IEEE Systems, Man and Cybernetics Magazine - October 2022 - 24

and his M.S. and Ph.D. degrees from Concordia University,
Montreal, Canada, in 2006 and 2011, respectively. He is
with the Helen and John C. Hartmann Department of
Electrical and Computer Engineering, New Jersey Institute
of Technology, Newark, NJ 07102 USA, and the
School of Applied Engineering and Technology, New Jersey
Institute of Technology, Newark, NJ 07102 USA. His
interests include cooperative and distributed control, networked
estimation, and fault diagnosis and prognosis in
microgrids and renewable energy systems. He is a Senior
Member of IEEE.
Mengchu Zhou (zhou@njit.edu) earned his B.S.
degree from Nanjing University of Science and Technology,
Nanjing, China in 1983, his M.S. from Beijing Institute
of Technology, Beijing, China in 1986, and his Ph. D.
degree from Rensselaer Polytechnic Institute, Troy, NY,
USA, in 1990. He is with the Helen and John C. Hartmann
Department of Electrical and Computer Engineering,
New Jersey Institute of Technology, Newark, NJ 07102
USA. His research interests include intelligent automation,
Petri nets, Internet of Things, edge/cloud computing,
and big data analytics. He has more than 1,000 publications,
including 12 books, more than 700 journal papers
(more than 600 in IEEE transactions), 31 patents, and 30
book-chapters. He is Fellow of IEEE, IFAC, AAAS, CAA
and NAI.
References
[1] C. Liu, Y. Wu, J. Liu, Z. Sun, and H. Xu, " Insulator faults detection in aerial images
from high-voltage transmission lines based on deep learning model, " Appl. Sci., vol. 11,
no. 10, p. 4647, 2021, doi: 10.3390/app11104647.
[2] Y. Liu, S. Pei, W. Fu, K. Zhang, X. Ji, and Z. Yin, " The discrimination method as
applied to a deteriorated porcelain insulator used in transmission lines on the basis of
a convolution neural network, " IEEE Trans. Dielectr. Electr. Insul., vol. 24, no. 6, pp.
3559-3566, Dec. 2017, doi: 10.1109/TDEI.2017.006840.
[3] Z. Zhao, Z. Zhen, L. Zhang, Y. Qi, Y. Kong, and K. Zhang, " Insulator detection
method in inspection image based on improved faster r-cnn, " Energies, vol. 12, no. 7, p.
1204, 2019, doi: 10.3390/en12071204.
[4] Y. Zhai, D. Wang, M. Zhang, J. Wang, and F. Guo, " Fault detection of insulator based
on saliency and adaptive morphology, " Multimedia Tools Appl., vol. 76, no. 9, pp.
12,051-12,064, 2017, doi: 10.1007/s11042-016-3981-2.
[5] Y. Li, W. Zhang, P. Li, Y. Ning, and C. Suo, " A method for autonomous navigation and
positioning of UAV based on electric field array detection, " Sensors, vol. 21, no. 4, p.
1146, 2021, doi: 10.3390/s21041146.
[6] Y. Ma, Q. Li, L. Chu, Y. Zhou, and C. Xu, " Real-time detection and spatial localization
of insulators for UAV inspection based on binocular stereo vision, " Remote Sens.,
vol. 13, no. 2, p. 230, 2021, doi: 10.3390/rs13020230.
[7] H. Cheng, Y. Zhai, R. Chen, D. Wang, Z. Dong, and Y. Wang, " Self-shattering defect
detection of glass insulators based on spatial features, " Energies, vol. 12, no. 3, p. 543,
2019, doi: 10.3390/en12030543.
[8] Y. Zhai, R. Chen, Q. Yang, X. Li, and Z. Zhao, " Insulator fault detection based on
spatial morphological features of aerial images, " IEEE Access, vol. 6, pp. 35,316-
35,326, Jun. 2018, doi: 10.1109/ACCESS.2018.2846293.
[9] S. Liao and J. An, " A robust insulator detection algorithm based on local features
and spatial orders for aerial images, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5,
pp. 963-967, 2014, doi: 10.1109/LGRS.2014.2369525.
24 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2022
[10] W. Wang, Y. Wang, J. Han, and Y. Liu, " Recognition and drop-off detection of
insulator based on aerial image, " in Proc. 9th IEEE Int. Symp. Comput. Intell. Design
(ISCID), vol. 1, pp. 162-167, 2016, doi: 10.1109/ISCID.2016.1045.
[11] A. Muzahid, W. Wan, F. Sohel, L. Wu, and L. Hou, " CurveNet: Curvature-based
multitask learning deep networks for 3d object recognition, " IEEE/CAA J. Automat.
Sinica, vol. 8, no. 6, pp. 1177-1187, 2020, doi: 10.1109/JAS.2020.1003324.
[12] F. M. Shakiba and M. Zhou, " Novel analog implementation of a hyperbolic tangent
neuron in artificial neural networks, " IEEE Trans. Ind. Electron., vol. 68, no. 11, pp.
10,856-10,867, Nov. 2020, doi: 10.1109/TIE.2020.3034856.
[13] F. M. Shakiba, CMOS-Based Implementation of Hyperbolic Tangent Activation
Function for Artificial Neural Network. Carbondale, IL, USA: Southern Illinois Univ.
Carbondale, 2018.
[14] Y. Wang, Z. Li, X. Yang, N. Luo, Y. Zhao, and G. Zhou, " Insulator defect recognition
based on faster R-CNN, " in Proc. IEEE Int. Conf. Comput., Inf. Telecommun. Syst.
(CITS), pp. 1-4, 2020, doi: 10.1109/CITS49457.2020.9232614.
[15] X. Lei and Z. Sui, " Intelligent fault detection of high voltage line based on the faster
R-CNN, " Measurement, vol. 138, pp. 379-385, May 2019, doi: 10.1016/j.measurement.
2019.01.072.
[16] Y. Wang et al., " Image classification towards transmission line fault detection via
learning deep quality-aware fine-grained categorization, " J. Vis. Commun. Image Represent.,
vol. 64, p. 102,647, Oct. 2019, doi: 10.1016/j.jvcir.2019.102647.
[17] F. M. Shakiba, M. Shojaee, S. M. Azizi, and M. Zhou, " Generalized fault diagnosis
method of transmission lines using transfer learning technique, " Neurocomputing, vol.
500, pp. 556-566, Aug. 2022, doi: 10.1016/j.neucom.2022.05.022.
[18] S. R. Fahim et al., " Self attention convolutional neural network with time
series imaging based feature extraction for transmission line fault detection and
classification, " Elect. Power Syst. Res., vol. 187, p. 106,437, Oct. 2020, doi: 10.1016/j.
epsr.2020.106437.
[19] P. Rai et al., " Fault classification in power system distribution network integrated
with distributed generators using CNN, " Elect. Power Syst. Res., vol. 192, p. 106,914,
Mar. 2021, doi: 10.1016/j.epsr.2020.106914.
[20] S. Fuada et al., " A high-accuracy of transmission line faults (TLFs) classification
based on convolutional neural network, " Int. J. Electron. Telecommun., vol. 66, no. 4,
pp. 655-664, 2020, doi: 10.24425/ijet.2020.134024.
[21] H. A. Shiddieqy et al., " Power line transmission fault modeling and dataset generation
for AI based automatic detection, " in Proc. 2019 Int. Symp. Electron. Smart
Devices (ISESD), pp. 1-5, doi: 10.1109/ISESD.2019.8909594.
[22] K. Chen et al., " Detection and classification of transmission line faults based on
unsupervised feature learning and convolutional sparse autoencoder, " IEEE Trans.
Smart Grid, vol. 9, no. 3, pp. 1748-1758, May 2016, doi: 10.1109/TSG.2016.2598881.
[23] E. F. Ohata et al., " Automatic detection of covid-19 infection using chest X-ray
images through transfer learning, " IEEE/CAA J. Automat. Sinica, vol. 8, no. 1,
pp. 239-248, Jan. 2020, doi: 10.1109/JAS.2020.1003393.
[24] Q. Kang, S. Yao, M. Zhou, K. Zhang, and A. Abusorrah, " Effective visual domain
adaptation via generative adversarial distribution matching, " IEEE Trans. Neural
Netw. Learn. Syst., vol. 32, no. 9, pp. 3919-3929, Sep. 2020, doi: 10.1109/
TNNLS.2020.3016180.
[25] G. Cai, Y. Wang, L. He, and M. Zhou, " Unsupervised domain adaptation with adversarial
residual transform networks, " IEEE Trans. Neural Netw. Learn. Syst., vol. 31,
no. 8, pp. 3073-3086, Aug. 2019, doi: 10.1109/TNNLS.2019.2935384.
[26] G. Cai, L. He, M. Zhou, H. Alhumade, and D. Hu, " Learning smooth representation
for unsupervised domain adaptation, " IEEE Trans. Neural Netw. Learn. Syst., early
access, 2021, doi: 10.1109/TNNLS.2021.3119889.
[27] G. Wang, J. Qiao, J. Bi, W. Li, and M. Zhou, " Tl-GDBN: Growing deep belief network
with transfer learning, " IEEE Trans. Autom. Sci. Eng., vol. 16, no. 2, pp. 874-885,
Apr. 2018, doi: 10.1109/TASE.2018.2865663.
IEEE Systems, Man and Cybernetics Magazine - October 2022

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - October 2022
