approach requires proprietary hardware, is not easy to port to another system and is closed-source. KW - Rapid-prototyping KW - Arduino KW - IO-Link KW - Industrial Communication Y1 - 2022 SN - 978-3-910103-00-9 U6 - https://doi.org/10.33968/2022.28 N1 - 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022 CY - Leipzig ER - TY - CHAP A1 - Ulmer, Jessica A1 - Mostafa, Youssef A1 - Wollert, Jörg T1 - Digital Twin Academy: From Zero to Hero through individual learning experiences T2 - Tagungsband AALE 2022: Wissenstransfer im Spannungsfeld von Autonomisierung und Fachkräftemangel N2 - Digital twins are seen as one of the key technologies of Industry 4.0. Although many research groups focus on digital twins and create meaningful outputs, the technology has not yet reached a broad application in the industry. The main reasons for this imbalance are the complexity of the topic, the lack of specialists, and the unawareness of the twin opportunities. The project "Digital Twin Academy" aims to overcome these barriers by focusing on three actions: Building a digital twin community for discussion and exchange, offering multi-stage training for various knowledge levels, and implementing realworld use cases for deeper insights and guidance. In this work, we focus on creating a flexible learning platform that allows the user to select a training path adjusted to personal knowledge and needs. Therefore, a mix of basic and advanced modules is created and expanded by individual feedback options. The usage of personas supports the selection of the appropriate modules. KW - Digital Twins KW - Knowledge Transfer KW - Training Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:l189-qucosa2-776097 SN - 978-3-910103-00-9 N1 - 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022 SP - 1 EP - 9 ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Wollert, Jörg T1 - Adaptive VR-Produktionsumgebungen für Evaluations- und Schulungstätigkeiten T1 - Adaptive VR production environments for evaluation and training purposes T2 - Automation 2021: Navigating towards resilient Production N2 - Industrie 4.0 stellt viele Herausforderungen an produzierende Unternehmen und ihre Beschäf-tigten. Innovative und effektive Trainingsstrategien sind erforderlich, um mit den sich schnell verändernden Produktionsumgebungen und neuen Fertigungstechnologien Schritt halten zu können. Virtual Reality (VR) bietet neue Möglichkeiten für On-the-Job, On-Demand- und Off-Premise-Schulungen. Diese Arbeit stellt ein neues VR Schulungssystem vor, welches sich flexible an unterschiedliche Trainingsobjekte auf Grundlage von Rezepten und CAD Modellen anpassen lässt. Das Konzept basiert auf gerichteten azyklischen Graphen und einem Level-system. Es ermöglicht eine benutzerindividuelle Lerngeschwindigkeit mittels visueller Ele-mente. Das Konzept wurde für einen mechanischen Anwendungsfall mit Industriekomponen-ten implementiert und in der Industrie 4.0-Modellfabrik der FH Aachen umgesetzt. N2 - Industry 4.0 poses many challenges for manufacturing companies and their employees. Inno-vative and effective training strategies are needed to keep pace with rapidly changing produc-tion environments and new manufacturing technologies. Virtual reality (VR) offers new oppor-tunities for on-the-job, on-demand, and off-premise training. This work presents a new VR training system that can be flexibly adapted to different training objects based on recipes and CAD models. The concept is based on directed acyclic graphs and a level system. It allows a user-individual learning speed by means of visual elements. The concept was implemented for a mechanical use case with industrial components and implemented in the industry 4.0 model factory of the FH Aachen University of Applied Sciences. Y1 - 2021 SN - 978-3-18-092392-5 U6 - https://doi.org/10.51202/9783181023921-55 SN - 0083-5560 N1 - 22. Leitkongress der Mess- und Automatisierungstechnik AUTOMATION 2021 - Navigating towards resilient Production, 29. und 30. Juni 2021 SP - 55 EP - 64 PB - VDI CY - Düsseldorf ER - TY - CHAP A1 - Freyer, Nils A1 - Thewes, Dustin A1 - Meinecke, Matthias ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Cuzzocrea, Alfredo T1 - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts T2 - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1 N2 - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low. KW - Natural Language Processing KW - Text Mining KW - Process Model Extraction KW - Business Process Intelligence Y1 - 2023 SN - 978-989-758-664-4 U6 - https://doi.org/10.5220/0012084400003541 SN - 2184-285X N1 - 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy. SP - 335 EP - 342 ER - TY - CHAP A1 - Klöser, Lars A1 - Büsgen, André A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - Explaining relation classification models with semantic extents T2 - Deep Learning Theory and Applications N2 - In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models. KW - Relation classification KW - Natural language processing KW - Natural language understanding KW - Information extraction KW - Trustworthy artificial intelligence Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - https://doi.org/10.1007/978-3-031-39059-3_13 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 189 EP - 208 PB - Springer CY - Cham ER - TY - CHAP A1 - Ketelhut, Maike A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Iterative learning control of an industrial robot for neuromuscular training T2 - 2019 IEEE Conference on Control Technology and Applications N2 - Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations. KW - Knee KW - Training KW - Load modeling KW - Force KW - Iterative learning control Y1 - 2019 SN - 978-1-7281-2767-5 (ePub) SN - 978-1-7281-2766-8 (USB) SN - 978-1-7281-2768-2 (PoD) U6 - https://doi.org/10.1109/CCTA.2019.8920659 N1 - 2019 IEEE Conference on Control Technology and Applications (CCTA) Hong Kong, China, August 19-21, 2019 PB - IEEE CY - New York ER -