해외전문학술지
36. Duck Bong Kim, Hamin Chong, Mohammad Mahruf Mahdi, Seung-Jun Shin (2024) Data-fused and concatenated-ensemble learning for in-situ anomaly detection in wire and arc-based direct energy deposition. Journal of Manufacturing Processes, 112, 273–289.
35. Seung‑Jun Shin, Ju‑Hong Lee, Sainand Jadhav, Duck Bong Kim (2023) Material‑Adaptive Anomaly Detection Using Property‑Concatenated Transfer Learning in Wire Arc Additive Manufacturing. International Journal of Precision Engineering and Manufacturing, https://doi.org/10.1007/s12541-023-00924-2.
34. Seung-Jun Shin, Sung-Ho Hong, Sainand Jadhav, Duck Bong Kim (2023) Detecting balling defects using multisource transfer learning in wire arc additive manufacturing. Journal of Computational Design and Engineering, 10, 1-20, DOI: 10.1093/jcde/qwad067.
33. Eun-Su Kim, Dong-Hee Lee, Gi-Jeong Seo, Duck-Bong Kim, Seung-Jun Shin (2023) Development of a CNN-based real-time monitoring algorithm for additively manufactured molybdenum. Sensors and Actuators: A. Physical, 352, 114205.
32. Jin‑Soo Cho, Dong‑Hee Lee, Gi‑Jeong Seo, Duck‑Bong Kim, Seung‑Jun Shin (2022) Optimizing the mean and variance of bead geometry in the wire + arc additive manufacturing using a desirability function method. The International Journal of Advanced Manufacturing Technology, https://doi.org/10.1007/s00170-022-09237-6
31. Jumyung Um, Taebyeong Park, Hae-Won Cho, Seung-Jun Shin (2022) Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory. Sustainability, Vol. 14, 3816, https://doi.org/10.3390/su14073816
30. Hae-Won Cho, Seung-Jun Shin, Gi-Jeong Seo, Duck Bong Kim, Dong-Hee Lee (2022) Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: Molybdenum material. Journal of Materials Processing Technology, Vol. 302, 117495, https://doi.org/10.1016/j.jmatprotec.2022.117495
29. Prita Meilanitasari, Seung-Jun Shin (2021) A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems. Processes, vol. 9, 1391, https://doi.org/10.3390/pr9081391
28. Young‑Min Kim, Seung‑Jun Shin, Hae‑Won Cho (2021) Predictive Modeling for Machining Power Based on Multi‑source Transfer Learning in Metal Cutting. International Journal of Precision Engineering and Manufacturing-Green Technology, https://doi.org/10.1007/s40684-021-00327-6
27. Seung-Jun Shin. 2020. An OPC UA-compliant interface of data analytics models for interoperable manufacturing intelligence. IEEE Transactions on Industrial Informatics, http://dx.doi.org/10.1109/TII.2020.3024628
26. Farrell Samuel Kiling, Seung‑Jun Shin, Min‑Kyu Lee, Prita Meilanitasari. 2020. An Energy‑Related Products compliant Eco‑design method with durability‑embedded economic and environmental assessments. International Journal of Precision Engineering and Manufacturing - Green Technology, https://doi.org/10.1007/s40684-020-00213-7
25. Seung-Jun Shin, Young-Min Kim, Prita Meilanitasari. 2019. A holonic-based self-learning mechanism for energy-predictive planning in machining processes. Processes, 7, 739, doi:10.3390/pr7100739
24. Seung-Jun Shin. 2019. A hybrid process planning for energy-efficient machining: Application of predictive analytics. IOP Conference Series: Materials and Engineering, 635 012032
35. Seung‑Jun Shin, Ju‑Hong Lee, Sainand Jadhav, Duck Bong Kim (2023) Material‑Adaptive Anomaly Detection Using Property‑Concatenated Transfer Learning in Wire Arc Additive Manufacturing. International Journal of Precision Engineering and Manufacturing, https://doi.org/10.1007/s12541-023-00924-2.
34. Seung-Jun Shin, Sung-Ho Hong, Sainand Jadhav, Duck Bong Kim (2023) Detecting balling defects using multisource transfer learning in wire arc additive manufacturing. Journal of Computational Design and Engineering, 10, 1-20, DOI: 10.1093/jcde/qwad067.
33. Eun-Su Kim, Dong-Hee Lee, Gi-Jeong Seo, Duck-Bong Kim, Seung-Jun Shin (2023) Development of a CNN-based real-time monitoring algorithm for additively manufactured molybdenum. Sensors and Actuators: A. Physical, 352, 114205.
32. Jin‑Soo Cho, Dong‑Hee Lee, Gi‑Jeong Seo, Duck‑Bong Kim, Seung‑Jun Shin (2022) Optimizing the mean and variance of bead geometry in the wire + arc additive manufacturing using a desirability function method. The International Journal of Advanced Manufacturing Technology, https://doi.org/10.1007/s00170-022-09237-6
31. Jumyung Um, Taebyeong Park, Hae-Won Cho, Seung-Jun Shin (2022) Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory. Sustainability, Vol. 14, 3816, https://doi.org/10.3390/su14073816
30. Hae-Won Cho, Seung-Jun Shin, Gi-Jeong Seo, Duck Bong Kim, Dong-Hee Lee (2022) Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: Molybdenum material. Journal of Materials Processing Technology, Vol. 302, 117495, https://doi.org/10.1016/j.jmatprotec.2022.117495
29. Prita Meilanitasari, Seung-Jun Shin (2021) A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems. Processes, vol. 9, 1391, https://doi.org/10.3390/pr9081391
28. Young‑Min Kim, Seung‑Jun Shin, Hae‑Won Cho (2021) Predictive Modeling for Machining Power Based on Multi‑source Transfer Learning in Metal Cutting. International Journal of Precision Engineering and Manufacturing-Green Technology, https://doi.org/10.1007/s40684-021-00327-6
27. Seung-Jun Shin. 2020. An OPC UA-compliant interface of data analytics models for interoperable manufacturing intelligence. IEEE Transactions on Industrial Informatics, http://dx.doi.org/10.1109/TII.2020.3024628
26. Farrell Samuel Kiling, Seung‑Jun Shin, Min‑Kyu Lee, Prita Meilanitasari. 2020. An Energy‑Related Products compliant Eco‑design method with durability‑embedded economic and environmental assessments. International Journal of Precision Engineering and Manufacturing - Green Technology, https://doi.org/10.1007/s40684-020-00213-7
25. Seung-Jun Shin, Young-Min Kim, Prita Meilanitasari. 2019. A holonic-based self-learning mechanism for energy-predictive planning in machining processes. Processes, 7, 739, doi:10.3390/pr7100739
24. Seung-Jun Shin. 2019. A hybrid process planning for energy-efficient machining: Application of predictive analytics. IOP Conference Series: Materials and Engineering, 635 012032
23. Seung-Jun Shin, Jungyub Woo, Sudarsan Rachuri, Wonchul Seo. 2019. An energy-efficient process planning system using machine-monitoring data: A data analytics approach. Computer-Aided Design, Vol. 110, pp. 92-109
22. Prita Meilanitasari, Seung-Jun Shin. 2018. A model-driven predictive analytics approach for machining time using historical machine-monitoring data. ICIC Express Letters, Vol. 12, No. 11, pp. 1145-1153.
21. Eungchan Kim, Young Seok Ock, Seung-Jun Shin, Wonchul Seo (2018) An approach to generating reference information for technology evaluation, Sustainability, 10: 3200
20. Jungyub Woo, Seung-Jun Shin, Wonchul Seo, Prita Meilanitasari, (2018) Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation, International Journal of Advanced Manufacturing Technology, https://doi.org/10.1007/s00170-018-2416-9
19. Junhyeok Park, Seung-Jun Shin, Pyunghoi Koo, Wonchul Seo, (2018) Identifying technology opportunities based on internal capabilities and technical suitability, ICIC Express Letters, 12(6): 599-606
18. Ki-Hong Kim, Wonchul Seo, Seung-Jun Shin, (2018) Integration of Environmentally-Conscious Transportation Model with Life Cycle Assessment in Reverse Logistics, ICIC Express Letters, Part B: Applications, 9(8): 827-834
17. Seung-Jun Shin, Jungyub Woo, Sudarsan Rachuri, Prita Meilanitasari, (2018) Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining, Sustainability, 10: 598 (doi:10.3390/su10030598)
16. Sanjay Jain, Shao Guodong, Seung-Jun Shin, (2017) Manufacturing data analytics using a virtual factory representation, International Journal Of Production Research, 55(18): 5450-5464
15. David Lechevalier, Seung-Jun Shin, Sudarsan Rachuri, Sebti Foufou, Y. Tina Lee, Abdelaziz Bouras, (2017) Simulating a virtual machining model in an agent-based model for advanced analytics, Journal Of Intelligent Manufacturing, DOI 10.1007/s10845-017-1363-x
14. Seung-Jun Shin, Jungyub Woo, Sudarsan Rachuri, (2017) Energy efficiency of milling machining: Component modeling and online optimization of cutting parameters, Journal of Cleaner Production, Vol. 161, pp. 12-29
13. Seongwook Choi, Seung-Jun Shin, Dongjin Choi, Wonchul Seo, (2016) Analyzing efficiency of intellectual property-intensive industries of OECD countries, ICIC Express Letters, 10(5): 1207-1212
12. Duck Bong Kim, Seung-Jun Shin, Guodong Shao, Alexander Brodsky., (2015) A decision-guidance framework for sustainability performance analysis of manufacturing processes, The International Journal of Advanced Manufacturing Technology, DOI:10.1007/s00170-014-6711-9. ISSN: 0268-3768
11. Seung-Jun Shin, Duck Bong Kim, Guodong Shao, Alexander Brodsky, David Lechevalier, (2015) Developing a decision support system for improving sustainability performance of manufacturing processes, Journal of Intelligent Manufacturing, DOI:10.1007/s10845-015-1059-z. ISSN: 0956-5515
10. Seung-Jun Shin,Jungyub Woo, Duck Bong Kim, Senthilkumaran Kumaraguru, Sudarsan Rachuri, (2015) Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC, International Journal of Production Research, DOI: 10.1080/00207543.2015.1064182, ISSN: 0020-7543
9. Seung-Jun Shin, Suk-Hwan Suh, Ian Stroud, SooCheol Yoon, (2015) Process-oriented life cycle assessment framework for environmentally conscious manufacturing, Journal of Intelligent Manufacturing, DOI:10.1007/s10845-015-1062-4, ISSN: 0956-5515
8. Guodong Shao, Alexander Brodsky, Seung-Jun Shin, Duck Bong Kim, (2014) Decision guidance methodology for sustainable manufacturing using process analytics formalism, Journal of Intelligent Manufacturing, DOI:10.1007/s10845-014-0995-3, ISSN: 0956-5515.
7. Seung-Jun Shin, Suk-Hwan Suh, Ian Stroud, (2014) A green productivity based process planning system for a machining process, International Journal of Production Research, . DOI:10.1080/00207543.2014.988884, ISSN: 0020-7543
6. Ju Yeon Lee, Seung-Jun Shin, Y Tina Lee, Donald Libes, (2014) Toward development of a testbed for sustainable manufacturing, Concurrent Engineering: Research and Applications, DOI:10.1177/1063293X14559527, ISSN: 1063-293X
5. Joo-Sung Yoon, Seung-Jun Shin, Suk-Hwan Suh, (2012) A conceptual framework for the ubiquitous factory, International Journal of Production Research, 50 (8): 2174-2189. ISSN: 0020-7543.
4. Seung-Jun Shin, Jumyung Um, Joo-Sung Yoon, Suho Jeong, Jae-Min Cha, Suk-Hwan Suh, Dae-Hyuk Chung, (2009) Developing ISO 14649-based conversational programming system for multi-channel complex machine tools, International Journal of Computer Integrated Manufacturing, 22 (6): 562–575
3. Suk-Hwan Suh, Seung-Jun Shin, Joo-Sung Yoon, Ju-Myung Um, (2008) UbiDM: a new paradigm for product design and manufacturing via ubiquitous computing technology, International Journal of Computer Integrated Manufacturing, 21 (5): 540-549.
2. Seung-Jun Shin, Suk-Hwan Suh, Ian Stroud, (2007) Reincarnation of G-code based part programs into STEP-NC for turning applications, Computer-Aided Design, 39: 1-16
1. Suk-Hwan Suh, Dae-Hyuck Chung, Byeong-Eon Lee, Seung-Jun Shin, In-Jun Choi, Kwang-Myung Kim, (2006) STEP-compliant CNC system for turning: Data model, architecture and implementation, Computer-Aided Design, 38: 677-688