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Tobias Meisen

University Professor (Univ.-Prof.)

Bergische Universität Wuppertal

Germany

Research Interests

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Professors in Germany Professors in Medical Imaging Medical Imaging professors in Germany

About

Professor Tobias Meisen is a University Professor at the University of Wuppertal, Germany. His research encompasses various areas including deep learning applications in industry, e-commerce personalization, and sensor data exploration. Recent publications highlight his contributions to flood prediction modeling, job shop scheduling, and advancements in computed tomography techniques.

Articles (34)

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Collaborators (5)

Yannik Hahn

Bergische Universität Wuppertal

GERMANY

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Christoph Quix

Professor

Hochschule Niederrhein

GERMANY

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André Pomp

Bergische Universität Wuppertal

GERMANY

View details

Patrick Görrn

Professor

Bergische Universität Wuppertal

GERMANY

View details

Jannik Peters

Bergische Universität Wuppertal

GERMANY

View details

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Tobias Meisen

Research Interests

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About

Articles (34)

From Masks to Images: Mask-Conditional Diffusion for Realistic Synthetic SSS Data

A literature review on deep reinforcement learning for machine scheduling problems

Enabling Value Chain Interoperability: Integrating and Evaluating Robust Data Governance Aspects Into the Asset Administration Shell

Preference-Conditioned Reinforcement Learning for Energy-Aware Multi-Objective Flexible Job Shop Scheduling

Material-resolving computed tomography of lithium-ion batteries using deep learning

A Systematic Review and Taxonomy of Machine Learning Methods for Process Optimization and Control in Laser Welding

Comprehensive Robustness Analysis of LiDAR-Based 3D Object Detection in Autonomous Driving

EXCODER: EXplainable Classification Of DiscretE Time Series Representations

Efficient Inter-Task Attention for Multitask Transformer Models

Emergent language: a survey and taxonomy

Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem

Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings

Data-Driven Inverse Design of Hybrid Waveguide Gratings Using Reflection Spectra via Tandem Networks and Conditional VAEs

Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation

Challenges and Opportunities of LLM-Augmented Semantic Model Creation for Dataspaces

Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding

Task Weighting Through Gradient Projection for Multitask Learning

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data

Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning

Guided Exploration of Industrial Sensor Data

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

Simulation Study: Data-Driven Material Decomposition in Industrial X-ray Computed Tomography

Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps

Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System

TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

Time Series Dataset Survey for Forecasting with Deep Learning

Stop Guessing in the Dark: Identified Requirements for Digital Product Passport Systems

A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

Vision Transformer in Industrial Visual Inspection

Collaborators (5)

Yannik Hahn

Christoph Quix

André Pomp

Patrick Görrn

Jannik Peters

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