H. Y. Dai

2.6k total citations
25 papers, 958 citations indexed

About

H. Y. Dai is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Astronomy and Astrophysics. According to data from OpenAlex, H. Y. Dai has authored 25 papers receiving a total of 958 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Nuclear and High Energy Physics, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Astronomy and Astrophysics. Recurrent topics in H. Y. Dai's work include Dark Matter and Cosmic Phenomena (13 papers), Astrophysics and Cosmic Phenomena (12 papers) and Particle Detector Development and Performance (3 papers). H. Y. Dai is often cited by papers focused on Dark Matter and Cosmic Phenomena (13 papers), Astrophysics and Cosmic Phenomena (12 papers) and Particle Detector Development and Performance (3 papers). H. Y. Dai collaborates with scholars based in United States, China and Australia. H. Y. Dai's co-authors include E. C. Loh, J. W. Elbert, P. Sommers, J. K. K. Tang, D. J. Bird, S. C. Corbató, C. G. Larsen, M. H. Salamon, M. Huang and P. Sokolsky and has published in prestigious journals such as The Astrophysical Journal, Remote Sensing and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

H. Y. Dai

20 papers receiving 908 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
H. Y. Dai United States	11	882	396	72	46	16	25	958
M. Brienza Italy	19	713 0.8×	906 2.3×	19 0.3×	15 0.3×	4 0.3×	55	947
Isabella P. Carucci Italy	11	280 0.3×	442 1.1×	23 0.3×	16 0.3×	9 0.6×	18	479
Amir Hajian United States	12	175 0.2×	416 1.1×	37 0.5×	27 0.6×	18 1.1×	29	478
Lloyd Knox United States	8	446 0.5×	747 1.9×	30 0.4×	6 0.1×	8 0.5×	10	774
Hayato Shimabukuro Japan	9	223 0.3×	332 0.8×	24 0.3×	12 0.3×	12 0.8×	17	361
Sultan Hassan United States	12	164 0.2×	341 0.9×	17 0.2×	17 0.4×	9 0.6×	24	363
Alexandre Amblard United States	12	186 0.2×	468 1.2×	15 0.2×	8 0.2×	4 0.3×	22	483
T. Herbig United States	8	312 0.4×	499 1.3×	40 0.6×	7 0.2×		14	514
José Fonseca United Kingdom	13	154 0.2×	395 1.0×	15 0.2×	15 0.3×	8 0.5×	27	431
M. López-Caniego Spain	13	229 0.3×	320 0.8×	10 0.1×	13 0.3×	4 0.3×	39	353

Countries citing papers authored by H. Y. Dai

Since Specialization

Citations

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

Fields of papers citing papers by H. Y. Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by H. Y. Dai. 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 H. Y. Dai. The network helps show where H. Y. Dai may publish in the future.

Co-authorship network of co-authors of H. Y. Dai

This figure shows the co-authorship network connecting the top 25 collaborators of H. Y. Dai. A scholar is included among the top collaborators of H. Y. Dai 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 H. Y. Dai. H. Y. Dai 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

Dai, H. Y., et al.. (2025). Cooperative push-grasping in clutter via deep reinforcement learning. Intelligent Service Robotics. 18(4). 723–743.

Zhao, Shengjie, et al.. (2024). AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding. Remote Sensing. 16(21). 4103–4103.

Dai, H. Y., et al.. (2024). Memory-Efficient Batch Normalization by One-Pass Computation for On-Device Training. IEEE Transactions on Circuits & Systems II Express Briefs. 71(6). 3186–3190.

Ding, Fengzhi, et al.. (2024). Mechanisms of pathogenicity in the hypertrophic cardiomyopathy-associated TNNI3 c.235C > T variant. International Journal of Cardiology. 419. 132627–132627.

Wang, Jiaqi, H. Y. Dai, Chong Chen, et al.. (2021). Relationship between lung function impairment, hypertension, and major adverse cardiovascular events: A 10‐year follow‐up study. Journal of Clinical Hypertension. 23(10). 1930–1938. 5 indexed citations

Dai, H. Y., Xuchong Zhang, Yongli Zhao, Hongbin Sun, & Nanning Zheng. (2021). Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching. IEEE Transactions on Circuits and Systems for Video Technology. 32(5). 3099–3110. 27 indexed citations

Yang, Dongyuan, et al.. (2001). RESEARCH ON STRATEGIC PLANNING OF INTELLIGENT TRANSPORTATION SYSTEM. Journal of Tongji University. 1 indexed citations

Dai, H. Y., E. C. Loh, & P. Sokolsky. (1998). The OWL detector: Aperture and resolution. 382–389. 1 indexed citations

Ding, Lei, Q. Q. Zhu, H. Y. Dai, et al.. (1997). Reexamination of Cosmic‐Ray Composition around 10¹⁸eV from Fly's Eye Data. The Astrophysical Journal. 474(1). 490–495. 11 indexed citations

10.

Dawson, B. R., H. Y. Dai, P. Sommers, & S. Yoshida. (1996). Simulations of a giant hybrid air shower detector. Astroparticle Physics. 5(3-4). 239–247. 13 indexed citations

11.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1995). Results from the fly’s eye experiment. AIP conference proceedings. 839–854. 2 indexed citations

12.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1995). Detection of a cosmic ray with measured energy well beyond the expected spectral cutoff due to cosmic microwave radiation. The Astrophysical Journal. 441. 144–144. 325 indexed citations

13.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1994). The cosmic-ray energy spectrum observed by the Fly's Eye. The Astrophysical Journal. 424. 491–491. 304 indexed citations

14.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1994). The calibration of the absolute sensitivity of photomultiplier tubes in the high resolution Fly's eye detector. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 349(2-3). 592–599. 9 indexed citations

15.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1993). The Fly's Eye Extremely High Energy Cosmic Ray Spectrum. ICRC. 2. 34. 1 indexed citations

16.

Gaisser, T. K., Todor Stanev, Serap Tilav, et al.. (1993). Cosmic-ray composition around1018eV. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 47(5). 1919–1932. 65 indexed citations

17.

Bird, D. J., S. C. Corbató, H. Y. Dai, et al.. (1993). The Cosmic Ray Composition Above 0.1 EeV. 2. 38.

18.

Nagano, M., M. Teshima, Y. Matsubara, et al.. (1992). Energy spectrum of primary cosmic rays above 10^17.0eV determined from extensive air shower experiments at Akeno. Journal of Physics G Nuclear and Particle Physics. 18(2). 423–442. 99 indexed citations

19.

Hazen, W. E., H. Y. Dai, & E. Hazen. (1989). Study of a mini-array for the Linsley effect in cosmic-ray air showers. Journal of Physics G Nuclear and Particle Physics. 15(1). 113–120. 3 indexed citations

20.

Dai, H. Y., K. Kasahara, Y. Matsubara, M. Nagano, & M. Teshima. (1988). On the energy estimation of ultra-high-energy cosmic rays observed with the surface detector array. Journal of Physics G Nuclear Physics. 14(6). 793–805. 25 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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