Tobias Meisen

Scopus Publications

A literature review on deep reinforcement learning for machine scheduling problems
Constantin Waubert de Puiseau, Furkan Ercan, Jannik Peters, Marvin Brune, Hasan Tercan, Christopher Prinz, Tobias Meisen, Bernd Kuhlenkötter
Journal of Manufacturing Systems, 2026
Machine scheduling represents a core challenge in industrial production systems due to its inherently complex combinatorial nature and its critical role in enhancing operational efficiency. With recent advances in artificial intelligence, deep reinforcement learning (DRL) has gained increasing attention as an innovative tool to address scheduling tasks with a self-adapting, data-driven approach. This survey presents a comprehensive review of 143 publications between 2018 and February 2025 that apply DRL to machine scheduling problems. We develop a structured framework to classify and compare problem settings, algorithmic designs, and evaluation methodologies. Key aspects such as action and observation space design, reward functions, neural network architectures, and experimental benchmarks are systematically analyzed. The review identifies current trends, outlines promising patterns, and highlights open research opportunities for DRL-based scheduling solutions. The goal of this survey is to make the rapidly evolving research landscape more accessible to both academics and practitioners and to identify the next steps in research and application. To facilitate reproducible research and customized analysis, we publish the dataset underpinning this review, which includes 61 annotated features per publication, allowing for customizable filtering and further in-depth exploration of niches within the field. This dataset is publicly accessible online .
Material-resolving computed tomography of lithium-ion batteries using deep learning
M. Weiss, K. Mrzljak, M. von Schmid, G. Erbach, N. Brierley, T. Meisen
NDT and E International, 2026
The demand for batteries as portable energy storages increases drastically. Especially for electric mobility, battery safety is crucial which begins at seamless quality control during and after manufacturing. Recent developments in high-speed computed tomography (high-speed CT) enable scan times around 10 s, roughly matching the speed of a typical battery production line. While the majority of defects in batteries can be detected using the CT scan data directly, data post-processing such as material identification can reveal further insights. As the complexity of modern battery production grows, traditional material-resolving CT methods face challenges in delivering the precision and efficiency required. To meet these demands, more advanced, data-driven approaches are becoming essential. This has led to an ongoing paradigm shift in material-resolving CT, introducing deep learning techniques that promise enhanced accuracy and processing speed. In the scope of this paper, we propose an end-to-end deep learning approach, which is designed to resolve materials in CT scans in presence of heavy CT artifacts by exploiting context knowledge with a convolution-based neural network. The model computes atomic numbers and densities directly from the dual-energy CT volume slices for each pixel. Our approach uses simulation-generated training data, thereby avoiding the need for manual annotation. CT scans from two fundamentally different systems — one providing slow, high-quality scans and the other fast, medium-quality scans — are compared in terms of material identification performance. Especially for high-speed CT, increasing the scanning time can influence the data quality drastically. We believe, that the combination of a high-speed scanner for pre-screening together with a slower high-quality scanner provides comprehensive in-line inspection, where only critical candidates, revealing anomalies in the high-speed scan, will be send to the high-quality scanner.
Dataset Curation for a Domain-specific People Detection System
Johannes Benkert, Philip Wagner, Mathias Zinnen, Vincent Christlein, Tobias Meisen
Proceedings of SPIE the International Society for Optical Engineering, 2026
Container terminals are moving toward full automation, yet tasks such as truck maneuvering, container unlocking, and twist‑lock removal still necessitate human involvement, creating safety hazards at the interface between workers and automated cranes. Although SOTA people‑detection models can process multiple camera feeds in real time, their performance degrades sharply when deployed in port environments due to a pronounced domain mismatch with standard training datasets. Traditionally, this domain gap is bridged by manually annotating data from the target domain and finetuning the model accordingly. In port environments, which are considered critical infrastructure, capturing and annotating new images for supervised training is often prohibited due to data privacy concerns. Therefore, this work investigates the extent to which a state-of-the-art, lightweight object detection model (YOLOv8) can automatically adapt to the new target environment without requiring labeled images from that environment. To achieve this goal, a curated dataset is created which is used to fine-tune the object detection model. The curated dataset is composed of images from public datasets that closely resemble the desired target domain. Since only the feature maps are needed to create this curated dataset, the data privacy requirements can be adhered to. As we show, by using our dataset curation and model fine-tuning process improved model performance can be achieved with minimal training effort. Furthermore, data privacy and security are preserved, as no labeled images from the target domain are required.
A Systematic Review and Taxonomy of Machine Learning Methods for Process Optimization and Control in Laser Welding
Jan Voets, Hasan Tercan, Tobias Meisen, Cemal Esen
Applied Sciences Switzerland, 2026
Laser welding is widely used in complex manufacturing processes and valued for its reliability, flexibility, and high energy density. However, achieving the desired weld quality requires the detection and, ideally, the prevention of defects. Besides other methods, machine learning (ML) has been integrated into laser welding with the primary goal of process optimization and quality improvement, for example, by enabling process adaptation before or during welding to reduce defects. This survey systematically reviews publications from 2015 to 2025 that integrate machine learning and deep learning methods into laser welding optimization or adaptation processes. An extensive analysis identifies which parts of the process and for what purposes ML methods are researched and implemented and how they are evaluated, as well as the sensors, lasers, and materials involved. Furthermore, the findings are analyzed and organized into taxonomies that define overarching meta-categories into which existing approaches can be classified and contextualized. The results reveal that various ML approaches are applied for tasks, such as surrogate modeling, process planning, direct control, and virtual sensing and monitoring. Although many different control parameters and optimization targets are considered, laser power and welding speed dominate as the most frequently adjusted parameters, while penetration depth and weld geometry-related properties are the most common optimization targets. Finally, the survey identifies major challenges, including the lack of benchmarking datasets, standardized evaluation protocols, and interpretable models.
Bridging the Synchrony Gap: A Deterministic GSMDP Approach for Integrating Deep Reinforcement Learning with Commercial Discrete Event Simulators
Merve Demir Varici, Hasan Tercan, Tobias Meisen
Proceedings of the Conference on Production Systems and Logistics, 2026
Although Deep Reinforcement Learning (DRL) offers adaptive control for manufacturing processes, training agents within high-fidelity commercial Discrete Event Simulation (DES) tools is hindered by the temporal mismatch between the continuous-time event logic of DES and the discrete-step nature of RL. Current loose-coupling methods (e.g., socket communication) frequently introduce latency and state drift, violating the Markov assumption necessary for stable training. This study introduces a strictly blocking, in-memory synchronization architecture that aligns DES execution with Generalized Semi-Markov Decision Process (GSMDP) decision epochs. Unlike asynchronous approaches, our framework freezes the simulation clock at decision points to ensure near-zero-latency state observations. We validated the architecture using a Dual-Resource Constrained Job Shop Scheduling Problem (DRC-JSSP) involving sequence-dependent setup times. Evaluations on this test bed demonstrate that the agent can learn near-optimal policies with a deviation of only 2.24% from the optimal CP-SAT solver, even under complex constraints where synchronization errors would otherwise lead to infeasible schedules.
Partial observability in vision-based forklift navigation with compressed visual information
Simon Hadwiger, David Kube, Tobias Meisen
International Journal of Intelligent Robotics and Applications, 2026
In industry, transportation tasks are more frequently handled by mobile robotic systems such as Automated Guided Vehicles (AGVs). The deployment of such systems alongside humans entails the need to handle load carriers with imprecisely known or unknown position. In this work, we apply our previously introduced method to control a forklift AGV based on a single RGB camera and a Deep Reinforcement Learning (DRL) agent. This agent utilizes compressed visual information in form of bounding box data to perform the final approaching and precise alignment in front of these load carriers. Hereby, the limited field of view of the camera results in a partially observable environment state, a typical issue for vision-based vehicles. To address this problem, we propose a direction estimation module, which uses a Long Short-Term Memory (LSTM) network to keep track of previous interactions with the environment. We design the proposed module to provide an additional input for the agent, enabling an independent training and verification of the system components. Through this extension, our DRL agent achieves a reduction of the lateral mean absolute error of up to 78% compared to the DRL baseline without the direction estimation module. The application of Domain Randomization (DR) to investigate the influences of inaccurate bounding box detections revealed an even higher importance of the direction estimation module, if combined with an imprecise detector. We also apply two distinct methods to speed up our training process. Firstly, a privileged agent is employed to generate expert demonstrations for the training of the DRL agent and LSTM network. Secondly, we accelerate the generation of bounding box data through the projection of tightly-fitted 3D bounding boxes. This method reduces the time required for the generation of observations by more than 89%.
Efficient Inter-Task Attention for Multitask Transformer Models
Christian Bohn, Thomas Kurbiel, Klaus Friedrichs, Hasan Tercan, Tobias Meisen
Communications in Computer and Information Science, 2026
Enabling Value Chain Interoperability: Integrating and Evaluating Robust Data Governance Aspects Into the Asset Administration Shell
Mario Angos-Mediavilla, Michael Gorenzweig, Lena Beil, Christian Kosel, Wanja Zemke, André Pomp, Matthias Freund, Gerome Pahnke, Tobias Meisen
Lecture Notes in Business Information Processing, 2026
Beyond performance: Explaining generalisation failures of Robotic Foundation Models in industrial simulation
David Kube, Simon Hadwiger, Tobias Meisen
Biomimetic Intelligence and Robotics, 2025
This study investigates the generalisation and explainability challenges of Robotic Foundation Models (RFMs) in industrial applications, using Octo as a representative case study. Motivated by the scarcity of domain-specific data and the need for safe evaluation environments, we adopt a simulation-first approach: instead of transitioning from simulation to real-world scenarios, we aim to adapt real-world-trained RFMs to synthetic, simulated environments — a critical step towards their safe and effective industrial deployment. While Octo promises zero-shot generalisation, our experiments reveal significant performance degradation when applied in simulation, despite minimal task and observation domain shifts. To explain this behaviour, we introduce a modified Grad-CAM technique that enables insight into Octo’s internal reasoning and focus areas. Our results highlight key limitations in Octo’s visual generalisation and language grounding capabilities under distribution shifts. We further identify architectural and benchmarking challenges across the broader RFM landscape. Based on our findings, we propose concrete guidelines for future RFM development, with an emphasis on explainability, modularity, and robust benchmarking — critical enablers for applying RFMs in safety-critical and data-scarce industrial environments.
Data-Driven Inverse Design of Hybrid Waveguide Gratings Using Reflection Spectra via Tandem Networks and Conditional VAEs
Shahrzad Dehghani, Christopher Knoth, Shaghayegh Eskandari, Maximilian Buchmüller, Tobias Meisen, Patrick Görrn
Optics, 2025
This study presents a data-driven inverse design approach for one-dimensional hybrid waveguide gratings using full reflection spectra across the visible range and a complete span of incident angles. Traditionally, designing such structures to achieve specific optical responses relies on parameter sweeps and iterative simulations which are computationally expensive, time-consuming, and often inefficient. To overcome this, we generated a comprehensive dataset using rigorous coupled-wave analysis (RCWA) simulations and trained two machine learning models: a deterministic tandem network and a generative conditional Variational Autoencoder (cVAE). Both models were trained on noisy reflection spectra to mimic real-world measurements. They both predict structural parameters accurately on clean and noisy data. On clean data, the mean absolute error (MAE) for silver thickness and grating period is below 1 nm. For the dielectric layer, the error is about 13–15 nm. When noise is added, the Tandem network performs best with low to moderate noise. The cVAE, however, stays more stable under high noise conditions. At σ=0.3, the cVAE model reliably predicts the silver thickness and grating period, with MAEs below 6 nm. The main error comes from the dielectric thickness. Sensitivity analysis of reflection spectra confirms this trend. The reflection is least sensitive to the dielectric thickness, while silver thickness and grating period dominate. This analysis provides physical insight for waveguide design as well in which, accurate control of silver thickness and grating period is far more critical than small errors in dielectric thickness. In general, our approach enables rapid prediction of structural parameters of hybrid waveguide gratings from reflection spectra. This reduces design time and reliance on complex microscopic measurements, with potential applications in sensing, communication, and integrated photonics.
Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding
Yannik Hahn, Jan Voets, Antonin Königsfeld, Hasan Tercan, Tobias Meisen
Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, 2025
Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation
Zhipeng Ma, Ali Rida Bahja, Andreas Burgdorf, André Pomp, Tobias Meisen, Bo Nørregaard Jørgensen, Zheng Grace Ma
Applied Sciences Switzerland, 2025
Emergent language: a survey and taxonomy
Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas de Carvalho, Christian Bitter, Tobias Meisen
Autonomous Agents and Multi Agent Systems, 2025
Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings
Miguel Alves Gomes, Philipp Meisen, Tobias Meisen
Journal of Theoretical and Applied Electronic Commerce Research, 2025
Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem
Constantin Waubert de Puiseau, Fabian Wolz, Merlin Montag, Jannik Peters, Hasan Tercan, Tobias Meisen
AI Switzerland, 2025
The Synergy of Large Language Models and Dataspaces: A Functional Exploration
Ceur Workshop Proceedings, 2025
Dynamic Capacity Expansion in Continual Learning: The eSECL Approach
Basile Tousside, Jörg Frochte, Tobias Meisen
Lecture Notes in Computer Science, 2025
Timbre Transfer For Ship Radiated Noise
Nils Müller, Jens Reermann, Tobias Meisen
European Signal Processing Conference, 2025
Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
Adwait Chandorkar, Hasan Tercan, Tobias Meisen
Proceedings 2025 IEEE Cvf International Conference on Computer Vision Workshops Iccv W 2025, 2025
Enhancing 3D Human Pose Estimation: A Novel Post-Processing Method
Elham Iravani, Frederik Hasecke, Lukas Hahn, Tobias Meisen
Proceedings of the International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications, 2025
AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
Loveneet Saini, Mirko Meuter, Hasan Tercan, Tobias Meisen
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2025
Deep reinforcement learning for assembly robot end effector scheduling
Marvin Brune, Constantin Waubert de Puiseau, Hasan Tercan, Tobias Meisen
Procedia CIRP, 2025
Task Weighting Through Gradient Projection for Multitask Learning
Christian Bohn, Ido Freeman, Hasan Tercan, Tobias Meisen
Communications in Computer and Information Science, 2025
LMFormer: Lane Based Motion Prediction Transformer
Harsh Yadav, Maximilian Schäfer, Kun Zhao, Tobias Meisen
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2025
Smart City Urban Heat Monitoring using a Solid-based Dataspace
Ceur Workshop Proceedings, 2025
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
Harsh Yadav, Maximilian Schaefer, Kun Zhao, Tobias Meisen
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025
Beyond Labels: Self-Attention-Driven Semantic Separation Using Principal Component Clustering in Latent Diffusion Models
Felix Stillger, Frederik Hasecke, Lukas Hahn, Tobias Meisen
Proceedings of the International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications, 2025
Inferring Driving Maps by Deep Learning-Based Trail Map Extraction
Michael Hubbertz, Pascal Colling, Qi Han, Tobias Meisen
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2025
Towards Connecting General DPP Ontology Frameworks with Domain-specific Information Sources
Ceur Workshop Proceedings, 2025
PCAD: A Real-World Dataset for 6D Pose Industrial Anomaly Detection
Robert Maack, Lars Thun, Thomas Liang, Hasan Tercan, Tobias Meisen
Proceedings 2025 IEEE Cvf Winter Conference on Applications of Computer Vision Workshops Wacvw 2025, 2025
Challenges and Opportunities of LLM-Augmented Semantic Model Creation for Dataspaces
Sayed Hoseini, Andreas Burgdorf, Alexander Paulus, Tobias Meisen, Christoph Quix, André Pomp
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025
AI-driven Configuration for Filling Systems using Generative Adversarial Networks
Antonin Königsfeld, Dirk Bergmann, Alexander Wunn, Kai-Henning Tybussek, Mahdie Karbasi, Mavin Heim, Hasan Tercan, Tobias Meisen
Procedia CIRP, 2025
Designing an Ontology Network for Digital Product Passports
Maike Jansen, Eva Blomqvist, Robin Keskisärkkä, Huanyu Li, Mikael Lindecrantz, Karin Wannerberg, André Pomp, Tobias Meisen, Holger Berg
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025
Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data
Yannik Hahn, Philip Kienitz, Mark Wönkhaus, Richard Meyes, Tobias Meisen
Water Switzerland, 2024
Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised-VAE
Yannik Hahn, Robert Maack, Hasan Tercan, Tobias Meisen, Marion Purrio, Guido Buchholz, Matthias Angerhausen
International Conference on Information and Knowledge Management Proceedings, 2024
Evaluating Text Placement in Information-Rich Virtual Environments: A User Study on Controller-Anchored Text
Nils A. Mack, Florian Schümmer, Marion Rose, Meisen Tobias
ACM International Conference Proceeding Series, 2024
Researchers' Concerns on Artificial Intelligence Ethics: Results from a Scenario-Based Survey
Marianna Jantunen, Richard Meyes, Veronika Kurchyna, Tobias Meisen, Pekka Abrahamsson, Rahul Mohanani
Proceedings 2024 IEEE ACM International Workshop on Software Intensive Business Iwsib 2024, 2024
Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
Marvin Brune, Tobias Meisen, André Pomp
Applied Sciences Switzerland, 2024
It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation
Miguel Alves Gomes, Richard Meyes, Philipp Meisen, Tobias Meisen
Journal of Theoretical and Applied Electronic Commerce Research, 2024
Guided Exploration of Industrial Sensor Data
Tristan Langer, Richard Meyes, Tobias Meisen
Computer Graphics Forum, 2024
Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers
Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes, Tobias Meisen
Applied System Innovation, 2024
CNNs Sparsification and Expansion for Continual Learning
Basile Tousside, Jörg Frochte, Tobias Meisen
International Conference on Agents and Artificial Intelligence, 2024
Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition
Nils Müller, Jens Reermann, Tobias Meisen
IEEE Access, 2024
Towards Precision in Motion: Investigating the Influences of Curriculum Learning based on a Multi-Purpose Performance Metric for Vision-Based Forklift Navigation
Simon Hadwiger, David Kube, Vladimir Lavrik, Tobias Meisen
IEEE International Conference on Automation Science and Engineering, 2024
CenterPoint Transformer for BEV Object Detection with Automotive Radar
Loveneet Saini, Yu Su, Hasan Tercan, Tobias Meisen
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2024
Improved Single Camera BEV Perception Using Multi-Camera Training
Daniel Busch, Ido Freeman, Richard Meyes, Tobias Meisen
IEEE Conference on Intelligent Transportation Systems Proceedings ITSC, 2024
Advancing Industry 4.0: Integrating Data Governance into Asset Administration Shell for Enhanced Interoperability
Mario Angos-Mediavilla, Michael Gorenzweig, Gerome Pahnke, André Pomp, Matthias Freund, Tobias Meisen
International Conference on Enterprise Information Systems Iceis Proceedings, 2024
Towards LLM-augmented Creation of Semantic Models for Dataspaces
Ceur Workshop Proceedings, 2024
Modelling Digital Product Passports for the Circular Economy
Ceur Workshop Proceedings, 2024
Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling
Vladimir Samsonov, Enslin Chrismarie, Hans-Georg Köpken, Schirin Bär, Daniel Lütticke, Tobias Meisen
Production Engineering, 2023
State of health forecasting of Lithium-ion batteries operated in a battery electric vehicle fleet
Friedrich von Bülow, Markus Wassermann, Tobias Meisen
Journal of Energy Storage, 2023
A review on customer segmentation methods for personalized customer targeting in e-commerce use cases
Miguel Alves Gomes, Tobias Meisen
Information Systems and E Business Management, 2023
Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System
Johannes Benkert, Robert Maack, Tobias Meisen
Journal of Marine Science and Engineering, 2023
TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
Miguel Alves Gomes, Mark Wönkhaus, Philipp Meisen, Tobias Meisen
Journal of Theoretical and Applied Electronic Commerce Research, 2023
Online Quality Prediction in Windshield Manufacturing using Data-Efficient Machine Learning
Hasan Tercan, Tobias Meisen
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2023
An Evaluation of Link Prediction Approaches in Few-Shot Scenarios
Rebecca Braken, Alexander Paulus, André Pomp, Tobias Meisen
Electronics Switzerland, 2023
schlably: A Python framework for deep reinforcement learning based scheduling experiments
Constantin Waubert de Puiseau, Jannik Peters, Christian Dörpelkus, Hasan Tercan, Tobias Meisen
Softwarex, 2023
Towards a Data Space for Interoperability of Analytic Provenance
Tristan Langer, André Pomp, Tobias Meisen
ACM Web Conference 2023 Companion of the World Wide Web Conference Www 2023, 2023
The PLASMA Framework: Laying the Path to Domain-Specific Semantics in Dataspaces
Alexander Paulus, André Pomp, Tobias Meisen
ACM Web Conference 2023 Companion of the World Wide Web Conference Www 2023, 2023
SPACE-DS: Towards a Circular Economy Data Space
André Pomp, Maike Jansen, Holger Berg, Tobias Meisen
ACM Web Conference 2023 Companion of the World Wide Web Conference Www 2023, 2023
Time Series Dataset Survey for Forecasting with Deep Learning
Yannik Hahn, Tristan Langer, Richard Meyes, Tobias Meisen
Forecasting, 2023
Stop Guessing in the Dark: Identified Requirements for Digital Product Passport Systems
Maike Jansen, Tobias Meisen, Christiane Plociennik, Holger Berg, André Pomp, Waldemar Windholz
Systems, 2023
A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions
Friedrich von Bülow, Tobias Meisen
Journal of Energy Storage, 2023
Curriculum Learning In Job Shop Scheduling Using Reinforcement Learning
C. W. D. Puiseau, Hasan Tercan, Tobias Meisen
Proceedings of the Conference on Production Systems and Logistics, 2023
Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps
Friedrich von Bülow, Yannik Hahn, Richard Meyes, Tobias Meisen
International Journal of Energy Research, 2023
Using IoT Technology for the Skilled Crafts
André Pomp, Andreas Burgdorf, Alexander Paulus, Tobias Meisen
Lecture Notes in Business Information Processing, 2023
On the detection and classification of objects in scarce sidescan sonar image dataset with deep learning methods
Underwater Acoustic Conference and Exhibition Series, 2023
Tackling data scarcity in sonar image classification with hybrid scattering neural networks
Yannik Steiniger, Angel Bueno, Dieter Kraus, Tobias Meisen
Oceans 2023 Limerick Oceans Limerick 2023, 2023
Asset administration shell-based engineering change management process: Challenges and ways forward
Mario Angos Mediavilla, Michele Lagnese, André Pomp, Tobias Meisen
Procedia CIRP, 2023
Industrial Cross-Robot Transfer Learning
Christian Bitter, Jannik Peters, Hasan Tercan, Tobias Meisen
Procedia CIRP, 2023
Simulation-to-Reality Transfer of a Two-Stage Deep Reinforcement Learning Controller for Autonomous Load Carrier Approaching
Simon Hadwiger, Vladimir Lavrik, Li Xin Liao, Tobias Meisen
2023 IEEE International Conference on Autonomous Robot Systems and Competitions Icarsc 2023, 2023
Filtering Recommender System for Semantic Model Refinement
Alexander Paulus, Andreas Burgdorf, André Pomp, Tobias Meisen
Proceedings 17th IEEE International Conference on Semantic Computing ICSC 2023, 2023
Extending PLASMA for Industrial Semantic Modeling
Ceur Workshop Proceedings, 2023
On The Effectiveness Of Bottleneck Information For Solving Job Shop Scheduling Problems Using Deep Reinforcement Learning
Waubert De Puiseau, Constantin, Zey, Lennart, Demir, Merve, Tercan, Hasan, Meisen, Tobias
Proceedings of the Conference on Production Systems and Logistics, 2023
Visual Interactive Exploration and Labeling of Large Volumes of Industrial Time Series Data
Tristan Langer, Viktor Welbers, Yannik Hahn, Mark Wönkhaus, Richard Meyes, Tobias Meisen
Lecture Notes in Business Information Processing, 2023
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes
Yannik Hahn, Robert Maack, Guido Buchholz, Marion Purrio, Matthias Angerhausen, Hasan Tercan, Tobias Meisen
Procedia CIRP, 2023
Vision Transformer in Industrial Visual Inspection
Nils Hütten, Richard Meyes, Tobias Meisen
Applied Sciences Switzerland, 2022
Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning
Constantin Waubert de Puiseau, Dimitri Tegomo Nanfack, Hasan Tercan, Johannes Löbbert-Plattfaut, Tobias Meisen
Technologies, 2022
Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings
Miguel Alves Gomes, Richard Meyes, Philipp Meisen, Tobias Meisen
International Conference on Information and Knowledge Management Proceedings, 2022
DocSemMap 2.0: Semantic Labeling based on Textual Data Documentations Using Seq2Seq Context Learner
Andreas Burgdorf, Alexander Paulus, André Pomp, Tobias Meisen
International Conference on Information and Knowledge Management Proceedings, 2022
PLASMA: A Semantic Modeling Tool for Domain Experts
Alexander Paulus, Andreas Burgdorf, Tristan Langer, André Pomp, Tobias Meisen, Sebastian Pol
International Conference on Information and Knowledge Management Proceedings, 2022
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Hasan Tercan, Tobias Meisen
Journal of Intelligent Manufacturing, 2022
Survey on deep learning based computer vision for sonar imagery
Yannik Steiniger, Dieter Kraus, Tobias Meisen
Engineering Applications of Artificial Intelligence, 2022
Reducing the Sim2Real-Gap in Extrusion Blow Molding using Random Forest Regressors
Robert F. Maack, Constantin Waubert de Puiseau, Anna Sokolova, Haile Atsbha, Hasan Tercan, Tobias Meisen
Manufacturing Letters, 2022
Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward
Vladimir Samsonov, Karim Ben Hicham, Tobias Meisen
Engineering Applications of Artificial Intelligence, 2022
Predicting the Progress of Vehicle Development Projects using an Attention-based Encoder-Decoder Model
Oliver Bohme, Tobias Meisen
ACM International Conference Proceeding Series, 2022
On reliability of reinforcement learning based production scheduling systems: a comparative survey
Constantin Waubert de Puiseau, Richard Meyes, Tobias Meisen
Journal of Intelligent Manufacturing, 2022
VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models
Andreas Burgdorf, Alexander Paulus, André Pomp, Tobias Meisen
Data, 2022
Autonomous Load Carrier Approaching Based on Deep Reinforcement Learning with Compressed Visual Information
Simon Hadwiger, Tobias Meisen
Proceedings 2022 5th International Conference on Artificial Intelligence for Industries Ai4i 2022, 2022
Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer
Hasan Tercan, Philipp Deibert, Tobias Meisen
Journal of Intelligent Manufacturing, 2022
Gideon Replay: A library to replay interactions in web-applications
Tristan Langer, Richard Meyes, Tobias Meisen
Softwarex, 2022
Deep Learning based Visual Quality Inspection for Industrial Assembly Line Production using Normalizing Flows
Robert F. Maack, Hasan Tercan, Tobias Meisen
IEEE International Conference on Industrial Informatics Indin, 2022
Gideon-TS: Efficient Exploration and Labeling of Multivariate Industrial Sensor Data
Tristan Langer, Viktor Welbers, Tobias Meisen
International Conference on Enterprise Information Systems Iceis Proceedings, 2022
DocSemMap: Leveraging Textual Data Documentations for Mapping Structured Data Sets into Knowledge Graphs
Andreas Burgdorf, Alexander Paulus, Andre Pomp, Tobias Meisen
Proceedings 16th IEEE International Conference on Semantic Computing ICSC 2022, 2022
Towards Unlocking the Potential of the Internet of Things for the Skilled Crafts
André Pomp, Andreas Burgdorf, Alexander Paulus, Tobias Meisen
International Conference on Enterprise Information Systems Iceis Proceedings, 2022
Investigating the training of convolutional neural networks with limited sidescan sonar image datasets
Yannik Steiniger, Jannis Stoppe, Dieter Kraus, Tobias Meisen
Oceans Conference Record IEEE, 2022
A Filter is Better Than None: Improving Deep Learning-Based Product Recommendation Models by Using a User Preference Filter
Miguel Alves Gomes, Hasan Tercan, Todd Bodnar, Philipp Meisen, Tobias Meisen
2021 IEEE 23rd International Conference on High Performance Computing and Communications 7th International Conference on Data Science and Systems 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor Cloud and Big Data Systems and Applications Hpcc Dss Smartcity Dependsys 2021, 2022
Using Node Embeddings to Generate Recommendations for Semantic Model Creation
Alexander Paulus, Andreas Burgdorf, Alina Stephan, André Pomp, Tobias Meisen
International Conference on Enterprise Information Systems Iceis Proceedings, 2022
Insights and Example Use Cases on Industrial Transfer Learning
Benjamin Maschler, Hannes Vietz, Hasan Tercan, Christian Bitter, Tobias Meisen, Michael Weyrich
Procedia CIRP, 2022
State of health forecasting of Lithium-ion batteries applicable in real-world operational conditions
Friedrich von Bülow, Joshua Mentz, Tobias Meisen
Journal of Energy Storage, 2021
A Semantic Data Marketplace for Easy Data Sharing within a Smart City
André Pomp, Alexander Paulus, Andreas Burgdorf, Tobias Meisen
International Conference on Information and Knowledge Management Proceedings, 2021
A Reference Framework for the Performance-Based Decision Support of City Authorities in Urban Freight Transport
Thomas Otte, Tobias Meisen
8th International Conference on ICT for Smart Society Digital Twin for Smart Society Iciss 2021 Proceeding, 2021
A study on modern deep learning detection algorithms for automatic target recognition in sidescan sonar images
Yannik Steiniger, Johannes Groen, Jannis Stoppe, Dieter Kraus, Tobias Meisen
Proceedings of Meetings on Acoustics, 2021
System design to utilize domain expertise for visual exploratory data analysis†
Tristan Langer, Tobias Meisen
Information Switzerland, 2021
Generating synthetic sidescan sonar snippets using transfer-learning in generative adversarial networks
Yannik Steiniger, Dieter Kraus, Tobias Meisen
Journal of Marine Science and Engineering, 2021
Applied feature-oriented project life cycle classification
Oliver Böhme, Tobias Meisen
Proceedings of the 10th International Conference on Data Science Technology and Applications Data 2021, 2021
Predicting the progress of vehicle development projects: An approach for the identification of input features
Icaart 2021 Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2021
Visual Analytics for Industrial Sensor Data Analysis
International Conference on Enterprise Information Systems Iceis Proceedings, 2021
Fleet Management Approach for Manufacturers displayed at the Use Case of Battery Electric Vehicles
Friedrich von Bulow, Felix Heinrich, Tobias Meisen
Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2021
Discovering Heuristics And Metaheuristics For Job Shop Scheduling From Scratch Via Deep Reinforcement Learning
Tilo van Ekeris, Richard Meyes, Tobias Meisen
Proceedings of the Conference on Production Systems and Logistics, 2021
Transparent and Interpretable Failure Prediction of Sensor Time Series Data with Convolutional Neural Networks
Richard Meyes, Nils Hütten, Tobias Meisen
Procedia CIRP, 2021
Erratum: DeepKnit: Learning-based Generation of Machine Knitting Code (Procedia Manufacturing)
Fabian Scheidt, Jifei Ou, Hiroshi Ishii, Tobias Meisen
Procedia Manufacturing, 2021
Recent Advances and Future Challenges of Semantic Modeling
Alexander Paulus, Andreas Burgdorf, Andre Pomp, Tobias Meisen
Proceedings 2021 IEEE 15th International Conference on Semantic Computing ICSC 2021, 2021
When to Message: Investigating User Response Prediction with Machine Learning for Advertisement Emails
Christian Bitter, Hasan Tercan, Tobias Meisen, Todd Bodnar, Philipp Meisen
Proceedings 2021 4th International Conference on Artificial Intelligence for Industries Ai4i 2021, 2021
Fault Detection in Railway Switches using Deformable Convolutional Neural Networks
Robert F. Maack, Hasan Tercan, Alexia F. Solvay, Maximilian Mieth, Tobias Meisen
IEEE International Conference on Industrial Informatics Indin, 2021
Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application
Hasan Tercan, Christian Bitter, Todd Bodnar, Philipp Meisen, Tobias Meisen
International Conference on Enterprise Information Systems Iceis Proceedings, 2021
Global Reward Design for Cooperative Agents to Achieve Flexible Production Control under Real-time Constraints
International Conference on Enterprise Information Systems Iceis Proceedings, 2021
PLASMA: Platform for Auxiliary Semantic Modeling Approaches
International Conference on Enterprise Information Systems Iceis Proceedings, 2021
Manufacturing control in job shop environments with reinforcement learning
Icaart 2021 Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2021
Transfer of Hierarchical Reinforcement Learning Structures for Robotic Manipulation Tasks
Christian Scheiderer, Malte Mosbach, Andres Felipe Posada-Moreno, Tobias Meisen
Proceedings 2020 International Conference on Computational Science and Computational Intelligence Csci 2020, 2020
Neural Combinatorial Optimization for Production Scheduling with Sequence-Dependent Setup Waste
Aymen Gannouni, Vladimir Samsonov, Mohamed Behery, Tobias Meisen, Gerhard Lakemeyer
Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2020
Dealing with Highly Unbalanced Sidescan Sonar Image Datasets for Deep Learning Classification Tasks
Yannik Steiniger, Jannis Stoppe, Tobias Meisen, Dieter Kraus
2020 Global Oceans 2020 Singapore U S Gulf Coast, 2020
Semantic Concept Recommendation for Continuously Evolving Knowledge Graphs
André Pomp, Vadim Kraus, Lucian Poth, Tobias Meisen
Lecture Notes in Business Information Processing, 2020
DeepKnit: Learning-based generation of machine knitting code
Fabian Scheidt, Jifei Ou, Hiroshi Ishii, Tobias Meisen
Procedia Manufacturing, 2020
Integration of a reactive scheduling solution using integration of a reactive scheduling solution using reinforcement learning in a manufacturing system
VDI Berichte, 2020
Simulation-as-a-service for reinforcement learning applications by example of heavy plate rolling processes
Christian Scheiderer, Timo Thun, Christian Idzik, Andrés Felipe Posada-Moreno, Alexander Krämer, Johannes Lohmar, Gerhard Hirt, Tobias Meisen
Procedia Manufacturing, 2020
More Machine Learning for Less: Comparing Data Generation Strategies in Mechanical Engineering and Manufacturing
Vladimir Samsonov, Johannes Lipp, Philipp Noodt, Alexia Fenollar Solvay, Tobias Meisen
2019 IEEE Symposium Series on Computational Intelligence Ssci 2019, 2019
Evaluation and Comparison of Cross-lingual Text Processing Pipelines
Robert Jungnickel, Andre Pomp, Andreas Kirmse, Xiang Li, Vladimir Samsonov, Tobias Meisen
2019 IEEE Symposium Series on Computational Intelligence Ssci 2019, 2019
Towards utilizing domain expertise for exploratory data analysis
Tristan Langer, Tobias Meisen
ACM International Conference Proceeding Series, 2019
Multi-agent reinforcement learning for job shop scheduling in flexible manufacturing systems
Schirin Baer, Jupiter Bakakeu, Richard Meyes, Tobias Meisen
Proceedings 2019 2nd International Conference on Artificial Intelligence for Industries Ai4i 2019, 2019
Data-Driven Recognition and Extraction of PDF Document Elements
Matthias Hansen, André Pomp, Kemal Erki, Tobias Meisen
Technologies, 2019
Transferring Knowledge from Monitored to Unmonitored Areas for Forecasting Parking Spaces
Andrei Ionita, André Pomp, Michael Cochez, Tobias Meisen, Stefan Decker
International Journal on Artificial Intelligence Tools, 2019
Combined learning processes for injection moulding based on simulation and experimental data
Christian Hopmann, Sabina Jeschke, Tobias Meisen, Thomas Thiele, Hasan Tercan, Martin Liebenberg, Julian Heinisch, Matthias Theunissen
Aip Conference Proceedings, 2019
Industrial transfer learning: Boosting machine learning in production
Hasan Tercan, Alexandro Guajardo, Tobias Meisen
IEEE International Conference on Industrial Informatics Indin, 2019
Model to assess the Economic Profitability of Predictive Maintenance Projects
Christian Wolf, Andreas Kirmse, Maximilian Burkhalter, Max Hoffmann, Tobias Meisen
2019 International Conference on High Performance Computing and Simulation Hpcs 2019, 2019
How to RAMI 4.0: Towards An Agent-based Information Management Architecture
Andreas Kirmse, Vadim Kraus, Tristan Langer, Andre Pomp, Tobias Meisen
2019 International Conference on High Performance Computing and Simulation Hpcs 2019, 2019
Towards an infrastructure enabling the internet of production
Jan Pennekamp, Rene Glebke, Martin Henze, Tobias Meisen, Christoph Quix, Rihan Hai, Lars Gleim, Philipp Niemietz, Maximilian Rudack, Simon Knape, Alexander Epple, Daniel Trauth, Uwe Vroomen, Thomas Bergs, Christian Brecher, Andreas Buhrig-Polaczek, Matthias Jarke, Klaus Wehrle
Proceedings 2019 IEEE International Conference on Industrial Cyber Physical Systems Icps 2019, 2019
You are Missing a Concept Enhancing Ontology-Based Data Access with Evolving Ontologies
Andre Pomp, Johannes Lipp, Tobias Meisen
Proceedings 13th IEEE International Conference on Semantic Computing ICSC 2019, 2019
Bézier curve based continuous and smooth motion planning for self-learning industrial robots
Christian Scheiderer, Timo Thun, Tobias Meisen
Procedia Manufacturing, 2019
A recurrent neural network architecture for failure prediction in deep drawing sensory time series data
Richard Meyes, Johanna Donauer, Andre Schmeing, Tobias Meisen
2019
Enhancing knowledge graphs with data representatives
André Pomp, Lucian Poth, Vadim Kraus, Tobias Meisen
Iceis 2019 Proceedings of the 21st International Conference on Enterprise Information Systems, 2019
Recommending Semantic Concepts for Improving the Process of Semantic Modeling
Alexander Paulus, André Pomp, Lucian Poth, Johannes Lipp, Tobias Meisen
Lecture Notes in Business Information Processing, 2019
User-centered integration of automated air mobility into urban transportation networks
Thomas Otte, Nora Metzner, Johannes Lipp, Max Schulze Schwienhorst, Alexia Fenollar Solvay, Tobias Meisen
AIAA IEEE Digital Avionics Systems Conference Proceedings, 2018
Design of an automated measuring system for RFID transponders in complex environments
Daniel Luetticke, Tobias Meisen
Rfid Ta 2018 2018 IEEE International Conference on Rfid Technology and Applications, 2018
Similarity recognition of interval-based sleep data
Marc Habler, Andreas Burgdorf, Christian Kohlschein, Tobias Meisen
2018 IEEE 20th International Conference on E Health Networking Applications and Services Healthcom 2018, 2018
An extensible semantic search engine for biomedical publications
Christian Kohlschein, Daniel Klischies, Alexander Paulus, Andreas Burgdorf, Tobias Meisen, Markus Kipp
2018 IEEE 20th International Conference on E Health Networking Applications and Services Healthcom 2018, 2018
Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures
André Pomp, Alexander Paulus, Andreas Kirmse, Vadim Kraus, Tobias Meisen
Technologies, 2018
STIDes Revisited - Tackling Global Time Shifts and Scaling
Marc Hassler, Andre Pomp, Christian Kohlschein, Tobias Meisen
Proceedings of the 2018 13th International Conference on Innovations in Information Technology Iit 2018, 2018
Where to park? predicting free parking spots in unmonitored city areas
Andrei Ionita, André Pomp, Michael Cochez, Tobias Meisen, Stefan Decker
ACM International Conference Proceeding Series, 2018
Towards Combining Semantic Concepts from Multiple Knowledge Bases
Alexander Paulus, Andre Pomp, Lucian Poth, Johannes Lipp, Tobias Meisen
Proceedings 12th IEEE International Conference on Semantic Computing ICSC 2018, 2018
Continuous Motion Planning for Industrial Robots based on Direct Sensory Input
Richard Meyes, Christian Scheiderer, Tobias Meisen
Procedia CIRP, 2018
Enabling semantics in enterprises
André Pomp, Alexander Paulus, Sabina Jeschke, Tobias Meisen
Lecture Notes in Business Information Processing, 2018
An architecture for efficient integration and harmonization of heterogeneous, distributed data sources enabling big data analytics
Andreas Kirmse, Vadim Kraus, Max Hoffmann, Tobias Meisen
Iceis 2018 Proceedings of the 20th International Conference on Enterprise Information Systems, 2018
Self-learning adaptive robot control
Productivity Management, 2018
A web-based UI to enable semantic modeling for everyone
André Pomp, Alexander Paulus, Daniel Klischies, Christian Schwier, Tobias Meisen
Procedia Computer Science, 2018
Gathering and combining semantic concepts from multiple knowledge bases
Alexander Paulus, André Pomp, Lucian Poth, Johannes Lipp, Tobias Meisen
Iceis 2018 Proceedings of the 20th International Conference on Enterprise Information Systems, 2018
Transfer-Learning: Bridging the Gap between Real and Simulation Data for Machine Learning in Injection Molding
Hasan Tercan, Alexandro Guajardo, Julian Heinisch, Thomas Thiele, Christian Hopmann, Tobias Meisen
Procedia CIRP, 2018
Flow Chart Based Information Modeling for Factory Planning
Chadi Bejjani, Julian Utsch, Thomas Thiele, Tobias Meisen, Sabina Jeschke, Peter Burggräf
Procedia CIRP, 2018
Interdisciplinary Data Driven Production Process Analysis for the Internet of Production
R. Meyes, H. Tercan, T. Thiele, A. Krämer, J. Heinisch, M. Liebenberg, G. Hirt, Ch. Hopmann, G. Lakemeyer, T. Meisen, S. Jeschke
2018
A data-based method for industrial big data project prioritization
Felix Kuschicke, Thomas Thiele, Tobias Meisen, Sabina Jeschke
ACM International Conference Proceeding Series, 2017
Cyber-Physical Production Systems: A Teaching Concept in Engineering Education
Daniela Antkowiak, Daniel Luetticke, Tristan Langer, Thomas Thiele, Tobias Meisen, Sabina Jeschke
Proceedings 2017 6th Iiai International Congress on Advanced Applied Informatics Iiai Aai 2017, 2017
Advanced data enrichment and data analysis in manufacturing industry by an example of laser drilling process
You Wang, Hasan Tercan, Thomas Thiele, Tobias Meisen, Sabina Jeschke, Wolfgang Schulz
Proceedings of the 2017 ITU Kaleidoscope Academic Conference Challenges for A Data Driven Society ITU K 2017, 2017
Improving the laser cutting process design by machine learning techniques
Hasan Tercan, Toufik Al Khawli, Urs Eppelt, Christian Büscher, Tobias Meisen, Sabina Jeschke
Production Engineering, 2017
ESKAPE: Platform for Enabling Semantics in the Continuously Evolving Internet of Things
Andre Pomp, Alexander Paulus, Sabina Jeschke, Tobias Meisen
Proceedings IEEE 11th International Conference on Semantic Computing ICSC 2017, 2017
Shifting Virtual Reality Education to the Next Level – Experiencing Remote Laboratories Through Mixed Reality
Max Hoffmann, Tobias Meisen, Sabina Jeschke
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
Virtuelle Produktion - Die Virtual Production Intelligence im Einsatz
Daniel Schilberg, Tobias Meisen, Rudolf Reinhard
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
OPC UA based ERP agents: Enabling scalable communication solutions in heterogeneous automation environments
Max Hoffmann, Tobias Meisen, Sabina Jeschke
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Virtual Production Intelligence – Process Analysis in the Production Planning Phase
Daniel Schilberg, Tobias Meisen, Rudolf Reinhard
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
ESKAPE: Information platform for enabling semantic data processing
André Pomp, Alexander Paulus, Sabina Jeschke, Tobias Meisen
Iceis 2017 Proceedings of the 19th International Conference on Enterprise Information Systems, 2017
Virtual Production Intelligence—A Contribution to the Digital Factory
Rudolf Reinhard, Christian Büscher, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
TIDAQL: A Query Language Enabling On-line Analytical Processing of Time Interval Data
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
Bitmap-Based On-Line Analytical Processing of Time Interval Data
Philipp Meisen, Tobias Meisen, Diane Keng, Marco Recchioni, Sabina Jeschke
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
Enhancing the Learning Success of Engineering Students by Virtual Experiments
Max Hoffmann, Lana Plumanns, Laura Lenz, Katharina Schuster, Tobias Meisen, Sabina Jeschke
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
The Need of Dynamic and Adaptive Data Models for Cyber-Physical Production Systems
C. Brecher, C. Ecker, W. Herfs, M. Obdenbusch, S. Jeschke, M. Hoffmann, T. Meisen
Cyber Physical Systems Foundations Principles and Applications, 2017
Engineering Education 4.0: Excellent Teaching and Learning in Engineering Sciences
A. Erman Tekkaya, Uwe Wilkesmann, Sabina Jeschke, Marcus Petermann, Anja Richert, et al.
Engineering Education 4 0 Excellent Teaching and Learning in Engineering Sciences, 2017
Motion Planning for Industrial Robots using Reinforcement Learning
Richard Meyes, Hasan Tercan, Simon Roggendorf, Thomas Thiele, Christian Büscher, Markus Obdenbusch, Christian Brecher, Sabina Jeschke, Tobias Meisen
Procedia CIRP, 2017
Language therapy of aphasia supported by augmented reality applications
Daniela Antkowiak, Christian Kohlschein, Roksaneh Kroob, Maximilian Speicher, Tobias Meisen, Sabina Jeschke, Cornelius J. Werner
2016 IEEE 18th International Conference on E Health Networking Applications and Services Healthcom 2016, 2016
Semantic integration of multi-agent systems using an OPC UA information modeling approach
Max Hoffmann, Philipp Thomas, Daniel Schutz, Birgit Vogel-Heuser, Tobias Meisen, Sabina Jeschke
IEEE International Conference on Industrial Informatics Indin, 2016
How will the Internet of Things and big data analytics impact the education of learning-disabled students? A Concept Paper
Laura Lenz, Andre Pomp, Tobias Meisen, Sabina Jeschke
2016 3rd Mec International Conference on Big Data and Smart City Icbdsc 2016, 2016
VPI-FP: An integrative information system for factory planning
Christian Büscher, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
International Journal of Production Research, 2016
Similarity search of bounded TIDASETs within large time interval databases
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Proceedings 2015 International Conference on Computational Science and Computational Intelligence Csci 2015, 2016
Virtual Production Intelligence-Process Analysis in the Production Planning Phase
Daniel Schilberg, Tobias Meisen, Rudolf Reinhard
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Shifting Virtual Reality Education to the Next Level-Experiencing Remote Laboratories Through Mixed Reality
Max Hoffmann, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Ontologiebasiertes Informationsmanagement für die Fabrikplanung
Christian Büscher, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
An Agile Information Processing Framework for High Pressure Die Casting Applications in Modern Manufacturing Systems
Michael Rix, Bernd Kujat, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Continuous Integration of Field Level Production Data into Top-level Information Systems Using the OPC Interface Standard
Max Hoffmann, Christian Büscher, Tobias Meisen, Sabina Jeschke
Procedia CIRP, 2016
Intelligent automation systems based on multi-agent systems and the OPC UA interface standard
Productivity Management, 2016
Continuous Integration of Field Level Production Data into Top-Level Information Systems Using the OPC Interface Standard
Max Hoffmann, Christian Büscher, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
An Agile Information Processing Framework for High Pressure Die Casting Applications in Modern Manufacturing Systems
Michael Rix, Bernd Kujat, Tobias Meisen, Sabina Jeschke
Procedia CIRP, 2016
TIDAQL: A Query Language Enabling On-line Analytical Processing of Time Interval Data
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
An open source framework enabling the integration of industry 4.0 solutions in existing production facilities
Productivity Management, 2016
Modeling and Processing of Time Interval Data for Data-Driven Decision Support
Philipp Meisen, Tobias Meisen, Marco Recchioni, Daniel Schilberg, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Implementing a Volunteer Notification System into a Scalable, Analytical Realtime Data Processing Environment
Jesko Elsner, Tomas Sivicki, Philipp Meisen, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Bitmap-Based On-Line Analytical Processing of Time Interval Data
Philipp Meisen, Tobias Meisen, Diane Keng, Marco Recchioni, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
A Framework for Semantic Integration and Analysis of Measurement Data in Modern Industrial Machinery
Tobias Meisen, Michael Rix, Max Hoffmann, Daniel Schilberg, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Enhancing the Learning Success of Engineering Students by Virtual Experiments
Max Hoffmann, Lana Plumanns, Laura Lenz, Katharina Schuster, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
AUDIME: Augmented Disaster Medicine
Alexander Paulus, Michael Czaplik, Frederik Hirsch, Philipp Meisen, Tobias Meisen, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Use of Classification Techniques to Design Laser Cutting Processes
Hasan Tercan, Toufik Al Khawli, Urs Eppelt, Christian Büscher, Tobias Meisen, Sabina Jeschke
Procedia CIRP, 2016
Semantic Information Modelling for Factory Planning Projects
Christian Büscher, Hanno Voet, Moritz Krunke, Peter Burggräf, Tobias Meisen, Sabina Jeschke
Procedia CIRP, 2016
Improving Factory Planning by Analyzing Process Dependencies
Christian Büscher, Hanno Voet, Tobias Meisen, Moritz Krunke, Kai Kreisköther, Achim Kampker, Daniel Schilberg, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
How Virtual Production Intelligence Can Improve Laser-Cutting Planning Processes
Rudolf Reinhard, Urs Eppelt, Toufik Al-Khawly, Tobias Meisen, Daniel Schilberg, Wolfgang Schulz, Sabina Jeschke
Automation Communication and Cybernetics in Science and Engineering 2015 2016, 2016
Bitmap-Based On-line Analytical Processing of Time Interval Data
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Proceedings 12th International Conference on Information Technology New Generations Itng 2015, 2015
Querying time interval data
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Lecture Notes in Business Information Processing, 2015
A methodological implementation of a pervasive information system in high pressure die casting manufacturing
23rd International Conference for Production Research Icpr 2015, 2015
TIDAQL: A query language enabling on-line analytical processing of time interval data
Philipp Meisen, Diane Keng, Tobias Meisen, Marco Recchioni, Sabina Jeschke
Iceis 2015 17th International Conference on Enterprise Information Systems Proceedings, 2015
AUDIME: Augmented disaster medicine
Alexander Paulus, Philipp Meisen, Tobias Meisen, Sabina Jeschke, Michael Czaplik, Frederik Hirsch
2015 17th International Conference on E Health Networking Application and Services Healthcom 2015, 2015
Enhancing the learning success of engineering students by virtual experiments
Max Hoffmann, Lana Plumanns, Laura Lenz, Katharina Schuster, Tobias Meisen, Sabina Jeschke
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
Work area monitoring in dynamic environments using multiple auto-aligning 3-D sensors
Y. Wang, D. Ewert, T. Meisen, D. Schilberg, S. Jeschke
Journal of Sensors and Sensor Systems, 2014
Virtual production intelligence: Process analysis in the production planing phase
Daniel Schilberg, Tobias Meisen, Rudolf Reinhard
Transactions on Engineering Technologies Special Issue of the World Congress on Engineering and Computer Science 2013, 2014
A modular factory planning approach based on vertical integration of the information flow
Max Hoffmann, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
Applied Mechanics and Materials, 2014
Modeling and processing of time interval data for data-driven decision support
Philipp Meisen, Marco Recchioni, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2014
Improving factory planning by analyzing process dependencies
Christian Büscher, Hanno Voet, Tobias Meisen, Moritz Krunke, Kai Kreisköther, Achim Kampker, Daniel Schilberg, Sabina Jeschke
Procedia CIRP, 2014
Virtual production intelligence
Productivity Management, 2013
Virtual production - The connection of the modules through the Virtual Production intelligence
Lecture Notes in Engineering and Computer Science, 2013
Multi-dimensional production planning using a vertical data integration approach: A contribution to modular factory design
Max Hoffmann, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
2013 10th International Conference and Expo on Emerging Technologies for A Smarter World Cewit 2013, 2013
Virtual production intelligence - A contribution to the digital factory
Rudolf Reinhard, Christian Büscher, Tobias Meisen, Daniel Schilberg, Sabina Jeschke
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Visualization
Thomas Beer, Tobias Meisen
Integrative Computational Materials Engineering Concepts and Applications of A Modular Simulation Platform, 2012
Distributed Simulations
Thomas Beer, Tobias Meisen, Rudolf Reinhard
Integrative Computational Materials Engineering Concepts and Applications of A Modular Simulation Platform, 2012
Adaptive information integration: Bridging the semantic gap between numerical simulations
Tobias Meisen, Philipp Meisen, Daniel Schilberg, Sabina Jeschke
Lecture Notes in Business Information Processing, 2012
Virtual Production Systems
Wolfgang Schulz, Christian Bischof, Kirsten Bobzin, Christian Brecher, Thomas Gries, Sabina Jeschke, Achim Kampker, Fritz Klocke, Torsten Kuhlen, Günther Schuh, Markus Apel, Tim Arping, Nazlim Bagcivan, Markus Bambach, Thomas Baranowski, Stephan Bäumler, Thomas Beer, Stefan Benke, Thomas Bergs, Peter Burggräf, Gustavo Cabral, Urs Eppelt, Patrick Fayek, Marcel Fey, Bastian Franzkoch, Stephan Freyberger, Lothar Glasmacher, Barbara Heesel, Thomas Henke, Werner Herfs, Ulrich Jansen, Tatyana Kashko, Sergey Konovalov, Britta Kuckhoff, Gottfried Laschet, Markus Linke, Wolfram Lohse, Tobias Meisen, Meysam Minoufekr, Jan Nöcker, Ulrich Prahl, Hendrik Quade, Matthias Rasim, Marcus Rauhut, Rudolf Reinhard, Jan Rosenbaum, Eduardo Rossiter, Daniel Schilberg, Georg J. Schmitz, Johannes Triebs, Hagen Wegner, Cathrin Wesch-Potente
Integrative Production Technology for High Wage Countries, 2012
Application integration of simulation tools considering domain specific knowledge
Iceis 2011 Proceedings of the 13th International Conference on Enterprise Information Systems, 2011
A multi level time model for interactive multiple dataset visualization: The dataset sequencer
Thomas Beer, Gerrit Garbereder, Tobias Meisen, Rudolf Reinhard, Torsten Kuhlen
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011
IT-infrastructure for an integrated visual analysis of distributed heterogeneous simulations
Tobias Meisen, Rudolf Reinhard, Thomas Beer, Daniel Schilberg, Sabina Jeschke
Advanced Materials Research, 2011
The virtual production simulation platform: From collaborative distributed simulation to integrated visual analysis
Thomas Beer, Tobias Meisen, Rudolf Reinhard, Sergey Konovalov, Daniel Schilberg, Sabina Jeschke, Torsten Kuhlen, Christian Bischof
Production Engineering, 2011
A framework for adaptive data integration in digital production
21st International Conference on Production Research Innovation in Product and Production Icpr 2011 Conference Proceedings, 2011
Simulation and interoperability in the planning phase of production processes
Daniel Schilberg, Tobias Meisen, Rudolf Reinhard, Sabina Jeschke
ASME 2011 International Mechanical Engineering Congress and Exposition Imece 2011, 2011
Enterprise Application Integration for virtual production
Daniel Schilberg, Tobias Meisen, Philippe Cerfontaine, Sabina Jeschke
ASME International Mechanical Engineering Congress and Exposition Proceedings Imece, 2010
Ontology based semantic interconnection of distributed numerical simulations for virtual production
Daniel Schilberg, Tobias Meisen
Ie and EM 2009 Proceedings 2009 IEEE 16th International Conference on Industrial Engineering and Engineering Management, 2009
Semantic data integration for virtual production
Daniel Schilberg, Tobias Meisen, Klaus Henning
2009 International Conference on Computer and Electrical Engineering Iccee 2009, 2009
Efficient similarity search using the Earth Mover's Distance for large multimedia databases
Ira Assent, Marc Wichterich, Tobias Meisen, Thomas Seidl
Proceedings International Conference on Data Engineering, 2008

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