Zhongyang Han

33 papers receiving 849 citations

Hit Papers

A Review of Deep Learning Models for Time Series Prediction 2019 · 314 citations
3142019202620212023100200300

Peers

Zhongyang Han
Comparison fields: 5 of 110
  • Signal Processing 101
  • Artificial Intelligence 270
  • Management Science and Operations Research 104
  • Computational Theory and Mathematics 124
  • Control and Systems Engineering 163
Replace İbrahim Berkan Aydilek with:
İbrahim Berkan Aydilek Türkiye
Jinsha Yuan China
Tinghui Ouyang China
Juan Zhao China
Yong Qi China
Jian Guo China
Russell C. Eberhart United States
Guangyu Li China
Qun Dai China
Zhongyang Han relative to İbrahim Berkan Aydilek Türkiye İbrahim Berkan Aydilek's profile →
Citations per field
00.5×2.6×
İbrahim Berkan Aydilek · 1×
Citations per year

Countries citing papers authored by Zhongyang Han

Since Specialization
Citations

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

Fields of papers citing papers by Zhongyang Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Zhongyang Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zhongyang Han Line = papers co-authored together Zhongyang Han links everyone, so they are left out of the graph.

All Works

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A Review of Deep Learning Models for Time Series Prediction
Hit paper breakdown →
2019314
18 201631
19 201628
20
Hybrid use of steel and carbon-fiber reinforced concrete for monitoring of crack behavior
20128

About Zhongyang Han

Zhongyang Han is a scholar working on Nuclear Energy and Engineering, General Energy, Computational Theory and Mathematics, Aging and Control and Systems Engineering, having authored 35 papers that have together received 867 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Rough Sets and Fuzzy Logic (7 papers), Process Optimization and Integration (5 papers), Integrated Energy Systems Optimization (4 papers), Fault Detection and Control Systems (4 papers), Energy Load and Power Forecasting (4 papers), Metaheuristic Optimization Algorithms Research (4 papers) and Advanced Multi-Objective Optimization Algorithms (3 papers). The work is most often cited by research in Signal Processing (101 citations), Artificial Intelligence (270 citations), Management Science and Operations Research (104 citations), Computational Theory and Mathematics (124 citations) and Control and Systems Engineering (163 citations). Zhongyang Han has collaborated with scholars based in China, Canada and Poland. Frequent co-authors include Jun Zhao, Wei Wang, Henry Leung, King Ma, Linqing Wang, Witold Pedrycz, Ying Liu, Wei Wang, Quanli Liu and Wei Wang. Their work appears in journals such as Control Engineering Practice, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Cybernetics, Information Sciences and IEEE Transactions on Industrial Electronics.

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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