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Smart grid-based manufacturing by nanoparticle analysis with evolutionary optimization probability detection

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Abstract

In smart grid-based manufacturing, nanoparticles can play a significant role in enhancing efficiency, productivity, and sustainability. They can be employed in areas such as materials synthesis, fabrication, sensing, and energy storage. With combining smart grid technologies and nanoparticles, manufacturers can benefit from improved process control, reduced energy consumption, enhanced product quality, and increased resource efficiency. Hence, this paper concentrate on the design of smart grid with nanoparticles with unique properties with the evolutionary optimization process. The paper uses Dempster–Shafer probability framework (DS-PF) in smart grid-based manufacturing using nanoparticles. The DS-PF perform the mathematical reasoning under uncertainty and combining evidence, making it suitable for addressing the complex and uncertain nature of smart grid manufacturing processes. With the estimation of the mathematical derivatives, an evolutionary process is implemented with the probability detection–based fuzzy rules. Based on the optimal value of the evolutionary optimization fuzzy rules, the nanoparticles are integrated with the smart grid manufacturing. Through experimental results and simulations, the paper demonstrates the effectiveness of DS-PF in handling uncertainties and optimizing decision-making in smart grid-based manufacturing. The results expressed that the with DS-PF model energy consumption is significantly reduced to 235 J, with network lifetime of 24 h and packet delivery ratio of 96.5%. The findings of this research contribute to the advancement of smart grid-based manufacturing by offering a comprehensive framework for handling uncertainties and making informed decisions. The proposed methodologies and approaches based on DS-PF have the potential to enhance the efficiency, reliability, and optimization of manufacturing processes in smart grid environments.

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References

  1. Park S, Park HS, Dao TT, Song SH, Lee SI, Van Tran H et al (2022) Solvothermal synthesis of oxygen deficient tungsten oxide nano-particle for dual band electrochromic devices. Solar Energy Mater Solar Cells 242:111759

    Article  Google Scholar 

  2. Jafarmadar S, Amini Niaki SR (2022) Experimental exergy analyses in a DI diesel engine fuelled with a mixture of diesel fuel and TiO2 nano-particle. Environ Prog Sustain Energy 41(1):e13703

    Article  Google Scholar 

  3. Wang C, Kou K, Yan J (2022) Frequency-shifted nano-particle sizing using laser self-mixing interferometry under linear current tuning. Laser Physics Lett 19(6):066202

    Article  Google Scholar 

  4. Sreenilayam SP, McCarthy É, McKeon L, Ronan O, McCann R, Fleischer K et al (2022) Additive-free silver nanoparticle ink development using flow-based laser ablation synthesis in solution and aerosol jet printing. Chem Eng J 449:137817

    Article  Google Scholar 

  5. Yadav NK, Rajput NS, Gupta MK (2023) Investigation of the mechanical and wear properties of epoxy resin composite (ERCs) made with nano particle TiO2 and cotton fiber reinforcement. Evergreen 10(1):63–77:2023–03. Kyushu University Graduate School of Research and Education Information found

  6. Kumar TS, Ashok B, Kumar MS, Vignesh R, Saiteja P, Hire KRB et al (2022) Biofuel powered engine characteristics improvement through split injection parameter multivariate optimization with titanium based nano-particle additives. Fuel 322:124178

    Article  Google Scholar 

  7. Praveenkumar S, Agyekum EB, Kumar A, Velkin VI (2023) Thermo-enviro-economic analysis of solar photovoltaic/thermal system incorporated with u-shaped grid copper pipe, thermal electric generators, and nanofluids: an experimental investigation. J Energy Storage 60:106611. https://doi.org/10.1016/j.est.2023.106611

  8. Zhu E, Chen Z, Cui J, Zhong H (2022) MOE/RF: a novel phishing detection model based on revised multiobjective evolution optimization algorithm and random forest. IEEE Trans Network Serv Manag 19(4):4461–4478

    Article  Google Scholar 

  9. Jiao B, Guo Y, Yang S, Pu J, Gong D (2023) Reduced-space multistream classification based on multiobjective evolutionary optimization. IEEE Trans Evol Comput 27(4):764–777. https://doi.org/10.1109/TEVC.2022.3232466

  10. Shen X, Yao X, Tu H, Gong D (2022) Parallel multi-objective evolutionary optimization based dynamic community detection in software ecosystem. Knowl-Based Syst 252:109404

    Article  Google Scholar 

  11. Liang S, Zhu Y, Li H (2022) Evolutionary optimization based set joint integrated probabilistic data association filter. Electronics 11(4):582

    Article  Google Scholar 

  12. Abouhawwash M, Alessio AM (2022) Evolutionary optimization of multiple machine-learned objectives for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci 7(3):273–283

    Article  Google Scholar 

  13. Murugesh C, Murugan S (2022) Evolutionary optimization with variational auto encoder based denial of service attack detection and classification in wireless sensor networks. In: In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, pp 994–1000

    Google Scholar 

  14. Chatterjee S, Das A (2023) An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer. Soft Comput 27(11):7147–7178

    Article  Google Scholar 

  15. Qi Z, Chang L, Shi F, Xu X, Feng J (2022) Evolutionary optimization for the belief-rule-based system: method and applications. Symmetry 14(8):1622

    Article  Google Scholar 

  16. Su ZL, Jiang XL, Li N, Ling HF, Zheng YJ (2022) Optimization of false target jamming against UAV detection. Drones 6(5):114

    Article  Google Scholar 

  17. Heller R, Klingner N, Claessens N, Merckling C, Meersschaut J (2022) Differential evolution optimization of Rutherford backscattering spectra. J Appl Phys 132(16):165302. https://doi.org/10.1063/5.0096497

  18. Han W, Li H, Gong M (2022) Automatic binary and ternary change detection in SAR images based on evolutionary multiobjective optimization. Appl Soft Comput 125:109200

    Article  Google Scholar 

  19. Abdulateef SK (2022) Evolutionary optimization of geometrical image contour detection. Int J Intell Eng Syst 15(2):287–297. https://doi.org/10.22266/ijies2022.0430.26

  20. Louati H, Bechikh S, Louati A, Aldaej A, Said LB (2022) Evolutionary optimization for CNN compression using thoracic X-ray image classification. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer International Publishing, Cham, pp 112–123

    Google Scholar 

  21. Li C, Yao W, Wang H, Jiang T, Zhang X (2023) Bayesian evolutionary optimization for crafting high-quality adversarial examples with limited query budget. Appl Soft Comput 142:110370. https://doi.org/10.1016/j.asoc.2023.110370

  22. Yang J, Zhang Y-D (2023) APPSO-NN: an adaptive-probability particle swarm optimization neural network for sensorineural hearing loss detection. IET Biome 12(4):211–221 . https://doi.org/10.1049/bme2.12114

  23. Chai ZY, Liu X, Li YL (2023) A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing. Eng Appl Artif Intell 121:105966

    Article  Google Scholar 

  24. Gao F, Gao W, Huang L, Xie J, Gong M (2022) An effective knowledge transfer method based on semi-supervised learning for evolutionary optimization. Inf Sci 612:1127–1144

    Article  Google Scholar 

  25. Ma J, Chang F, Yu X (2022) Large-scale evolutionary optimization approach based on decision space decomposition. Front Energy Res 10:926161

    Article  Google Scholar 

  26. Lyu C, Shi Y, Sun L, Lin C-T (2023) “Community detection in multiplex networks based on evolutionary multitask optimization and evolutionary clustering ensemble,” In: IEEE Trans Evolution Comput 27(3):728–742. https://doi.org/10.1109/TEVC.2022.3184988

  27. González Prieto PE (2022) Evolutionary optimization techniques for 3D simultaneous localization and mapping. https://doi.org/10.3390/s22103690

  28. Praveena HD, Srilakshmi V, Rajini S, Kolluri R, Manohar M (2023) Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks. Phys Commun 56:101956

    Article  Google Scholar 

  29. Jia T, Song J, Niu Y, Chen B, Cao Z (2022) Optimized hybrid design with stabilizing transition probability for stochastic Markovian jump systems under hidden Markov mode detector. Asian J Control 24(5):2787–2795

    Article  MathSciNet  Google Scholar 

  30. Falcetelli F, Yue N, Di Sante R, Zarouchas D (2022) Probability of detection, localization, and sizing: the evolution of reliability metrics in Structural Health Monitoring. Struct Health Monit 21(6):2990–3017

    Article  Google Scholar 

  31. Zhang H, Li J, Xia X, Hao K, Xiao X (2022) Multi-objective evolutionary for object detection mobile architectures search. arXiv preprint arXiv:2211.02791

  32. Gu D, Gao Y, Chen K, Shi J, Li Y, Cao Y (2022) Electricity theft detection in AMI with low false positive rate based on deep learning and evolutionary algorithm. IEEE Trans Power Syst 37(6):4568–4578

    Article  Google Scholar 

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  1. Zhejiang College of Security Technology, Wenzhou, 325016, China

    JiYong Wang

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Wang, J. Smart grid-based manufacturing by nanoparticle analysis with evolutionary optimization probability detection. Int J Adv Manuf Technol (2023). https://doi.org/10.1007/s00170-023-12613-5

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