Tobias Meisen

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Computer Science

Scopus Publications

Emergent language: a survey and taxonomy
Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas de Carvalho, Christian Bitter, and Tobias Meisen
Springer Science and Business Media LLC
Abstract The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of relevant scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.


Designing an Ontology Network for Digital Product Passports
Maike Jansen, Eva Blomqvist, Robin Keskisärkkä, Huanyu Li, Mikael Lindecrantz, Karin Wannerberg, André Pomp, Tobias Meisen, and Holger Berg
Springer Nature Switzerland



Challenges and Opportunities of LLM-Augmented Semantic Model Creation for Dataspaces
Sayed Hoseini, Andreas Burgdorf, Alexander Paulus, Tobias Meisen, Christoph Quix, and André Pomp
Springer Nature Switzerland



CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
Harsh Yadav, Maximilian Schaefer, Kun Zhao, and Tobias Meisen
Springer Nature Switzerland



Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data
Yannik Hahn, Philip Kienitz, Mark Wönkhaus, Richard Meyes, and Tobias Meisen
MDPI AG
The increasing frequency and severity of floods due to climate change underscores the need for precise flood forecasting systems. This study focuses on the region surrounding Wuppertal in Germany, known for its high precipitation levels, as a case study to evaluate the effectiveness of flood prediction through deep learning models. Our primary objectives are twofold: (1) to establish a robust dataset from the Wupper river basin, containing over 19 years of time series data from three sensor types such as water level, discharge, and precipitation at multiple locations, and (2) to assess the predictive performance of nine advanced machine learning algorithms, including Pyraformer, TimesNet, and SegRNN, in providing reliable flood warnings 6 to 48 h in advance, based on 48 h of input data. Our models, trained and validated using k-fold cross-validation, achieved high quantitative performance metrics, with an accuracy reaching up to 99.7% and F1-scores up to 91%. Additionally, we analyzed model performance relative to the number of sensors by systematically reducing the sensor count, which led to a noticeable decline in both accuracy and F1-score. These findings highlight critical trade-offs between sensor coverage and predictive reliability. By publishing this comprehensive dataset alongside performance benchmarks, we aim to drive further innovation in flood risk management and resilience strategies, addressing urgent needs in climate adaptation.


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, and Matthias Angerhausen
ACM



Evaluating Text Placement in Information-Rich Virtual Environments: A User Study on Controller-Anchored Text
Nils A. Mack, Florian Schümmer, Marion Rose, and Meisen Tobias
ACM



Researchers' Concerns on Artificial Intelligence Ethics: Results from a Scenario-Based Survey
Marianna Jantunen, Richard Meyes, Veronika Kurchyna, Tobias Meisen, Pekka Abrahamsson, and Rahul Mohanani
ACM
The ethical impacts of Artificial Intelligence (AI) are causing con-cern in many areas of AI research and development. The implemen-tation of AI ethics is still, in many ways, a work in progress, but various initiatives are tackling the issues by creating guidelines and implementation methods. This study investigates concerns about the negative impacts of AI systems posed by researchers working with AI. The study was conducted as a scenario-based survey, in which participants answered the question, “What could go wrong?” regarding five scenarios depicting fictional AI systems. The study concludes with the results from 33 survey participants who gave 161 responses to the scenarios. The results suggest that researchers can identify threats posed by AI systems, particularly regarding their social and ethical consequences. This is even though half of the participants reported limited involvement with AI ethics in their work. The widespread understanding of ethics among researchers could positively impact AI software development due to increased capabilities to bring theoretical AI ethics to practice.


Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
Marvin Brune, Tobias Meisen, and André Pomp
MDPI AG
This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.


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, and Tobias Meisen
MDPI AG
Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.


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, and Tobias Meisen
MDPI AG
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.


Guided Exploration of Industrial Sensor Data
Tristan Langer, Richard Meyes, and Tobias Meisen
Wiley
AbstractIn recent years, digitization in the industrial sector has increased steadily. Digital data not only allows us to monitor the underlying production process using machine learning methods (anomaly detection, behaviour analysis) but also to understand the underlying production process. Insights from Exploratory Data Analysis (EDA) play an important role in building data‐driven processes because data scientists learn essential characteristics of the data in the context of the domain. Due to the complexity of production processes, it is usually difficult for data scientists to acquire this knowledge by themselves. Hence, they have to rely on continuous close collaboration with domain experts and their acquired domain expertise. However, direct communication does not promote documentation of the knowledge transfer from domain experts to data scientists. In this respect, changing team constellations, for example due to a change in personnel, result in a renewed high level of effort despite the same knowledge transfer problem. As a result, EDA is a cost‐intensive iterative process. We, therefore, investigate a system to extract information from the interactions that domain experts perform during EDA. Our approach relies on recording interactions and system states of an exploration tool and generating guided exploration sessions for domain novices. We implement our approach in a software tool and demonstrate its capabilities using two real‐world use cases from the manufacturing industry. We evaluate its feasibility in a user study to investigate whether domain novices can reproduce the most important insights from domain experts about the datasets of the use cases based on generated EDA sessions. From the results of this study, we conclude the feasibility of our system as participants are able to reproduce on average 86.5% of insights from domain experts.


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, and Tobias Meisen
IEEE
The automation of logistics is an overarching goal and provides various application and research potentials. In this work, we address the task of vision-based navigation to precisely align an Automated Guided Vehicle (AGV) in front of a load carrier. We envision to solve this task with a maximal allowed position and orientation error of 0.01m and 1.0°, utilizing a learning-based controller and a single RGB camera. Therefore, we apply an actor-critic Reinforcement Learning (RL) agent to steer the AGV. As the precision requirement results in the sparsity of high rewards, we propose a Curriculum Learning (CL) framework to investigate the influence of two curricula, PositionSampling and PrecisionSampling, and their combination on the resulting agent’s performance. We introduce a multipurpose performance measure to investigate the capabilities of the trained agents, which in turn can be used for task difficulty evaluation, episode termination, control of curriculum learning strategies and as reward calculation. Further, we show that CL methods bring us one step closer to our vision of precision in motion.


Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition
Nils Müller, Jens Reermann, and Tobias Meisen
Institute of Electrical and Electronics Engineers (IEEE)
The field of deep learning is a rapidly developing research area with numerous applications across multiple domains. Sonar (SOund Navigation And Ranging) processing has traditionally been a field of statistical analysis. However, in the past ten to fifteen years, the rapid growth of deep learning has challenged classical approaches with modern deep learning-based methods. This survey provides a systematic overview of the Underwater Acoustic Target Recognition (UATR) domain within the area of deep learning. The objective is to highlight popular design choices and evaluate the commonalities and differences of the investigated techniques in relation to the selected architectures and pre-processing methods. Furthermore, this survey examines the state of UATR literature through the identification of prominent conferences and journals which points new researchers in directions where to allocate UATR related publications. Additionally, popular datasets and available benchmarks are identified and analysed for complexity coverage. This work targets researchers new to the field as well as experienced researchers that want to get a broader overview. Nonetheless, experienced sonar engineers with a strong background within classical analysis also benefit from this survey.


CenterPoint Transformer for BEV Object Detection with Automotive Radar
Loveneet Saini, Yu Su, Hasan Tercan, and Tobias Meisen
IEEE
Object detection in Bird’s Eye View (BEV) has emerged as a prevalent approach in automotive radar perception systems. Recent methods use Feature Pyramid Networks(FPNs) with large yet limited receptive fields to encode object properties. In contrast, Detection Transformers (DETRs), known for their application in image-based object detection, use a global receptive field and object queries with set losses. However, applying DETRs to sparse radar inputs is challenging due to limited object definition, resulting in inferior set matching. This paper addresses such limitations by introducing a novel approach that uses transformers to extract global context information and encode it into the object’s center point. This approach aims to provide each object with individualized global context awareness to extract richer feature representations. Our experiments, conducted on the public NuScenes dataset, show a significant increase in mAP for the car category by 23.6% over the best radar-only submission, alongside notable improvements for object detectors on the Aptiv dataset. Our modular architecture allows for easy integration of additional tasks, providing benefits as evidenced by a reduction in the mean L2 error in velocity prediction across different classes.


Modelling Digital Product Passports for the Circular Economy




Towards LLM-augmented Creation of Semantic Models for Dataspaces




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, and Tobias Meisen
SCITEPRESS - Science and Technology Publications



CNNs Sparsification and Expansion for Continual Learning
Basile Tousside, Jörg Frochte, and Tobias Meisen
SCITEPRESS - Science and Technology Publications
: Learning multiple sequentially arriving tasks without forgetting previous knowledge, known as Continual Learning (CL), remains a long-standing challenge for neural networks. Most existing CL methods rely on data replay. However, they are not applicable when past data is unavailable or is not allowed to be synthetically generated. To address this challenge, we propose Sparification and Expansion-based Continual Learning (SECL). SECL avoids forgetting of previous tasks by ensuring the stability of the CNN via a stability regularization term, which prevents filters detected as important for past tasks to deviate too much when learning a new task. On top of that, SECL makes the network plastic via a plasticity regularization term that leverage the over-parameterization of CNNs to efficiently sparsify the network and tunes unimportant filters making them relevant for future tasks. Also, SECL enhances the plasticity of the network through a simple but effective heuristic mechanism that automatically decides when and where (at which layers) to expand the network. Experiments on popular CL vision benchmarks show that SECL leads to significant improvements over state-of-the-art method in terms of overall CL performance, as measured by classification accuracy as well as in terms of avoiding catastrophic forgetting.


Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling
Vladimir Samsonov, Enslin Chrismarie, Hans-Georg Köpken, Schirin Bär, Daniel Lütticke, and Tobias Meisen
Springer Science and Business Media LLC
AbstractComputer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).


State of health forecasting of Lithium-ion batteries operated in a battery electric vehicle fleet
Friedrich von Bülow, Markus Wassermann, and Tobias Meisen
Elsevier BV



TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
Miguel Alves Gomes, Mark Wönkhaus, Philipp Meisen, and Tobias Meisen
MDPI AG
Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.


Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System
Johannes Benkert, Robert Maack, and Tobias Meisen
MDPI AG
In recent years, a steady increase in maritime business and annual container throughput has been recorded. To meet this growing demand, terminal operators worldwide are turning to automated container handling. For the automated operation of a crane, a reliable capture of the environment is required. In current state-of-the-art applications this is mostly achieved with light detection and ranging (LiDAR) sensors. These sensors enable precise three-dimensional sampling of the surroundings, even at great distances. However, the use of LiDAR sensors has a number of disadvantages, such as high acquisition costs and limited mounting positions. This raises the question of whether the LiDAR systems of automated container terminals (ACT) can be replaced with cameras. However, this transformation is not easy to accomplish and is explored in more depth in this paper. The field of camera-based container automation presented in this publication is largely unexplored. To the best of our knowledge, there is currently no automated container terminal in real-world operation that exclusively uses cameras. This publication aims to create a basis for further scientific research towards the goal of a fully camera-based container automation. Therefore, the authors present a narrative review providing a broad overview of the mentioned transformation, identifying research gaps, and suggesting areas for future research. In order to achieve this, this publication examines the fundamentals of an automated container terminal, the existing automation solutions and sensor technologies, as well as the opportunities and challenges of a transformation from LiDAR to camera.


A review on customer segmentation methods for personalized customer targeting in e-commerce use cases
Miguel Alves Gomes and Tobias Meisen
Springer Science and Business Media LLC
AbstractThe importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.


Online Quality Prediction in Windshield Manufacturing using Data-Efficient Machine Learning
Hasan Tercan and Tobias Meisen
ACM
The digitization of manufacturing processes opens up the possibility of using machine learning methods on process data to predict future product quality. Based on the model predictions, quality improvement actions can be taken at an early stage. However, significant challenges must be overcome to successfully implement the predictions. Production lines are subject to hardware and memory limitations and are characterized by constant changes in quality influencing factors. In this paper, we address these challenges and present an online prediction approach for real-world manufacturing processes. On the one hand, it includes methods for feature extraction and selection from multimodal process and sensor data. On the other hand, a continual learning method based on memory-aware synapses is developed to efficiently train an artificial neural network over process changes. We deploy and evaluate the approach in a windshield production process. Our experimental evaluation shows that the model can accurately predict windshield quality and achieve significant process improvement. By comparing with other learning strategies such as transfer learning, we also show that the continual learning method both prevents catastrophic forgetting of the model and maintains its data efficiency.

RECENT SCHOLAR PUBLICATIONS

Emergent language: a survey and taxonomy
J Peters, C Waubert de Puiseau, H Tercan, A Gopikrishnan, ...
Autonomous Agents and Multi-Agent Systems 39 (1), 1-73 2025



LMFormer: Lane based Motion Prediction Transformer
H Yadav, M Schaefer, K Zhao, T Meisen
arXiv preprint arXiv:2504.10275 2025



AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
L Saini, M Meuter, H Tercan, T Meisen
arXiv preprint arXiv:2504.00559 2025



Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem
C Waubert de Puiseau, F Wolz, M Montag, J Peters, H Tercan, T Meisen
AI 6 (3), 48 2025



Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings
M Alves Gomes, P Meisen, T Meisen
Journal of Theoretical and Applied Electronic Commerce Research 20 (1), 12 2025



Enhancing 3D Human Pose Estimation: A Novel Post-Processing Method
E Iravani, F Hasecke, L Hahn, T Meisen
Proceedings of the 20th International Joint Conference on Computer Vision 2025



Beyond Labels: Self-Attention-Driven Semantic Separation Using Principal Component Clustering in Latent Diffusion Models
F Stillger, F Hasecke, L Hahn, T Meisen
Proceedings of the 20th International Joint Conference on Computer Vision 2025



PCAD: A Real-World Dataset for 6D Pose Industrial Anomaly Detection
R Maack, L Thun, T Liang, H Tercan, T Meisen
Proceedings of the Winter Conference on Applications of Computer Vision 2025



CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
H Yadav, M Schaefer, K Zhao, T Meisen
International Conference on Pattern Recognition, 420-434 2025



Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data
Y Hahn, P Kienitz, M Wnkhaus, R Meyes, T Meisen
Water 16 (23), 3368 2024



Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised-VAE
Y Hahn, R Maack, H Tercan, T Meisen, M Purrio, G Buchholz, ...
Proceedings of the 33rd ACM International Conference on Information and 2024



Navigating the Depths: A Comprehensive Survey of Deep Learning for Passive Underwater Acoustic Target Recognition
N Mller, J Reermann, T Meisen
IEEE Access 2024



Improved Single Camera BEV Perception Using Multi-Camera Training
D Busch, I Freeman, R Meyes, T Meisen
arXiv preprint arXiv:2409.02676 2024



Task Weighting through Gradient Projection for Multitask Learning
C Bohn, I Freeman, H Tercan, T Meisen
arXiv preprint arXiv:2409.01793 2024



Towards Precision in Motion: Investigating the Influences of Curriculum Learning based on a Multi-Purpose Performance Metric for Vision-Based Forklift Navigation
S Hadwiger, D Kube, V Lavrik, T Meisen
2024 IEEE 20th International Conference on Automation Science and 2024



Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings
M Alves Gomes, P Meisen, T Meisen
arXiv e-prints, arXiv: 2408.14118 2024



Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning
M Weiss, T Meisen
NDT 2 (3), 286-310 2024



Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation
F Stillger, F Hasecke, T Meisen
arXiv preprint arXiv:2406.17541 2024



Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
CW de Puiseau, C Drpelkus, J Peters, H Tercan, T Meisen
arXiv preprint arXiv:2406.07325 2024



Designing an ontology network for digital product passports
M Jansen, E Blomqvist, R Keskisrkk, H Li, M Lindecrantz, ...
European Semantic Web Conference, 220-237 2024

MOST CITED SCHOLAR PUBLICATIONS

Industrial internet of things and cyber manufacturing systems
S Jeschke, C Brecher, T Meisen, D zdemir, T Eschert
Industrial Internet of Things: Cybermanufacturing Systems, 3-19 2017
Citations: 1220



Ablation studies in artificial neural networks
R Meyes, M Lu, CW de Puiseau, T Meisen
arXiv preprint arXiv:1901.08644 2019
Citations: 350



Machine learning and deep learning based predictive quality in manufacturing: a systematic review
H Tercan, T Meisen
Journal of Intelligent Manufacturing 33 (7), 1879-1905 2022
Citations: 227



Towards an infrastructure enabling the internet of production
J Pennekamp, R Glebke, M Henze, T Meisen, C Quix, R Hai, L Gleim, ...
2019 IEEE international conference on industrial cyber physical systems 2019
Citations: 167



Transfer-learning: Bridging the gap between real and simulation data for machine learning in injection molding
H Tercan, A Guajardo, J Heinisch, T Thiele, C Hopmann, T Meisen
Procedia Cirp 72, 185-190 2018
Citations: 152



Motion planning for industrial robots using reinforcement learning
R Meyes, H Tercan, S Roggendorf, T Thiele, C Bscher, M Obdenbusch, ...
Procedia CIRP 63, 107-112 2017
Citations: 113



A review on customer segmentation methods for personalized customer targeting in e-commerce use cases
M Alves Gomes, T Meisen
Information Systems and e-Business Management 21 (3), 527-570 2023
Citations: 92



Stop guessing in the dark: Identified requirements for digital product passport systems
M Jansen, T Meisen, C Plociennik, H Berg, A Pomp, W Windholz
Systems 11 (3), 123 2023
Citations: 89



Survey on deep learning based computer vision for sonar imagery
Y Steiniger, D Kraus, T Meisen
Engineering Applications of Artificial Intelligence 114, 105157 2022
Citations: 89



Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer
H Tercan, P Deibert, T Meisen
Journal of Intelligent Manufacturing 33 (1), 283-292 2022
Citations: 67



A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions
F von Blow, T Meisen
Journal of Energy Storage 57, 105978 2023
Citations: 66



Multi-agent reinforcement learning for job shop scheduling in flexible manufacturing systems
S Baer, J Bakakeu, R Meyes, T Meisen
2019 Second International Conference on Artificial Intelligence for 2019
Citations: 66



Continuous integration of field level production data into top-level information systems using the OPC interface standard
M Hoffmann, C Bscher, T Meisen, S Jeschke
Procedia Cirp 41, 496-501 2016
Citations: 56



Industrial transfer learning: Boosting machine learning in production
H Tercan, A Guajardo, T Meisen
2019 IEEE 17th international conference on industrial informatics (INDIN) 1 2019
Citations: 53



On reliability of reinforcement learning based production scheduling systems: a comparative survey
C Waubert de Puiseau, R Meyes, T Meisen
Journal of Intelligent Manufacturing 33 (4), 911-927 2022
Citations: 52



Manufacturing Control in Job Shop Environments with Reinforcement Learning.
V Samsonov, M Kemmerling, M Paegert, D Ltticke, F Sauermann, ...
ICAART (2), 589-597 2021
Citations: 50



Efficient similarity search using the earth mover's distance for large multimedia databases
I Assent, M Wichterich, T Meisen, T Seidl
2008 IEEE 24th International conference on data engineering, 307-316 2008
Citations: 48



Where to park? predicting free parking spots in unmonitored city areas
A Ionita, A Pomp, M Cochez, T Meisen, S Decker
Proceedings of the 8th International Conference on Web Intelligence, Mining 2018
Citations: 46



Shifting virtual reality education to the next level–Experiencing remote laboratories through mixed reality
M Hoffmann, T Meisen, S Jeschke
Engineering education 4.0: Excellent teaching and learning in engineering 2017
Citations: 45



A recurrent neural network architecture for failure prediction in deep drawing sensory time series data
R Meyes, J Donauer, A Schmeing, T Meisen
Procedia Manufacturing 34, 789-797 2019
Citations: 43