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The Transactions of
the Korean Institute of Electrical Engineers
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ISSN : 2287-4364 (Online)
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The Transactions of the Korean Institute of Electrical Engineers
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Trans. Korean. Inst. Elect. Eng.
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2021-07
(Vol.70 No.7)
10.5370/KIEE.2021.70.7.1029
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References
1
M. Iorgulescu, R. Beloiu, M. O. Popescu, 2010, Vibration monitoring for diagnosis of electrical equipment’s faults, 12th International Conference on Optimization of Electrical and Electronic Equipment
2
M. Iorgulescu, M. Alexandru, R. Beloiu, 2012, Noise and vibration monitoring for diagnosis of DC motor’s faults, 13th International Conference on Optimization of Electrical Electronic Equipment
3
D. Kim, H. Jo, M. Kim, J. Roh, J. Park, 2019, Short-Term Load Forecasting Based on Deep Learning Model, The transactions of The Korean Institute of Electrical Engineers, Vol. 68, No. 9, pp. 1094-1099
4
J. Park, S. Oh, 2021, A Comparative Study on CNN-based Pattern Classifier through Partial Discharge Data Processing Methods, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 3, pp. 515-525
5
D. Lee, Y. Sun, I. Sim, Y. Hwang, S. Kim, J. Kim, 2019, Prediction of Power Consumptions Based on Gated Recurrent Unit for Internet of Energy, Journal of IKEE, Vol. 23, No. 1, pp. 120-126
6
J. Schmidhuber, 2015, Deep Learning in Neural Networks: An Overview, Neural Networks, Vol. 61, pp. 85-117
7
Y. LeCun, Y. Bengio, G. Hinton, 2015, Deep learning, Nature, Vol. 521, pp. 436-444
8
LeCun, Yann, 1998, Gradient based learning applied to document recognition, Proceeding of the IEEE
9
Tianmei Guo, Jiwen Dong, Henjian Li, Yunxing Gao, 2017, Simple convolutional neural network on image classification, IEEE 2nd International Conference on Big Data Analysis, pp. 721-724
10
K. Simonyan, A. Zisserman, 2014, Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning Representations
11
K. He. X. Zhang, S. Ren, J. Sun, 2016, Deep Residual Learning for Image Recognition, Computer Vision and Pattern Recognition
12
G. Huang, Z. Liu, K. Weinberger, 2016, Densely Connected Convolutional Networks, arXiv: 1609.06993
13
Aurelien Geron, 2019, Hand-On Machine Learning with ScikitLearn, Keras & TensorFlow 2nd, O’Reilly, pp. 497-524
14
T. Baba, 2012, Time-Frequency Analysis Using Short Time Fourier Transform, Open Acoustics Journal
15
K. Yabe, 1997, Power Differential Method for Discrimination between Fault and Magnetizing Inrush Current in Transformers, IEEE Transactions on Power Delivery, Vol. 12, No. 3, pp. 1109-1118
16
H. Zhang, J. Wen, P. Liu, O. Malik, 2002, Discrimination between fault and magnetizing inrush current in transformer using short-time correlation transform, Electrical Power and Energy Systems, Vol. 24, pp. 557-562
17
S. Paraskar, M. Beg, G. Dhole, 2002, Discrimination between inrush and fault condition in transformer: a probabilistic neural network approach, Int. J. Computational Systems Engineering, Vol. 1, No. 1, pp. 50-57
18
X. Yaoheng, L. Binding, L. Hongcai, S. Lipeng, L. Yun, H. Haibo, L. Xinwen, 2016, A Robust Discrimination Method for Inrush Current Based on Neural Network, 2016 First IEEE International Conference on Computer Communication and the Internet, pp. 543-546
19
Y. U. Kim, 2020, Artificial Intelligence and Power Systems, Trans of the KIEE, Vol. 69, No. 7, pp. 24-30