Richard Meyes

641 total citations
20 papers, 245 citations indexed

About

Richard Meyes is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Richard Meyes has authored 20 papers receiving a total of 245 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Industrial and Manufacturing Engineering, 7 papers in Artificial Intelligence and 3 papers in Control and Systems Engineering. Recurrent topics in Richard Meyes's work include Industrial Vision Systems and Defect Detection (4 papers), Time Series Analysis and Forecasting (3 papers) and Reinforcement Learning in Robotics (3 papers). Richard Meyes is often cited by papers focused on Industrial Vision Systems and Defect Detection (4 papers), Time Series Analysis and Forecasting (3 papers) and Reinforcement Learning in Robotics (3 papers). Richard Meyes collaborates with scholars based in Germany and Finland. Richard Meyes's co-authors include Tobias Meisen, Thomas Thiele, Sabina Jeschke, Hasan Tercan, Christian Büscher, Christian Brecher, G. Hirt, Christian Hopmann, Gerhard Lakemeyer and Pekka Abrahamsson and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Sciences and International Journal of Energy Research.

In The Last Decade

Richard Meyes

17 papers receiving 238 citations

Peers

Richard Meyes

IME

CSE

CVPR

Xuelei Meng China

Yuanping Xu China

Guixiang Shen China

Zeid Kootbally United States

Sang Won Yoon United States

Jinkang Guo China

Chao Guan China

Chenxu Dai China

Luis Piardi Portugal

Xuelei Meng China

Richard Meyes

96 ×0.9

IME

128 ×2.4

38 ×0.7

CSE

24 ×0.5

CVPR

35 ×0.8

Citations per year, relative to Richard Meyes Richard Meyes (= 1×) peers Xuelei Meng

Countries citing papers authored by Richard Meyes

Since Specialization

Citations

This map shows the geographic impact of Richard Meyes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Richard Meyes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Meyes more than expected).

Fields of papers citing papers by Richard Meyes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard Meyes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard Meyes. The network helps show where Richard Meyes may publish in the future.

Co-authorship network of co-authors of Richard Meyes

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Meyes. A scholar is included among the top collaborators of Richard Meyes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard Meyes. Richard Meyes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Meyes, Richard, et al.. (2024). Guided Exploration of Industrial Sensor Data. Computer Graphics Forum. 43(1). 1 indexed citations

Meyes, Richard, et al.. (2024). Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data. Water. 16(23). 3368–3368. 3 indexed citations

Meyes, Richard, et al.. (2024). It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation. Journal of theoretical and applied electronic commerce research. 19(1). 135–151.

Meyes, Richard, et al.. (2024). Researchers’ Concerns on Artificial Intelligence Ethics: Results from a Scenario-Based Survey. Jyväskylä University Digital Archive (University of Jyväskylä). 24–31. 1 indexed citations

Meyes, Richard, et al.. (2024). Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers. Applied System Innovation. 7(1). 11–11. 23 indexed citations

Busch, Daniel A., et al.. (2024). Improved Single Camera BEV Perception Using Multi-Camera Training. 3982–3988.

Meyes, Richard, et al.. (2023). Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps. International Journal of Energy Research. 2023. 1–23. 8 indexed citations

Meyes, Richard, et al.. (2023). Time Series Dataset Survey for Forecasting with Deep Learning. SHILAP Revista de lepidopterología. 5(1). 315–335. 6 indexed citations

Meyes, Richard, et al.. (2022). Vision Transformer in Industrial Visual Inspection. Applied Sciences. 12(23). 11981–11981. 21 indexed citations

10.

Meyes, Richard, et al.. (2022). Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2873–2882. 10 indexed citations

11.

Meyes, Richard, et al.. (2022). On reliability of reinforcement learning based production scheduling systems: a comparative survey. Journal of Intelligent Manufacturing. 33(4). 911–927. 31 indexed citations

12.

Meyes, Richard. (2022). Transparency and Interpretability for Learned Representations of Artificial Neural Networks.

13.

Meyes, Richard, et al.. (2021). Gideon Replay: A library to replay interactions in web-applications. SoftwareX. 17. 100964–100964. 1 indexed citations

14.

Meyes, Richard, et al.. (2021). Transparent and Interpretable Failure Prediction of Sensor Time Series Data with Convolutional Neural Networks. Procedia CIRP. 104. 1446–1451. 4 indexed citations

15.

Meyes, Richard, et al.. (2019). Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems. 22–25. 33 indexed citations

16.

Meyes, Richard, et al.. (2019). A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data. Procedia Manufacturing. 34. 789–797. 24 indexed citations

17.

Meyes, Richard, et al.. (2018). Continuous Motion Planning for Industrial Robots based on Direct Sensory Input. Procedia CIRP. 72. 291–296. 11 indexed citations

18.

Meyes, Richard, Hasan Tercan, Thomas Thiele, et al.. (2018). Interdisciplinary Data Driven Production Process Analysis for the Internet of Production. Procedia Manufacturing. 26. 1065–1076. 13 indexed citations

19.

Meyes, Richard, Hasan Tercan, Thomas Thiele, et al.. (2017). Motion Planning for Industrial Robots using Reinforcement Learning. Procedia CIRP. 63. 107–112. 54 indexed citations

20.

Meyes, Richard. (2015). Emulation of Bursting Neurons in Neuromorphic Hardware Based on Phase-Change Materials. CERN Document Server (European Organization for Nuclear Research). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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