Eva H. Dulf

2.5k citations
146 papers · 1.7k · 1 hit paper · h-index 21

Impact in

Papers in

Eva H. Dulf

125 papers receiving 1.7k citations

Eva H. Dulf's Hit Papers

A review on modern defect detection models using DCNNs – Deep convolutional neural networks 2021 · 230 citations
2300+1+3Years since publication50100150200

Peers

Eva H. Dulf
Comparison fields: 5 of 141
  • Modeling and Simulation 183
  • Control and Systems Engineering 761
  • Biochemistry 147
  • Industrial and Manufacturing Engineering 116
  • Biotechnology 69
Replace Farman Ullah with:
Farman Ullah Pakistan
Jun‐Wei Wang China
Xuegang Huang China
Mohamed Hammami Tunisia
Francesco Villecco Italy
Masayuki Fujita Japan
Yu Tang China
Junfeng Zhang China
Shiqi Zheng China
Shuo Tang China
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Citations per year

Countries citing papers authored by Eva H. Dulf

Since Specialization
Citations

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

Fields of papers citing papers by Eva H. Dulf

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eva H. Dulf. 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 Eva H. Dulf. The network helps show where Eva H. Dulf may publish in the future.

Co-authors

The 25 scholars most cited alongside Eva H. Dulf, 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 Eva H. Dulf Line = papers co-authored together Eva H. Dulf links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 146 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A review on modern defect detection models using DCNNs – Deep convolutional neural networks
Hit paper breakdown →
2021230
2 2017101
3 202291
4 201389
5 201583
6 201564
7 202261
8 201958
9 201758
10 202148
11 201240
12 201940
13 201529
14 201428
15 202427
16 202326
17 202124
18 202222
19 202222
20 202022

About Eva H. Dulf

Eva H. Dulf is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering, Aerospace Engineering, Artificial Intelligence and Modeling and Simulation, having authored 146 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Control Systems Design (58 papers), Advanced Control Systems Optimization (38 papers), Extremum Seeking Control Systems (26 papers), Advanced Data Processing Techniques (15 papers), Fractional Differential Equations Solutions (10 papers), Fault Detection and Control Systems (9 papers), Control Systems and Identification (8 papers) and Field-Flow Fractionation Techniques (6 papers). The work is most often cited by research in Modeling and Simulation (183 citations), Control and Systems Engineering (761 citations), Biochemistry (147 citations), Industrial and Manufacturing Engineering (116 citations) and Biotechnology (69 citations). Eva H. Dulf has collaborated with scholars based in Romania, Hungary and Belgium. Frequent co-authors include Cristina I. Mureşan, Francisc Vasile Dulf, Dan Cristian Vodnar, Clara M. Ionescu, Monica Ioana Toșa, Isabela Birs, Silviu Folea, Adela Pintea, George Moiş and Ancuța Jurj. Their work appears in journals such as Applied Sciences, Sensors, IEEE Access, Chemical Engineering & Technology and Frontiers in Bioengineering and Biotechnology.

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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