Young-Doo Lee

1.4k total citations
37 papers, 1.0k citations indexed

About

Young-Doo Lee is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Young-Doo Lee has authored 37 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 16 papers in Computer Networks and Communications and 11 papers in Control and Systems Engineering. Recurrent topics in Young-Doo Lee's work include Cognitive Radio Networks and Spectrum Sensing (6 papers), Smart Grid Security and Resilience (5 papers) and Fault Detection and Control Systems (5 papers). Young-Doo Lee is often cited by papers focused on Cognitive Radio Networks and Spectrum Sensing (6 papers), Smart Grid Security and Resilience (5 papers) and Fault Detection and Control Systems (5 papers). Young-Doo Lee collaborates with scholars based in South Korea, United Kingdom and Japan. Young-Doo Lee's co-authors include Insoo Koo, Sana Ullah Jan, Saeed Ahmed, Seung-Ho Hyun, Jungpil Shin, Umer Saeed, Seong Woo Jeon, Chang-Min Cho, Won Young Tak and Young Oh Kweon and has published in prestigious journals such as IEEE Access, Sensors and Reliability Engineering & System Safety.

In The Last Decade

Young-Doo Lee

34 papers receiving 977 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young-Doo Lee South Korea 13 452 407 393 243 133 37 1.0k
Paulo Gil Portugal 16 343 0.8× 219 0.5× 287 0.7× 217 0.9× 40 0.3× 113 938
Antoni Morell Spain 19 167 0.4× 493 1.2× 563 1.4× 174 0.7× 106 0.8× 84 1.2k
Peter Hellinckx Belgium 17 162 0.4× 338 0.8× 251 0.6× 144 0.6× 36 0.3× 91 1.2k
Khmaies Ouahada South Africa 20 363 0.8× 380 0.9× 657 1.7× 260 1.1× 35 0.3× 114 1.3k
Ting Yang China 21 633 1.4× 385 0.9× 1.0k 2.6× 270 1.1× 122 0.9× 120 1.9k
Mehran Mehrandezh Canada 15 470 1.0× 193 0.5× 304 0.8× 123 0.5× 30 0.2× 71 1.2k
Zahid Wadud Pakistan 21 392 0.9× 408 1.0× 832 2.1× 134 0.6× 273 2.1× 42 1.3k
Dengyin Zhang China 19 148 0.3× 346 0.9× 579 1.5× 93 0.4× 95 0.7× 154 1.7k
Yinggao Yue China 20 126 0.3× 434 1.1× 354 0.9× 357 1.5× 41 0.3× 53 1.2k
Jingqi Fu China 16 261 0.6× 207 0.5× 251 0.6× 129 0.5× 20 0.2× 66 727

Countries citing papers authored by Young-Doo Lee

Since Specialization
Citations

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

Fields of papers citing papers by Young-Doo Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young-Doo Lee. 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 Young-Doo Lee. The network helps show where Young-Doo Lee may publish in the future.

Co-authorship network of co-authors of Young-Doo Lee

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

All Works

20 of 20 papers shown
1.
Lee, Young-Doo, et al.. (2022). Deep Learning‐Based Scheduling Scheme for IEEE 802.15.4e TSCH Network. Wireless Communications and Mobile Computing. 2022(1). 2 indexed citations
2.
Saeed, Umer, Young-Doo Lee, Sana Ullah Jan, & Insoo Koo. (2021). CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning. Sensors. 21(2). 617–617. 36 indexed citations
3.
Saeed, Umer, Sana Ullah Jan, Young-Doo Lee, & Insoo Koo. (2020). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering & System Safety. 205. 107284–107284. 163 indexed citations
4.
Lee, Young-Doo, et al.. (2020). Timely Sensor Fault Detection Scheme based on Deep Learning. The Journal of the Institute of Webcasting, Internet and Telecommunication. 20(1). 163–169. 3 indexed citations
5.
Saeed, Umer, Sana Ullah Jan, Young-Doo Lee, & Insoo Koo. (2020). Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi. Edinburgh Napier Research Repository (Edinburgh Napier University). 1–7. 16 indexed citations
6.
Lee, Young-Doo, et al.. (2019). An RNN-based Fault Detection Scheme for Digital Sensor. The Journal of the Institute of Webcasting, Internet and Telecommunication. 19(1). 29–35. 2 indexed citations
7.
Ahmed, Saeed, Young-Doo Lee, Seung-Ho Hyun, & Insoo Koo. (2019). Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest. IEEE Transactions on Information Forensics and Security. 14(10). 2765–2777. 202 indexed citations
8.
Lee, Young-Doo, et al.. (2018). Implementation and Measurement of Spectrum Sensing for Cognitive Radio Networks Based on LoRa and GNU Radio. International journal of advanced smart convergence. 7(3). 23–36. 3 indexed citations
9.
Lee, Young-Doo, et al.. (2018). Sensor Fault Detection Scheme based on Deep Learning and Support Vector Machine. The Journal of the Institute of Webcasting, Internet and Telecommunication. 18(2). 185–195. 2 indexed citations
10.
Lee, Young-Doo, et al.. (2018). Convolution Neural Network-Based Spectrum Sensing for Cognitive Radio Systems Using USRP with GNU Radio. 45. 862–864. 4 indexed citations
11.
Lee, Young-Doo, et al.. (2018). Convolutional Autoencoder-Based Sensor Fault Classification. 5. 865–867. 2 indexed citations
12.
Ahmed, Saeed, Young-Doo Lee, Seung-Ho Hyun, & Insoo Koo. (2018). Feature Selection–Based Detection of Covert Cyber Deception Assaults in Smart Grid Communications Networks Using Machine Learning. IEEE Access. 6. 27518–27529. 69 indexed citations
13.
Lee, Young-Doo, et al.. (2017). An adaptive network allocation vector timer-based carrier sense multiple access with collision avoidance medium access control protocol for underwater acoustic sensor networks. International Journal of Distributed Sensor Networks. 13(1). 812021544–812021544. 11 indexed citations
14.
Lee, Young-Doo, et al.. (2017). Application of Navigating System based on Bluetooth Smart. The Journal of The Institute of Internet Broadcasting and Communication. 17(1). 69–76.
15.
Lee, Young-Doo, et al.. (2006). Comparison of Hemostatic Efficacy between Epinephrine Injection Alone and a Combined Therapy with Hemoclip for Bleeding Peptic Ulcers. Clinical Endoscopy. 32(1). 9–14. 1 indexed citations
16.
Lee, Young-Doo, Seong Woo Jeon, Chang-Min Cho, et al.. (2006). Solid-pseudopapillary Tumor of the Pancreas. Journal of Clinical Gastroenterology. 40(10). 919–922. 22 indexed citations
17.
Lee, Young-Doo, Seong Woo Jeon, Dong‐Seok Lee, et al.. (2005). Risk Factors Related to Bleeding after Endoscopic Mucosal Resection of Gastric Tumors. Clinical Endoscopy. 30(6). 297–304. 1 indexed citations
18.
Lee, Young-Doo & Tae‐Eog Lee. (2005). Stochastic cyclic flow lines with blocking: Markovian models. OR Spectrum. 27(4). 551–568. 2 indexed citations
19.
Lee, Young-Doo, et al.. (1995). 엔트로피최대화방법을 이용한 극미부 확률의 추정. 대한산업공학회 춘계공동학술대회 논문집. 966–966.
20.
Cho, Woohyung, et al.. (1989). Cost-benefit Analysis of Health Screening Test for the Insured. Journal of Preventive Medicine and Public Health. 22(2). 248–258. 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.

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