Intelligent Control of Multilegged Robot Smooth Motion: A Review

Zhao, Yongyong; Wang, Jinghua; Cao, Guohua; Yuan, Ye; Xu, Yang; Qi, Le

doi:10.1109/access.2023.3304992

Cited by 66 publications

(2 citation statements)

References 145 publications

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“…A comprehensive literature review was conducted to gather experimental results regarding the CS of GrNCC. Relevant data from a variety of published articles 16 , 17 , 26 , 28 , 29 , 68 – 93 that provide thorough insights into the factors and characteristics that affect compressive strength was compiled, resulting in a database comprising 172 data points as depicted in Appendix A. The selective method of data compilation looked to enhance the dataset with high-quality and appropriate information to conduct a thorough analysis of factors influencing compressive strength.…”

Section: Methodsmentioning

confidence: 99%

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Fawad,

Alabduljabbar,

Farooq

et al. 2024

Sci Rep

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Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

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Section: Methodsmentioning

confidence: 99%

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Fawad,

Alabduljabbar,

Farooq

et al. 2024

Sci Rep

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“…The concept of integrals for set-valued functions originated from Aumann's research [27], which is based on Lebesgue integrals and is commonly referred to as set-valued Aumann integrals. Moreover, interval-valued Riemanian and fuzzy Aumann's integrals are discussed in [28][29][30], respectively, and the references are therein. Further main concepts related to fuzzy theory are discussed by Anastassiou [31] and then applied by Bede [32] to introduce gH-differentiability, as well as by Noor [33] to define nonconvex mapping under the umbrella of fuzzy mapping.…”

Section: Introductionmentioning

confidence: 99%

A New Class of Coordinated Non-Convex Fuzzy-Number-Valued Mappings with Related Inequalities and Their Applications

Rakhmangulov,

Aljohani,

Mubaraki

et al. 2024

Axioms

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Both theoretical and applied mathematics depend heavily on integral inequalities with generalized convexity. Because of its many applications, the theory of integral inequalities is currently one of the areas of mathematics that is evolving at the fastest pace. In this paper, based on fuzzy Aumann’s integral theory, the Hermite–Hadamard’s type inequalities are introduced for a newly defined class of nonconvex functions, which is known as U·D preinvex fuzzy number-valued mappings (U·D preinvex F·N·V·Ms) on coordinates. Some Pachpatte-type inequalities are also established for the product of two U·D preinvex F·N·V·Ms, and some Hermite–Hadamard–Fejér-type inequalities are also acquired via fuzzy Aumann’s integrals. Additionally, several new generalized inequalities are also obtained for the special situations of the parameters. Additionally, some of the interesting remarks are provided to acquire the classical and new exceptional cases that can be considered as applications of the main outcomes. Lastly, a few suggested uses for these inequalities in numerical integration are made.

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Development and characterization of UV‐curable PCL/AESO/CNT nanocomposites for biomedical engineering

Mohammadi,

Mirzaei,

Moradi

et al. 2024

Polymers for Advanced Techs

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This study investigates the development and characterization of UV‐curable Poly(ε‐caprolactone) (PCL), Acrylated Epoxidized Soybean Oil (AESO), and Carbon Nanotubes (CNT) nanocomposites for biomedical engineering applications. The PCL/AESO blends were prepared in various ratios, and CNTs were incorporated at concentrations of 0.5, 1.0, and 1.5 wt% to enhance mechanical properties. The UV‐curable formulations aimed to leverage rapid curing times, precise control over material properties, and the ability to fabricate complex structures. Results indicated that the incorporation of CNTs improved the tensile strength, modulus, and toughness of the composites. The PCL/AESO/CNT nanocomposites exhibited a tensile strength increase of 25%, a modulus improvement of 30%, and a toughness enhancement of 20% compared to pure PCL. Thermal analysis showed an increase in crystallization temperature and thermal stability, with a crystallinity degree of 63.31% and a maximum degradation temperature of 407°C for the B/C 50/50/1.5 sample. Biocompatibility assessments using L929 fibroblast cells revealed that the composites supported cell viability and proliferation over 7 days with negligible cytotoxicity. Cell attachment studies indicated favorable morphology and adherence, suggesting a conducive environment for cell growth and differentiation. Hydrolytic biodegradation studies demonstrated adjustable degradation rates, making these composites suitable for various biomedical applications requiring controlled biodegradation.

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Intelligent Control of Multilegged Robot Smooth Motion: A Review

Cited by 66 publications

References 145 publications

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

A New Class of Coordinated Non-Convex Fuzzy-Number-Valued Mappings with Related Inequalities and Their Applications

Development and characterization of UV‐curable PCL/AESO/CNT nanocomposites for biomedical engineering

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