Xu Zhou, Rui Zhang, Guogen Li, Gang Wei, and Baishuang Liu
[1] C. Shao, S. Zheng, C. Gu, Y. Hu, and X. Qin, A novel outlierdetection method for monitoring data in dam engineering,Expert Systems with Application, 193(5), 2022, 2–16. [2] A.M. Usman and M.K, Abdullah, An assessment of buildingenergy consumption characteristics using analytical energy andcarbon footprint assessment model, Green and Low-CarbonEconomy, 1(1), 2023, 28–40. [3] H. Wang, J. Zhao, B. Wang, and L. Tong, A quantumapproximate optimization algorithm with metalearning forMaxCut problem and its simulation via TensorFlow quan-tum, Mathematical Problems in Engineering, 21(13), 2021,2–11. [4] B. Lla, G. Xi, B. Jfda, and S. Me, Machine learningaccelerated discrete element modeling of granular flows,Chemical Engineering Science, 21(245), 2021, 32–33. [5] J.M. Topple and J.A. Fawcett, MiNet: Efficient deep learningautomatic target recognition for small autonomous vehicles,IEEE Geoscience and Remote Sensing Letters, 18(6), 2021,14–19. [6] E. Zhao and C. Wu, Centroid deformation-based nonlinearsafety monitoring model for arch dam performance evaluation,Engineering Structures, 243(15), 2021, 2–13. [7] Q.Z. Wang and X.X. Wang, A fault detection diagnosis predictobserver based on resource allocation network, MechatronicSystems and Control, 50(2), 2022, 96–101. [8] A. Kumar, Reinforcement learning application and advancetowards stable control strategies, Mechatronic Systems andControl, 51(1), 2023, 53–57. [9] V.T. Minh, R. Reza Moezzi, J. Cyrus, and J. Hlava, Feasibleand optimal trajectories generation for autonomous drivingvehicles, Mechatronic Systems and Control, 51(1), 2023,11–24. [10] R. Cardoso, D. Golubovic, I.P. Lozada, R. Rocha, J. Fernandes,and S. Vallecorsa, Accelerating GAN training using highlyparallel hardware on public cloud, The European PhysicalJournal Conferences, 251(10), 2021, 20–73. [11] P. Thomadakis, A. Angelopoulos, G. Gavalian, and N.Chrisochoides, Using machine learning for particle trackidentification in the CLAS12 detector, Computer PhysicsCommunications, 276(10), 2022, 108–121. [12] S. Ashry, T. Ogawa, and W. Gomaa, CHARM-Deep:Continuous human activity recognition model based on deepneural network using IMU sensors of smartwatch, IEEE SensorsJournal, 20(15), 2020, 57–70. [13] M.N. Anh and D.X. Bien, Voice recognition and inversekinematics control for a redundant manipulator based on amultilayer artificial intelligence network, Journal of Robotics,2021(4), 2021, 1–10. [14] N. Chen, Y. Zhang, J. Wu, H. Zhang, V. Chamola, andV.C. Albuquerque, Brain–computer interface-based targetrecognition system using transfer learning: A deep learningapproach, Computational Intelligence, 38(1), 2022, 139–155. [15] C. Han and R. Xue, Differentially private GANs by addingnoise to discriminator’s loss, Computers & Security, 107(7),2021, 102–116. [16] M. ¨Unver, M. Olgun, and E. T¨urkarslan, Cosine and cotangentsimilarity measures based on Choquet integral for Sphericalfuzzy sets and applications to pattern recognition, Journalof Computational and Cognitive Engineering, 1(1), 2022,21–31. [17] Y. Li, K. Min, Y. Zhang, and L. Wen, Prediction of the failurepoint settlement in rockfill dams based on spatial-temporal dataand multiple-monitoring-point models, Engineering Structures,243(15), 2021, 2–12. [18] S. Qin, H. Liu, Q. Meng, Y. Zhou, S. Xu, E. Lichtfouse, and Z.Chen, Enhanced nutrient removal from mixed black water bya microbial ultra-low weak electrical stimulated anaerobic-twostage anoxic/aerobic process, Chemical Engineering Journal,434(1), 2022, 2–11. [19] D. Moutevelis, J. Rold´an-P´erez, M. Prodanovic, and S. Sanchez-Acevedo, Bifurcation analysis of active electrical distributionnetworks considering load tap changers and power convertercapacity limits, IEEE Transactions on Power Electronics,37(6), 2022, 7230–7246. [20] E.M, Thomas, M.K. Mcbride, O.A. Lee, R.C Hayward,and A.J. Crosby, Predicting the electrical, mechanical, andgeometric contributions to soft electroadhesives throughfracture mechanics, ACS Applied Materials and Interfaces,15(25), 2023, 30956–30963.5
Important Links:
Go Back