Prediction of Particle Packing Density of Alternative Fine Aggregates by Artificial Neural Network Applications

Singh, Siddharth; Pranathi, B. M.; Kirthika, S. K.

doi:10.1007/s40030-019-00412-9

Cited by 2 publications

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References 29 publications

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“…Reference [21] uses a deep neural network to determine one-dimensional fast ion velocity distribution function from ion cyclotron emission data. Reference [22] uses neural networks to process phase-time measurement information. The novelty of the proposed method lies in the selection of classification attributes and the binary classification of perceptron algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Niu²

2022

Journal of Sensors

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Language is the cornerstone of children’s learning knowledge and exploring the world. Language teaching is an indispensable part of early childhood education, which can help children improve their communication ability and help children communicate with classmates and teachers in school. The teaching form of preschool language education is too single, which does not conform to children’s own learning ability. Therefore, in teaching, teachers should actively optimize the language environment to make children willing to express themselves in language: introducing game activities to make children want to express themselves in language. With the help of situational display, children like to express themselves in language and carry out performance activities to give children the opportunity to express themselves in language. With the help of home force, children dare to express themselves in language. Children are in a dynamic stage of rapid growth. In order to enable children to learn language effectively, teachers should make clear the development center of children and their mastery of language. To guide language teaching in accordance with students’ aptitude for a long time, at the same time, we should update our rigid views on children at any time, boldly innovate the content and thinking mode of children’s language teaching activities, and mobilize children’s initiative and enthusiasm to participate in language learning, so that they can achieve the language learning goals of being able to speak, loving to speak and bravely speaking. This paper selects the learning situation of preschool children as the object, establishes an evaluation model by using the hybrid GA-BP neural network method, and makes an empirical analysis on the development of preschool education in Liaoning Province. The GA-BP neural network model is trained by MATLAB 7.0, and the simulation results show that the model can evaluate the development of preschool education scientifically, and it is scientific and practical. According to the results of this paper, the corresponding evaluation model of preschool education language teaching quality is put forward, which makes a good prediction preparation for children to learn language better in advance.

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Section: Introductionmentioning

confidence: 99%

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Niu²

2022

Journal of Sensors

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“…As matérias primas utilizadas estão em inúmeras publicações, tanto para areia 11,13,14,18,19,20,31,37,38,62,63,64,65,66 quanto para vidro 11,15,18,24,28,62,67,68,69,70 , e são utilizadas vastamente como modelos ou matérias-primas na área de empacotamento, sendo umas das principais ênfases a área de concreto.…”

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Modelagem de empacotamento de partículas multifatorial utilizando redes neurais

Martos

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Gostaria de mencionar minha profunda gratidão ao meu orientador Prof. Dr. Eduardo Bellini Ferreira pelos conselhos acadêmicos e ensinamentos sobre a ciência dos materiais no trabalho apresentado, bem como o esforço para a viabilização da pesquisa e minha construção profissional. Sou grato ao auxílio, à gestão do projeto e acompanhamento de experimentos pelo Dr. Paulo Giovany Quadros do Amaral, em que seu aconselhamento foi fundamental durante todo o trabalho. Expresso profunda gratidão ao Departamento de Engenharia de Materiais pela oportunidade e o amparo à pesquisa, bem como à agência de fomento Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) -Código de Financiamento 001pela bolsa de mestrado, que viabilizou a realização deste trabalho acadêmico e minha capacitação profissional. Agradeço ao acesso aos equipamentos fornecidos pela Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), fundamental para a concretização da pesquisa. RESUMO Martos, L. G. C. Modelagem de empacotamento de partículas multifatorial utilizando redes neurais. 83p. Dissertação (Mestrado) -

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Prediction of Particle Packing Density of Alternative Fine Aggregates by Artificial Neural Network Applications

Cited by 2 publications

References 29 publications

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Intelligent Analysis and Application of Preschool Education Language Teaching Quality Based on Deep Neural Network

Modelagem de empacotamento de partículas multifatorial utilizando redes neurais

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