Integrating signal pairing evaluation metrics with deep learning for wind power forecasting through coupled multiple modal decomposition and aggregation

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113394 - 113394

Опубликована: Апрель 1, 2025

Язык: Английский

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A deep reinforcement learning approach for wind speed forecasting

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2025, Номер 19(1)

Опубликована: Май 7, 2025

Язык: Английский

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Convergence of machine learning with microfluidics and metamaterials to build smart materials

International Journal on Interactive Design and Manufacturing (IJIDeM), Год журнала: 2024, Номер 18(10), С. 6909 - 6917

Опубликована: Янв. 11, 2024

Abstract Recent advances in machine learning have revolutionized numerous research domains by extracting the hidden features and properties of complex systems, which are not otherwise possible using conventional ways. One such development can be seen designing smart materials, intersects ability microfluidics metamaterials with to achieve unprecedented abilities. Microfluidics involves generating manipulating fluids form liquid streams or droplets from microliter femtoliter regimes. However, analysis fluid flows is always tiresome challenging due complexity involved integration detection various chemical biological processes. On other hand, acoustic manipulate waves unparalleled properties, natural materials. Nonetheless, design relies on expertise specialists analytical models that require an enormous number expensive function evaluations, making this method extremely time-consuming. These complexities exorbitant evaluations both fluidic metamaterial systems embark need for support computational tools identify, process, quantify large amounts intricacy, thus techniques. This review discusses shortcomings metamaterials, overcome neoteric approaches building The following ends providing importance future perspective integrating optimization microfluidic-based build efficient intelligent next-generation

Язык: Английский

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Intelligent Building Construction Cost Optimization and Prediction by Integrating BIM and Elman Neural Network

Heliyon, Год журнала: 2024, Номер 10(18), С. e37525 - e37525

Опубликована: Сен. 1, 2024

This study aims to address the challenges of capturing design changes, supply chain fluctuations, and labor cost variations improve accuracy real-time nature intelligent building construction predictions. It seeks accurately forecast optimize project costs. The innovatively constructs an prediction model based on Building Information Modeling (BIM) Elman neural networks (ENNs), denoted as BIM-ENN model. first introduces BIM technology digitize visualize information related structures, electromechanical systems, pipelines. digitized data obtained through is then used input for ENN, which optimizes network parameters predict Finally, experimentally evaluated. results demonstrate that value by this closely matches original price, with a 95.83 %. Compared single root mean squared error less than 75, determination coefficient above 0.95. indicates can explain more 95 % results, making it feasible solution actual problems achieving satisfactory results. reported here exhibits high reliability. successfully costs, providing robust decision support digitalization development enterprises. practical significance lies in industry accurate management tool helps enterprises budget control resource allocation, enhancing risk assessment capabilities. Moreover, potential impact its ability elevate standards within industry, promote technological integration innovation, enhance enterprise competitiveness, drive industry's transition towards sustainable development.

Язык: Английский

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Heuristic computing for the novel singular third order perturbed delay differential model arising in thermal explosion theory

Arabian Journal of Chemistry, Год журнала: 2022, Номер 16(3), С. 104509 - 104509

Опубликована: Дек. 15, 2022

In this study, a novel singular third order perturbed delay differential model (STO-PDDM) is designed with its two types using the traditional Lane-Emden model. The descriptions of delay/shape perturbed, and factors are also presented for both STO-PDDM. artificial neural networks (ANNs) along optimization global/local performances based on genetic algorithm (GA) interior-point (IPA) have been used to solve performed GAIPA activation function through form For solving STO-PDDM, system's accuracy, substantiation, authenticity by comparison obtained exact solutions. accessible approximate solutions evaluate computational approach's robustness, stability, correctness, convergence. reliability scheme different statistical measures

Язык: Английский

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