Taichi Nakamura

1.4k total citations · 1 hit paper
70 papers, 970 citations indexed

About

Taichi Nakamura is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Taichi Nakamura has authored 70 papers receiving a total of 970 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Taichi Nakamura's work include Model Reduction and Neural Networks (6 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Software Engineering Techniques and Practices (5 papers). Taichi Nakamura is often cited by papers focused on Model Reduction and Neural Networks (6 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Software Engineering Techniques and Practices (5 papers). Taichi Nakamura collaborates with scholars based in Japan, United States and United Kingdom. Taichi Nakamura's co-authors include Koji Fukagata, Hiroyuki Kono, Kai Fukami, Kazuto Hasegawa, Hisaho Hashimoto, Yuuichi Shimizu, Kazuo Kondo, Hitoshi Sakano, Naoki Mukawa and Naoki Okamoto and has published in prestigious journals such as Journal of Biological Chemistry, Scientific Reports and Journal of Computational Physics.

In The Last Decade

Taichi Nakamura

63 papers receiving 944 citations

Hit Papers

Convolutional neural network and long short-term memory b... 2021 2026 2022 2024 2021 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
    2021 144 citations Taichi Nakamura, Kai Fukami et al. Physics of Fluids profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Taichi Nakamura Japan 15 288 248 112 100 99 70 970
Jin Li China 21 106 0.4× 65 0.3× 366 3.3× 104 1.0× 32 0.3× 110 1.4k
Adel Mhamdi Germany 21 121 0.4× 58 0.2× 14 0.1× 75 0.8× 68 0.7× 79 1.1k
Hooman Fatoorehchi Iran 21 99 0.3× 202 0.8× 6 0.1× 17 0.2× 171 1.7× 58 1.2k
Guangcai Zhang China 25 1.3k 4.4× 56 0.2× 64 0.6× 335 3.4× 79 0.8× 106 2.3k
Zhi Wang China 22 176 0.6× 41 0.2× 260 2.3× 73 0.7× 113 1.1× 120 1.8k
Fanghui Liu China 19 40 0.1× 28 0.1× 211 1.9× 55 0.6× 27 0.3× 72 1.1k
Zhao Zhang China 19 45 0.2× 97 0.4× 32 0.3× 56 0.6× 16 0.2× 102 1.1k
Mei Liu China 21 56 0.2× 49 0.2× 20 0.2× 168 1.7× 24 0.2× 79 1.4k
Li Lee Taiwan 15 73 0.3× 18 0.1× 14 0.1× 48 0.5× 242 2.4× 75 1.2k
Kon Max Wong Canada 23 96 0.3× 7 0.0× 81 0.7× 114 1.1× 381 3.8× 64 2.0k

Countries citing papers authored by Taichi Nakamura

Since Specialization
Citations

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

Fields of papers citing papers by Taichi Nakamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taichi Nakamura

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

All Works

20 of 20 papers shown
1.
Otomo, Ryoichi, Taichi Nakamura, & Yuichi Κamiya. (2024). Characterization of acid sites on Hf-Beta zeolite by CD3CN IR-TPD. Microporous and Mesoporous Materials. 378. 113246–113246. 1 indexed citations
2.
Nakamura, Taichi, et al.. (2024). Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks. SN Computer Science. 5(3). 4 indexed citations
3.
4.
Nakamura, Taichi, Yuichi Κamiya, & Ryoichi Otomo. (2022). A rapid synthesis of Hf-Beta zeolite as highly active catalyst for Meerwein-Ponndorf-Verley reduction by controlling water content of precursor gel. Microporous and Mesoporous Materials. 333. 111743–111743. 6 indexed citations
5.
Nakamura, Taichi, Kai Fukami, & Koji Fukagata. (2022). Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions. Scientific Reports. 12(1). 3726–3726. 22 indexed citations
6.
Kondo, Kazuaki, Taichi Nakamura, Yuichi Nakamura, & Shin’ichi Satoh. (2021). Siamese-structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling. 4605–4612. 2 indexed citations
7.
Nakamura, Taichi, et al.. (2021). Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow. Physics of Fluids. 33(2). 144 indexed citations breakdown →
8.
Fukami, Kai, Taichi Nakamura, & Koji Fukagata. (2020). Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data. Physics of Fluids. 32(9). 138 indexed citations
9.
Fukami, Kai, et al.. (2020). Model order reduction with neural networks: Application to laminar and turbulent flows. arXiv (Cornell University). 43 indexed citations
10.
Nakamura, Taichi. (2017). A Study on Total Serviceability Evaluation for Disaster Monitoring by Satellites (2nd Report). JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES. 65(3). 103–110. 1 indexed citations
11.
Maruyama, Hiroshi, et al.. (2012). An analysis of the relation between the behavior of a learner and acquired skill level in role-play training. 112(165). 67–72. 4 indexed citations
12.
13.
Kondo, Kazuo, et al.. (2007). Via Filling Electrodeposition by Using Periodic Reverse Pulse Current. Journal of The Surface Finishing Society of Japan. 58(4). 244–248. 2 indexed citations
14.
Nakamura, Taichi, Koichi Shinoda, & Sadaoki Furui. (2006). TokyoTech's TRECVID2006 Notebook. TRECVID. 1 indexed citations
15.
Nakamura, Taichi, et al.. (2003). A High Speed Distributed Video Transcoder for Multiple Rates and Formats. ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications. 921–924. 1 indexed citations
16.
Sohmura, Taiji, et al.. (2003). A new method to remove metal artifact of 3D-CT image. 22(2). 168. 1 indexed citations
17.
Nakamura, Taichi, Akio Tsuneda, & Takahiro Inoue. (2002). A New Stochastic Binary Neural Network Based on Hopfield Model and Its Application. ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications. 35–38. 3 indexed citations
18.
Nakamura, Taichi. (1991). Active pixel image sensors : Recent developments of the CMD. Medical Entomology and Zoology. 70–79. 1 indexed citations
19.
Maejima, T., et al.. (1989). A study of image recognition system. International Conference on Image Processing. 112–116. 2 indexed citations
20.
Nakamura, Taichi, et al.. (1987). A systematized approach to undercoat plating for high-density computer hard disk.. Journal of the Metal Finishing Society of Japan. 38(12). 554–560.

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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