Skip to main content

Advertisement

SpringerLink
Log in

Early prediction of cooling load in energy-efficient buildings through novel optimizer of shuffled complex evolution

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The crucial significance of proper management of heating, ventilating, and air conditioning systems in energy-efficient buildings were the main reason for dedicating this study to test a novel approach for this task. Shuffled complex evolution (SCE) is an efficient metaheuristic technique that is used to optimize the performance of a multi-layer perceptron neural network (MLP) for accurate prediction of cooling load (CL). The CL information of 768 residential buildings, obtained from a vast computer simulation in the published literature, is used to train and validate the performance of the proposed model. The results showed that the SCE could properly surmount the computational drawbacks of the MLP, as its learning and prediction accuracies are enhanced by 19.52 and 22.84%, respectively. Also, the SCE outperformed two benchmark optimizers of moth–flame optimization and optics inspired optimization in both training and testing phases. Another advantage of the tested SCE-MLP was the considerably simpler structure, and consequently, shorter computation time (722 vs. 1050 and 46,192 s). Therefore, the proposed model can be promisingly used in practice for the early prediction of CL in energy-efficient buildings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. McQuiston FC, Parker JD (1982) Heating, ventilating, and air conditioning: analysis and design. Wiley, USA

    Google Scholar 

  2. Mi C, Cao L, Zhang Z, Feng Y, Yao L, Wu Y (2020) A port container code recognition algorithm under natural conditions. J Coastal Res 103:822–829. https://doi.org/10.2112/SI103-170.1

    Article  Google Scholar 

  3. Tian X, Song Z, Wang B, Zhou G (2020) A theoretical calculation method of influence radius of settlement based on the slices method in tunnel construction. Math Probl Eng 2020:1–9. https://doi.org/10.1155/2020/5804823

    Article  Google Scholar 

  4. Baek J, Kim E, Park M (2008) Adaptive fuzzy output feedback control for the nonlinear heating, ventilating, and air conditioning system. ITC-CSCC. In: International Technical Conference on Circuits Systems, Computers and Communications

  5. Yan X, Ren Q, Meng Q (2010) Iterative learning control in large scale HVAC system. In: 2010 8th World Congress on Intelligent Control and Automation

  6. Sun G, Yang B, Yang Z, Xu G (2019) An adaptive differential evolution with combined strategy for global numerical optimization. Soft Comput 24:6877–6296

    Google Scholar 

  7. Zhou G, Moayedi H, Bahiraei M, Lyu Z (2020) Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J Clean Prod 254:120082

    Google Scholar 

  8. Guo Z, Moayedi H, Foong LK, Bahiraei M (2020) Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing. Energy Build 214:109866

    Google Scholar 

  9. Tien Bui D, Moayedi H, Anastasios D, Kok Foong L (2019) Predicting heating and cooling loads in energy-efficient buildings using two hybrid intelligent models. Appl Sci 9:3543

    Google Scholar 

  10. Moayedi H, Nguyen H, Kok Foong L (2019) Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Eng Comput. https://doi.org/10.1007/s00366-019-00882-2

    Article  Google Scholar 

  11. Huang P, Huang G, Wang Y (2015) HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique. Energy Build 91:26–36

    Google Scholar 

  12. Wemhoff A, Frank M (2010) Predictions of energy savings in HVAC systems by lumped models. Energy Build 42:1807–1814

    Google Scholar 

  13. Korolija I, Zhang Y, Marjanovic-Halburd L, Hanby VI (2013) Regression models for predicting UK office building energy consumption from heating and cooling demands. Energy Build 59:214–227

    Google Scholar 

  14. Ji Q, Guo J-F (2015) Oil price volatility and oil-related events: an internet concern study perspective. Appl Energy 137:256–264. https://doi.org/10.1016/j.apenergy.2014.10.002

    Article  Google Scholar 

  15. Lv Q, Liu H, Wang J, Liu H, Shang Y (2020) Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets. Sci Total Environ 703:134394. https://doi.org/10.1016/j.scitotenv.2019.134394

    Article  Google Scholar 

  16. Zhu B, Ye S, Han D, Wang P, He K, Wei Y-M, Xie R (2019) A multiscale analysis for carbon price drivers. Energy Econ 78:202–216

    Google Scholar 

  17. Cao Y, Li Y, Zhang G, Jermsittiparsert K, Nasseri M (2020) An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Rep 6:530–542

    Google Scholar 

  18. Zhu B, Ye S, Jiang M, Wang P, Wu Z, Xie R, Chevallier J, Wei Y-M (2019) Achieving the carbon intensity target of China: a least squares support vector machine with mixture kernel function approach. Appl Energy 233:196–207

    Google Scholar 

  19. Zhu B, Zhang M, Huang L, Wang P, Su B, Wei Y-M (2020) Exploring the effect of carbon trading mechanism on China’s green development efficiency: a novel integrated approach. Energy Econ 85:104601

    Google Scholar 

  20. Das S, Swetapadma A, Panigrahi C (2019) A study on the application of artificial intelligence techniques for predicting the heating and cooling loads of buildings. J Green Build 14:115–128

    Google Scholar 

  21. Moayedi H, Mu'azu MA, Foong LK (2019) Novel swarm-based approach for predicting the cooling load of residential buildings based on social behavior of elephant herds. Energy Build 206:109579. https://doi.org/10.1016/j.enbuild.2019.109579

    Article  Google Scholar 

  22. Moayedi H, Bui TD, Dounis A, Lyu Z, Foong KL (2019) Predicting heating load in energy-efficient buildings through machine learning techniques. Appl Sci 9:438. https://doi.org/10.3390/app9204338

    Article  Google Scholar 

  23. Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci 9 Doi: 10.3390/app9132714

  24. Gao W, Alsarraf J, Moayedi H, Shahsavar A, Nguyen H (2019) Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Appl Soft Comput 84:105748. https://doi.org/10.1016/j.asoc.2019.105748

    Article  Google Scholar 

  25. Bui X-N, Moayedi H, Rashid ASA (2019) Developing a predictive method based on optimized M5Rules-GA predicting heating load of an energy-efficient building system. Eng Comput 1–10

  26. Hou Z, Lian Z, Yao Y, Yuan X (2006) Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl Energy 83:1033–1046

    Google Scholar 

  27. Cao B, Zhao J, Lv Z, Gu Y, Yang P, Halgamuge SK (2020) Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Trans Fuzzy Syst 28:939–952

    Google Scholar 

  28. Pezeshki Z, Mazinani SM (2019) Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif Intell Rev 52:495–525

    Google Scholar 

  29. Li Q, Meng Q, Cai J, Yoshino H, Mochida A (2009) Applying support vector machine to predict hourly cooling load in the building. Appl Energy 86:2249–2256

    Google Scholar 

  30. Qu S, Zhao L, Xiong Z (2020) Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Comput Appl 1–16

  31. Sun G, Xu G, Jiang N (2020) A simple differential evolution with time-varying strategy for continuous optimization. Soft Comput 24:2727–2747

    Google Scholar 

  32. Yang L, Chen H (2019) Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Comput Appl 31:4463–4478

    Google Scholar 

  33. Chen F, Yang Y, Tang B, Chen B, Xiao W, Zhong X (2020) Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. Measurement 151:107116

    Google Scholar 

  34. Gu F, Ma B, Guo J, Summers PA, Hall P (2017) Internet of things and big data as potential solutions to the problems in waste electrical and electronic equipment management: an exploratory study. Waste Manage 68:434–448. https://doi.org/10.1016/j.wasman.2017.07.037

    Article  Google Scholar 

  35. Zeng H-B, Teo KL, He Y, Wang W (2019) Sampled-data-based dissipative control of TS fuzzy systems. Appl Math Model 65:415–427. https://doi.org/10.1016/j.apm.2018.08.012

    Article  MathSciNet  MATH  Google Scholar 

  36. Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203

    MathSciNet  Google Scholar 

  37. Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490

    Google Scholar 

  38. Chen H, Zhang Q, Luo J, Xu Y, Zhang X (2020) An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl Soft Comput 86:105884

    Google Scholar 

  39. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput 88:105946

    Google Scholar 

  40. Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567. https://doi.org/10.1016/j.enbuild.2012.03.003

    Article  Google Scholar 

  41. Navarro-Gonzalez FJ, Villacampa Y (2019) An octahedric regression model of energy efficiency on residential buildings. Appl Sci 9:4978

    Google Scholar 

  42. Sha H, Xu P, Hu C, Li Z, Chen Y, Chen Z (2019) A simplified HVAC energy prediction method based on degree-day. Sustain Cities Soc 51:101698

    Google Scholar 

  43. Koschwitz D, Frisch J, Van Treeck C (2018) Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: a comparative study on district scale. Energy 165:134–142

    Google Scholar 

  44. Kumar S, Pal SK, Singh RP (2018) A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes. Energy Build 176:275–286

    Google Scholar 

  45. Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2018) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 1–18

  46. Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324

    Google Scholar 

  47. Xu X, Chen H-L (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18:797–807

    Google Scholar 

  48. Zhao X, Li D, Yang B, Ma C, Zhu Y, Chen H (2014) Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl Soft Comput 24:585–596

    Google Scholar 

  49. Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75

    Google Scholar 

  50. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Google Scholar 

  51. Qiao W, Moayedi H, Foong KL (2020) Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption. Energy Build. https://doi.org/10.1016/j.enbuild.2020.110023

    Article  Google Scholar 

  52. Moayedi H, Gör M, Khari M, Foong LK, Bahiraei M, Bui DT (2020) Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength. Measurement 156:107576

    Google Scholar 

  53. Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA (2019) A novel swarm intelligence—harris hawks optimization for spatial assessment of landslide susceptibility. Sensors 19:3590

    Google Scholar 

  54. Zhao D, Zhang W, Zhang Z, Yang F (2019) Prediction of cooling load of an energy station based on GA-SVR. IOP Conf Ser Earth Environ Sci 300:042007

    Google Scholar 

  55. Zhou G, Moayedi H, Foong LK (2020) Teaching–learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building. Eng Comput 1–12

  56. Wu D, Foong LK, Lyu Z (2020) Two neural-metaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings. Eng Comput 1–14

  57. Jitkongchuen D, Pacharawongsakda E prediction heating and cooling loads of building using evolutionary grey wolf algorithms. In: 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON)

  58. Gu J, Wang J, Qi C, Min C, Sundén B (2018) Medium-term heat load prediction for an existing residential building based on a wireless on–off control system. Energy 152:709–718

    Google Scholar 

  59. Moayedi H, Nguyen H, Foong L (2019) Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Eng Comput

  60. Tien Bui D, Moayedi H, MaM A, Rashid SA, Nguyen H (2019) Prediction of pullout behavior of belled piles through various machine learning modelling techniques. Sensors 19:3678

    Google Scholar 

  61. Moré JJ (1978) The Levenberg-Marquardt algorithm: implementation and theory, numerical analysis. Springer, Berlin, pp 105–116

    MATH  Google Scholar 

  62. Duan Q, Gupta VK, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521

    MathSciNet  MATH  Google Scholar 

  63. Baroni MDV, Varejão FM (2015) A shuffled complex evolution algorithm for the multidimensional knapsack problem. In: Iberoamerican Congress on Pattern Recognition

  64. Meshkat Razavi H, Shariatmadar H (2015) Optimum parameters for tuned mass damper using shuffled complex evolution (SCE) algorithm. Civil Eng Infrastruct J 48:83–100

    Google Scholar 

  65. Stewart I, Aye L, Peterson T (2017) Global optimisation of chiller sequencing and load balancing using Shuffled Complex Evol

  66. Ira J, Hasalová L, Jahoda M (2015) The use of optimization in fire development modeling, The use of optimization techniques for estimation of pyrolysis model input parameters. Appl Struct Fire Eng

  67. Gao X, Cui Y, Hu J, Xu G, Wang Z, Qu J, Wang H (2018) Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Convers Manage 157:460–479

    Google Scholar 

  68. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Google Scholar 

  69. Yamany W, Fawzy M, Tharwat A, Hassanien AE (2015) Moth-flame optimization for training multi-layer perceptrons. In: 2015 11th International Computer Engineering Conference (ICENCO)

  70. Moayedi H, Bui DT, Ngo T, Thao P (2019) Neural computing improvement using four metaheuristic optimizers in bearing capacity analysis of footings settled on two-layer soils. Appl Sci 9:5264

    Google Scholar 

  71. Yıldız BS, Yıldız AR (2017) Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Mat Test 59:425–429

    Google Scholar 

  72. Savsani V, Tawhid MA (2017) Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. Eng Appl Artif Intell 63:20–32

    Google Scholar 

  73. Kashan AH (2015) A new metaheuristic for optimization: optics inspired optimization (OIO). Comput Oper Res 55:99–125

    MathSciNet  MATH  Google Scholar 

  74. Alatas B, Bingol H (2019) A physics based novel approach for travelling tournament problem: optics inspired optimization. Inform Technol Contr 48:373–388

    Google Scholar 

  75. Özdemr M, Öztürk D (2017) Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies 10:2134

    Google Scholar 

  76. Roberts A, Marsh A (2001) ECOTECT: environmental prediction in architectural education

  77. Yang T, Asanjan AA, Faridzad M, Hayatbini N, Gao X, Sorooshian S (2017) An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis. Inf Sci 418:302–316

    Google Scholar 

  78. Jalili S, Husseinzadeh Kashan A (2018) Optimum discrete design of steel tower structures using optics inspired optimization method. Struct Design Tall Special Build 27:e1466

    Google Scholar 

  79. Alsarraf A, Moayedi H, Rashid ASA, Muazu MA, Shahsavar A (2019) Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system. Eng Comput 35:1–14. https://doi.org/10.1007/s00366-019-00721-4

    Article  Google Scholar 

  80. Liu L, Moayedi H, Rashid ASA, Rahman SSA, Nguyen H (2019) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput 35:1–13. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  81. Moayedi H, Foong LK, Nguyen H, Bui DT, Jusoh WAW, Rashid ASA (2019) Optimizing ANN models with PSO for predicting short building seismic response. Engineering with Computers 35:1–16. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  82. Tien Bui D, MaM A, Ghareh S, Moayedi H, Nguyen H (2019) Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete. Eng Comput. https://doi.org/10.1007/s00366-019-00850-w

    Article  Google Scholar 

  83. Yuan C, Moayedi H (2019) Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for prediction of landslide occurrence. Eng Comput 36. Doi: 10.1007/s00366-019-00798-x

  84. Moayedi H, Aghel B, Foong LK, Bui DT (2019) Feature validity during machine learning paradigms for predicting biodiesel purity. Fuel 116498. Doi: 10.1016/j.fuel.2019.116498

  85. Moayedi H, Rezaei A (2017) An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl 31:327–336. https://doi.org/10.1007/s00521-017-2990-z

    Article  Google Scholar 

  86. Bahiraei M, Nazari S, Moayedi H, Safarzadeh H (2020) Using neural network optimized by imperialist competition method and genetic algorithm to predict water productivity of a nanofluid-based solar still equipped with thermoelectric modules. Powder Technol 366:571–286

    Google Scholar 

  87. Bui DT, Moayedi H, Mu’azu MA, Rashid ASA, Nguyen H (2019) Prediction of pullout behaviour of belled piles thorough various machine learning modelling techniques. Sensors 19:3678. https://doi.org/10.3390/s19173678

    Article  Google Scholar 

  88. Mehrabi M, Pradhan B, Moayedi H, Alamri A (2020) Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques. Sensors 20:1723. https://doi.org/10.3390/s20061723

    Article  Google Scholar 

  89. Moayedi H, Osouli A, Tien Bui D, Foong LK (2019) Spatial landslide susceptibility assessment based on novel neural-metaheuristic geographic information system based ensembles. Sensors 19:4698

    Google Scholar 

  90. Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315

    Google Scholar 

  91. Rajabioun R (2011) Cuckoo optimization algorithm. ApplSoft Comput 11:5508–5518

    Google Scholar 

  92. Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: International Conference of Soft Computing and Pattern Recognition

  93. Bayraktar Z, Komurcu M, Werner DH (2010) Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. In: 2010 IEEE Antennas and Propagation Society International Symposium

  94. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  95. Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a Multi-objective optimization algorithm for engineering problems. Knowl Based Syst 150:175–197

    Google Scholar 

  96. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering and Transportation, Guangdong University of Technology, Outer Ring West Road 100, Guangzhou, China

    Senlin Zheng

  2. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Zongjie Lyu

  3. Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Zongjie Lyu

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loke Kok Foong

Authors
  1. Senlin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zongjie Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Loke Kok Foong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zongjie Lyu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, S., Lyu, Z. & Foong, L.K. Early prediction of cooling load in energy-efficient buildings through novel optimizer of shuffled complex evolution. Engineering with Computers 38 (Suppl 1), 105–119 (2022). https://doi.org/10.1007/s00366-020-01140-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01140-6

Keywords

Search

Navigation