Yu Ding

5.2k total citations
161 papers, 3.8k citations indexed

About

Yu Ding is a scholar working on Industrial and Manufacturing Engineering, Mechanical Engineering and Artificial Intelligence. According to data from OpenAlex, Yu Ding has authored 161 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Industrial and Manufacturing Engineering, 36 papers in Mechanical Engineering and 27 papers in Artificial Intelligence. Recurrent topics in Yu Ding's work include Manufacturing Process and Optimization (34 papers), Advanced Multi-Objective Optimization Algorithms (15 papers) and Advanced machining processes and optimization (15 papers). Yu Ding is often cited by papers focused on Manufacturing Process and Optimization (34 papers), Advanced Multi-Objective Optimization Algorithms (15 papers) and Advanced machining processes and optimization (15 papers). Yu Ding collaborates with scholars based in United States, China and South Korea. Yu Ding's co-authors include Dariusz Ceglarek, Jianjun Shi, Eunshin Byon, Shiyu Zhou, Chiwoo Park, Lewis Ntaimo, Yong Chen, Li Zeng, Ahmed Aziz Ezzat and Jianhua Z. Huang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Yu Ding

151 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yu Ding United States 34 1.3k 808 736 695 549 161 3.8k
Achille Messac United States 39 636 0.5× 1.3k 1.6× 985 1.3× 1.1k 1.5× 678 1.2× 186 5.8k
Laibin Zhang China 39 1.1k 0.9× 1.2k 1.5× 1.6k 2.1× 2.0k 2.9× 591 1.1× 275 6.2k
N. Srinivas India 3 717 0.6× 316 0.4× 822 1.1× 745 1.1× 892 1.6× 14 5.5k
Runwei Cheng Japan 18 2.7k 2.1× 167 0.2× 1.0k 1.4× 435 0.6× 568 1.0× 31 6.1k
Agus Sudjianto United States 25 362 0.3× 1.9k 2.3× 196 0.3× 438 0.6× 234 0.4× 74 4.0k
Himanshu Jain India 11 717 0.6× 242 0.3× 634 0.9× 552 0.8× 800 1.5× 23 6.8k
Alessandro Beghi Italy 28 676 0.5× 236 0.3× 1.2k 1.6× 607 0.9× 392 0.7× 193 3.1k
David A. Van Veldhuizen United States 9 710 0.5× 262 0.3× 680 0.9× 528 0.8× 555 1.0× 18 5.6k
Indraneel Das United States 10 344 0.3× 504 0.6× 641 0.9× 338 0.5× 401 0.7× 18 3.4k
Ping Jiang China 42 496 0.4× 740 0.9× 221 0.3× 3.5k 5.0× 675 1.2× 316 6.4k

Countries citing papers authored by Yu Ding

Since Specialization
Citations

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

Fields of papers citing papers by Yu Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Ding. A scholar is included among the top collaborators of Yu Ding 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 Yu Ding. Yu Ding 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.
Sutherland, Melissa A., et al.. (2024). Health screenings in college health centers: Variations in practice. Journal of American College Health. 73(8). 3031–3038.
2.
Ahmed, Imtiaz, et al.. (2024). Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy. IEEE Transactions on Automation Science and Engineering. 22. 7912–7926. 1 indexed citations
3.
Liu, Xin, et al.. (2023). A Bayesian Hierarchical Model to Understand the Effect of Terrain on Wind Turbine Power Curves. IEEE Transactions on Sustainable Energy. 15(2). 1127–1137. 1 indexed citations
4.
Tuo, Rui, et al.. (2022). The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling. Technometrics. 65(1). 70–82. 13 indexed citations
5.
6.
Ahmed, Imtiaz, Mikyoung Jun, & Yu Ding. (2022). A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data. IEEE Transactions on Intelligent Transportation Systems. 23(11). 20783–20797. 11 indexed citations
7.
Tuo, Rui, et al.. (2022). Hypothesis tests with functional data for surface quality change detection in surface finishing processes. IISE Transactions. 55(9). 940–956. 5 indexed citations
8.
Zhou, Lan, et al.. (2021). Joint estimation of monotone curves via functional principal component analysis. Computational Statistics & Data Analysis. 166. 107343–107343. 3 indexed citations
9.
Ezzat, Ahmed Aziz, Jiong Tang, & Yu Ding. (2020). A model-based calibration approach for structural fault diagnosis using piezoelectric impedance measurements and a finite element model. Structural Health Monitoring. 19(6). 1839–1855. 24 indexed citations
10.
Qian, Yanjun, et al.. (2020). Effective Super-Resolution Methods for Paired Electron Microscopic Images. IEEE Transactions on Image Processing. 29. 7317–7330. 6 indexed citations
11.
Ezzat, Ahmed Aziz, Mikyoung Jun, & Yu Ding. (2018). Spatio-Temporal Asymmetry of Local Wind Fields and Its Impact on Short-Term Wind Forecasting. IEEE Transactions on Sustainable Energy. 9(3). 1437–1447. 70 indexed citations
12.
Hwangbo, Hoon, et al.. (2018). Evaluation of alternative power production efficiency metrics for offshore wind turbines and farms. Renewable Energy. 128. 81–90. 23 indexed citations
13.
Ahmed, Imtiaz, Aldo Dagnino, & Yu Ding. (2018). Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines. IEEE Transactions on Automation Science and Engineering. 16(2). 654–667. 39 indexed citations
14.
Hwangbo, Hoon, et al.. (2017). Quantifying the effect of vortex generator installation on wind power production: An academia-industry case study. Renewable Energy. 113. 1589–1597. 38 indexed citations
15.
Qian, Yanjun, Jianhua Z. Huang, Xiaodong Li, & Yu Ding. (2016). Robust Nanoparticles Detection From Noisy Background by Fusing Complementary Image Information. IEEE Transactions on Image Processing. 25(12). 5713–5726. 11 indexed citations
16.
Ding, Yu, et al.. (2006). A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes. IIE Transactions. 38(4). 309–328. 95 indexed citations
17.
Ding, Yu, et al.. (2006). NUMERICAL SIMULATION OF THE TRUSS SPAR 'HORN MOUNTAIN'. 5 indexed citations
18.
Ding, Yu, et al.. (2005). Optimal coordinate sensor placements for estimating mean and variance components of variation sources. IIE Transactions. 37(9). 877–889. 38 indexed citations
19.
Jin, Jionghua & Yu Ding. (2004). Online automatic process control using observable noise factors for discrete-part manufacturing. IIE Transactions. 36(9). 899–911. 24 indexed citations
20.
Ding, Yu. (2001). Modeling and analysis of stream -of -variation in multistage manufacturing processes.. Deep Blue (University of Michigan). 1 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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