D. Yang

60 papers receiving 1.4k citations

Peers

D. Yang
Comparison fields: 5 of 108
  • Signal Processing 352
  • Metals and Alloys 83
  • Information Systems 540
  • Computer Networks and Communications 467
  • Artificial Intelligence 603
Replace Yaohui Jin with:
Yaohui Jin China
Lidong Wang China
Minghao Zhao China
Rinku Dewri United States
Tao Yue Norway
Shingo Yamaguchi Japan
Sheng Xiao China
Gigliola Vaglini Italy
Christian Prehofer Germany
Wen-Chih Peng Taiwan
D. Yang relative to Yaohui Jin China Yaohui Jin's profile →
Citations per field
00.5×10×13.8×
Yaohui Jin · 1×
Citations per year

Countries citing papers authored by D. Yang

Since Specialization
Citations

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

Fields of papers citing papers by D. Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside D. Yang, 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 D. Yang Line = papers co-authored together D. Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1986458
2 2002244
3 2015111
4 201989
5 200758
6 202049
7 201638
8 201137
9 202036
10 201030
11 202127
12 200826
13 200825
14 202221
15 201319
16 200618
17 202117
18 202116
19 201816
20 202015

About D. Yang

D. Yang is a scholar working on Mechanical Engineering, Computer Networks and Communications, Information Systems, Artificial Intelligence and Signal Processing, having authored 70 papers that have together received 1.6k indexed citations. Recurring topics across this work include Welding Techniques and Residual Stresses (18 papers), Additive Manufacturing Materials and Processes (17 papers), Advanced Database Systems and Queries (14 papers), Data Management and Algorithms (13 papers), Data Mining Algorithms and Applications (10 papers), Rough Sets and Fuzzy Logic (8 papers), High Entropy Alloys Studies (6 papers) and Advanced Computational Techniques and Applications (5 papers). The work is most often cited by research in Signal Processing (352 citations), Metals and Alloys (83 citations), Information Systems (540 citations), Computer Networks and Communications (467 citations) and Artificial Intelligence (603 citations). D. Yang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include James P. Fry, Toby J. Teorey, Shiwei Tang, Guangjun Zhang, Jian Pei, Hongjun Lü, Shojiro Nishio, Kehong Wang, Jiawei Han and Tengjiao Wang. Their work appears in journals such as Journal of Manufacturing Processes, Materials Science and Engineering A, Journal of Materials Engineering and Performance, Journal of Materials Research and Technology and Journal of Materials Processing Technology.

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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