Ping Wu

1.1k total citations
74 papers, 760 citations indexed

About

Ping Wu is a scholar working on Control and Systems Engineering, Mechanical Engineering and Analytical Chemistry. According to data from OpenAlex, Ping Wu has authored 74 papers receiving a total of 760 indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Control and Systems Engineering, 34 papers in Mechanical Engineering and 15 papers in Analytical Chemistry. Recurrent topics in Ping Wu's work include Fault Detection and Control Systems (40 papers), Mineral Processing and Grinding (23 papers) and Spectroscopy and Chemometric Analyses (15 papers). Ping Wu is often cited by papers focused on Fault Detection and Control Systems (40 papers), Mineral Processing and Grinding (23 papers) and Spectroscopy and Chemometric Analyses (15 papers). Ping Wu collaborates with scholars based in China, Netherlands and Canada. Ping Wu's co-authors include Siwei Lou, Jinfeng Gao, Yichao Liu, Chunjie Yang, Xujie Zhang, Jiajun He, Riccardo Ferrari, Jan‐Willem van Wingerden, Jiaqi Wang and Lingling Guo and has published in prestigious journals such as Scientific Reports, Optics Express and IEEE Access.

In The Last Decade

Ping Wu

64 papers receiving 739 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ping Wu China	15	449	296	104	75	75	74	760
Jinglin Zhou China	20	629 1.4×	246 0.8×	151 1.5×	47 0.6×	155 2.1×	94	887
Arun K. Tangirala India	16	682 1.5×	150 0.5×	120 1.2×	166 2.2×	85 1.1×	89	1.1k
Torsten Jeinsch Germany	14	1.0k 2.2×	182 0.6×	92 0.9×	51 0.7×	34 0.5×	129	1.3k
Alireza Fatehi Iran	18	1.0k 2.3×	189 0.6×	275 2.6×	119 1.6×	55 0.7×	114	1.3k
Xun Lang China	18	517 1.2×	150 0.5×	116 1.1×	97 1.3×	54 0.7×	64	824
Sofiane Khadraoui United Arab Emirates	16	491 1.1×	150 0.5×	155 1.5×	213 2.8×	24 0.3×	67	871
Krzysztof Patan Poland	14	647 1.4×	183 0.6×	198 1.9×	48 0.6×	17 0.2×	63	787
Shaoyuan Sun China	14	225 0.5×	127 0.4×	104 1.0×	69 0.9×	24 0.3×	49	828
Ali Chaibakhsh Iran	13	393 0.9×	168 0.6×	74 0.7×	121 1.6×	15 0.2×	62	727
Jiongqi Wang China	14	291 0.6×	127 0.4×	214 2.1×	84 1.1×	26 0.3×	79	600

Countries citing papers authored by Ping Wu

Since Specialization

Citations

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

Fields of papers citing papers by Ping Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ping Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Ping Wu. A scholar is included among the top collaborators of Ping Wu 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 Ping Wu. Ping Wu 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

Wu, Ping, Ke Wang, Jinfeng Gao, et al.. (2025). FA-SconvAE-LSTM: Feature-Aligned Stacked Convolutional Autoencoder with Long Short-Term Memory Network for Soft Sensor Modeling. Engineering Applications of Artificial Intelligence. 150. 110535–110535. 3 indexed citations

Miao, Zhiwei, et al.. (2025). GRU‐based stacked sparse autoencoder with attention mechanism for process monitoring. The Canadian Journal of Chemical Engineering. 103(8). 3767–3785. 1 indexed citations

Lou, Siwei, et al.. (2025). Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP. IEEE/CAA Journal of Automatica Sinica. 12(5). 894–912. 1 indexed citations

He, Yuchen, Xueqin Yang, Lijuan Qian, et al.. (2025). Soft Sensing for Time Series With Irregular Sampling Internals Based on a Denoising Interval Attention LSTM Network. IEEE Transactions on Neural Networks and Learning Systems. 36(12). 20133–20145.

Wu, Ping, et al.. (2024). Fiber Bragg grating sensing multiplexing method using photonic lantern spatial mode diversity. Optics Communications. 574. 131118–131118. 1 indexed citations

Li, Xiaojun, et al.. (2024). VMD-SE-DCNN-CBAM: An Intelligent Cavitation Recognition Method for Canned Motor Pump. IEEE Transactions on Instrumentation and Measurement. 74. 1–13. 7 indexed citations

Lou, Siwei, et al.. (2024). TKS-BLS: Temporal Kernel Stationary Broad Learning System for Enhanced Modeling, Anomaly Detection, and Incremental Learning With Application to Ironmaking Processes. IEEE Transactions on Systems Man and Cybernetics Systems. 55(1). 645–660. 1 indexed citations

Zhang, Xi, Yaochu Jin, Ping Wu, & Qiqi Liu. (2024). Dynamic Multi-Objective Home Appliance Scheduling Using Transfer Learning Based Evolutionary Algorithm. 438–444. 1 indexed citations

Lou, Siwei, Ping Wu, Chunjie Yang, & Yonghong Jade Xu. (2023). Structured fault information-aided canonical variate analysis model for dynamic process monitoring. Journal of Process Control. 124. 54–69. 13 indexed citations

10.

Hu, Xuguang, Ping Wu, Haipeng Pan, & Yuchen He. (2023). A fault detection method based on sparse dynamic canonical correlation analysis. The Canadian Journal of Chemical Engineering. 102(3). 1188–1202. 2 indexed citations

11.

Wu, Ping, et al.. (2023). SSAE‐KPLS : A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares. The Canadian Journal of Chemical Engineering. 101(10). 5858–5873. 3 indexed citations

12.

Wang, Ke, et al.. (2023). Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process. Electronics. 12(23). 4773–4773.

13.

Wu, Ping, et al.. (2022). Data-driven fault detection of a 10 MW floating offshore wind turbine benchmark using kernel canonical variate analysis. Measurement Science and Technology. 34(3). 34001–34001. 6 indexed citations

14.

Wu, Ping, et al.. (2021). Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM. Measurement Science and Technology. 33(3). 34001–34001. 26 indexed citations

15.

Wang, Xuemei, Ping Wu, & Siwei Lou. (2021). Quality-Relevant Process Monitoring Based on Improved Concurrent Canonical Correlation Analysis. 565–570.

16.

Wang, Jiaqi, Jinfeng Gao, & Ping Wu. (2021). Attack-resilient event-triggered formation control of multi-agent systems under periodic DoS attacks using complex Laplacian. ISA Transactions. 128(Pt A). 10–16. 41 indexed citations

17.

Wu, Ping, Daniel Domingo‐Fernándéz, Henri A. Vrooman, et al.. (2020). Clustering of Alzheimer’s and Parkinson’s disease based on genetic burden of shared molecular mechanisms. Scientific Reports. 10(1). 19097–19097. 12 indexed citations

18.

Yang, Chunjie, et al.. (2014). Active surge control for variable speed axial compressors. ISA Transactions. 53(5). 1389–1395. 11 indexed citations

19.

Wu, Ping, et al.. (2011). Application of least squares support vector machine to harmonic sources modeling based on genetic algorithm. Power System Protection and Control.

20.

Wu, Ping. (2007). Edge Drop and Flatness Decoupling Control on UCMW Cold Mill. Ironmaking & Steelmaking Processes Products and Applications. 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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