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Category: teaching

Control of a Brushless DC Motor (Thesis-Proposal)

Posted on October 9, 2020November 2, 2020 By ayaz No Comments on Control of a Brushless DC Motor (Thesis-Proposal)
Control of a Brushless DC Motor (Thesis-Proposal)
teaching

contact: Muhammad Ayaz Hussain, M. Sc., Tel: +49 208 88254806muhammad.hussain@hs-ruhrwest.de A brushless dc (BLDC) motor drive is characterized by higher efficiency, lower maintenance, and higher cost. In a market driven by profit margins, the appliance industry is reluctant to replace the conventional motor drives with the advanced motor drives (BLDC) due to their higher cost. … Read More “Control of a Brushless DC Motor (Thesis-Proposal)” »

Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)

Posted on October 9, 2020November 2, 2020 By felix No Comments on Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)
Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)
teaching

Reinforcement Learning (RL) is a subdomain of machine learning that has developed rapidly in recent years and has become increasingly popular. In reinforcement learning an agent learns from experience using a scalar reward signal, in contrast to learning from examples of labelled data as it is done in supervised machine learning. The agent interacts with … Read More “Comparing and Evaluating Prioritized Experience Replay Methods in Reinforcement Learning (Thesis-Proposal)” »

Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)

Posted on October 9, 2020November 2, 2020 By felix No Comments on Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)
Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)
teaching

Reinforcement Learning (RL) is a subdomain of machine learning that has developed rapidly in recent years and has become increasingly popular. In reinforcement learning an agent learns from experience using a scalar reward signal, in contrast to learning from examples of labelled data as it is done in supervised machine learning. The agent interacts with … Read More “Comparing and Evaluating Distributional Reinforcement Learning Methods (Thesis Proposal)” »

PECCS2012: A Versatile Simulated Reality Framework: From Embedded Components to ADAS

Posted on February 17, 2012December 30, 2021 By jannis
PECCS2012: A Versatile Simulated Reality Framework: From Embedded Components to ADAS
teaching

The developmental process of any kind of systems, either single embedded components or complex composite system like ADAS, is supposed to reflect all constraints of the desired task and boundary conditions of the environment in order to be part of the solution. Advanced driver assistant systems acting in natural unstructured environments, interacting with human operators, … Read More “PECCS2012: A Versatile Simulated Reality Framework: From Embedded Components to ADAS” »

BC11: Benefits of ego motion feedback for interactive experiments in virtual reality scenarios

Posted on September 6, 2011December 30, 2021 By jannis
BC11: Benefits of ego motion feedback for interactive experiments in virtual reality scenarios
teaching

The investigation of neuronal accounts of cognition is closely linked to collaboration between behavioral experiments, theory and application and supports the process of moving from pure behaviorist correlation analysis to gaining a real understanding of the underlying mechanisms. Cognition builds upon the individual behavioral history, and  the understanding of cognition is based on neuronal principles.

The study of human behavior incorporates in particular interactive, dynamically changing scenarios with multiple human individuals. Both the acquisition of behavioral data of human subjects, the modeling of behavior, as well as the evaluation in interactive scenarios, makes it necessary to generate simulated images of reality. Simulations allow the investigator to precisely control the structure of the environment the subject interacts with. Furthermore, situations that would be too dangerous in the real world (e.g. near-crash driving situations) can be investigated using virtual reality

Read More “BC11: Benefits of ego motion feedback for interactive experiments in virtual reality scenarios” »

Dynamic Mobile Platform lib for NAO

Posted on March 18, 2010December 30, 2021 By jannis
Dynamic Mobile Platform lib for NAO
teaching

Die Gruppe Autonome Robotik verfügt zur Zeit über eine Vielzahl verschiedener mobiler Roboter. Die Roboterfamilie besteht aus den radgetriebenen Plattformen Khepera, E-Puck und Koala, sowie dem hu- manoiden Laufroboter NAO. Die Übertragung der in der Gruppe entwickelten Ansätze zur Erzeugung von Motorverhalten in robotischen Systemen bedingen eine effiziente und generische Art der Kommunikation mit den … Read More “Dynamic Mobile Platform lib for NAO” »

Simulation des KUKA-LBR Manipulators

Posted on March 18, 2010December 30, 2021 By jannis
Simulation des KUKA-LBR Manipulators
teaching

Seit kurzem hat das Institut für Neuroinformatik mit dem KUKA-LBR einen neuen Roboterarm. Dieser Arm bietet viele Steuerungsmöglichkeiten, wie Einstelllung der Steifheit einzelner Gelenke oder Kraftkontrolle. Momentsensoren in jedem Gelenk erlauben außer- dem genaues Überwachen des aktuellen Zustands.Die Projektgruppe soll für diesen Arm eine Software-Simulation für das in der Robotikgruppe verwendete Framework erstellen. Dazu gehört … Read More “Simulation des KUKA-LBR Manipulators” »

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Recent Posts

  • (no title) June 18, 2026
  • New journal article: Predicting Muscle Activity from Upper Limb Kinematics with LSTM Networks June 7, 2026
  • New preprint: The Spatial and Temporal Resolution of Motor Intention in Multi-Target Prediction June 7, 2026
  • New paper: Performance Boundaries for BCIs Using Error-Related Potentials and Reinforcement Learning June 7, 2026
  • New paper: Distributional Properties of ReLU-Activations in Neural Networks That Learn by Memorization June 7, 2026

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