Advanced search
Publication Cover
Biotechnology & Biotechnological Equipment Volume 35, 2021 - Issue 1
Submit an article Journal homepage
1,031
Views
4
CrossRef citations to date
0
Altmetric
Articles

Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression

Zhao Juna Department of Mechanical Engineering, College of Mechanical and Electronic Engineering, Tarim University, Aral, Xinjiang Uygur Autonomous Region, PR ChinaView further author information
,
Zhang Youqianga Department of Mechanical Engineering, College of Mechanical and Electronic Engineering, Tarim University, Aral, Xinjiang Uygur Autonomous Region, PR ChinaCorrespondence[email protected]
View further author information
,
Cao Weib Department of Abdominal Oncology, West China Hospital, Sichuan University, ChengDu, Sichuan, PR ChinaView further author information
&
Chen Fuc Department of Blood Transfusion, West China Hospital, Sichuan University, ChengDu, Sichuan, PR ChinaView further author information
Pages 462-468 | Received 13 Oct 2020, Accepted 15 Feb 2021, Published online: 04 Mar 2021

References

  • Maxian TA, Brown TD, Pedersen DR, et al. A sliding-distance-coupled finite element formulation for polyethylene wear in total hip arthroplasty. Techn Note. 1996;29(5):687–692.
  • Wang L, Liu XH, Li DC, et al. Contact mechanics studies of an ellipsoidal contact bearing surface of metal-on-metal hip prostheses under micro-lateralization . Med Eng Phys. 2014;36(4):419–424.
  • Hua X, Li JJ, Jin ZM, et al. The contact mechanics and occurrence of edge loading in modular metal-on-polyethylene total hip replacement during daily activities. Med Eng Phys. 2016;38(6):518–525.
  • Cilingir AC. Finite element analysis of the contact mechanics of ceramic-on-ceramic hip resurfacing prostheses. J Bionic Eng. 2010;7(3):244–253.
  • KošakKralj-Iglič RV, Iglič A, et al. Polyethylene Wear is Related to Patient-specific Contact Stress in THA. Clin Orthop Relat Res. 2011; 469(12):3415–3422.
  • Lizhang J, Fisher J, Jin Z, et al. The effect of contact stress on cartilage friction, deformation and wear. Proc Inst Mech Eng H. 2011; 225(5):461–475.
  • Jangid V, Singh A, Mishra KA. Wear Simulation of artificial hip joints: effect of materials. Mater Today Proc. 2019; 18:3867–3875.
  • Balabin RM, Lomakina EI. Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data . Analyst. 2011; 136(8):1703–1712.
  • Guo F, Ren LH, Jin YC, et al. A dynamic SVR–ARMA model with improved fruit fly algorithm for the nonlinear fiber stretching process. Nat Comput. 2019;18(4):747–756.
  • García-Nieto PJ, García-Gonzalo E, Sánchez Lasheras F, et al. A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study. J Comput Appl Math. 2020;366:112417.
  • Li Y, Sun MH, Yan WZ, et al. Multi-output parameter-insensitive kernel twin SVR model. Neural Netw. 2020;121:276–293.
  • DundarTopkaya ST, Solmaz MY, et al. Finite element analysis of the stress distributions in peri-implant bone in modified and standard-threaded dental implants. Biotechnol Biotechnolog Equip. 2016;30(1):127–133.
  • Hua X, Wroblewski BM, Jin ZM, et al. The effect of cup inclination and wear on the contact mechanics and cement fixation for ultra high molecular weight polyethylene total hip replacements. Med Eng Phys. 2012;34(3):318–325.
  • ISO 14242-3. 2009. Implants for surgery — Wear of total hip-joint prostheses — Part 3: loading and displacement parameters for orbital bearing type wear testing machines and corresponding environmental conditions for test.Avilable from https://www.iso.org/standard/44997.html.
  • Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. ). 2011; 2(3):1–65.
  • Wu CH, Tzeng GH, Lin RH. A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl. 2009;36(3):4725–4735.
  • Smola AJ, Schölkopf B. A tutorial on support vector regression. Statist Comput. 2004;14(3):199–222.
  • Kavousi-Fard A, Samet H, Marzbani F. A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl. 2014;41(13):6047–6056.
  • Ling H, Qian CX, Kang W, et al. Combination of support vector machine and K-Fold cross validation to predict compressive strength of concrete in marine environment. Constr Build Mater. 2019;206:355–363.
  • Chen HZ, Liu ZY, Cai K, et al. Grid search parametric optimization for FT-NIR quantitative analysis of solid soluble content in strawberry samples. Vib Spectrosc. 2018;94:7–15.
  • Tutun S, Chou CA, Canıyılmaz E. A new forecasting framework for volatile behavior in net electricity consumption: a case study in Turkey. Energy. 2015;93:2406–2422.
  • Karmy JP, Maldonado S. Hierarchical time series forecasting via support vector regression in the European Travel Retail Industry. Expert Syst Appl. 2019;137:59–73.
  • Lu SZ, Jiang MS, Wang XH, et al. Damage degree prediction method of CFRP structure based on fiber. Optik. 2019;180:244–253.
  • Xue Z, Zhang W, Cheng YC, et al. Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing. 2020;376:95–102.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.