TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - JOUR A1 - Elsen, Ingo A1 - Kraiss, Karl-Friedrich T1 - System concept and realization of a scalable neurocomputing architecture JF - Systems Analysis Modelling Simulation N2 - This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies. Y1 - 1999 SN - 0232-9298 SN - 1029-4902 VL - 35 IS - 4 SP - 399 EP - 419 PB - Gordon and Breach Science Publishers CY - Amsterdam ER - TY - JOUR A1 - Elsen, Ingo A1 - Kraiss, Karl-Friedrich A1 - Krumbiegel, Dirk A1 - Walter, Peter A1 - Wickel, Jochen T1 - Visual information retrieval for 3D product identification: a midterm report JF - KI - Künstliche Intelligenz Y1 - 1999 SN - 1610-1987 SN - 0933-1875 VL - 13 IS - 1 SP - 64 EP - 67 PB - Springer CY - Berlin ER - TY - CHAP A1 - Elsen, Ingo T1 - A pixel based approach to view based object recognition with self-organizing neural networks T2 - IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society N2 - This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images. Y1 - 1998 SN - 0-7803-4503-7 U6 - http://dx.doi.org/10.1109/IECON.1998.724032 N1 - Aachen, 31 August 1998 - 04 September 1998 SP - 2040 EP - 2044 PB - IEEE CY - New York ER - TY - CHAP A1 - Elsen, Ingo A1 - Kraiss, Karl-Friedrich A1 - Krumbiegel, Dirk T1 - Pixel based 3D object recognition with bidirectional associative memories T2 - International Conference on Neural Networks 1997 N2 - This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand. Y1 - 1997 SN - 0-7803-4122-8 N1 - June 9 - 12, 1997, Westin Galleria Hotel Houston, Texas, USA. SP - 1679 EP - 1684 PB - IEEE CY - New York ER - TY - CHAP A1 - Dey, Thomas A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Frauenrath, Tobias A1 - Reke, Michael A1 - Schiffer, Stefan ED - Makedon, Fillia T1 - CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom T2 - PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference N2 - In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway. KW - embedded hardware KW - sensor networks KW - information systems KW - education KW - do-it-yourself Y1 - 2021 SN - 9781450387927 U6 - http://dx.doi.org/10.1145/3453892.3462697 N1 - PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference Corfu Greece 29 June 2021- 2 July 2021 SP - 292 EP - 299 PB - Association for Computing Machinery CY - New York ER - TY - CHAP A1 - Elsen, Ingo A1 - Schmalzbauer, Michael ED - Reussner, Ralf ED - Grund, Matthias ED - Andreas, Oberweis ED - Tichy, Walter T1 - Messsystematik zur Steuerung der Produkt- und Prozessqualität in Systemintegrationsprojekten – ein Erfahrungsbericht T2 - Software Engineering 2011 - Fachtagung des GI-Fachbereichs Softwaretechnik, 21. - 25. Februar 2011 in Karlsruhe N2 - Der Erfolg eines Softwarenentwicklungsprojektes insbesondere eines Systemintegrationsprojektes wird mit der Erfüllung des „Teufelsdreiecks“, „In-Time“, „In-Budget“, „In-Quality“ gemessen. Hierzu ist die Kenntnis der Software- und Prozessqualität essenziell, um die Einhaltung der Qualitätskriterien festzustellen, aber auch, um eine Vorhersage hinsichtlich Termin- und Budgettreue zu treffen. Zu diesem Zweck wurde in der T-Systems Systems Integration ein System aus verschiedenen Key Performance Indikatoren entworfen und in der Organisation implementiert, das genau das leistet und die Kriterien für CMMI Level 3 erfüllt. Y1 - 2011 SN - 9783885792772 SN - 1617-5468 PB - Gesellschaft für Informatik eV CY - Bonn ER - TY - CHAP A1 - Elsen, Ingo A1 - Hawari, Asma A1 - Johnen, Uwe ED - Pietsch, Wolfram ED - Krams, Benedikt T1 - Produktkernel in der Systemintegration (Erfahrungsbericht aus der Praxis) T2 - Vom Projekt zum Produkt - Fachtagung des GI-Fachausschusses Management der Anwendungsentwicklung und -wartung im Fachbereich Wirtschaftsinformatik (WI-MAW), 1. - 3. Dezember 2010 in Aachen N2 - In der Vergangenheit basierten große Systemintegrationsprojekte in der Regel auf Individualentwicklungen für einzelne Kunden. Getrieben durch Kostendruck steigt aber der Bedarf nach standardisierten Lösungen, die gleichzeitig die individuellen Anforderungen des jeweiligen Umfelds berücksichtigen. T-Systems GEI GmbH wird beiden Anforderungen mit Produktkerneln gerecht. Neben den technischen Aspekten der Kernelentwicklung spielen besonders organisatorische Aspekte eine Rolle, um Kernel effizient und qualitativ hochwertig zu entwickeln, ohne deren Funktionalitäten ins Uferlose wachsen zu lassen. Umgesetzt hat T-Systems dieses Konzept für Flughafeninformationssysteme. Damit kann dem wachsenden Bedarf der Flughafenbetreiber nach einer effizienten und kostengünstigen Softwarelösung zur Unterstützung Ihrer Geschäftsprozesse entsprochen werden. Y1 - 2010 SN - 9783885792727 SN - 1617-5468 SP - 93 EP - 102 PB - Gesellschaft für Informatik eV CY - Bonn ER - TY - JOUR A1 - Elsen, Ingo A1 - Hartung, Frank A1 - Horn, Uwe A1 - Kampmann, Markus A1 - Peters, Liliane ED - Voas, Jeffrey T1 - Streaming technology in 3G mobile communication systems JF - Computer : innovative technology for computer professionals N2 - Third-generation mobile communication systems will combine standardized streaming with a range of unique services to provide high-quality Internet content that meets the specific needs of the rapidly growing mobile market. Y1 - 2001 SN - 0018-9162 SN - 1558-0814 VL - 34 IS - 9 Seiten SP - 46 EP - 52 PB - IEEE CY - New York ER - TY - THES A1 - Elsen, Ingo T1 - Ansichtenbasierte 3D-Objekterkennung mit erweiterten selbstorganisierenden Merkmalskarten KW - Dreidimensionale Bildverarbeitung KW - Objekterkennung KW - CCD-Bildwandler KW - Vorverarbeitung KW - Klassifikator Y1 - 2000 SN - 978-3-18-363110-0 SN - 3-18-363110-5 SN - 0341-1796 SN - 0178-9627 N1 - Fortschritt-Berichte VDI : Reihe 10, Informatik, Kommunikation 631 PB - VDI-Verlag CY - Düsseldorf ER - TY - CHAP A1 - Walter, Peter A1 - Elsen, Ingo A1 - Müller, Holger A1 - Kraiss, Karl-Friedrich T1 - 3D object recognition with a specialized mixtures of experts architecture T2 - IJCNN'99. International Joint Conference on Neural Networks. Proceedings N2 - Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications. Y1 - 1999 SN - 0-7803-5529-6 U6 - http://dx.doi.org/10.1109/IJCNN.1999.836243 SN - 1098-7576 N1 - Washington, DC 10-16.07.1999 SP - 3563 EP - 3568 PB - IEEE CY - New York ER - TY - CHAP A1 - Altherr, Lena A1 - Conzen, Max A1 - Elsen, Ingo A1 - Frauenrath, Tobias A1 - Lyrmann, Andreas ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - Sensor retrofitting of existing buildings in an interdisciplinary teaching project at university level T2 - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel N2 - Existing residential buildings have an average lifetime of 100 years. Many of these buildings will exist for at least another 50 years. To increase the efficiency of these buildings while keeping costs at reasonable rates, they can be retrofitted with sensors that deliver information to central control units for heating, ventilation and electricity. This retrofitting process should happen with minimal intervention into existing infrastructure and requires new approaches for sensor design and data transmission. At FH Aachen University of Applied Sciences, students of different disciplines work together to learn how to design, build, deploy and operate such sensors. The presented teaching project already created a low power design for a combined CO2, temperature and humidity measurement device that can be easily integrated into most home automation systems KW - Building Automation KW - Smart Building KW - CO2 KW - Carbon Dioxide KW - Education Y1 - 2023 SN - 978-3-910103-01-6 U6 - http://dx.doi.org/10.33968/2023.04 N1 - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023. BTS Connected Buildings & Cities Luxemburg (Tagungsband unter https://doi.org/10.33968/2023.01) SP - 31 EP - 40 PB - le-tex publishing services GmbH CY - Leipzig ER - TY - CHAP A1 - Arndt, Tobias A1 - Conzen, Max A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Galla, Oskar A1 - Köse, Hakan A1 - Schiffer, Stefan A1 - Tschesche, Matteo T1 - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers T2 - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments N2 - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products. KW - Datasets KW - Neural networks KW - Anomaly detection KW - Quality control KW - Process optimization Y1 - 2023 SN - 9798400700699 U6 - http://dx.doi.org/10.1145/3594806.3596558 N1 - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023. SP - 535 EP - 542 PB - ACM ER -