Bing Long

61 papers receiving 1.1k citations

Peers

Bing Long
Comparison fields: 5 of 69
  • Hardware and Architecture 386
  • Automotive Engineering 391
  • Safety, Risk, Reliability and Quality 248
  • Control and Systems Engineering 525
  • Software 50
Replace Sankalita Saha with:
Sankalita Saha United States
Scott Poll United States
R.P. Jones United Kingdom
Junyou Shi China
Carl S. Byington United States
W. M. Eden Hong Kong
Zhen Jia China
Mohammad Pirani Canada
Bing Long relative to Sankalita Saha United States Sankalita Saha's profile →
Citations per field
00.5×9.9×
Sankalita Saha · 1×
Citations per year

Countries citing papers authored by Bing Long

Since Specialization
Citations

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

Fields of papers citing papers by Bing Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 71 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013196
2 2012144
3 2013120
4 201483
5 201063
6 201347
7 200846
8 201246
9 202236
10 201035
11 201933
12 201032
13 202124
14 201123
15 200822
16 201421
17 201221
18 201021
19 201318
20 202017

About Bing Long

Bing Long is a scholar working on Hardware and Architecture, Software, Control and Systems Engineering, Safety, Risk, Reliability and Quality and Industrial and Manufacturing Engineering, having authored 71 papers that have together received 1.2k indexed citations. Recurring topics across this work include VLSI and Analog Circuit Testing (32 papers), Integrated Circuits and Semiconductor Failure Analysis (23 papers), Engineering and Test Systems (20 papers), Fault Detection and Control Systems (10 papers), Reliability and Maintenance Optimization (7 papers), Industrial Vision Systems and Defect Detection (7 papers), Advanced Battery Technologies Research (6 papers) and Software Testing and Debugging Techniques (6 papers). The work is most often cited by research in Hardware and Architecture (386 citations), Automotive Engineering (391 citations), Safety, Risk, Reliability and Quality (248 citations), Control and Systems Engineering (525 citations) and Software (50 citations). Bing Long has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Houjun Wang, Shulin Tian, Zhen Liu, Min Li, Michael Pecht, Chenglin Yang, Arvind Sai Sarathi Vasan, Lin Jiang, Jianguo Huang and Fang Chen. Their work appears in journals such as IEEE Transactions on Instrumentation and Measurement, Electronics, International Journal of Advancements in Computing Technology, Microelectronics Reliability and Measurement.

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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