A statistical multivariable optimization method using improved orthogonal algorithm based on large data

References

• Zhu W, Fan Y. Relabeling algorithms for mixture models with applications for large data sets. J Statist Comput Simul. 2016;86(2):394–413. doi: 10.1080/00949655.2015.1015129
   Web of Science ®Google Scholar
• Manyika J, Chui M, Brown B, et al. Big data: the next frontier for innovation, competition, and productivity. Analytics, 2011;5(1):41–47.
   Google Scholar
• Kambatla K, Kollias G, Kumar V. Trends in big data analytics. J Parallel Distrib Comput. 2014;74(7):2561–2573. doi: 10.1016/j.jpdc.2014.01.003
   Web of Science ®Google Scholar
• Lee SJ. Big data analysis using principal component analysis. J Korean Instit Intell Syst. 2015;25(6):592–599. doi: 10.5391/JKIIS.2015.25.6.592
   Google Scholar
• Baumann P, Mazzetti P, Ungar J., et al. Big data analytics for earth sciences: the earth server approach. Int J Digital Earth. 2016;9(1):1–27. doi: 10.1080/17538947.2014.1003106
   Web of Science ®Google Scholar
• Belaud JP, Negny S, Dupros F, et al. Collaborative simulation and scientific big data analysis: illustration for sustainability in natural hazards management and chemical process engineering. Comput Ind. 2014;65(3):521–535. doi: 10.1016/j.compind.2014.01.009
   Web of Science ®Google Scholar
• Lu SL. Considering Taguchi loss function on statistically constrained economic sum of squares exponentially weighted moving average charts. J Statist Comput Simul. 2015;85(3):572–586. doi: 10.1080/00949655.2013.829059
   Web of Science ®Google Scholar
• Baecher GB, Christian JT. Reliability and statistics in geotechnical engineering. Britain: Wiley; 2003.
   Google Scholar
• Xu XF. Reliability-based design in geotechnical engineering: computations and applications. Struct Infrastruct Eng. 2011;7(3):259–260. doi: 10.1080/15732470902940293
   Google Scholar
• Wang G, Ma Z. Hybrid particle swarm optimization for first-order reliability method. Comput Geotech. 2017;81:49–58. doi: 10.1016/j.compgeo.2016.07.013
   Web of Science ®Google Scholar
• Silva ECN, Fonseca JSO, Kikuchi N. Optimal design of piezoelectric microstructures. Comput Mech. 1997;19(5):397–410. doi: 10.1007/s004660050188
   Web of Science ®Google Scholar
• Nelli Silva EC, Nishiwaki S, Kikuchi N. Design of piezocomposite materials and piezoelectric transducers using topology optimization— part III. Arch Comput Methods Eng. 1999;6(4):305–329. doi: 10.1007/BF02818918
   Web of Science ®Google Scholar
• Luo Z, Tong L, Luo J, et al. Design of piezoelectric actuators using a multiphase level set method of piecewise constants. J Comput Phys. 2009;228(7):2643–2659. doi: 10.1016/j.jcp.2008.12.019
   Web of Science ®Google Scholar
• Chen S, Gonella S, Chen W, et al. A level set approach for optimal design of smart energy harvesters. Comput Methods Appl Mech Eng. 2010;199(37–40):2532–2543. doi: 10.1016/j.cma.2010.04.008
   Web of Science ®Google Scholar
• Kang Z, Wang R, Tong L. Combined optimization of bi-material structural layout and voltage distribution for in-plane piezoelectric actuation. Comput Methods Appl Mech Eng. 2011;200(13–16):1467–1478. doi: 10.1016/j.cma.2011.01.005
   Web of Science ®Google Scholar
• Fukushima DHLM. On the global convergence of the BFGs method for nonconvex unconstrained optimization problems. Siam J Optim. 2001;11(4):1054–1064. doi: 10.1137/S1052623499354242
   Web of Science ®Google Scholar
• Byrd RH. Global convergence of a cass of quasi-newton methods on convex problems. SIAM J Numer Anal. 1987;24(5):1171–1190. doi: 10.1137/0724077
   Web of Science ®Google Scholar
• Li DH, Fukushima M. A modified BFGS method and its global convergence in nonconvex minimization. J Comput Appl Math. 2001;129(1–2):15–35. doi: 10.1016/S0377-0427(00)00540-9
   Web of Science ®Google Scholar
• Zheng-Chun DU, Yang JG, Yao ZQ, et al. Modeling approach of regression orthogonal experiment design for the thermal error compensation of a CNC turning center. J Mater Process Technol. 2002;129(S1):619–623.
   Google Scholar
• Jourdan A. Space-filling orthogonal arrays of strength two. J Statist Comput Simul. 2015;85(7):1382–1397. doi: 10.1080/00949655.2014.973880
   Web of Science ®Google Scholar
• Üstün B, Melssen WJ, Oudenhuijzen M, et al. Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Anal. Chim. Acta. 2005;544(1):292–305. doi: 10.1016/j.aca.2004.12.024
   Web of Science ®Google Scholar
• Liu R, Zhang Y, Wen C, et al. The orthogonal experiment design and analysis method study. Exp Technol Manage. 2010;27(9):52–55.
   Google Scholar
• Pourbagian M, Habashi WG. Aero-thermal optimization of in-flight electro-thermal ice protection systems in transient de-icing mode. Int J Heat Fluid Flow. 2015;54:167–182. doi: 10.1016/j.ijheatfluidflow.2015.05.012
   Web of Science ®Google Scholar
• Coffman HJ. Helicopter rotor icing protection methods. J Amer Helicopter Soc. 1987;32(2):34–39. doi: 10.4050/JAHS.32.34
   Google Scholar
• Gent RW, Dart NP, Cansdale JT. Aircraft icing. Philosop Trans R Soc London A: Math, Phys Eng Sci. 2000;358(1776):2873–2911. doi: 10.1098/rsta.2000.0689
   Web of Science ®Google Scholar
• Jang WH, Chan DK, Jae SJ. A study on the parameters for icing airworthiness flight tests of surion military helicopter. J Korean Soc. Aeronaut. Space Sci. 2015;43(6):526–532.
   Google Scholar
• Cao HY, Li GZ, Hess RA. Helicopter flight characteristics in icing conditions. Aeronaut J. 2012;116(116):963–979. doi: 10.1017/S0001924000007375
   Google Scholar
• Cao Y, Li G, Yang Q. Studies of trims, stability, controllability, and some flying qualities of a tandem rotor helicopter. Proc Instit Mech Eng Part G: J Aerospace Eng. 2009;223(2):171–177. doi: 10.1243/09544100JAERO462
   Web of Science ®Google Scholar