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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  3. 2026-07 (Vol.75 No.7)
  4. 10.5370/KIEE.2026.75.7.1524
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References

1 
M. Zhou, "Ensemble-based algorithm for synchrophasor data anomaly detection in smart grid," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2979-2988, 2019. DOI
2 
G. Li, J. J. Jung, "Deep learning for anomaly detection in multivariate time-series: Approaches, applications, and challenges," Information Fusion, vol. 91, pp. 93-102, 2023. DOI
3 
J. Kim, K. Kim, W. Nah, "Diagnosis of fault and abnormal conditions in a single-phase transformer using S-parameter measurement," The Transactions of the Korean Institute of Electrical Engineers, vol. 67, no. 10, pp. 1344-1352, 2018. DOI
4 
H. Homayouni, "An autocorrelation-based LSTM-autoencoder for anomaly detection on time-series data," pp. 5068-5077, 2020. DOI
5 
D. L. John, "Identifying optimal window size configurations for big data time series forecasting," pp. 5138-5146, 2024. DOI
6 
H. Wen, "A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection," Applied Energy, vol. 378, pp. 1-12, 2025. DOI
7 
H. Kim, H. Kim, P. Kang, "Unsupervised anomaly detection with wider and deeper LSTM-GAN for energy consumption pattern," Journal of the Korean Institute of Industrial Engineers, vol. 47, no. 5, pp. 421-432, 2021. DOI
8 
G. Lai, "Modeling long- and short-term temporal patterns with deep neural networks," pp. 95-104, 2018. DOI
9 
I. Kozlov, "Self-supervised Transformer architecture for change detection in radio access networks," pp. 2227-2232, 2023. DOI
10 
A. Melo, "Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis," Comput. Chem. Eng., vol. 165, pp. 1-15, 2022. DOI
11 
N. Ki, D. Yang, S. Lee, B. Kim, S. S. Bang, "Time window selection for anomaly detection in power equipment," 2025. Google Search
12 
S. Putchala, Available: https://www.kaggle.com/datasets/sreshta140/ai-transformer-monitoring. Accessed: Apr, 21, 2025, "AI-transformer monitoring," Kaggle, 2020. URL
13 
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung, "Time Series Analysis: Forecasting and Control," John Wiley & Sons, Hoboken, NJ, USA, 2015. Google Search
14 
P. J. Brockwell, R. A. Davis, "Introduction to Time Series and Forecasting," Springer, Cham, Switzerland, 2016. DOI
15 
S. R. Putchala, R. Kotha, V. Guda, Y. Ramadevi, "Transformer data analysis for predictive maintenance," pp. 217-230, 2022. DOI