Refine
H-BRS Bibliography
- yes (42) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (42) (remove)
Document Type
- Part of a Book (42) (remove)
Year of publication
Has Fulltext
- no (42) (remove)
Keywords
- Adaptive Case Management (1)
- BG-Klinik (1)
- BPMS (1)
- Berufskrankheiten (1)
- Bond graphs (1)
- Business Case (1)
- CMMN (1)
- Case-Based Reasoning (1)
- Cleaning Task (1)
- Common Criteria (1)
A Bicycle Simulator Based on a Motion Platform in a Virtual Reality Environment - FIVIS Project
(2007)
In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to „map“ or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model.
Domestic Robotics
(2016)
Domestic Robotics
(2008)
Motivation is a key ingredient for learning: Only if the learner is motivated, successful learning is possible. Educational robotics has proven to be an excellent tool for motivating students at all ages from 8 to 80. Robot competitions for kids, like RoboCupJunior, are instrumental to sustain motivation over a significant period of time. This increases the chances that the learner acquires more in-depth knowledge about the subject area and develops a genuine interest in the field.
Der Mutterpass wurde als wichtiges Vorsorgeinstrument für Schwangere Anfang der sechziger Jahre in Papierform eingeführt. Er wird bei 90% aller Schwangerschaften genutzt. Seit seiner Einführung im Jahre 1968 hat jedoch die Komplexität der Vorsorgeuntersuchungen zugenommen, wie auch die Begleitumstände einer Schwangerschaft häufig komplexer geworden sind. Dies war Anlass dafür, die elektronische Abbildung des Papier basierten Mutterpasses zu entwickeln, um den gewachsenen Anforderungen der medizinischen Dokumentation und Evaluation gerecht zu werden. Eine große Herausforderung bei der Konzeption und Entwicklung des elektronischen Mutterpasses war dabei die Definition eines strukturierten und maschinenlesbaren Austauschformates. Darüber hinaus mussten weltweit neue eindeutige Identifier entwickelt werden, um den Mutterpass elektronisch abzubilden. Nach der prototypischen Realisierung einer vollständigen Version wurde im Frühjahr 2008 die Pilotierung in der Metropolregion Rhein-Neckar begonnen.
The objective of the FIVIS project is to develop a bicycle simulator which is able to simulate real life bicycle ride situations as a virtual scenario within an immersive environment. A sample test bicycle is mounted on a motion platform to enable a close to reality simulation of turns and balance situations. The visual field of the bike rider is enveloped within a multi-screen visualisation environment which provides visual data relative to the motion and activity of the test bicycle. That means the bike rider has to pedal and steer the bicycle as a usual bicycle, while the motion is recorded and processed to control the simulation. Furthermore, the platform is fed with real forces and accelerations that have been logged by a mobile data acquisition system during real bicycle test drives. Thus, using a feedback system makes the movements of the platform match to the virtual environment and the reaction of the driver (e.g. steering angle, step rate).
In the presented project, a new approach for the prevention of hand movements leading to hazards and for non-contact detection of fingers is intended to permit comprehensive and economical protection on circular saws. The basic principles may also be applied to other machines with manual loading and / or unloading. With an automatic blade guard an improved integration of the protection system can be achieved. In addition a new detection principle is explained. The distinction between skin and wood or other material is achieved by a dedicated spectral analysis in the near infrared region. Using LED and photodiodes it is possible to detect fingers and hands reliably. With a kind of light curtain the intrusion of hands or fingers into the dangerous zone near the blade guard can be prevented.
Improving the Performance of Parallel SpMV Operations on NUMA Systems with Adaptive Load Balancing
(2018)
For a parallel Sparse Matrix Vector Multiply (SpMV) on a multiprocessor, rather simple and efficient work distributions often produce good results. In cases where this is not true, adaptive load balancing can improve the balance and performance. This paper introduces a low overhead framework for adaptive load balancing of parallel SpMV operations. It uses statistical filters to gather relevant runtime performance data and detects an imbalance situation. Three different algorithms were compared that adaptively balance the load with high quality and low overhead. Results show that for sparse matrices, where the adaptive load balancing was enabled, an average speedup of 1.15 (regarding the total execution time) could be achieved with our best algorithm over 4 different matrix formats and two different NUMA systems.
Incremental Bond Graphs
(2011)