Journal Vol – 19 No – 5, May 2024

Abstract:

The authors of the article consider what features appear when using artificial intelligence (AI) in the generative design of construction facilities. Every day artificial intelligence becomes more and more important in various fields of human activity. One of the areas of activity in which AI is actively being implemented is construction, namely digital (BIM) and generative (GD) building design. These areas of design include the development of design solutions for an object using computer algorithms and mathematical models. The article examines the positive aspects of implementing AI in generative design, compared to traditional design methods. The use of AI in generative design can improve the quality of produced design documentation by reducing the number of unintentional mechanical and technical errors, providing designers with a more extensive amount of analytical data. The authors focus on the main AI methods that are involved in GD, as well as the problems and limitations that arise when using AI in design.

Keywords:

Artificial Intelligence (AI),generative model (GD),Information Model (BIM),Information Modelling Technologies (TIM),Generative design,

Refference:

I. A. A. Lapidus, V. I. Telichenko, D. K. Tumanov et al., : “Development of methods of technology and organization of construction production to solve energy efficiency problems.” Technol. and organizat. of construct. Product. 2 10–6. (2014).
II. Agkathidis, Ast., : “Generative Design.”, 160, (2015).
III. A. Pakhtaeva, : “Generative design methods.”, Noema (Architecture. Urbanistics. Art). No. 2(7), pp.213-221. (2021).
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V. D.O. Fedchun, : “Comparative analysis of generative, parametric and informational architectural design methods.”, Scientific and practical Online journal “Bulletin of the FEFU School of Engineering”. No. 2(50), pp.103-114. (2018).
VI. Duffy, Alex, H.B., David C., Brown, Mary Lou Maher., : “ Special Issue: Machine learning in design // Artificial Intelligence for Engineering Design, Analysis and Manufacturing.”, 10(2), pp.81-82. (1996).
VII. Fakhratov Mukhammet, Sinenko Sergey, Akbari Mohammad, Asayesh Farid. : “Determination of fundamental criteria in the selection of a construction system.”, E3S, Web. Conf. “Energy Efficient Building Design”, Volume 157, (2020), Key Trends in Transportation Innovation, (KTTI-2019) : https://doi.org/10.1051/e3sconf/202015706025
VIII. Fakhratov M., Sinenko S., Akbari M., Asayesh F. : “ Determination of fundamental criteria in the selection of a construction system” : E3S Web of Conferences, Key Trends in Transportation Innovation, KTTI 2019. (2020). С. 06025.
IX. K. Wong, : “Optimize or Generate?”, Digital Engineering, (2021), : https://www.digitalengineering247.com/article/optimize-or-generate/
X. Krawczyk R. J., : “ Experiments in Architectural Form Generation Using Cellular Automata” , Illinois Institute of Technology, College of Architecture, USA, (2002).
XI. Krish, Sivam, : “A practical generative design method.”, Computer-Aided Design, 43 (1): hhtps:/88–100.doi:10.1016/j.cad.2010.09.009
XII. Meintjes, Keith, : “Generative Design” – What’s That? – CIMdata
XIII. PlanRadar.com: PlanRadar: BIM- technology in Russia and Europe
XIV. R. Berger, : “Digitization in the construction industry.”, Munich, pp. 1—15., (2016)
XV. Raina A., McComb, C., and Cagan, J. : “Learning to Design from Humans: Imitating Human Designers Through Deep Learning”, ASME. J. Mech. Des. (2019)
XVI. S. A. Sinenko, I. M. Savin, : “Digitalization of the activities of construction contractors. Construction production”, No. 2, pp.147 – 151. (2023)
XVII. S.A. Sinenko, : “Selection of Organizational and Technological Solutions for Construction.”, ISEES., (2020)
XVIII. S. A. Sinenko, S. A. Aliev, : “Visualization of process maps for construction and installation works.”, ISEES (2020)
XIX. Sinenko S. A., Doroshin I. N. : “Use of Modern Means and Methods in the Organization and Management in Construction.”, The International Conference on Materials Research and Innovation, (ICMARI), 16-18 December 2019, Bangkok, Thailand. 2020 IOP Conf. Ser.: Mater. Sci. Eng. 753 042017, https://doi.org/10.1088/1757-899X/753/4/042017
XX. Sinenko Sergey, Hanitsch Pavel, Aliev Sheroz, and Volovik Mikhail, : “The implementation of BIM in construction projects”, E3S Web Conf., Volume 164, (2020), Topical Problems of Green Architecture, Civil and Environmental Engineering, 2019, (TPACEE 2019), https://doi.org/10.1051/e3sconf/202016408002
XXI. Sinenko S. A., Poznakhirko T. Y., : “On the Description of a Universal Model of Project System”, International science and technology conference, “EarthScience”, IOP, Conf., Series:, Earth and Environmental Science, 459 (2020) 052051. IOP, Publishing, doi: 10.1088/1755-1315/459/5/052051
XXII. Sinenko S A., an,d Doroshin I. N., : “Economical Aspects of the Cost Regulation for the Construction of Buildings”, International Science and Technology Conference, (FarEastСon 2020) IOP, Conf., Series, : Materials Science and Engineering, 1079, (2021), 052066. IOP Publishing doi:10.1088/1757-899X/1079/5/052066
XXIII. T.S. Metellik, : “Generative design method and ways of its implementation in graphic design.”, Business and design review: journal. Vol. 1, No. 2(6), p.11. (2017)
XXIV. Vishnivetskaya A.I., T. H. Ablyazov, ; “Digital generation as a basis for the digital transformation of construction organizations.” Economics: yesterday, today, tomorrow. vol. 9, pp. 11-20. (2019)

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Abstract:

This study examines the sliding wear behavior of aluminium 7075 composites supplemented with crab shell ash (CSA), a waste product from the seafood industry. The composites with different weight percentages of CSA (0%, 1%, 2%, and 3%) were created using the stir-casting procedure. Afterward, a pin-on-disc device was used to evaluate these composites under different sliding conditions. The primary aim of this research is to analyze the effects of CSA content and sliding parameters on composite wear performance. In the experiment, it was discovered that the stability of the composites differed depending on the amount of CSA that was present. The unreinforced aluminum 7075 alloy's wear resistance was enhanced with CSA particles, according to the data. Wear resistance is optimal at 3% CSA content and begins to decline somewhat above this concentration. As a contribution to sustainable material engineering, this study is significant since it improves metal matrix composites' properties by reusing waste materials. This research emphasizes the potential of using waste materials such as crab shell ash to enhance mechanical properties and wear resistance, to promote sustainability in material engineering approaches.

Keywords:

Aluminum 7075,Crab shell ash,Metal matrix composites,Sliding wear behavior,Stir casting,

Refference:

I. Alaneme KK, Adewale TM, Olubambi PA. Corrosion and wear behaviour of Al–Mg–Si alloy matrix hybrid composites reinforced with rice husk ash and silicon carbide. J Mat Res Technol. 2014;3:9-16.
II. Cai S, He Y, Song R. Study on the strengthening mechanism of two-stage double-peaks aging in 7075 aluminum alloy. Trans Indian Inst Met. 2020;73:109-17.
III. Ceschini L, Minak G, Morri A. Tensile and fatigue properties of the AA6061/20 vol% Al2O3p and AA7005/10 vol% Al2O3p composites. Compos Sci Technol. 2006;66:333-42.
IV. Das S. The influence of matrix microstructure and particle reinforcement on the two-body abrasive wear of an Al-Si alloy. J Mater Sci Lett. 1997;16:1757-60.
V. Devaganesh S, Kumar PD, Venkatesh N, Balaji R. Study on the mechanical and tribological performances of hybrid SiC-Al7075 metal matrix composites. J Mater Res Technol. 2020;9:3759-66.
VI. Dirisenapu G, Dumpala L, Reddy SP. Dry sliding tribological behavior of Al7010/B 4 C/BN hybrid metal matrix nanocomposites prepared by ultrasonic-assisted stir casting. Trans Indian Inst Met. 2020;74:149-158.
VII. Gopalakrishnan S, Murugan N. Production and wear characterisation of AA 6061 matrix titanium carbide particulate reinforced composite by enhanced stir casting method. Compos, Part B Eng. 2012;43:302-8.Budinski K.G, (1998). Surface Engg. For Wear Resistance, N.J, USA.
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X. Kumar PSR, Madindwa MP. Investigation on tribological behaviour of aluminosilicate reinforced AA7075 composites for aviation application. Trans Indian Inst Met. 2020;74:79-88.
XI. Kumar S, Balasubramanian V. Developing a mathematical model to evaluate wear rate of AA7075/SiCp powder metallurgy composites. Wear. 2008;264:1026-34.
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XVII. Sambathkumar M, Navaneethakrishnan P, Ponappa K, Sasikumar K. Mechanical and corrosion behavior of Al7075 (Hybrid) metal matrix composites by two step stir casting process. Lat Am J Solids Struct. 2017;14:243-55.
XVIII. Sardar S, Karmakar SK, Das D. Evaluation of abrasive wear resistance of Al 2 O 3/7075 composite by Taguchi experimental design technique. Trans Indian Inst Met. 2018;71:1847-58.
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XX. Zhu H, Wang H, Ge L. Wear properties of the composites fabricated by exothermic dispersion reaction synthesis in an Al–TiO2–B2O3 system. Wear. 2008;264:967-72.

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Abstract:

From its early days as a fledgling technology, satellite communication has come a long way to become a flourishing component of the global technological ecosystem that determines our increasingly interdependent world. This scholarly essay provides a comprehensive analysis of current developments in satellite communication technology and the several fields in which they might be applied. The essay dives into major inventions that have catapulted this discipline to unparalleled heights, and it spans from the historical origins to the modern accomplishments. This overview elucidates the enormous influence that satellite communication has had on modern civilization, highlighting its central position in allowing global connection, data dissemination, and transformational applications across a variety of industries.

Keywords:

GEO,ISL,LEO,MEO,Satellite communications,

Refference:

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III. Abdulwahid, M. M., Al-Ani, O. A. S., Mosleh, M. F., & Abd-Alhameed, R. A. : ‘A Comparison between Different C-band and mm Wave band Frequencies for Indoor Communication’. J. Commun., 14(10), (2019). PP. 892-899.‏
IV. Abdulwahid, M. M., Al-Hakeem, M. S., Mosleh, M. F., & Abd-Ahmed, R. A. : ‘Investigation and optimization method for wireless AP deployment-based indoor network’. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012031). IOP Publishing.‏ (2020, February).
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LXXIV. Y. S. Mezaal et al., “Cloud computing investigation for cloud computer networks using cloudanalyst,” Journal of Theoretical and Applied Information Technology, vol. 96, no. 20, 2018.
LXXV. Zaal, R. M., Mosleh, M. F., Abbas, E. I., & Abdulwahid, M. M. (2020, September). Optimal Coverage Area with Lower Number of Access Point. In IMDC-SDSP 2020: Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP (p. 230).‏
LXXVI. Zaal, R. M., Mustafa, F. M., Abbas, E. I., Mosleh, M. F., & Abdulwahid, M. M. (2020, July). Real measurement of optimal access point localizations. In IOP Conference Series: Materials Science and Engineering (Vol. 881, No. 1, p. 012119). IOP Publishing.‏

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Abstract:

This manuscript outlines various work carried out in the field of Distributed Generation (DG). Increase in power consumption and shortage in transmission capabilities are addressed by DGs. In order to maximize the potential benefits, it is imperative to place the DGs at optimal locations and the DGs should have optimal size pertaining to that location. There are several research works that are carried out on the placement and sizing of DGs. Nonetheless, the methodical principle for this issue is still unsettled. Various optimization strategies can be used to obtain the appropriate placement and sizing of distributed generation (DG) in grids. This study provides a comprehensive overview of several DG placement approaches, including stochastic fractal search algorithms, particle swarm optimization, symbiotic search algorithms, opposition-based tuneable chaotic differential evaluation, and more. The benefits and potential uses of each method are briefly covered in this study. The study sheds light on the efforts made to determine the best location and size of DGs.

Keywords:

Distributed Generation,DG Placement Techniques,Optimal Locations,Optimal Size,

Refference:

I. Abou El-Ela, Adel A., Ragab A. El-Sehiemy, and Ahmed Samir Abbas. “Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm.” IEEE Systems Journal 12.4 (2018): 3629-3636.
II. Acharya, Naresh, Pukar Mahat, and Nadarajah Mithulananthan. “An analytical approach for DG allocation in primary distribution network.” International Journal of Electrical Power & Energy Systems 28.10 (2006): 669-678.
III. Alam, Afroz, Bushra Zaheer, and Mohammad Zaid. “Optimal placement of DG in distribution system for power loss minimization and voltage profile improvement.” 2018 International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2018.
IV. Ali, E. S., S. M. Abd Elazim, and A. Y. Abdelaziz. “Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations.” Renewable Energy 101 (2017): 1311-1324.
V. Aman, M. M., et al. “A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm.” Energy 66 (2014): 202-215.
VI. Angalaeswari, S., et al. “Hybrid pipso-sqp algorithm for real power loss minimization in radial distribution systems with optimal placement of distributed generation.” Sustainability 12.14 (2020): 5787.
VII. Das, Bikash, V. Mukherjee, and Debapriya Das. “DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization.” Applied Soft Computing 49 (2016): 920-936.
VIII. Devi, Sudha, and M. Geethanjali. “Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm.” International Journal of Electrical Power & Energy Systems 62 (2014): 562-570.
IX. Devi, S., and M. Geethanjali. “Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation.” Expert Systems with Applications 41.6 (2014): 2772-2781.
X. Fandi, Ghaeth, et al. “Voltage regulation and power loss minimization in radial distribution systems via reactive power injection and distributed generation unit placement.” Energies 11.6 (2018): 1399.
XI. Ganthia, B. P., Barik, S., & Nayak, B. (2020). Application of hybrid facts devices in DFIG based wind energy system for LVRT capability enhancements. J. Mech. Cont. Math. Sci, 15(6), 245-256.
XII. Ganthia, B. P., Barik, S. K., & Nayak, B. (2020). Transient Analysis of Grid Integrated Stator Voltage Oriented Controlled Type-Iii DFIG Driven Wind Turbine Energy System. Journal of Mechanics of Continua and Mathematical Sciences, 15(6), 139-157.
XIII. Hung, Duong Quoc, Nadarajah Mithulananthan, and Kwang Y. Lee. “Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss.” International Journal of Electrical Power & Energy Systems 55 (2014): 179-186.
XIV. Hung, Duong Quoc, Nadarajah Mithulananthan, and R. C. Bansal. “Analytical strategies for renewable distributed generation integration considering energy loss minimization.” Applied Energy 105 (2013): 75-85.
XV. Jordehi, Ahmad Rezaee. “Allocation of distributed generation units in electric power systems: A review.” Renewable and Sustainable Energy Reviews 56 (2016): 893-905.
XVI. Kaushal, Pawan Kumar, and Minal Tomar. “Real and reactive power loss minimization of IEEE-33 bus by optimal DG placement using LSO in RDS.” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017.
XVII. Kumar, Sajjan, Kamal K. Mandal, and Niladri Chakraborty. “A novel opposition-based tuned-chaotic differential evolution technique for techno-economic analysis by optimal placement of distributed generation.” Engineering Optimization 52.2 (2020): 303-324.
XVIII. Nguyen, Tri Phuoc, and Dieu Ngoc Vo. “A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems.” Applied Soft Computing 70 (2018): 773-796.
XIX. Niknam, Taher, et al. “Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks.” Journal of Power Sources 196.20 (2011): 8881-8896.
XX. Niknam, T., A. M. Ranjbar, and A. R. Shirani. “Impact of distributed generation on volt/var control in distribution networks.” 2003 IEEE Bologna Power Tech Conference Proceedings,. Vol. 3. IEEE, 2003.
XXI. Prabha, D. Rama, and T. Jayabarathi. “Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm.” Ain Shams Engineering Journal 7.2 (2016): 683-694.
XXII. Prasad, C. Hari, K. Subbaramaiah, and P. Sujatha. “Cost–benefit analysis for optimal DG placement in distribution systems by using elephant herding optimization algorithm.” Renewables: Wind, Water, and Solar 6.1 (2019): 1-12.
XXIII. Prakash, D. B., and C. Lakshminarayana. “Multiple DG placements in radial distribution system for multi objectives using Whale Optimization Algorithm.” Alexandria engineering journal 57.4 (2018): 2797-2806.
XXIV. Raut, Usharani, Sivkumar Mishra, and Debani Prasad Mishra. “An adaptive NSGA II for optimal insertion of distributed generators in radial distribution systems.” 2019 International Conference on Information Technology (ICIT). IEEE, 2019.
XXV. Raut, Usharani, and Sivkumar Mishra. “An improved Elitist–Jaya algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems.” Renewable Energy Focus 30 (2019): 92-106.
XXVI. Reddy, P. Dinakara Prasad, VC Veera Reddy, and T. Gowri Manohar. “Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems.” Renewables: wind, water, and solar 4.1 (2017): 1-13.
XXVII. Siahbalaee, Jafar, Neda Rezanejad, and Gevork B. Gharehpetian. “Reconfiguration and DG sizing and placement using improved shuffled frog leaping algorithm.” Electric Power Components and Systems 47.16-17 (2019): 1475-1488.
XXVIII. Teimourzadeh, Hamid, and Behnam Mohammadi-Ivatloo. “A three-dimensional group search optimization approach for simultaneous planning of distributed generation units and distribution network reconfiguration.” Applied Soft Computing 88 (2020): 106012.
XXIX. Tran, Tung The, Khoa Hoang Truong, and Dieu Ngoc Vo. “Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations.” Ain Shams Engineering Journal 11.2 (2020): 389-407.
XXX. Truong, Khoa H., et al. “A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks.” Applied Soft Computing 88 (2020): 106067.
XXXI. Yuvaraj, T., K. R. Devabalaji, and K. Ravi. “Optimal allocation of DG in the radial distribution network using bat optimization algorithm.” Advances in power systems and energy management. Springer, Singapore, 2018. 563-569.
XXXII. Zongo, Oscar Andrew, and Anant Oonsivilai. “Optimal placement of distributed generator for power loss minimization and voltage stability improvement.” Energy Procedia 138 (2017): 134-139.

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Abstract:

Estimating the variance matrix has an important role in statistical applications and conclusions, in high–dimensional matrices if the number of variables is greater than the number of observations P > n, the traditional statistical methods are not reliable because they give uncontrolled estimates. Shrinkage methods are used to estimate the high–dimensional variance matrix. In this research, the high–dimensional variance matrix was estimated using the robust Nonparametric method Minimum Regularized Covariance Determinant (MRCD), which is based on Mahalanobis distance, and compared with the variance matrix estimated by the Oracle method, which is based on the Frobenius criterion.

Keywords:

Frobenius,High–Dimensional,Minimum Regularized Covariance Determinant,Mahalanobis,Oracle,Parameter regulation,Shrinkage,

Refference:

I. F. Abdul–Hammeed, and M. Sabah, : ‘Compared with genetic algorithm Fast-MCD-Nested Extension and neural network multilayer Back propagation’. JOURNAL OF ECONOMIC & ADMINISTRATIVE SCIENCE. Jun. No 22(89), 381-395, (2016).
II. F. Virgile, V. Gael, T. Benjamin, and Bertrand Thirion. : ‘Detecting outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinant’. pp. 264-271. https://hal.inria.fr/inria-00626857. 10.1007/978-3-642-23626-6_33
III. I. Clifferd, : ‘High Dimensional Covariance Matrix Estimation’. Department of Statistics, London School. http://stats.lse.ac.uk.
IV. J. Brian Williamson, : ‘Shrinkage Estimators for high-dimensional Covariance matrices’. POMONA COLLEGE , April 4, (2014). 10.1109/ICASSP.2009.4960239
V. K. Jan, and H. Jaroslav, : ‘Robust Regularized Discriminant Analysis Based on Implicit Weighting’. Technical report No.v-1241. December (2016). http://www.nusl.cz/ntk/nusl-262425
VI. K. Jan, T. Jurjen Duintjer, and S. Anna, : ‘Robustness of High-Dimensional Data’.
Mining.Kalina@cs.cas.cz. https://www.semantis/scholarory
VII. L. Olivier, W. Michael, : ‘Shrinkage Estimation of large covariance
matrices: keep it simple’. statistician. university of Zurich . Journal of Multivariate Analysis. 186, (2021) 104796. 10.1016/j.jmva.2021.104796
VIII. M. Abdul – Hammeed,and F. Abdul – Hammeed, : ‘Estimated between the two-stage summation shrinkage for the variance of a normal distribution and for equal sizes of the two samples’. Baghdad science journal. Jun. No 1009, (2011).
IX. M. Hubert, and M. Debruyne, : ‘Minimum Covariance Determinant’. Wiley Interdisciplinary Reviews:Computional Statistics. 2(2010). Pp.- 36-34. https://wis.kuleuven.be/stat/robust/papers/2010/wire-mcd.pdf
X. O. Ledoit ,and M. Wolf , : ‘Quadratic Shrinkage for Large Covariance Matrices’. University of Zurich , November (2019). http://dx.doi.org/10.2139/ssrn.3486378
XI. O. Ledoit ,and M. Wolf. : ‘A well-conditioned estimator for large-dimensional covariance matrices’. Journal of Multivariate Analysis. 88(2) (2004), pp. 365-411. 10.1016/S0047-259X(03)00096-4
XII. R. Maronnan, and R.H. Zamar. : ‘Robust Estimates of Location and Dispersion for High-Dimensional Datasets’. Technometrics. 44(4), 307-317 (2002). https://www.jstor.org/stable/1271538
XIII. P. Rousseeuw , S . Vanduffel and T. Verdonckl. : ‘Minimum Regularized Covariance Determinant Estimatimater’. june 1. (2018). **
XIV. P. Rousseeuw, V. Steven, and V. Tim. : ‘The Minimum Regularized Covariance Determinant Estimator’. ar Xiv:1701.07086v3, November 29 (2018). 10.2139/ssrn.2905259
XV. P. Rousseeuw, and D. Van. : ‘Afast algorithm for the Minimum Covariance Determinant estimator’. Technometrics. 41(3), (1991), pp. 212-223. doi.org/10.2307/1270566
XVI. Won J. H, Lim J. Kim S., J. Rajaratnan. : ‘Condition-number regularized covariance estimation’. J. R. Stat. Ser B (stat.Methodol) 75 (3), (2013) 427-450. doi.org/10.1111/j.1467-9868.2012.01049.x
XVII. Yilun C., Ami wiesel, Alfred O. Hero III. : ‘Shrinkage Estimtion of high Dimensional Covariance Matrices’. International Conference on Acoustics, Speech and Signal Processing. April (2009) 10.1109/ICASSP.2009.4960239
XVIII. Yilun C., Ami Wiesel, Alfredo. : ‘Robust Shrinkage Estimtion of high Dimensional Covariance Matrices’. arXiv:1009.5331v1 [stat.ME]. 27 sep (2010). 10.1109/TSP.2011.2138698
XIX . Zongliang Hu, Kai Dong, Wenlin Dai and Tiejan Tong. : ‘Acomparison of Methods for Estimating the Determinent of High-Dimensional Covariance Matrix’. The International Journal of Biostatistics. September, (2017). doi.org/10.1515/ijb-2017-0013

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Abstract:

This study conducts a bibliometric analysis to explore the evolution and practical applications of legged robots equipped with single-degree-of-freedom mechanisms from 2010 to 2023. Through comprehensive methodologies involving renowned academic databases such as Scopus, the research examines 127 relevant articles, employing statistical analysis and network assessments to discern trends and contributors in the field. Results indicate a peak in publication volume in 2019, with India emerging as the leading contributor, followed by Romania and China. The findings provide valuable insights into the global research landscape of legged robotics, highlighting key advancements and contributors and paving the way for future developments in the field.

Keywords:

Citation,Co-occurrences,Degrees of Freedom,Legged Robots,Walking Mechanisms,

Refference:

I. Armada, M. A., de González Santos, P., Ottaviano, E., Ceccarelli, M., & Tavolieri, C. (2005). Kinematic and dynamic analyses of a pantograph-leg for a biped walking machine. In Climbing and Walking Robots: Proceedings of the 7th International Conference CLAWAR 2004 (pp. 561-568). Springer Berlin Heidelberg.
II. Desai, Shivamanappa G., Anandkumar R. Annigeri, and A. TimmanaGouda. “Analysis of a new single degree-of-freedom eight link leg mechanism for walking machine.” Mechanism and machine theory 140 (2019): 747-764. 10.1016/j.mechmachtheory.2019.06.002
III. Frank, C. Modern Robotics-Mechanics, Planning, and Control. Cambridge University Press, 2017.
IV. Fukuoka, Y., Kimura, H., & Cohen, A. H. (2003). Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts. The International Journal of Robotics Research, 22(3-4), 187-202. 10.1177/0278364903022003004
V. Godoy, J. C., Campos, I. J., Pérez, L. M., & Muñoz, L. R. (2018). Nonanthropomorphic exoskeleton with legs based on eight-bar linkages. International Journal of Advanced Robotic Systems, 15(1), 1729881418755770. 10.1177/1729881418755770

VI. Ishihara, Hidenori, and Kiyoshi Kuroi. “A four-leg locomotion robot for heavy load transportation.” 2006 IEEE/RSJ International Conference on intilligent and robots and systems .IEEE,2006. 10.1109/IROS.2006.282379
VII. Jansen, Theo. The great pretender. 010 Publishers, 2007.
VIII. Jansen, Theo. The great pretender. 010 Publishers, 2007.
IX. Kashem, Saad Bin Abul, et al. “An experimental study of the amphibious robot inspired by biological duck foot.” 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018). IEEE, 2018. 10.1109/CPE.2018.8372507
X. Kashem, Saad Bin Abul, et al. “Design and implementation of a quadruped amphibious robot using duck feet.” Robotics 8.3 (2019): 77. 10.3390/robotics8030077
XI. Kim, H., Lee, D., Jeong, K., & Seo, T. (2015). Water and ground-running robotic platform by repeated motion of six spherical footpads. IEEE/ASME Transactions on Mechatronics, 21(1), 175-183. 10.1109/TMECH.2015.2435017
XII. Kulandaidaasan Sheba, J., Elara, M. R., Martínez-García, E., & Tan-Phuc, L. (2016). Trajectory generation and stability analysis for reconfigurable klann mechanism based walking robot. Robotics, 5(3), 13. 10.3390/robotics5030013
XIII. Liang, C., Ceccarelli, M., Takeda, Y. “Operation Analysis of a Chebyshev-Pantograph Leg Mechanism for a Single DOF Biped Robot.” Frontiers of Mechanical Engineering, vol. 7, no. 4, 2012, pp. 357–370. 10.1007/s11465-012-0340-5
XIV. Lockhande, N. G., and V. B. Emche. “Mechanical spider by using klann mechanisms.” International Journal of Mechanical Engineering and Computer Applications 1.5 (2013): 13-16.
XV. McCarthy, J. M., & Chen, K. Design of Mechanical Walking Robots. MDA, Press, 2021.
XVI. Núñez-Altamirano, Diego A., Felipe J. Torres, and Ignacio Juárez-Campos. “Kinematics of a Reconfigurable Robotic Leg based on the inverse Peaucellier-Lipkin mechanism.” 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2019. 10.1109/ROPEC48299.2019.9057073
XVII. Patnaik, Lalit, and Loganathan Umanand. “Kinematics and dynamics of Jansen leg mechanism: A bond graph approach.” Simulation Modelling Practice and Theory 60 (2016): 160-169. 10.1016/j.simpat.2015.10.003
XVIII. Rajkumar, A. “A microcontroller based spider bot using Klann mechanism.” AIP Conference Proceedings. Vol. 2460. No. 1. AIP Publishing, 2022. 10.1063/5.0096353
XIX. Regulan, Gopi Krishnan, Ganesan Kaliappan, and M. Santhakumar. “Development of an amphibian legged robot based on Jansen mechanism for exploration tasks.” Advancements in Automation, Robotics and Sensing: First International Conference, ICAARS 2016, Coimbatore, India, June 23-24, 2016, Revised Selected Papers. Springer Singapore, 2016.10.1007/978-981-10-2845-8_7
XX. Shah, Rushil, et al. “Advancement and application of Theo Jansen linkages: A review.” AIP Conference Proceedings. Vol. 2855. No. 1. AIP Publishing, 2023. 10.1063/5.0169581
XXI. Sheba, Jaichandar Kulandaidaasan, et al. “Design and evaluation of reconfigurable Klann mechanism based four legged walking robot.” 2015 10th International Conference on Information, Communications and Signal Processing (ICICS). IEEE, 2015. 10.1109/ICICS.2015.7459939
XXII. Silva, Manuel Fernando, and JA Tenreiro Machado. “A literature review on the optimization of legged robots.” Journal of Vibration and Control 18.12 (2012): 1753-1767.
XXIII. Varma, DS Mohan. “Synthesis and Analysis of Jansen’s Leg-Based Mechanism for Gait Rehabilitation.” Mechanism and Machine Science: Select Proceedings of Asian MMS 2018. Springer Singapore, 2021. 10.1007/978-981-15-4477-4_22

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Abstract:

In this article, we extract the diverse solitary wave solutions to the time-fractional perturbed nonlinear Schrödinger equation describing the dynamics of optical solitons travelling through nonlinear optical fibers. The nonlinear fractional differential equation is transformed into a nonlinear differential equation using a traveling wave transformation relating to the beta derivative. After that, the resulting equation is explained using the extended Riccati equation method. Abundant soliton and soliton-type solutions are extracted, comprising trigonometric and hyperbolic functions. The nature of the solutions varies qualitatively depending on distinct parameters. Additionally, graphical representations of the constructed solutions exhibit various physical forms, including kink, bell-shaped, periodic, anti-coupon etc. Moreover, the achieved solutions play a significant role in interpreting wave propagation studies and are essential for validating numerical and experimental findings in the fields of nonlinear optics, quantum mechanics, engineering, etc.

Keywords:

Beta Derivative,Extended Riccati Equation method,Optical Solitons,Time-fractional Perturbed Nonlinear Schrödinger Equation,Traveling Wave Transformation,

Refference:

I. Akbar, M. A., & Khatun, M. M. (2023). Optical soliton solutions to the space–time fractional perturbed Schrödinger equation in communication engineering. Optical and Quantum Electronics, 55(7), 645. 10.1007/s11082-023-04911-9
II. Ali, A., Ahmad, J., & Javed, S. (2023). Solitary wave solutions for the originating waves that propagate of the fractional Wazwaz-Benjamin-Bona-Mahony system. Alexandria Engineering Journal, 69, 121-133. 10.1016/j.aej.2023.01.063
III. Atangana, A., & Alqahtani, R. T. (2016). Modelling the spread of river blindness disease via the Caputo fractional derivative and the beta-derivative. Entropy, 18(2), 40. 10.3390/e18020040
IV. Atangana, A., Baleanu, D., & Alsaedi, A. (2016). Analysis of time-fractional Hunter-Saxton equation: a model of neumatic liquid crystal. Open Physics, 14(1), 145-149. 10.1515/phys-2016-0010
V. Beghami, W., Maayah, B., Bushnaq, S., & Abu Arqub, O. (2022). The Laplace optimized decomposition method for solving systems of partial differential equations of fractional order. International Journal of Applied and Computational Mathematics, 8(2), 52. 10.1007/s40819-022-01256-x
VI. Bekir, A., Aksoy, E., & Cevikel, A. C. (2015). Exact solutions of nonlinear time fractional partial differential equations by sub‐equation method. Mathematical Methods in the Applied Sciences, 38(13), 2779-2784. 10.1002/mma.3260
VII. Bekir, A., Guner, O., & Cevikel, A. (2016). The exp-function method for some time-fractional differential equations. IEEE/CAA Journal of Automatica Sinica, 4(2), 315-321. 10.1109/JAS.2016.7510172
VIII. Bilal, M., & Ren, J. (2022). Dynamics of exact solitary wave solutions to the conformable time-space fractional model with reliable analytical approaches. Optical and Quantum Electronics, 54, 1-19. 10.1007/s11082-021-03408-7
IX. Bilal, M., Ren, J., Inc, M., & Alhefthi, R. K. (2023). Optical soliton and other solutions to the nonlinear dynamical system via two efficient analytical mathematical schemes. Optical and Quantum Electronics, 55(11), 938. 10.1007/s11082-023-05103-1
X. Chen, W., Sun, H., & Li, X. (2022). Fractional derivative modeling in mechanics and engineering. Beijing, China: Springer.
XI. Esra Köse, G., Oruç, Ö., & Esen, A. (2022). An application of Chebyshev wavelet method for the nonlinear time fractional Schrödinger equation. Mathematical Methods in the Applied Sciences, 45(11), 6635-6649. 10.1002/mma.8196
XII. Islam, T., Akbar, M. A., & Azad, A. K. (2018). Traveling wave solutions to some nonlinear fractional partial differential equations through the rational (G′/G)-expansion method. Journal of Ocean Engineering and Science, 3(1), 76-81. 10.1016/j.joes.2017.12.003
XIII. Islam, M. T., Akter, M. A., Gómez-Aguilar, J. F., & Akbar, M. A. (2022). Novel and diverse soliton constructions for nonlinear space–time fractional modified Camassa–Holm equation and Schrodinger equation. Optical and Quantum Electronics, 54(4), 227. 10.1007/s11082-022-03602-1

XIV. Khater, M. M., Attia, R. A., & Lu, D. (2018). Modified auxiliary equation method versus three nonlinear fractional biological models in present explicit wave solutions. Mathematical and Computational Applications, 24(1), 1. 10.3390/mca24010001
XV. Kudryashov, N. A., & Biswas, A. (2022). Optical solitons of nonlinear Schrödinger’s equation with arbitrary dual-power law parameters. Optik, 252, 168497. 10.1016/j.ijleo.2021.168497
XVI. Laskin, N. (2002). Fractional Schrödinger equation. Physical Review E, 66(5), 056108. 10.1103/PhysRevE.66.056108
XVII. Lu, D., Wang, J., Arshad, M., & Ali, A. (2017). Fractional reduced differential transform method for space-time fractional order heat-like and wave-like partial differential equations. Journal of Advanced Physics, 6(4), 598-607. 10.1166/jap.2017.1383
XVIII. Odabasi, M., & Misirli, E. (2018). On the solutions of the nonlinear fractional differential equations via the modified trial equation method. Mathematical Methods in the Applied Sciences, 41(3), 904-911. 10.1002/mma.3533
XIX. Okposo, N. I., Veeresha, P., & Okposo, E. N. (2022). Solutions for time-fractional coupled nonlinear Schrödinger equations arising in optical solitons. Chinese Journal of Physics, 77, 965-984. 10.1016/j.cjph.2021.10.014
XX. Owyed, S., Abdou, M. A., Abdel-Aty, A., & Dutta, H. (2020). Optical solitons solutions for perturbed time fractional nonlinear Schrodinger equation via two strategic algorithms. Aims Math, 5(3), 2057-2070. 10.3934/math.2020136
XXI. Riaz, M. B., Atangana, A., Jahngeer, A., Jarad, F., & Awrejcewicz, J. (2022). New optical solitons of fractional nonlinear Schrodinger equation with the oscillating nonlinear coefficient: A comparative study. Results in Physics, 37, 105471. 10.1016/j.rinp.2022.105471
XXII. Rizvi, S. T. R., Seadawy, A. R., Younis, M., Ahmad, N., & Zaman, S. (2021). Optical dromions for perturbed fractional nonlinear Schrödinger equation with conformable derivatives. Optical and Quantum Electronics, 53(8), 477. 10.1007/s11082-021-03126-0
XXIII. Sarwar, A., Gang, T., Arshad, M., Ahmed, I., & Ahmad, M. O. (2023). Abundant solitary wave solutions for space-time fractional unstable nonlinear Schrödinger equations and their applications. Ain Shams Engineering Journal, 14(2), 101839. 10.1016/j.asej.2022.101839
XXIV. Valentim, C. A., Rabi, J. A., & David, S. A. (2021). Fractional mathematical oncology: On the potential of non-integer order calculus applied to interdisciplinary models. Biosystems, 204, 104377. 10.1016/j.biosystems.2021.104377
XXV. Wang, F., Salama, S. A., & Khater, M. M. (2022). Optical wave solutions of perturbed time-fractional nonlinear Schrödinger equation. Journal of Ocean Engineering and Science. 10.1016/j.joes.2022.03.014
XXVI. Wazwaz, A. M. (2022). Bright and dark optical solitons of the (2+ 1)-dimensional perturbed nonlinear Schrödinger equation in nonlinear optical fibers. Optik, 251, 168334. 10.1016/j.ijleo.2021.168334
XXVII. Younis, M., ur Rehman, H., Rizvi, S. T. R., & Mahmood, S. A. (2017). Dark and singular optical solitons perturbation with fractional temporal evolution. Superlattices and Microstructures, 104, 525-531. 10.1016/j.spmi.2017.03.006
XXVIII. Zaman, U. H. M., Arefin, M. A., Akbar, M. A., & Uddin, M. H. (2023). Utilizing the extended tanh-function technique to scrutinize fractional order nonlinear partial differential equations. Partial Differential Equations in Applied Mathematics, 8, 100563. 10.1016/j.padiff.2023.100563
XXIX. Zhu, S. D. (2008). The generalizing Riccati equation mapping method in non-linear evolution equation: application to (2+1)-dimensional Boiti-Leon-Pempinelle equation. Chaos, Solitons & Fractals, 37(5), 1335-1342. 10.1016/j.chaos.2006.10.015

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Abstract:

Over the last several years, there has been a significant increase in the amount of focus placed on the infrastructure development of smart cities. The primary issue that academics are attempting to address is the issue of energy efficiency. One of these issues was the identification of anomalies in energy usage, which was an essential component that needed to be taken into consideration when managing energy-saving systems that were efficient, hence lowering the total energy consumption and carbon emissions. Therefore, the proposal of a strong approach that is based on the Internet of Things (IoT) might provide more relevance for the identification of abnormal consumption in buildings and the provision of this information to customers and governments so that it can be handled in an appropriate manner to minimize payments. Consequently, the purpose of this work is to explore three different optimization methods, namely ADAM, AadMax, and Nadam, and to advocate for an optimization approach that makes use of the LSTM algorithm to identify anomalies. Statistical modelling techniques such as ARIMA and SARIMAX are used for the purpose of time series forecasting. The findings of the anomaly detection system reveal that the best results are obtained by using LSTM in conjunction with Nadar. The MSE and RMSE values reached were 0.15348 and 0.02356 respectively. Additionally, the ARIMA model yields the best overall results, with the AIC value being 0.13859 and the MSE value being 300.94365 correspondingly. Confirmation of the suggested model's dependability and flexibility in optimizing anomaly detection is provided by this particular fact.

Keywords:

Anomaly Detection System,Abnormal Consumption,Energy-saving Systems,Statistical Modelling Techniques,Time Series Forecasting.,

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Abstract:

In this paper, the author stated and proved Bhattacharyya’s Theorem: √{-(x² + y² )} = -√(x² + y²). With the help of this theorem, the author finds the square root of any negative number introducing Fermat’s last theorem without using the concept of complex numbers. The author has introduced Fermat’s Last Theorem in Bhattacharyya’s Theorem to find the square root of any negative number in real numbers in a very simple way. Indeed it is a new invention in mathematics in this era.

Keywords:

Extended form of Pythagoras Theorem,Fermat’s Last Theorem,Pythagoras Theorem,Rectangular Bhattacharyya’s Co-ordinate System,Theory of Dynamics of Numbers,

Refference:

I. A. Harripersaud. : ‘The quadratic equation concept’. American Journal of Mathematics and Statistics. 11.(3). pp. 67-71. (2021).

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III. G. H. Hardy and E. M. Wright. : ‘An Introduction to the Theory of Numbers’. Sixth Edition. Page 245 – 247.

IV. H. Lee Price, Frank R. Bernhart. : ‘Pythagorean Triples and a New Pythagorean Theorem’. arXiv:math/0701554 [math.HO]. 10.48550/arXiv.math/0701554
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