Nauman Munir

470 total citations
10 papers, 344 citations indexed

About

Nauman Munir is a scholar working on Mechanical Engineering, Mechanics of Materials and Electrical and Electronic Engineering. According to data from OpenAlex, Nauman Munir has authored 10 papers receiving a total of 344 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Mechanical Engineering, 4 papers in Mechanics of Materials and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Nauman Munir's work include Non-Destructive Testing Techniques (7 papers), Welding Techniques and Residual Stresses (5 papers) and Ultrasonics and Acoustic Wave Propagation (4 papers). Nauman Munir is often cited by papers focused on Non-Destructive Testing Techniques (7 papers), Welding Techniques and Residual Stresses (5 papers) and Ultrasonics and Acoustic Wave Propagation (4 papers). Nauman Munir collaborates with scholars based in South Korea, Pakistan and Greece. Nauman Munir's co-authors include Sung-Jin Song, Hak-Joon Kim, Sung-Sik Kang, Jinhyun Park, Chul‐Hwan Kim, Amy J.C. Trappey, Peter W. Tse and Ki-Bok Kim and has published in prestigious journals such as IEEE Access, IEEE Transactions on Power Delivery and Ultrasonics.

In The Last Decade

Nauman Munir

10 papers receiving 333 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Nauman Munir South Korea	8	254	202	66	61	59	10	344
Fangming Li China	7	276 1.1×	140 0.7×	76 1.2×	47 0.8×	61 1.0×	10	340
Cláudia Teresa Teles Farias Brazil	8	218 0.9×	181 0.9×	40 0.6×	38 0.6×	69 1.2×	21	304
Maria C.S. Albuquerque Brazil	8	211 0.8×	175 0.9×	39 0.6×	37 0.6×	65 1.1×	16	300
Antonio Alves de Carvalho Brazil	8	263 1.0×	157 0.8×	75 1.1×	30 0.5×	68 1.2×	15	325
Jianping Peng China	10	245 1.0×	224 1.1×	22 0.3×	34 0.6×	92 1.6×	65	389
See Yenn Chong South Korea	9	171 0.7×	289 1.4×	116 1.8×	15 0.2×	124 2.1×	19	371
Weichao Liu China	10	265 1.0×	272 1.3×	36 0.5×	30 0.5×	137 2.3×	27	382
Shaoping Zhou China	12	180 0.7×	225 1.1×	74 1.1×	12 0.2×	127 2.2×	35	366
Claudio Camerini Brazil	9	264 1.0×	118 0.6×	100 1.5×	12 0.2×	84 1.4×	28	387
Fengyuan Zuo China	10	156 0.6×	46 0.2×	17 0.3×	105 1.7×	16 0.3×	25	257

Countries citing papers authored by Nauman Munir

Since Specialization

Citations

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

Fields of papers citing papers by Nauman Munir

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nauman Munir

This figure shows the co-authorship network connecting the top 25 collaborators of Nauman Munir. A scholar is included among the top collaborators of Nauman Munir 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 Nauman Munir. Nauman Munir is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Munir, Nauman, et al.. (2024). Machine learning based eddy current testing: A review. Results in Engineering. 25. 103724–103724. 10 indexed citations

Park, Jinhyun, et al.. (2022). Discrimination of Poor Electrode Junctions within Lithium-Ion Batteries by Ultrasonic Measurement and Deep Learning. Batteries. 8(3). 21–21. 7 indexed citations

Tse, Peter W., et al.. (2022). Non-contact detection of railhead defects and their classification by using convolutional neural network. Optik. 253. 168607–168607. 13 indexed citations

Kim, Chul‐Hwan, et al.. (2022). Single-Phase Auto-Reclosing Scheme Using Particle Filter and Convolutional Neural Network. IEEE Transactions on Power Delivery. 37(6). 4775–4785. 11 indexed citations

Munir, Nauman, et al.. (2022). Pattern Recognition Based Auto-Reclosing Scheme Using Bi-Directional Long Short-Term Memory Network. IEEE Access. 10. 119734–119744. 4 indexed citations

Munir, Nauman, Jinhyun Park, Hak-Joon Kim, Sung-Jin Song, & Sung-Sik Kang. (2020). Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder. NDT & E International. 111. 102218–102218. 94 indexed citations

Munir, Nauman, et al.. (2019). Lamb Wave Flaw Classification in Al Plates Using Time Reversal and Deep Neural Networks. Journal of the Korean Physical Society. 75(12). 978–984. 6 indexed citations

Park, Jinhyun, et al.. (2019). MRPC eddy current flaw classification in tubes using deep neural networks. Nuclear Engineering and Technology. 51(7). 1784–1790. 9 indexed citations

Munir, Nauman, Hak-Joon Kim, Jinhyun Park, Sung-Jin Song, & Sung-Sik Kang. (2018). Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions. Ultrasonics. 94. 74–81. 133 indexed citations

10.

Munir, Nauman, Hak-Joon Kim, Sung-Jin Song, & Sung-Sik Kang. (2018). Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments. Journal of Mechanical Science and Technology. 32(7). 3073–3080. 57 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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