Seung‐Jun Shin

1.7k total citations
65 papers, 1.2k citations indexed

About

Seung‐Jun Shin is a scholar working on Industrial and Manufacturing Engineering, Mechanical Engineering and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, Seung‐Jun Shin has authored 65 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Industrial and Manufacturing Engineering, 14 papers in Mechanical Engineering and 14 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in Seung‐Jun Shin's work include Manufacturing Process and Optimization (29 papers), Flexible and Reconfigurable Manufacturing Systems (21 papers) and Digital Transformation in Industry (20 papers). Seung‐Jun Shin is often cited by papers focused on Manufacturing Process and Optimization (29 papers), Flexible and Reconfigurable Manufacturing Systems (21 papers) and Digital Transformation in Industry (20 papers). Seung‐Jun Shin collaborates with scholars based in South Korea, United States and Japan. Seung‐Jun Shin's co-authors include Jungyub Woo, Sudarsan Rachuri, Guodong Shao, Sanjay Jain, Duck Bong Kim, Suk‐Hwan Suh, Wonchul Seo, Young-Min Kim, Gi-Jeong Seo and Dong‐Hee Lee and has published in prestigious journals such as Nano Letters, Applied Physics Letters and Journal of Cleaner Production.

In The Last Decade

Seung‐Jun Shin

59 papers receiving 1.1k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Seung‐Jun Shin 714 281 257 135 131 65 1.2k
Shilong Wang 773 1.1× 251 0.9× 127 0.5× 49 0.4× 77 0.6× 70 1.4k
Chuang Wang 626 0.9× 114 0.4× 256 1.0× 21 0.2× 68 0.5× 61 1.2k
Fernando Castaño 475 0.7× 203 0.7× 185 0.7× 21 0.2× 92 0.7× 48 990
Paul Young 160 0.2× 226 0.8× 212 0.8× 32 0.2× 111 0.8× 59 810
R.J. Caudill 698 1.0× 240 0.9× 112 0.4× 42 0.3× 179 1.4× 85 1.4k
Moneer Helu 1.1k 1.5× 438 1.6× 263 1.0× 281 2.1× 127 1.0× 43 1.8k
A R Mileham 836 1.2× 404 1.4× 229 0.9× 26 0.2× 252 1.9× 101 1.6k
Li Zhou 258 0.4× 212 0.8× 303 1.2× 15 0.1× 48 0.4× 122 1.2k
Chien‐Yi Huang 427 0.6× 88 0.3× 277 1.1× 51 0.4× 35 0.3× 61 840
K. C. Morris 532 0.7× 114 0.4× 44 0.2× 49 0.4× 184 1.4× 80 999

Countries citing papers authored by Seung‐Jun Shin

Since Specialization
Citations

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

Fields of papers citing papers by Seung‐Jun Shin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seung‐Jun Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Seung‐Jun Shin. A scholar is included among the top collaborators of Seung‐Jun 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 Seung‐Jun Shin. Seung‐Jun Shin 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.
Kim, Duck Bong, et al.. (2025). Human-in-the-loop in smart manufacturing (H-SM): A review and perspective. Journal of Manufacturing Systems. 82. 178–199. 1 indexed citations
2.
Kang, Young Kee, Seung‐Jun Shin, Cheol‐Ho Kim, & Jaehyun Park. (2025). Asset Administration Shell-based Virtualized Model for Holonic Factory. Journal of the Korean Society for Precision Engineering. 42(3). 203–213. 1 indexed citations
3.
Kim, Duck Bong, et al.. (2024). Introduction of Human-in-the-Loop in Smart Manufacturing (H-SM). 2(2). 209–214. 5 indexed citations
4.
Shin, Seung‐Jun & Jumyung Um. (2024). Deploying data analytics models in asset administration shells: Energy prediction in manufacturing. Engineering Applications of Artificial Intelligence. 138. 109269–109269. 2 indexed citations
5.
Shin, Seung‐Jun & Jumyung Um. (2023). Integrating Predictive Model Markup Language with Asset Administration Shell. IFAC-PapersOnLine. 56(2). 9823–9830. 2 indexed citations
6.
Shin, Seung‐Jun, et al.. (2023). Material-Adaptive Anomaly Detection Using Property-Concatenated Transfer Learning in Wire Arc Additive Manufacturing. International Journal of Precision Engineering and Manufacturing. 25(2). 383–408. 18 indexed citations
7.
Shin, Seung‐Jun, et al.. (2023). Detecting balling defects using multisource transfer learning in wire arc additive manufacturing. Journal of Computational Design and Engineering. 10(4). 1423–1442. 15 indexed citations
8.
Um, Jumyung, et al.. (2022). Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory. Sustainability. 14(7). 3816–3816. 2 indexed citations
9.
Kim, Young-Min, et al.. (2021). Predictive Modeling for Machining Power Based on Multi-source Transfer Learning in Metal Cutting. International Journal of Precision Engineering and Manufacturing-Green Technology. 9(1). 107–125. 37 indexed citations
10.
Shin, Seung‐Jun, et al.. (2020). An OPC UA-based Representation Method of Data Analytics Models for Interoperable Manufacturing Intelligence. Journal of Korean Institute of Industrial Engineers. 46(6). 580–592. 1 indexed citations
11.
Shin, Seung‐Jun, et al.. (2018). A model-driven predictive analytics approach for machining time using historical machine-monitoring data. 12(11). 1145–1153. 1 indexed citations
12.
Shin, Seung‐Jun & Wonchul Seo. (2017). Identifying new technology areas based on firm’s internal capabilities. 3(3). 9 indexed citations
13.
Shin, Seung‐Jun, Jungyub Woo, & Wonchul Seo. (2017). Development of a Data and Model-Interconnected Holonic Architecture for Intelligent Decision-Making on Cyber-Physical Production Systems. Journal of Korean Institute of Industrial Engineers. 43(6). 451–463. 2 indexed citations
14.
Shin, Seung‐Jun, et al.. (2017). Energy Prediction Modeling for Numerical Control Programs Using MTConnect. Journal of the Korean Society for Precision Engineering. 34(5). 355–362. 4 indexed citations
15.
Jain, Sanjay, David Lechevalier, Jungyub Woo, & Seung‐Jun Shin. (2015). Towards a Virtual Factory Prototype | NIST. Winter Simulation Conference. 2 indexed citations
16.
Jain, Sanjay, David Lechevalier, Jungyub Woo, & Seung‐Jun Shin. (2015). Towards a virtual factory prototype. Winter Simulation Conference. 2207–2218. 11 indexed citations
17.
Shin, Seung‐Jun, Jungyub Woo, & Sudarsan Rachuri. (2014). Predictive Analytics Model for Power Consumption in Manufacturing. Procedia CIRP. 15. 153–158. 92 indexed citations
18.
Shin, Seung‐Jun, et al.. (2011). Room-Temperature Charge Stability Modulated by Quantum Effects in a Nanoscale Silicon Island. Nano Letters. 11(6). 2578–2578. 2 indexed citations
19.
Shin, Seung‐Jun, et al.. (2009). Developing an ISO 44649-based e-CAM System Supporting Multi-channel e-CNC for Composite Machine Tools. Journal of the Korean Society for Precision Engineering. 26(4). 23–32. 1 indexed citations
20.
Kim, Ki-Hong, et al.. (2008). A study on economical introduction of RFID system in the small and medium 3rd Party Logistics. Journal of the Korea Safety Management and Science. 10(3). 117–126. 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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