Yong Qin

10.0k total citations · 2 hit papers
495 papers, 6.9k citations indexed

About

Yong Qin is a scholar working on Control and Systems Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Yong Qin has authored 495 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 150 papers in Control and Systems Engineering, 111 papers in Mechanical Engineering and 95 papers in Artificial Intelligence. Recurrent topics in Yong Qin's work include Transportation Planning and Optimization (66 papers), Machine Fault Diagnosis Techniques (66 papers) and Traffic Prediction and Management Techniques (66 papers). Yong Qin is often cited by papers focused on Transportation Planning and Optimization (66 papers), Machine Fault Diagnosis Techniques (66 papers) and Traffic Prediction and Management Techniques (66 papers). Yong Qin collaborates with scholars based in China, United States and United Kingdom. Yong Qin's co-authors include Limin Jia, Limin Jia, Dandan Peng, Zhiliang Liu, Huan Wang, Xinwang Liu, Zhipeng Wang, Weizhong Wang, Yunpeng Wu and Honghui Dong and has published in prestigious journals such as Advanced Materials, SHILAP Revista de lepidopterología and Advanced Functional Materials.

In The Last Decade

Yong Qin

447 papers receiving 6.7k citations

Hit Papers

Understanding and Learnin... 2019 2026 2021 2023 2019 2024 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yong Qin China 42 2.5k 1.9k 926 865 807 495 6.9k
Xinping Yan China 58 1.5k 0.6× 2.9k 1.5× 535 0.6× 591 0.7× 983 1.2× 435 10.6k
Qing He China 39 1.2k 0.5× 1.7k 0.9× 528 0.6× 737 0.9× 1.2k 1.4× 231 5.2k
Zhenghua Chen Singapore 44 2.4k 1.0× 1.4k 0.7× 1.7k 1.8× 381 0.4× 193 0.2× 225 9.3k
Gang Xiong China 35 1.0k 0.4× 784 0.4× 544 0.6× 395 0.5× 486 0.6× 447 5.5k
Clive Roberts United Kingdom 43 1.4k 0.5× 3.4k 1.8× 329 0.4× 1.3k 1.5× 1.0k 1.3× 282 6.2k
David W. Coit United States 52 1.6k 0.6× 878 0.5× 966 1.0× 822 1.0× 126 0.2× 201 12.0k
Dimitri N. Mavris United States 38 1.6k 0.6× 727 0.4× 646 0.7× 501 0.6× 163 0.2× 1.2k 10.0k
Kincho H. Law United States 43 726 0.3× 952 0.5× 681 0.7× 3.3k 3.8× 315 0.4× 326 7.5k
Huimin Zhao China 33 2.4k 0.9× 1.2k 0.7× 2.2k 2.4× 341 0.4× 141 0.2× 103 6.7k
Robert Babuška Netherlands 51 5.2k 2.1× 1.3k 0.7× 5.0k 5.4× 798 0.9× 230 0.3× 336 12.4k

Countries citing papers authored by Yong Qin

Since Specialization
Citations

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

Fields of papers citing papers by Yong Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yong Qin

This figure shows the co-authorship network connecting the top 25 collaborators of Yong Qin. A scholar is included among the top collaborators of Yong Qin 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 Yong Qin. Yong Qin 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.
Wang, Jiali, et al.. (2025). Boundary Dynamic Perception Detection Network for Low-Light Railway Environment. IEEE Sensors Journal. 25(8). 14064–14079.
2.
Ren, Ziliang, et al.. (2025). Skeleton-guided and supervised learning of hybrid network for multi-modal action recognition. Journal of Visual Communication and Image Representation. 107. 104389–104389. 1 indexed citations
3.
Qin, Yong, et al.. (2025). RAE3D: Multiscale Aggregation-Enhanced 3D Object Detection for Rail Transit Obstacle Perception. IEEE Transactions on Industrial Informatics. 21(5). 4221–4232. 3 indexed citations
4.
Wang, Meiqi, Yijun Hao, Jiayi Yang, et al.. (2024). A hybrid triboelectric-piezoelectric-electromagnetic generator with the high output performance for vibration energy harvesting of high-speed railway vehicles. Nano Energy. 132. 110417–110417. 8 indexed citations
5.
Chen, Yuejian, et al.. (2024). Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions. Reliability Engineering & System Safety. 254. 110596–110596. 46 indexed citations
6.
Wang, Ning, Limin Jia, Yong Qin, et al.. (2023). Scale-independent shrinkage broad learning system for wheelset bearing anomaly detection under variable conditions. Mechanical Systems and Signal Processing. 200. 110653–110653. 7 indexed citations
7.
Wang, Ning, et al.. (2023). Manifold-Contrastive Broad Learning System for Wheelset Bearing Fault Diagnosis. IEEE Transactions on Intelligent Transportation Systems. 24(9). 9886–9900. 11 indexed citations
8.
Luo, Jie, Chao Wen, Qiyuan Peng, Yong Qin, & Ping Huang. (2023). Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method. Physica A Statistical Mechanics and its Applications. 621. 128793–128793. 8 indexed citations
9.
Ding, Ao, et al.. (2023). Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation. Engineering Applications of Artificial Intelligence. 127. 107382–107382. 21 indexed citations
10.
Yang, Yi, et al.. (2022). An Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis. Scientific Programming. 2022. 1–15. 3 indexed citations
11.
Li, Chunya, et al.. (2021). Statistical estimation in passenger‐to‐train assignment models based on automated data. Applied Stochastic Models in Business and Industry. 38(2). 287–307. 10 indexed citations
12.
Qin, Yong, et al.. (2019). Railway vehicle bearings risk monitoring based on normal region estimation for no-fault data situations. Journal of Transportation Safety & Security. 13(10). 1047–1065. 3 indexed citations
13.
He, Meng, et al.. (2015). Sparse Fast Fourier Transform and its application in intelligent diagnosis system of train rolling bearing. Journal of Vibroengineering. 17(8). 4219–4230. 5 indexed citations
14.
Qin, Yong, Lei Zhu, Zhenyu Zhang, Linlin Kou, & Xiaoqing Cheng. (2015). Railway Disaster Prevention and Snow Survey Design Optimization Technology. The Open Automation and Control Systems Journal. 7(1). 1895–1902. 1 indexed citations
15.
Wang, Xiaohao, et al.. (2014). Application of DEMATEL in metro door system reliability research. 618–622. 2 indexed citations
16.
Qin, Yong. (2009). Reliability analysis of gas alarm based on GO methodology. Transducer and Microsystem Technologies. 1 indexed citations
17.
Qin, Yong. (2009). Warped source spectrum for voice conversion and similarity. Journal of Tsinghua University(Science and Technology).
18.
Dong, Honghui, et al.. (2009). Road Traffic Flow Prediction with a Time-Oriented ARIMA Model. 1649–1652. 43 indexed citations
19.
Qin, Yong. (2008). Application of Improved AHP in Evaluation of Railway Emergency Plans. Journal of the China Railway Society. 5 indexed citations
20.
Qin, Yong & Limin Jia. (2007). Research on the System Framework and Application of Railway Transportation Emergency Management. Zhongguo anquan kexue xuebao. 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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