13 citations · 21 across the 6 of their papers we have counts for
9 papers
Cutting Through the Noise: An Empirical Comparison of Psychoacoustic and Envelope-based Features for Machinery Fault Detection
Peter Wißbrock, Yvonne Richter, David Pelkmann +2
Acoustic-based fault detection has a high potential to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively i…
Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation
Holger Eichelberger, Gregory Palmer, Svenja Reimer +6
For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial. This includes easing the integration of AI with industrial devices, support…
Market Making with Scaled Beta Policies
Joseph Jerome, Gregory Palmer, Rahul Savani
This paper introduces a new representation for the actions of a market maker in an order-driven market. This representation uses scaled beta distributions, and generalises three ap…
Data Valuation for Offline Reinforcement Learning
Amir Abolfazli, Gregory Palmer, Daniel Kudenko
The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many re…
Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning
Jeff Reimer, Yandong Wang, Sofiane Laridi +3
In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and…
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition
Yi Chang, Sofiane Laridi, Zhao Ren +3
Due to the development of machine learning and speech processing, speech emotion recognition has been a popular research topic in recent years. However, the speech data cannot be p…
Fully Convolutional One-Shot Object Segmentation for Industrial Robotics
Benjamin Schnieders, Shan Luo, Gregory Palmer +1
The ability to identify and localize new objects robustly and effectively is vital for robotic grasping and manipulation in warehouses or smart factories. Deep convolutional neural…
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Gregory Palmer, Rahul Savani, Karl Tuyls
In Multi-Agent Reinforcement Learning (MA-RL), independent cooperative learners must overcome a number of pathologies to learn optimal joint policies. Addressing one pathology ofte…
Lenient Multi-Agent Deep Reinforcement Learning
Gregory Palmer, Karl Tuyls, Daan Bloembergen +1
Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks…