Jin‐Song Pei

787 total citations
54 papers, 586 citations indexed

About

Jin‐Song Pei is a scholar working on Civil and Structural Engineering, Statistical and Nonlinear Physics and Control and Systems Engineering. According to data from OpenAlex, Jin‐Song Pei has authored 54 papers receiving a total of 586 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Civil and Structural Engineering, 17 papers in Statistical and Nonlinear Physics and 16 papers in Control and Systems Engineering. Recurrent topics in Jin‐Song Pei's work include Structural Health Monitoring Techniques (21 papers), Model Reduction and Neural Networks (16 papers) and Neural Networks and Applications (10 papers). Jin‐Song Pei is often cited by papers focused on Structural Health Monitoring Techniques (21 papers), Model Reduction and Neural Networks (16 papers) and Neural Networks and Applications (10 papers). Jin‐Song Pei collaborates with scholars based in United States, France and Singapore. Jin‐Song Pei's co-authors include Andrew W. Smyth, Joseph P. Wright, Sami F. Masri, Tat‐Seng Lok, Elias B. Kosmatopoulos, Michael D. Todd, François Gay–Balmaz, Walter Lacarbonara, David Wagg and Biagio Carboni and has published in prestigious journals such as Applied Physics Letters, Computer Methods in Applied Mechanics and Engineering and Journal of Applied Mechanics.

In The Last Decade

Jin‐Song Pei

53 papers receiving 550 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jin‐Song Pei United States 12 359 121 100 95 92 54 586
Houpu Yao United States 10 177 0.5× 72 0.6× 134 1.3× 83 0.9× 21 0.2× 21 560
Tehuan Chen China 13 92 0.3× 245 2.0× 89 0.9× 23 0.2× 46 0.5× 54 516
Morteza Mohammadzaheri Australia 18 114 0.3× 429 3.5× 240 2.4× 10 0.1× 71 0.8× 89 800
Amir Nasrollahi United States 13 231 0.6× 25 0.2× 73 0.7× 61 0.6× 35 0.4× 34 442
Pan Fang China 17 280 0.8× 202 1.7× 148 1.5× 253 2.7× 34 0.4× 79 783
Mohammad Mahdi Khatibi Iran 12 142 0.4× 84 0.7× 105 1.1× 25 0.3× 8 0.1× 36 426
Z. Hou United States 14 715 2.0× 168 1.4× 169 1.7× 31 0.3× 24 0.3× 25 1.1k
Jian Xing China 11 368 1.0× 69 0.6× 154 1.5× 7 0.1× 22 0.2× 40 590
M. R. Ashory Iran 11 229 0.6× 153 1.3× 119 1.2× 32 0.3× 5 0.1× 42 486
Ruijin Cang United States 5 134 0.4× 15 0.1× 137 1.4× 27 0.3× 23 0.3× 7 448

Countries citing papers authored by Jin‐Song Pei

Since Specialization
Citations

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

Fields of papers citing papers by Jin‐Song Pei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin‐Song Pei. 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 Jin‐Song Pei. The network helps show where Jin‐Song Pei may publish in the future.

Co-authorship network of co-authors of Jin‐Song Pei

This figure shows the co-authorship network connecting the top 25 collaborators of Jin‐Song Pei. A scholar is included among the top collaborators of Jin‐Song Pei 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 Jin‐Song Pei. Jin‐Song Pei 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.
Pei, Jin‐Song, Joseph P. Wright, Gerald A. Miller, François Gay–Balmaz, & Marco B. Quadrelli. (2024). Mem-modeling of strain ratcheting using early-time soil fatigue data. Nonlinear Dynamics. 113(9). 9189–9215.
2.
Wright, Joseph P., Stephen A. Sarles, & Jin‐Song Pei. (2023). DC operating points of Mott neuristor circuits. Microelectronic Engineering. 284-285. 112124–112124. 2 indexed citations
3.
Beck, James L. & Jin‐Song Pei. (2022). Demonstrating the power of extended Masing models for hysteresis through model equivalencies and numerical investigation. Nonlinear Dynamics. 108(2). 827–856. 6 indexed citations
4.
Hougen, Dean F., et al.. (2020). Toward Interpretable Machine Learning for Understanding Epidemic Data. 3677–3681. 1 indexed citations
5.
Wagg, David & Jin‐Song Pei. (2020). Modeling a helical fluid inerter system with time‐invariant mem‐models. Structural Control and Health Monitoring. 27(10). 13 indexed citations
6.
Floyd, Royce W., et al.. (2019). Experimental Testing of Older AASHTO Type II Bridge Girders with Corrosion Damage at the Ends. PCI Journal. 64(1). 10 indexed citations
7.
Brewick, Patrick T., et al.. (2018). Fusing State-Space and Data-Driven Strategies for Computational Shock Response Prediction. AIAA Journal. 56(6). 2308–2321. 1 indexed citations
8.
Pei, Jin‐Song, et al.. (2012). Mapping some basic functions and operations to multilayer feedforward neural networks for modeling nonlinear dynamical systems and beyond. Nonlinear Dynamics. 71(1-2). 371–399. 21 indexed citations
9.
10.
Lynch, Jerome P., et al.. (2011). Distributed neural computations for embedded sensor networks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7981. 79811U–79811U. 1 indexed citations
11.
Jones, Jonathan D., et al.. (2010). Embedded EMD algorithm within an FPGA-based design to classify nonlinear SDOF systems. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7647. 76470E–76470E. 3 indexed citations
12.
Jones, Jonathan D. & Jin‐Song Pei. (2009). Embedded Algorithms Within an FPGA to Classify Nonlinear Single-Degree-of-Freedom Systems. IEEE Sensors Journal. 9(11). 1486–1493. 7 indexed citations
13.
14.
Pei, Jin‐Song, et al.. (2008). ConstructingMultilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering MechanicsApplications. Journal of Applied Mechanics. 75(6). 18 indexed citations
15.
Pei, Jin‐Song, et al.. (2008). Mapping some functions and four arithmetic operations to multilayer feedforward neural networks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6935. 693512–693512. 4 indexed citations
16.
Pei, Jin‐Song, et al.. (2007). An experimental investigation of the data delivery performance of a wireless sensing unit designed for structural health monitoring. Structural Control and Health Monitoring. 15(4). 471–504. 10 indexed citations
17.
Pei, Jin‐Song, et al.. (2005). <title>Investigation of data quality in a wireless sensing unit composed of off-the-shelf components</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5768. 118–128. 1 indexed citations
18.
Pei, Jin‐Song, et al.. (2005). Development of an off-the-shelf field programmable gate array-based wireless sensing unit for structural health monitoring. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5765. 195–195. 2 indexed citations
19.
Pei, Jin‐Song & Andrew W. Smyth. (2003). More transparent neural network approach for modeling nonlinear hysteretic systems. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5057. 516–516. 2 indexed citations
20.
Smyth, Andrew W., Jin‐Song Pei, & Sami F. Masri. (2003). System identification of the Vincent Thomas suspension bridge using earthquake records. Earthquake Engineering & Structural Dynamics. 32(3). 339–367. 72 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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