Ying Wu


  1. [1] W. Zhu, Y. Xue, Z. Xu, and C. Peng, Symmetrical circulationgradient color system construction and gradient color yarnspun by a three-channel numerical control spinning system,Textile Research Journal, 92(11–12), 2022, 2046–2060.
  2. [2] F.J. Vasko, Y. Lu, and B. McNally, A simple methodologythat efficiently generates all optimal spanning trees for thecable-trench problem, Journal of Computational and CognitiveEngineering, 1(1), 2022, 13–20.
  3. [3] A. Sarkar, A. Biswas, and M. Kundu, Development of q-rung orthopair trapezoidal fuzzy einstein aggregation operatorsand their application in MCGDM problems, Journal ofComputational and Cognitive Engineering, 1(3), 2022, 109–121.
  4. [4] Z. Xu, K. Zhang, J. He, and X. Liu, A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems, Information Sciences, 596, 2022,236–263.
  5. [5] G.B. Libotte, F.S. Lobato, F.D. Moura Neto, and G.M.Platt, A novel reliability-based robust design multiobjectiveoptimization formulation applied in chemical engineering,Industrial and Engineering Chemistry Research, 61(9), 2022,3483–3501.
  6. [6] N. Bahrami-Novin, E. Mahdavi, M. Shaban, and H. Mazaheri,Multi-objective optimization of tensile properties of thecorrugated composite sheet, Journal of Composite Materials,56(5), 2022, 811–821.
  7. [7] J.Y. Ji and M.L. Wong, An improved dynamic multi-objective optimization approach for nonlinear equationsystems, Information Sciences, 576, 2021, 204–227.
  8. [8] Z.G. Xiao, N.M.S. Rao, K. Kannan, and D. Sinharoy, Multi-objective optimization of feature selection using hybrid catswarm optimization, Science in China: Technical Sciences,64(3), 2021, 508–520.
  9. [9] H. Wang, B. Sheng, Q. Lu, X. Yin, F. Zhao, X. Lu, R. Luo,and G. Fu, A novel multi-objective optimization algorithmfor the integrated scheduling of flexible job shops consideringpreventive maintenance activities and transportation processes,Soft Computing, 25(4), 2021, 2863–2889.
  10. [10] S. Xu, Q. Yue, and B. Lu, Grey correlation analysis on thesynergistic development between innovation-driven strategyand marine industrial agglomeration: Based on China’s coastalprovinces, Grey Systems: Theory and Application, 12(1), 2022,269–289.
  11. [11] X. Yan, J. Jiao, B. Tang, and Y. Liang, and Z. Wang, Assessingsediment connectivity and its spatial response on land useusing two flow direction algorithms in the catchment on theChinese Loess Plateau, Journal of Mountain Science, 19(4),2022, 1119–1138.
  12. [12] J. Zhou, E.S. Huebner, J. Chen, and L. Tian, Co–developmentof aggression in elementary school children: The predictiveroles of victimization experiences, Aggressive Behavior, 48(2),2022, 173–186.
  13. [13] A. Palanisamy, N. Jeyaprakash, V. Sivabharathi, and S.Sivasankaran, Effects of dry turning parameters of Incoloy800H superalloy using Taguchi-based Grey relational analysisand modeling by response surface methodology, Proceedings ofthe Institution of Mechanical Engineers, Part C: Journal ofMechanical Engineering Science, 236(1), 2022, 607–623.
  14. [14] Y. Han, G. Song, F. Liu, and Z. Geng, Fault monitoring usingnovel adaptive kernel principal component analysis integratinggrey relational analysis, Process Safety and EnvironmentalProtection, 157, 2022, 397–410.
  15. [15] V.T. Minh, R. Moezzi, J. Cyrus, and J. Hlava, Fuzzy systemfor clutch engagement and vibration control in parallel hybridelectric vehicle, Mechatronic Systems and Control, 51(1), 2023,25–33.
  16. [16] X. Qian, C. Wu, Y. Liu, and C. Wang, Derating-basedverification method for rated fatigue pressure of pressure-containing envelopes, Mechatronic Systems and Control, 50(4),2022, 182–188.
  17. [17] T. Zhang and L. Shi, Fault analysis of transmission line basedon big data algorithm, Mechatronic Systems and Control,50(4), 2022, 216–223.
  18. [18] S. Velchev, I. Kolev, K. Ivanov, and S. Gechevski, Empiricalmodels for specific energy consumption and optimization ofcutting parameters for minimizing energy consumption duringturning, Journal of Cleaner Production, 80(1), 2014, 139–149.
  19. [19] L.C. Moreira, W.D. Li, X. Lu, and M.E. Fitzpatrick, Energy-Efficient machining process analysis and optimisation based onBS EN24T alloy steel as case studies, Robotics and Computer-Integrated Manufacturing, 58, 2019, 1–12.
  20. [20] X. Chen, C. Li, Y. Tang, L. Li, and H. Li, Energy efficient cuttingparameter optimization, Frontiers of Mechanical Engineering,16, 2021, 221–248.
  21. [21] S. Jia, S. Wang, J. Lv, W. Cai, N. Zhang, Z. Zhang,and S. Bai, Multi-objective optimization of CNC turningprocess parameters considering transient-steady state energyconsumption, Sustainability, 13(24), 2021, 13803–13803.8
  22. [22] C. Feng, Y. Wu, W. Li, B. Qiu, J. Zhang, and X. Xu,Energy consumption optimisation for machining processesbased on numerical control programs, Advanced EngineeringInformatics, 57(1), 2023, 102101.
  23. [23] Z. Feng, X. Ding, H. Zhang, and Y. Liu, An energy consumptionestimation method for the tool setting process in CNC millingbased on the modular arrangement of predetermined timestandards, Energies, 16(20), 2023, 7064–7064.
  24. [24] Y. Hu, S. Lv, J. Wan C, Zheng, S. Dan, H Wang, Y. Tao,M. Li, and Y. Luo, Recent advances in nanomaterials forprostate cancer detection and diagnosis, Journal of MaterialsChemistry, B. materials for biology and medicine, 26(10),2022, 4907–4934.
  25. [25] M. Versaci, G. Angiulli, P. Barba, and F.C. Morabito, Jointuse of eddy current imaging and fuzzy similarities to assess theintegrity of steel plates, Open Physics, 18(1), 2020, 230–240.

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