Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A survey on long short-term memory networks for time series prediction
2021347 citationsB. Lindemann, Timo Müller et al.Procedia CIRPprofile →
A survey on anomaly detection for technical systems using LSTM networks
2021242 citationsB. Lindemann, Benjamin Maschler et al.Computers in Industryprofile →
An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System
This map shows the geographic impact of B. Lindemann'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 B. Lindemann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites B. Lindemann more than expected).
This network shows the impact of papers produced by B. Lindemann. 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 B. Lindemann. The network helps show where B. Lindemann may publish in the future.
Co-authorship network of co-authors of B. Lindemann
This figure shows the co-authorship network connecting the top 25 collaborators of B. Lindemann.
A scholar is included among the top collaborators of B. Lindemann 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 B. Lindemann. B. Lindemann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
13 of 13 papers shown
1.
Lindemann, B., Timo Müller, Hannes Vietz, Nasser Jazdi, & Michael Weyrich. (2021). A survey on long short-term memory networks for time series prediction. Procedia CIRP. 99. 650–655.347 indexed citations breakdown →
Lindemann, B., Benjamin Maschler, Nada Sahlab, & Michael Weyrich. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry. 131. 103498–103498.242 indexed citations breakdown →
Talkhestani, Behrang Ashtari, Tobias Jung, B. Lindemann, et al.. (2019). An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System. at - Automatisierungstechnik. 67(9). 762–782.214 indexed citations breakdown →
10.
Liewald, Mathias, et al.. (2018). VOLATILE MEDIA AS LUBRICANT SUBSTITUTES IN DEEP DRAWING AND TRACKING OF INDIVIDUAL WORKPIECES IN HOT FORGING PLANTS. ACTA TECHNICA NAPOCENSIS - Series: APPLIED MATHEMATICS, MECHANICS, and ENGINEERING. 61(4).1 indexed citations
Lindemann, B., et al.. (1977). USED GLASS AS AN AGGREGATE FOR THE PRODUCTION OF ROLLED ASPHALT. 17(3).2 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.