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*ÇØ¿Ü Àú³Î [1] Yoon, J., & Lee, J. (2017). Altitude and roll control of a hovering quad-rotor air vehicle using the multi-objective approximate optimization of proportional?integral?differential control. Engineering optimization, 49(10), 1704-1718. [2] Yoon, J., & Lee, J. (2018). Parameter analysis and design for the hovering thrust of a quad-rotor air vehicle using CFD and design of experiment. Journal of Mechanical Science and Technology, 32(2), 781-791. [3] Yoo, Y., Lee, S., Yoon, J., & Lee, J. (2018). Modelica-based dynamic analysis and design of lift-generating disk-type wind blade using computational fluid dynamics and wind tunnel test data. Mechatronics, 55, 1-12. [4] Joo, M., Yoon, J., Junejo, A. R., & Doh, J. (2022). Optimization: Drone-Operated Metal Detection Based on Machine Learning and PID Controller. International Journal of Precision Engineering and Manufacturing, 23(5), 503-515. [5] Yoon, J., & Doh, J. (2022). Optimal PID control for hovering stabilization of quadcopter using long short term memory. Advanced Engineering Informatics, 53, 101679. [6] Yoon, J., Lim, K., & Doh, J. (2023). Multi-objective Optimization of Aerodynamic Blade Shapes for Quadcopter System to Enhance Hovering Thrust and Power Consumption Efficiency. International Journal of Aeronautical and Space Sciences, 24(3), 689-700. [7] Yoon, J., & Doh, J. (2025). A Study on Hybrid-architecture Deep Learning Model for Predicting Pressure Distribution in 2D Airfoils, Nature Scientific Reports, 2155. [8] Yoon, J., Kim, M., Bang, J., Kim, S., & Doh, J., improvement of hovering stability for UAVs under crosswinds via evolutionary learning-based optimal PID control, Journal of Mechanical Science and Technology, Vol. 39, No. 4, pp. 2151-2162, 2025.
*ÇØ¿ÜÇмúȸ [1] Yun, J. H., & Lee, J. (2014). Effect of Rotor-Rotor Interactions in Aerodynamic Performance of Multi-Rotor Air Vehicle. Technology, 2011, 0024829. [2] Yoon, J., & Lee, J. (2015). Optimal Blade Design of Quad-Rotor Air Vehicle Considering Hovering Thrust and Position Disturbance. Proceedings of the 11th World Congress on Structural and Multidisciplinary Optimization (WCSMO-11). [3] Yoon, J., & Lee, J. (2016). Validation of CFD Based Optimized Quad-Copter Blade Using 3D Printer and Thrust Measurement. Asian Congress of Structural and Multidisciplinary Optimization. [4] Yoon, J., & Lee, J. (2017). Multi-Objective Optimization of Multi-Rotor Air Vehicle Considering Uncertain Environment. International Conference on Engineering & Technology, Computer, Basic & Applied Sciences (ECBA-2017). [5] Yoon, J., & Lee, J. (2017). Enhanced PID Control Optimization of Hovering Quad-Copter for Robust Flight Stabilization. 12th World Congress on Structural and Multidisciplinary Optimization (WCSMO-12). [6] Yoon, J., & Lee, J. (2018). Image Realization of Fluid Flow Distributions Using Convolutional Neural Networks. 6th European Conference on Computational Fluid Dynamics (ECFD 7). [7] Lim, K., Lee, H., Kim, T.H., Yoon, J., Kim, S., Park, S., & Doh, J., Deep Learning-based Prediction of Cutting Force to Support Tool Path Optimization on Cutting Process (Proceedings of the 9th Internat ional Conferenc e on Manufacturing, Machine Design and Tribology 2023). [8] Kim, H., Yoon, J., & Park, C. S., "Integration of Digital Humans into Online Courses," International Workshop on Advanced Image Technology (IWAIT2024). [9] Yoon, J., Park, C. S., & Doh, J. (2024) "Implementing On-Flex and PPBL Innovations in Online Education for Self-Directed Learning," International Conference on Education, Economics, Psychology and Social Science.(9th ICEEPS) [10] Kim, H., Lee, S. M., Yoon, J., Choi, J. M., & Lee, J. (2024) "Development and Implementation of a Remote AI Competency Education Model for Disconnected Youth: A Case Study from Education Innovation Project," The International Conference on Education and Language Learning in a post-COVID world.(ICELL2024) [11] Jeong, S., Park, S., Jin, M., Lee, J., Yoon, J., & Doh, J.(2025) "The Implementation of a Unity-Mapbox-Integrated Digital Twin Platform for Deep Learning-Based UAM Flight Control and Monitoring," Asia-Pacific Conference on Engineering and Applied Sciences(APCEAS2025)
*±¹³» Àú³Î [1] À±ÀçÇö, ÃÖÇÏ¿µ, & ÀÌÁ¾¼ö. (2014). ¹«ÀκñÇàü ºí·¹À̵å Çü»ó º¯È¿¡ µû¸¥ ´ÜÀÏ·ÎÅÍÀÇ Á¦ÀÚ¸® ºñÇà Ã߷¼º´É ºÐ¼®. ´ëÇѱâ°èÇÐȸ ³í¹®Áý A ±Ç, 38(5), 513-520. [2] À±ÀçÇö, & ÀÌÁ¾¼ö. (2015). PID Á¦¾î¸¦ ÅëÇÑ ÄõµåÄßÅÍ ´ÙÁ߸ñÀû ±Ù»çÃÖÀû¼³°è. ´ëÇѱâ°èÇÐȸ ³í¹®Áý A ±Ç, 39(7), 673-679. [3] Á¤½Ã¿µ, ÀÌÁ¾¼ö, & À±ÀçÇö. (2017). ½ÇÇè°èȹ¹ýÀ» ÀÌ¿ëÇÑ ³¯°³ ¾ø´Â ¼±Ç³±âÀÇ ³ëÁñ Çü»ó ÃÖÀû ¼³°è. ´ëÇѱâ°èÇÐȸ ³í¹®Áý A ±Ç, 41(8), 711-719. [4] À±ÀçÇö, & ÀÌÁ¾¼ö. (2018). ȸÀüÀÍ ¹«ÀÎÇ×°ø±â ºí·¹À̵åÀÇ Ãß·Â ¼º´É °ËÁõÀ» À§ÇÑ Ãß·Â ÃøÁ¤ ½ÇÇè±â ¼³°è. ´ëÇѱâ°èÇÐȸ ³í¹®Áý A ±Ç, 42(6), 533-539. [5] À±ÀçÇö, ³ë¿ì½Â, & µµÀçÇõ. (2022). Àü»êÀ¯Ã¼¿ªÇÐÀ» ÅëÇÑ PAV ÀÇ ·ÎÅÍ ºí·¹À̵å Ãà°£°Å¸®¿¡ µû¸¥ È£¹ö¸µ ¼º´É º¯È ¿¬±¸. Çѱ¹±â°è°¡°øÇÐȸÁö, 21(5), 53-60. [6] À±ÀçÇö, ¹®Çöö, & µµÀçÇõ. (2022). °È PID Á¦¾î ½Ã¹Ä·¹À̼ÇÀ» ÀÌ¿ëÇÑ ÄõµåÄßÅÍ ½Ã½ºÅÛÀÇ Á¦ÀÚ¸® ºñÇà ¾ÈÁ¤¼º¿¡ °üÇÑ ¿¬±¸. ´ëÇѱâ°èÇÐȸ ³í¹®Áý A ±Ç, 46(9), 843-852. [7] À±ÀçÇö, ³ë¿ì½Â, & µµÀçÇõ. (2022). Ȥµî°í·¡ °¡½¿Áö´À·¯¹Ì¸¦ ÀÚ¿¬ ¸ð»çÇÑ °íÁ¤ÀÍ Ç¥¸é¿¡ Ȥ Å©±â¿¡ µû¸¥ ¾ç·Â°è¼ö ¿µÇâ¿¡ °üÇÑ ¿¬±¸. ±â°è°¡°øÇÐȸÁö, 21(11), 17-22. [8] À±ÀçÇö, & ¹ÚÂù¼ö. (2024). PPBL ¸ðµ¨À» Àû¿ëÇÑ Çù¾÷°ú âÀǼº °È¸¦ À§ÇÑ ±³À° ¹æ¹ý·Ð Àû¿ë »ç·Ê. ´ëÇѱâ°èÇÐȸ ³í¹®Áý C ±Ç, 12(1), 39-46.
*±¹³»Çмú´ëȸ [1] À±ÀçÇö, °íÀçõ, & ÀÌÁ¾¼ö. (2012). ÅÄ´ý¹è¿ ÆÒ ºí·¹ÀÌµå ·ÎÅÍÀÇ À¯µ¿Çؼ® ¹× Ã߷¼º´É ºÐ¼®. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 331-334. [2] °íÀçõ, ¹æÀοÏ, Â÷»ó¿ì, À̰æ¿ì, ½Å¼º¿ø, À±ÀçÇö, & ÀÌÁ¾¼ö(2012). ´Ù¾çÇÑ ÇÏÁßÁ¶°ÇÀ» °í·ÁÇÑ ¿£Áø¹ëºê½ÃÆ®ÀÇ º¯Çü ¹× ¸¶¸ð ÇØ¼® ±âÃÊ ¿¬±¸. ´ëÇѱâ°èÇÐȸ ÃáÃß Çмú´ëȸ, 326-330. [3] Á¤Èñ¼®, À±ÀçÇö, °íÀçõ, ÀÌÀÍȯ, & ÀÌÁ¾¼ö. (2012). ¿À·ù¿ªÀüÆÄ ½Å°æ¸Á À̷нĿ¡ ÀÇÇÑ 1 Â÷ ¹Î°¨µµ ±Ù»çÇØ¼®. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 2207-2210. [4] À±ÀçÇö, & ÀÌÁ¾¼ö. (2014). ½ÇÇè°èȹ¹ýÀ» ÀÌ¿ëÇÑ ÄõµåÄßÅÍ ºí·¹À̵å Çü»ó ÃÖÀû¼³°è. Çѱ¹Á¤¹Ð°øÇÐȸ Çмú¹ßÇ¥´ëȸ ³í¹®Áý, 89-89. [5] À±ÀçÇö, & ÀÌÁ¾¼ö. (2014). PID Á¦¾î¸¦ ÅëÇÑ ÄõµåÄßÅÍ ÀÚ¼¼ Á¦¾î. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 834-835. [6] À±ÀçÇö, & ÀÌÁ¾¼ö. (2015). Àü»êÀ¯Ã¼¿ªÇÐ ÇØ¼®À» ÅëÇÑ ³¯°³ ¾ø´Â ¼±Ç³±âÀÇ ³ëÁñ Çü»ó ¿¬±¸. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 409-410. [7] À±ÀçÇö, À±À缺, & ÀÌÁ¾¼ö. (2015). ¿©¼º¿ë ±¸µÎ Ç÷§Æû Çü»ó¿¡ µû¸¥ º¸Çà±³Á¤. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 316-317. [8] À±ÀçÇö, ÀÌÁ¾¼ö, & À±À缺. (2015). ±¸µÎ ¹ØÃ¢ º¯ÇüÀ» ÅëÇÑ º¸Çà ÆÐÅÏ ºÐ¼®. Çѱ¹Á¤¹Ð°øÇÐȸ Çмú¹ßÇ¥´ëȸ ³í¹®Áý, 624-624. [9] À±ÀçÇö, & ÀÌÁ¾¼ö. (2016). ¸ÖƼ·ÎÅÍ ºí·¹À̵åÀÇ Ãß·Â ½ÇÇè±â Á¦ÀÛ ¹× ¼º´É °ËÁõ. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 56-57. [10] À±ÀçÇö &ÀÌÁ¾¼ö. (2016). ºí·¹À̵åÀÇ Ãß·Â ÃøÁ¤ ½ÇÇè±â ¼³°è Á¦ÀÛ ¹× ½ÇÇè. Çѱ¹ CED ÇÐȸ ºÎ¹®¿¬ÇÕÇмú´ëȸ. [11] À¯¿µ¹Î, À±ÀçÇö, & ÀÌÁ¾¼ö. (2016). ºÐ¼®Àû ¸ñÇ¥ Àü°³ ¹æ¹ýÀ» ÀÌ¿ëÇÑ ´ÙºÐ¾ßÅëÇÕ ÃÖÀû¼³°è¿¡ °üÇÑ ¿¬±¸. Çѱ¹ CED ÇÐȸ ºÎ¹®¿¬ÇÕÇмú´ëȸ. [12] ±è¿¹Áø, À¯¿µ¹Î, À̼ҿµ, À±ÀçÇö, & ÀÌÁ¾¼ö. (2016). ºí·¹À̵å Çü»ó º¯È ¹× À¯µ¿ÇÏÁß ºÐÆ÷¿¡ µû¸¥ µð½ºÅ©Çü dz·Â¹ßÀü ±¸Á¶ °µµÇؼ®. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 3277-3279. [13] À±ÀçÇö, & ÀÌÁ¾¼ö. (2017). ÇÕ¼º°ö ½Å°æ¸Á ±â¹Ý Ç×°ø±â Airfoil ´Ü¸é Çü»ó À̹ÌÁö ¿¹Ãø. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 1545-1547. [14] À̼ҿµ, ÀÌÁ¾¼ö, & À±ÀçÇö. (2017). Convolutional Neural Networks ¸¦ ÀÌ¿ëÇÑ NACA0012 ÀÍÇü ÁÖÀ§ À¯µ¿Çؼ® °á°ú¿¹Ãø. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 331-332. [15] À±ÀçÇö, & ÀÌÁ¾¼ö. (2017). PID °È Á¦¾î¸¦ ÀÌ¿ëÇÑ Á¦ÀÚ¸® ºñÇà»óÅÂÀÇ ÄõµåÄßÅÍ °ÀÎÁ¦¾î. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 180-181. [16] À̼ҿµ, À¯¿µ¹Î, À±ÀçÇö,& ÀÌÁ¾¼ö. (2017). µð½ºÅ©Çü dz·Â ¹ßÀü±âÀÇ Çü»ó º¯È¿¡ µû¸¥ ¼öÁ÷ º¯À§ dzµ¿ ½ÇÇè. dz·Â¿¡³ÊÁö ÇÐȸ 2017³âµµ Ãß°èÇмú´ëȸ. [17] À±ÀçÇö. (2017) ÇÕ¼º°ö ½Å°æ¸ÁÀ» ÀÌ¿ëÇÑ Ç×°ø±â ÀÍÇü ÁÖÀ§ À¯µ¿Çؼ® °á°ú ¿¹Ãø. Á¦ 2ȸ ¿¬¼¼´ëÇб³ Á¤±â Çмú´ëȸ [18] À±ÀçÇö, & ÀÌÁ¾¼ö. (2018). ¼ÒºñÀü·ÂÀ» °í·ÁÇÑ ÄõµåÄßÅÍÀÇ ºí·¹À̵å Çü»ó ¼³°è. Çѱ¹ CED ÇÐȸ 2018³âµµ µ¿°èÇмú´ëȸ [19] ÁÖ¹ÎÈ£, ÀÌÁ¾¼ö, & À±ÀçÇö. (2018). ±â°èÇнÀ±â¹Ý ºÐ·ù ¾Ë°í¸®ÁòÀ» ÅëÇÑ ±Ý¼Ó ŽÁö µå·Ð °³¹ß. ´ëÇѱâ°èÇÐȸ ÃáÃßÇмú´ëȸ, 264-265. [20] À±ÀçÇö, & ÀÌÁ¾¼ö. (2019). ÄõµåÄßÅÍ ºí·¹À̵å Çü»óÃÖÀûÈ¿¡ ±â¹ÝÇÑ ¹èÅ͸® ¼º´É ¹× ¼ö¸íÆò°¡. Çѱ¹ PHM ÇÐȸ 2019³âµµ Ãá°èÇмú´ëȸ. [21] ÃÖ¿µ·Ï, & À±ÀçÇö. (2023). ´ë±âÀü·Â °¨¼Ò¸¦ À§ÇÑ Àü·Â Â÷´Ü ½Ã½ºÅÛ. Çѱ¹Á¤¹Ð°øÇÐȸ 2023 Ãá°èÇмú´ëȸ. [22] À±ÀçÇö, & ¹ÚÂù¼ö. (2023). ¿Â¶óÀÎ ´ëÇп¡¼ÀÇ PPBL ¸ðµ¨ ¼ö¾÷ Àû¿ë »ç·Ê. 2023 ´ëÇѱâ°èÇÐȸ ±³À°ºÎ¹® Ãá°èÇмú´ëȸ. [23] ¹ÚÂù¼ö, & À±ÀçÇö. (2023). ±º ±â¼ú±³À° °úÁ¤ °³¼³ ¹× ¿î¿µ. 2023 ´ëÇѱâ°èÇÐȸ ±³À°ºÎ¹® Ãá°èÇмú´ëȸ. [24] À±ÀçÇö, ÀÌ¿ø±¹, & µµÀçÇõ. (2023). Àü»êÀ¯Ã¼¿ªÇÐÀ» ÀÌ¿ëÇÑ Â÷·®¿ë ¸®¾î ½ºÆ÷ÀÏ·¯ °ø·Â Ư¼º Æò°¡. ´ëÇѱâ°èÇÐȸ °æ³²Áöȸ¡¤È£³²Áöȸ 2023³â ÅëÇÕÇмú´ëȸ. [25] Á¤»óÁØ, À±ÀçÇö & µµÀçÇõ. (2024). UAM ºñÇà ¸ð´ÏÅ͸µ ¹× Á¦¾î¸¦ À§ÇÑ Unity ±â¹Ý µðÁöÅÐ Æ®À© ±¸Ãà ¿¬±¸. 2024³âµµ ´ëÇѱâ°èÇÐȸ »ý»ê ¹× ¼³°è°øÇкι® Ãá°èÇмú´ëȸ. [26] À±ÀçÇö, & µµÀçÇõ. (2024). µð½ºÆæ¼ ÅäÃâ»óÅ ±ÕÀϵµ Çâ»óÀ» À§ÇÑ ±â±¸ °¡ÀÌµå º¯°æ. 2024³âµµ ´ëÇѱâ°èÇÐȸ »ý»ê ¹× ¼³°è°øÇкι® Ãá°èÇмú´ëȸ. [27] À±ÀçÇö, & µµÀçÇõ. (2024). ÁÖÇà ¾ÈÁ¤¼º Çâ»óÀ» À§ÇÑ Â÷·® ¸®¾î ½ºÆ÷ÀÏ·¯ ÃÖÀûÈ. 2024³âµµ ´ëÇѱâ°èÇÐȸ »ý»ê ¹× ¼³°è°øÇкι® Ãá°èÇмú´ëȸ. [28] À±ÀçÇö, ¹ÚÂù¼ö, & µµÀçÇõ. (2024). ÄõµåÄßÅÍ ÀÚ¼¼Á¦¾î ±³À°¿¡¼ÀÇ µðÁöÅÐ Æ®À© Àû¿ë. 2024³âµµ ´ëÇѱâ°èÇÐȸ ±³À°ºÎ¹® Ãá°èÇмú´ëȸ. [29] Á¤°¡Àº, ±Ç¼ÒÀ±, À±¿¹Áö, ÀÌ´ÙÇý, Á¤½Ã¿ì & À±ÀçÇö. (2024) ½ÇÇè°èȹ¹ý¿¡ ÀÇÇÑ ¹ÙÄû ¸ñ Æ÷ȹ ±â±âÀÇ ¼¾¼ °¨Áö ¼º´É Çâ»ó ¿¬±¸. ´ëÇѱâ°èÇÐȸ 2024³â Çмú´ëȸ [30] Á¤½Ã¿ì, ±Ç¼ÒÀ±, À±¿¹Áö, ÀÌ´ÙÇý, Á¤°¡Àº & À±ÀçÇö. (2024) Àü»êÀ¯Ã¼¿ªÇÐ ±â¹Ý ¹ÙÄû¸ñ Æ÷ȹ ÀåÄ¡ÀÇ ÈíÀÔ ÆÒ ºí·¹À̵å Çü»ó ±Ù»ç ÃÖÀû¼³°è. ´ëÇѱâ°èÇÐȸ 2024³â Çмú´ëȸ [31] Á¤»óÁØ, ÀÌÁ¤¿ì, Áø¹Î¼ö, ¹Ú»óÇö, À±ÀçÇö & µµÀçÇõ. (2024) PID ±â¹Ý µå·Ð ÀÚ¼¼ Á¦¾î ¹× ºñÇà ¸ð´ÏÅ͸µÀ» À§ÇÑ Unity ±â¹Ý µðÁöÅÐ Æ®À© ±¸Ãà ¿¬±¸. ´ëÇѱâ°èÇÐȸ 2024³â Çмú´ëȸ
*¿¬±¸°úÁ¦ [1] ´öÆ®ÆÒÀ» Àû¿ëÇÑ Äõµå·ÎÅÍ Á¦ÀÚ¸®ºñÇà ¼º´É ¿¬±¸. ¼¿ï»çÀ̹ö´ëÇб³(2022). [2] Â÷·® ÁÖÇà ¾ÈÁ¤¼º Çâ»óÀ» À§ÇÑ ¸®¾î ½ºÆ÷ÀÏ·¯ ÃÖÀû¼³°è. (ÁÖ)Çѱ¹»ê¾÷±â¼úÁøÈïÇùȸ(2023).
*ƯÇã ¹× Ãâ¿ø [1] º¸È£¼ö´ÜÀ» Æ÷ÇÔÇÏ´Â ºñÇàÀåÄ¡. (2014). Ãâ¿ø 10-2014-006819. [2] ¹°¼Ó º¸Çà°ú °°Àº È¿°ú¸¦ °®´Â ÀçȰ º¸Á¶ ÀåÄ¡.(2016) µî·Ï 10-161782. [3] µö ·¯´× ±â¹Ý ¼³°è ÃÖÀûÈ ¹æ¹ý ¹× ½Ã½ºÅÛ. (2020). Ãâ¿ø 10-2020-0159605
*Àú¼ [1] ±è¼ºµµ, À±ÀçÇö, ¾çÇö»ó, ±èÁ¤Çõ, À̰溹, Á¤Àç¿, ±èÀº±¤, ¹Ú¼öÈ£. (2023) ¾Ë±â ½¬¿î ¹Ì·¡ ±¹¹æ±â¼ú. ¼¿ï»çÀ̹ö´ëÇб³ ÃâÆÇºÎ, 978-89-94578-18-7
*¼ö»ó [1] ¿¬¼¼´ëÇб³ Á¦2ȸ Á¤±âÇмú´ëȸ ³í¹® ¿ì¼ö»ó, Prediction of Flow Around Aircraft Airfoil Using Convolution Neural Networks. (2017) [2] ´ëÇѱâ°èÇÐȸ ±³À°ºÎ¹® 2023³â Ãá°èÇмú´ëȸ ¹ßÇ¥, ¿ì¼ö³í¹®»ó, ±º ±â¼ú±³À°°úÁ¤ °³¼³ ¹× ¿î¿µ.(2024) |