Ruru Pan

1.8k total citations
131 papers, 1.3k citations indexed

About

Ruru Pan is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Polymers and Plastics. According to data from OpenAlex, Ruru Pan has authored 131 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Industrial and Manufacturing Engineering, 75 papers in Computer Vision and Pattern Recognition and 65 papers in Polymers and Plastics. Recurrent topics in Ruru Pan's work include Industrial Vision Systems and Defect Detection (95 papers), Textile materials and evaluations (65 papers) and Optical measurement and interference techniques (32 papers). Ruru Pan is often cited by papers focused on Industrial Vision Systems and Defect Detection (95 papers), Textile materials and evaluations (65 papers) and Optical measurement and interference techniques (32 papers). Ruru Pan collaborates with scholars based in China, United States and Ghana. Ruru Pan's co-authors include Weidong Gao, Jun Xiang, Jie Zhang, Jihong Liu, Jingan Wang, Lei Wang, Hongbo Wang, Dandan Zhu, Jian Zhou and Jihong Liu and has published in prestigious journals such as Advanced Functional Materials, IEEE Transactions on Image Processing and Expert Systems with Applications.

In The Last Decade

Ruru Pan

120 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ruru Pan China	19	842	626	538	191	186	131	1.3k
Weidong Gao China	21	844 1.0×	610 1.0×	780 1.4×	182 1.0×	187 1.0×	169	1.7k
Anindya Ghosh India	14	134 0.2×	30 0.0×	420 0.8×	16 0.1×	32 0.2×	73	603
Ming‐Shyan Huang Taiwan	26	556 0.7×	19 0.0×	103 0.2×	10 0.1×	45 0.2×	83	1.7k
A. García‐Alonso Spain	15	118 0.1×	190 0.3×	11 0.0×	75 0.4×	35 0.2×	58	879
Chenming Wu China	16	183 0.2×	172 0.3×	97 0.2×	7 0.0×	55 0.3×	58	994
Yi-Ming Chang Taiwan	16	41 0.0×	102 0.2×	164 0.3×	53 0.3×	22 0.1×	46	880
You Wu China	9	267 0.3×	178 0.3×	11 0.0×	53 0.3×	6 0.0×	29	580
Tarek Taha United Arab Emirates	13	50 0.1×	341 0.5×	126 0.2×	10 0.1×	8 0.0×	55	873
Koichi OZAKI Japan	16	31 0.0×	330 0.5×	27 0.1×	17 0.1×	34 0.2×	168	967

Countries citing papers authored by Ruru Pan

Since Specialization

Citations

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

Fields of papers citing papers by Ruru Pan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ruru Pan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Peng, Yangyang, Fengxin Sun, & Ruru Pan. (2025). Multiplexed Sensing Textiles Enabled by Reconfigurable Weaving Meso-Structures for Intricate Kinematic Posture Recognition and Thermal Therapy Healthcare. ACS Sensors. 10(4). 3051–3060. 3 indexed citations

Xiang, Jun, et al.. (2025). An Efficient Measurement Method for Fabric Defect Size Based on Multilight-Source Computer Vision. IEEE Transactions on Instrumentation and Measurement. 74. 1–15.

Wang, Lei, et al.. (2024). Characterizing fabric crease recovery through sequential image analysis. Measurement Science and Technology. 35(10). 105902–105902.

Peng, Yangyang, et al.. (2024). Switchable Pseudo‐Triaxial Structure Enabled Mechanosensory Textiles with Ultra‐Wide Detection Range for Flexible E‐Wearables. Advanced Functional Materials. 34(52). 5 indexed citations

Zhang, Ning, et al.. (2024). Toward accurate and realistic garment texture transfer with attention to details. Neural Computing and Applications. 36(21). 12991–13007. 1 indexed citations

Zhang, Xiaoting, Pengyu Zhao, Ruru Pan, & Weidong Gao. (2024). Plaid fabric image retrieval based on hand-crafted features and relevant feedback. Displays. 86. 102899–102899.

Wang, Xiaohu, et al.. (2023). Real-time textile fabric flaw inspection system using grouped sparse dictionary. Journal of Real-Time Image Processing. 20(4). 2 indexed citations

Yuan, Fang, Xin Ren, Ruru Pan, et al.. (2023). Tensile Construction Monitoring and Progressive Collapse Test of Suspen-Dome Structure Based on UAV-Assisted Close-Range Photogrammetry and Multi-Camera Stereo-Digital Image Correlation. Experimental Mechanics. 63(8). 1371–1389. 9 indexed citations

Gao, Weidong, et al.. (2022). Image recoloring of printed fabric based on the salient map and local color transfer. Textile Research Journal. 92(21-22). 4422–4436. 1 indexed citations

10.

Meng, Shuo, et al.. (2022). Automatic recognition of woven fabric structural parameters: a review. Artificial Intelligence Review. 55(8). 6345–6387. 11 indexed citations

11.

Wang, Lei, et al.. (2021). Appearance change for colored spun yarn fabric based on image color transfer. Textile Research Journal. 91(13-14). 1439–1451. 7 indexed citations

12.

Wang, Jingan, Lei Wang, Zhengxin Li, et al.. (2020). Automatic Assessment of Fabric Smoothness Appearance Based on a Compact Convolutional Neural Network With Label Smoothing. IEEE Access. 8. 26966–26974. 12 indexed citations

13.

Meng, Shuo, et al.. (2020). A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern. Journal of Intelligent Manufacturing. 32(4). 1147–1161. 32 indexed citations

14.

Wang, Jingan, Shuo Meng, Lei Wang, et al.. (2020). Objective evaluation of fabric smoothness appearance with an ordinal classification framework based on label noise estimation. Textile Research Journal. 91(3-4). 316–334. 4 indexed citations

15.

Meng, Shuo, et al.. (2020). Recognition of the layout of colored yarns in yarn-dyed fabrics. Textile Research Journal. 91(1-2). 100–114. 3 indexed citations

16.

Meng, Shuo, et al.. (2020). Weaving scheduling based on an improved ant colony algorithm. Textile Research Journal. 91(5-6). 543–554. 11 indexed citations

17.

Wang, Jingan, Lei Wang, Zhengxin Li, et al.. (2020). Fusing Convolutional Neural Network Features With Hand-Crafted Features for Objective Fabric Smoothness Appearance Assessment. IEEE Access. 8. 110678–110692. 3 indexed citations

18.

Meng, Shuo, et al.. (2019). Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks. IEEE Access. 7. 75810–75821. 18 indexed citations

19.

Pan, Ruru, et al.. (2015). Measuring Thread Densities of Woven Fabric Using the Fourier Transform. Fibres and Textiles in Eastern Europe. 7 indexed citations

20.

Pan, Ruru, Weidong Gao, Jihong Liu, & Hongbo Wang. (2010). Automatic Inspection of Woven Fabric Density of Solid Colour Fabric Density by the Hough Transform. Fibres and Textiles in Eastern Europe. 18 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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