Simon Malinowski

26 papers receiving 1.3k citations

Hit Papers

Direct Remaining Useful Life Estimation Based on Support Vector Regression 2016 · 337 citations
3370+4+8Years since publication100200300

Peers

Simon Malinowski
Comparison fields: 5 of 113
  • Medical Laboratory Technology 48
  • Control and Systems Engineering 644
  • Safety, Risk, Reliability and Quality 247
  • Signal Processing 185
  • Mechanical Engineering 316
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Countries citing papers authored by Simon Malinowski

Since Specialization
Citations

This map shows the geographic impact of Simon Malinowski'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 Simon Malinowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Malinowski more than expected).

Fields of papers citing papers by Simon Malinowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Simon Malinowski. 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 Simon Malinowski. The network helps show where Simon Malinowski may publish in the future.

Co-authors

The 25 scholars most cited alongside Simon Malinowski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Simon Malinowski Line = papers co-authored together Simon Malinowski links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network
Hit paper breakdown →
2014398
2
Direct Remaining Useful Life Estimation Based on Support Vector Regression
Hit paper breakdown →
2016337
3
Data Augmentation for Time Series Classification using Convolutional Neural Networks
Hit paper breakdown →
2016258
4 200752
5 201944
6 200732
7 201532
8 201321
9 200920
10 201519
11 201915
12 20159
13 20238
14 20078
15 20217
16 20145
17 20124
18 20074
19 20134
20 20232

About Simon Malinowski

Simon Malinowski is a scholar working on Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Computational Theory and Mathematics and Electrical and Electronic Engineering, having authored 30 papers that have together received 1.3k indexed citations. Recurring topics across this work include Algorithms and Data Compression (7 papers), Error Correcting Code Techniques (6 papers), Cellular Automata and Applications (6 papers), Time Series Analysis and Forecasting (4 papers), Machine Fault Diagnosis Techniques (3 papers), Coding theory and cryptography (2 papers), Fault Detection and Control Systems (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Medical Laboratory Technology (48 citations), Control and Systems Engineering (644 citations), Safety, Risk, Reliability and Quality (247 citations), Signal Processing (185 citations) and Mechanical Engineering (316 citations). Simon Malinowski has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Brigitte Chebel‐Morello, Farhat Fnaiech, Romain Tavenard, Jaouher Ben Ali, Lotfi Saïdi, Noureddine Zerhouni, Racha Khelif, Emna Laajili, Christine Guillemot and Hervé Jeǵou. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Water Resources Research, IEEE Transactions on Industrial Electronics, Computer Communications and Mechanical Systems and Signal Processing.

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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