Yoji Okabe

4.5k total citations
204 papers, 3.5k citations indexed

About

Yoji Okabe is a scholar working on Electrical and Electronic Engineering, Mechanics of Materials and Civil and Structural Engineering. According to data from OpenAlex, Yoji Okabe has authored 204 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Electrical and Electronic Engineering, 75 papers in Mechanics of Materials and 46 papers in Civil and Structural Engineering. Recurrent topics in Yoji Okabe's work include Advanced Fiber Optic Sensors (86 papers), Ultrasonics and Acoustic Wave Propagation (58 papers) and Photonic and Optical Devices (40 papers). Yoji Okabe is often cited by papers focused on Advanced Fiber Optic Sensors (86 papers), Ultrasonics and Acoustic Wave Propagation (58 papers) and Photonic and Optical Devices (40 papers). Yoji Okabe collaborates with scholars based in Japan, China and United States. Yoji Okabe's co-authors include Nobuo Takeda, Qi Wu, N. Takeda, Fengming Yu, Shin‐ichi Takeda, Kazuya Saito, Shu Minakuchi, Shigeki Yashiro, Toshimichi Ogisu and Tadahito Mizutani and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Applied Physics Letters.

In The Last Decade

Yoji Okabe

196 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoji Okabe Japan 32 1.9k 1.3k 954 922 526 204 3.5k
Marcelo J. Dapino United States 33 867 0.5× 578 0.4× 718 0.8× 2.1k 2.3× 635 1.2× 243 4.1k
Kara Peters United States 24 1.5k 0.8× 493 0.4× 435 0.5× 157 0.2× 386 0.7× 177 2.3k
Chaofeng Lü China 27 1.0k 0.5× 649 0.5× 470 0.5× 840 0.9× 1.9k 3.7× 120 3.2k
Fuh‐Gwo Yuan United States 39 745 0.4× 3.0k 2.3× 1.7k 1.8× 1.7k 1.9× 1.1k 2.1× 172 5.1k
Kon‐Well Wang United States 29 1.0k 0.5× 336 0.3× 1.7k 1.8× 2.8k 3.0× 1.8k 3.4× 119 4.0k
Alison B. Flatau United States 35 1.3k 0.7× 592 0.5× 1.1k 1.2× 2.4k 2.6× 976 1.9× 219 5.6k
Jinkyu Yang United States 38 643 0.3× 881 0.7× 1.2k 1.2× 1.9k 2.0× 1.8k 3.3× 140 4.4k
Shuncong Zhong China 20 713 0.4× 619 0.5× 347 0.4× 414 0.4× 430 0.8× 91 1.8k
John Lambros United States 41 496 0.3× 2.7k 2.0× 1.5k 1.6× 1.7k 1.8× 486 0.9× 138 5.3k
S.M. Spearing United States 54 2.5k 1.3× 4.3k 3.3× 1.3k 1.3× 2.6k 2.8× 2.0k 3.8× 216 8.3k

Countries citing papers authored by Yoji Okabe

Since Specialization
Citations

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

Fields of papers citing papers by Yoji Okabe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoji Okabe

This figure shows the co-authorship network connecting the top 25 collaborators of Yoji Okabe. A scholar is included among the top collaborators of Yoji Okabe 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 Yoji Okabe. Yoji Okabe 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.
Okabe, Yoji, et al.. (2025). Bayesian-optimized 1D-CNN for delamination classification in CFRP laminates using raw ultrasonic guided waves. Composites Science and Technology. 264. 111101–111101. 3 indexed citations
2.
Saito, Osamu, et al.. (2025). Baseline-free defects localization in CFRP laminates by acoustic nonlinear response of broadband guided waves. Applied Acoustics. 240. 110916–110916. 2 indexed citations
3.
Saito, Osamu, et al.. (2024). Delamination detection in CFRP laminates using a chirp guided wave mixing technique. NDT & E International. 144. 103086–103086. 13 indexed citations
4.
Saito, Osamu, et al.. (2024). An insight on local defect resonance based on modal decomposition analysis: A two-dimensional case. Journal of Sound and Vibration. 596. 118718–118718. 1 indexed citations
5.
Saito, Osamu, et al.. (2023). Impact damage detection in woven CFRP laminates based on a local defect resonance technique with laser ultrasonics. Mechanical Systems and Signal Processing. 207. 110929–110929. 22 indexed citations
6.
7.
Takahashi, Kazuki, et al.. (2013). Damage Detection Technology for CFRP Structure Using MFC/FBG Hybrid Sensor System. Structural Health Monitoring. 1 indexed citations
8.
Wu, Qi, Yoji Okabe, Fengming Yu, & Kazuya Saito. (2013). Ultrasensitive Optical-Fiber Ultrasonic Sensor Based on Phase-Shifted Fiber Bragg Gratings. Structural Health Monitoring. 2063–2070. 2 indexed citations
9.
Yokozeki, Tomohiro, et al.. (2012). Identification of Wrinkle States in Membranes Based on Dispersion of Elastic Wave. TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES AEROSPACE TECHNOLOGY JAPAN. 10(ists28). Pc_1–Pc_6. 1 indexed citations
10.
Ōyama, Toshiyuki, et al.. (2010). Application of Biomass-Derived Lignophenol to Epoxy Resins. KOBUNSHI RONBUNSHU. 67(9). 497–505. 4 indexed citations
11.
Okabe, Yoji, et al.. (2007). Smart Honeycomb Sandwich Panels with Damage Detection and Shape Recovery Functions. Journal of the Japan Society for Composite Materials. 33(1). 30–37. 1 indexed citations
12.
Takeda, N. & Yoji Okabe. (2005). Durability Analysis and Structural Health Management of Smart Composite Structures Using Small-Diameter Fiber Optic Sensors. Science and Engineering of Composite Materials. 12(1-2). 1–12. 6 indexed citations
13.
Okabe, Yoji, et al.. (2005). Detection of Delaminations in CFRP Laminates by Using FBG Sensors as Lamb Wave Receiver. JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES. 53(615). 166–173. 2 indexed citations
14.
Okabe, Yoji, Ryohei Tsuji, & Nobuo Takeda. (2004). Measurement of Non-Axisymmetric Thermal Residual Strain in CFRP Laminates Using FBG Sensors. Journal of the Japan Society for Composite Materials. 30(5). 199–206. 4 indexed citations
15.
Takeda, Nobuo, Yoji Okabe, & Shigeki Yashiro. (2000). Real-time damage detection of composite laminates with embedded bragg grating sensors. Proceedings of SPIE, the International Society for Optical Engineering. 4185. 42–45. 2 indexed citations
16.
Okabe, Yoji & Hideki Kakeya. (2000). Fast combinatorial optimization with parallel digital computers. IEEE Transactions on Neural Networks. 11(6). 1323–1331. 6 indexed citations
17.
Okabe, Yoji, N. Takeda, M. Yanaka, & Yusuke Tsukahara. (1999). Determination of the orthotropic elastic constants of thin PET film by an ultrasonic micro-spectrometer. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 46(5). 1269–1275. 7 indexed citations
18.
Okayama, Hideaki, et al.. (1998). Optical switch network based on two stage module architecture. 98(481). 43–48. 1 indexed citations
19.
Okabe, Yoji & Nobuo Takeda. (1998). Evaluation of Moisture-Induced Change in Viscous Coefficient of CFRP by Ultrasonic Wave Propagation Characteristics.. Journal of the Society of Materials Science Japan. 47(5). 472–477. 1 indexed citations
20.
Takeda, N., Tatsuro KOSAKA, & Yoji Okabe. (1996). High-Resolution Ultrasonic Detection of Subsurface Transverse Cracks in CFRP Laminates. Simulation and Experiments. Science and Engineering of Composite Materials. 5(3-4). 169–184. 2 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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