Shiyuan Fu

483 total citations
22 papers, 344 citations indexed

About

Shiyuan Fu is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Shiyuan Fu has authored 22 papers receiving a total of 344 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 7 papers in Computer Networks and Communications and 7 papers in Signal Processing. Recurrent topics in Shiyuan Fu's work include Anomaly Detection Techniques and Applications (15 papers), Time Series Analysis and Forecasting (7 papers) and Imbalanced Data Classification Techniques (7 papers). Shiyuan Fu is often cited by papers focused on Anomaly Detection Techniques and Applications (15 papers), Time Series Analysis and Forecasting (7 papers) and Imbalanced Data Classification Techniques (7 papers). Shiyuan Fu collaborates with scholars based in China and Hong Kong. Shiyuan Fu's co-authors include Rui Yu, Zhonghe Shui, Xin Gao, Bing Xue, Qiulei Song, Dingqiang Fan, Chunyuan Jiang, Liang Yue, Kangning Liu and Zijian Huang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Construction and Building Materials and Expert Systems with Applications.

In The Last Decade

Shiyuan Fu

20 papers receiving 338 citations

Peers

Shiyuan Fu

CSE

CNC

Junping Zhang China

Héctor Guerrero Mexico

Linjun Yan United States

J.P.B. Leite United Kingdom

Abdellah Jamali Morocco

Ehsan Jahani Iran

Kaustav Basu France

Phuong Nguyen Thanh Vietnam

Simon Letzgus Germany

Junping Zhang China

Shiyuan Fu

282 ×1.5

CSE

27 ×0.2

179 ×2.1

4 ×0.1

7 ×0.1

CNC

Citations per year, relative to Shiyuan Fu Shiyuan Fu (= 1×) peers Junping Zhang

Countries citing papers authored by Shiyuan Fu

Since Specialization

Citations

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

Fields of papers citing papers by Shiyuan Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shiyuan Fu

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

All Works

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20 of 20 papers shown

Lv, Wei, Enlai Dong, Shiyuan Fu, et al.. (2025). Stepping into the era of all-solid waste and low carbon: Phosphogypsum and other solid wastes cooperatively construct green buildings. Construction and Building Materials. 486. 141926–141926. 3 indexed citations

Fu, Shiyuan, et al.. (2024). Multivariate time series anomaly detection via separation, decomposition, and dual transformer-based autoencoder. Applied Soft Computing. 159. 111671–111671. 7 indexed citations

Xue, Bing, et al.. (2024). A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection. Neural Networks. 177. 106395–106395. 3 indexed citations

Lv, Wei, Enlai Dong, Shiyuan Fu, et al.. (2024). Regulating sulfur migration and transformation in low water-binder ratio cementitious system incorporating phosphogypsum aggregate: Environmentally friendly clean materials. Journal of Building Engineering. 91. 109586–109586. 13 indexed citations

Gao, Xin, et al.. (2023). An adversarial contrastive autoencoder for robust multivariate time series anomaly detection. Expert Systems with Applications. 245. 123010–123010. 17 indexed citations

Fu, Shiyuan, et al.. (2023). A time series anomaly detection method based on series-parallel transformers with spatial and temporal association discrepancies. Information Sciences. 657. 119978–119978. 14 indexed citations

Gao, Xin, et al.. (2023). A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection. Neural Networks. 170. 478–493. 16 indexed citations

Xue, Bing, Xin Gao, Shuwei Zhang, et al.. (2023). A feature-level mask self-supervised assisted learning approach based on transformer for remaining useful life prediction. Intelligent Data Analysis. 28(1). 217–237.

Dong, Enlai, Shiyuan Fu, Wei Lv, et al.. (2023). Value-added utilization of phosphogypsum industrial by-products in producing green Ultra-High performance Concrete: Detailed reaction kinetics and microstructure evolution mechanism. Construction and Building Materials. 389. 131726–131726. 64 indexed citations

10.

Xue, Bing, et al.. (2023). A contrastive autoencoder with multi-resolution segment-consistency discrimination for multivariate time series anomaly detection. Applied Intelligence. 53(23). 28655–28674. 3 indexed citations

11.

Li, Chao, et al.. (2023). Requirement-Oriented TT&C Method for Satellite Based on BDS-3 Short-Message Communication System. SHILAP Revista de lepidopterología. 3. 3 indexed citations

12.

Liu, Zhiyu, Xin Gao, Xin Jia, et al.. (2022). Correlation-based feature partition regression method for unsupervised anomaly detection. Applied Intelligence. 52(13). 15074–15090. 4 indexed citations

13.

Li, Chao, et al.. (2022). Access once encountered TT&C mode based on space–air–ground integration network. Open Astronomy. 31(1). 390–404.

14.

Gao, Xin, Xin Jia, Bing Xue, et al.. (2022). Detection of local and clustered outliers based on the density–distance decision graph. Engineering Applications of Artificial Intelligence. 110. 104719–104719. 20 indexed citations

15.

Huang, Zijian, et al.. (2022). An imbalanced binary classification method via space mapping using normalizing flows with class discrepancy constraints. Information Sciences. 623. 493–523. 4 indexed citations

16.

Jia, Xin, Xin Gao, Wenli Chen, et al.. (2022). Global reliable data generation for imbalanced binary classification with latent codes reconstruction and feature repulsion. Applied Intelligence. 53(13). 16922–16960. 1 indexed citations

17.

Gao, Xin, Shiyuan Fu, Bing Xue, et al.. (2022). An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss. Knowledge-Based Systems. 258. 110003–110003. 7 indexed citations

18.

Fu, Shiyuan, Xin Gao, Baofeng Li, et al.. (2022). Two Outlier-Sensitive Measures for Semi-supervised Dynamic Ensemble Anomaly Detection Models. Neural Processing Letters. 55(3). 3429–3470. 1 indexed citations

19.

Zhang, Guangyao, Xin Gao, Lei Wang, et al.. (2022). Probabilistic autoencoder with multi-scale feature extraction for multivariate time series anomaly detection. Applied Intelligence. 53(12). 15855–15872. 6 indexed citations

20.

Fan, Dingqiang, Rui Yu, Shiyuan Fu, et al.. (2021). Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques. Cement and Concrete Composites. 122. 104171–104171. 117 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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