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2025 Vol.29, Issue 4 Preview Page

RESEARCH PAPERS

31 August 2025. pp. 26-32
Abstract
References
1

Zhao, F., Lai, M.C. and Harrington, D.L, “Automotive Spark-Ignited Direct -Injection Gasoline Engines,” Progress in Energy and Combustion Science, Vol. 25, No. 5, pp. 437-562, 1999.

10.1016/S0360-1285(99)00004-0
2

Duronio, F., De Vita, A., Allocca, L. and Anatone, M., “Gasoline direct injection engines–A review of latest technologies and trends, Part 1: Spray breakup process,” Fuel, Vol. 265, pp. 116948, 2020.

10.1016/j.fuel.2019.116948
3

Duronio, F., De Vita, A., Montanaro, A. and Villante, C., “Gasoline direct injection engines–A review of latest technologies and trends, Part 2,” Fuel, Vol. 265, pp. 116947, 2020.

10.1016/j.fuel.2019.116947
4

Spicher, U, Reissing, J., Kech, J.M. and Gindele, J., “Gasoline Direct Injection (GDI) engines-Development Potentialities,” Technical Report, SAE Technical Paper 1999-01-2938, 1999.

10.4271/1999-01-2938
5

Cavina, N., Businaro, A., Rojo, N., De Cesare, M., Paiano, L. and Cerofolini, A., “Combustion and intake/exhaust systems diagnosis based on acoustic emissions of a GDI TC engine,” Energy Procedia, Vol. 101, pp. 677–684, 2016.

10.1016/j.egypro.2016.11.086
6

Chen, H., Liu, H., Chu, X., Liu, Q. and Xue, D., “Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network,” Renewable Energy, Vol. 172, pp. 829-840, 2021.

10.1016/j.renene.2021.03.078
7

Li, Z., Li, J., Wang, Y. and Wang, K., “A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment,” The International Journal of Advanced Manufacturing Technology, Vol. 103, pp. 499-510, 2019.

10.1007/s00170-019-03557-w
8

Qu, C., Zhou, Z., Liu, Z. and Jia, S., “Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder,” Energy Reports, Vol. 8, pp. 998-1003, 2022.

10.1016/j.egyr.2022.01.225
9

Wielgosz, M., Skoczeń, A. and De Matteis, E., “Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor,” Sensors, Vol. 18, No. 11, p. 3933, 2018.

10.3390/s1811393330441813PMC6264111
10

Lee, M., “Early warning detection of thermoacoustic instability using three-dimensional complexity-entropy causality space,” Experimental Thermal and Fluid Science, Vol. 130, p. 110517, 2022.

10.1016/j.expthermflusci.2021.110517
11

Han, E., Kim, D., Lee, J., Kim, Y., Yi, M. and Lee, M., “Analysis of the Hall-Effect Thruster Discharge Blowoff Using Complexity-Entropy Causality Plane,” Journal of the Korean Society for Aeronautical & Space Sciences, Vol. 51, No. 4, pp. 263-271, 2023.

10.5139/JKSAS.2023.51.4.263
12

Son, H. and Lee, M., “A PINN approach for identifying governing parameters of noisy thermoacoustic systems,” Journal of Fluid Mechanics, Vol. 984, p. A21, 2024.

10.1017/jfm.2024.219
13

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., “Attention is all you need,” Advances in Neural Information Processing Systems, Vol. 30, 2017.

14

Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A. and Eickhoff, C., “A transformer-based framework for multivariate time series representation learning,” 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, pp. 2114-2124, Aug. 2021.

10.1145/3447548.3467401
15

Jin, Y., Hou, L. and Chen, Y., “A time series transformer based method for the rotating machinery fault diagnosis,” Neurocomputing, Vol. 494, pp. 379-395, 2022.

10.1016/j.neucom.2022.04.111
16

Li, Z., Zhang, X. and Dong, Z., “TSF-transformer: a time series forecasting model for exhaust gas emission using transformer,” Applied Intelligence, Vol. 53, No. 13, pp. 17211-17225, 2023.

10.1007/s10489-022-04326-136590990PMC9788662
17

Lee, M., Kim, K.T., Gupta, V. and Li, L.K.B., “System identification and early warning detection of thermoacoustic oscillations in a turbulent combustor using its noise-induced dynamics,” Proceedings of the Combustion Institute, Vol. 38, No. 4, pp. 6025-6033, 2021.

10.1016/j.proci.2020.06.057
18

Guk, S., Seo, S. and Lee, M., “Thermoacoustic dynamics in an annular model gas-turbine combustor under transverse stochastic forcing,” Journal of the Korean Society of Combustion, Vol. 28, No. 3, pp. 20-27, 2023.

10.15231/jksc.2023.28.3.020
19

Guk, S., Seo, S. and Lee, M., “An image-based spatiotemporal approach for detecting coherence resonance in annular model gas-turbine combustor,” Physics of Fluids, Vol. 36, No. 5, 2024.

10.1063/5.0208950
Information
  • Publisher :The Korean Society of Propulsion Engineers
  • Publisher(Ko) :한국추진공학회
  • Journal Title :Journal of the Korean Society of Propulsion Engineers
  • Journal Title(Ko) :한국추진공학회지
  • Volume : 29
  • No :4
  • Pages :26-32
  • Received Date : 2025-03-10
  • Revised Date : 2025-04-10
  • Accepted Date : 2025-04-22