Dongjoo Shin

1.9k total citations
64 papers, 1.4k citations indexed

About

Dongjoo Shin is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Dongjoo Shin has authored 64 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Electrical and Electronic Engineering, 22 papers in Computer Vision and Pattern Recognition and 11 papers in Biomedical Engineering. Recurrent topics in Dongjoo Shin's work include Advanced Neural Network Applications (12 papers), Advanced Memory and Neural Computing (11 papers) and Calibration and Measurement Techniques (7 papers). Dongjoo Shin is often cited by papers focused on Advanced Neural Network Applications (12 papers), Advanced Memory and Neural Computing (11 papers) and Calibration and Measurement Techniques (7 papers). Dongjoo Shin collaborates with scholars based in South Korea, India and Russia. Dongjoo Shin's co-authors include Hoi‐Jun Yoo, Jinmook Lee, Sanghoon Kang, Jinsu Lee, Changhyeon Kim, Sangyeob Kim, Sungpill Choi, Kyeongryeol Bong, Seong‐Wook Park and Taesung Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Proceedings of the IEEE and Scientific Reports.

In The Last Decade

Dongjoo Shin

61 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dongjoo Shin South Korea	17	916	642	339	163	115	64	1.4k
Sangyeob Kim South Korea	17	671 0.7×	460 0.7×	284 0.8×	127 0.8×	91 0.8×	88	1.2k
Donghyeon Han South Korea	17	564 0.6×	464 0.7×	246 0.7×	132 0.8×	47 0.4×	70	989
Kailash Gopalakrishnan United States	14	938 1.0×	473 0.7×	464 1.4×	146 0.9×	58 0.5×	29	1.5k
Fengbo Ren United States	18	405 0.4×	443 0.7×	252 0.7×	103 0.6×	294 2.6×	48	1.3k
Priyanka Raina United States	14	1.3k 1.5×	460 0.7×	401 1.2×	400 2.5×	43 0.4×	64	1.8k
Xuecheng Zou China	19	1.1k 1.2×	221 0.3×	338 1.0×	268 1.6×	263 2.3×	254	1.9k
Miguel Figueroa Chile	18	468 0.5×	164 0.3×	299 0.9×	167 1.0×	169 1.5×	92	1.3k
Xuan Zhang United States	19	791 0.9×	165 0.3×	286 0.8×	258 1.6×	196 1.7×	90	1.4k
Xing Hu China	19	851 0.9×	327 0.5×	618 1.8×	273 1.7×	27 0.2×	64	1.4k
Hyoukjun Kwon United States	16	829 0.9×	703 1.1×	414 1.2×	576 3.5×	35 0.3×	38	1.6k

Countries citing papers authored by Dongjoo Shin

Since Specialization

Citations

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

Fields of papers citing papers by Dongjoo Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongjoo Shin

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

All Works

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20 of 20 papers shown

Shin, Dongjoo, et al.. (2023). Stretchable optical fiber strain sensor comprising zinc oxide and PDMS for human motion monitoring. Journal of Mechanical Science and Technology. 37(6). 3205–3212. 5 indexed citations

Shin, Dongjoo & Taesung Kim. (2021). Wearable Sensor based on Fiber Bragg Grating with Flexible Polymer for Squat Exercise. 478–481. 4 indexed citations

Shin, Dongjoo, Hyeong‐U Kim, Atul Kulkarni, Young‐Hak Kim, & Taesung Kim. (2021). Development of Force Sensor System Based on Tri-Axial Fiber Bragg Grating with Flexure Structure. Sensors. 22(1). 16–16. 10 indexed citations

Lee, Jinsu, Sanghoon Kang, Jinmook Lee, et al.. (2020). The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 67(10). 3458–3470. 36 indexed citations

Shin, Dongjoo, et al.. (2020). Report on the APMP.PR-S6 : 2012-2013 supplementary comparison of spectral radiance from 250 nm to 2500 nm. Metrologia. 57(1A). 2001–2001. 1 indexed citations

Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision. 218–220. 232 indexed citations

Lee, Jinsu, Dongjoo Shin, Youchang Kim, & Hoi‐Jun Yoo. (2017). A 17.5-fJ/bit Energy-Efficient Analog SRAM for Mixed-Signal Processing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 25(10). 2714–2723. 15 indexed citations

Shin, Dongjoo & Hoi‐Jun Yoo. (2017). DNPU: An Energy-Efficient Deep Neural Network Processor with On-Chip Stereo Matching. 1 indexed citations

Shin, Dongjoo, Jinmook Lee, Jinsu Lee, & Hoi‐Jun Yoo. (2017). 14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks. 240–241. 239 indexed citations

10.

Lee, Jinsu, Dongjoo Shin, Kyuho Lee, & Hoi‐Jun Yoo. (2017). A 31.2pJ/disparity· pixel stereo matching processor with stereo SRAM for mobile UI application. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). C158–C159. 4 indexed citations

11.

Kim, Hyeong‐U, Atul Kulkarni, Chisung Ahn, et al.. (2017). Highly uniform wafer-scale synthesis ofα-MoO₃by plasma enhanced chemical vapor deposition. Nanotechnology. 28(17). 175601–175601. 29 indexed citations

12.

Lee, Jinsu, Dongjoo Shin, Youchang Kim, & Hoi‐Jun Yoo. (2016). A 17.5 fJ/bit energy-efficient analog SRAM for mixed-signal processing. 1010–1013. 3 indexed citations

13.

Kim, Youchang, Dongjoo Shin, Jinsu Lee, Yongsu Lee, & Hoi‐Jun Yoo. (2016). 14.3 A 0.55V 1.1mW artificial-intelligence processor with PVT compensation for micro robots. 258–259. 18 indexed citations

14.

Yoo, Hoi‐Jun, Seong‐Wook Park, Kyeongryeol Bong, et al.. (2015). A 1.93 TOPS/W Scalable Deep Learning/Inference Processor with Tetra-parallel MIMD Architecture for Big Data Applications. 80–81. 34 indexed citations

15.

Park, Seong‐Wook, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). 4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications. 1–3. 78 indexed citations

16.

Hong, Injoon, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). 18.1 A 2.71nJ/pixel 3D-stacked gaze-activated object-recognition system for low-power mobile HMD applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3. 16 indexed citations

17.

Hong, Injoon, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). A 2.71 nJ/Pixel Gaze-Activated Object Recognition System for Low-Power Mobile Smart Glasses. IEEE Journal of Solid-State Circuits. 51(1). 45–55. 17 indexed citations

18.

Shin, Dongjoo, et al.. (2009). Optimal Design of an In-Wheel Permanent Magnet Synchronous Motor for mobile robot. 688–689. 1 indexed citations

19.

Shaw, Ping-Shine, Uwe Arp, Robert D. Saunders, et al.. (2006). Synchrotron radiation-based irradiance calibration from 200 to 400 nm at the Synchrotron Ultraviolet Radiation Facility III. Applied Optics. 46(1). 25–25. 13 indexed citations

20.

Shin, Dongjoo, et al.. (2005). A novel linearity tester for optical detectors using high-brightness light emitting diodes. Metrologia. 42(2). 154–158. 12 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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