Artificial Neural Network Model
Mostrando 1-12 de 145 artigos, teses e dissertações.
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1. PREDICTING THE PERFORMANCE PARAMETERS OF CHISEL PLOW USING NEURAL NETWORK MODEL
ABSTRACT This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed so
Eng. Agríc.. Publicado em: 2020-12
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2. EVALUATION OF MECHANICAL AND FLAME RETARDANT PROPERTIES OF MEDIUM DENSITY FIBERBOARD USING ARTIFICIAL NEURAL NETWORK
ABSTRACT The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatur
CERNE. Publicado em: 2020-06
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3. Enurese noturna: uma condição comórbida,
ABSTRACT The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatur
J. Pediatr. (Rio J.). Publicado em: 2020-06
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4. Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neural network (ANN). Furthermore, we evaluate and compare the pr
J. Microw. Optoelectron. Electromagn. Appl.. Publicado em: 2020-03
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5. Kinetics of Lumefantrine Thermal Decomposition Employing Isoconversional Models and Artificial Neural Network
Thermal analysis can be used to determine shelf-life and kinetic parameters in pharmaceutical systems. This work investigates the kinetic of lumefantrine thermal decomposition, an antimalarial, using non-isothermal and isothermal experimental data. The non-isothermal conditions are analyzed applying Vyazovkin method, while isothermal conditions employ models
J. Braz. Chem. Soc.. Publicado em: 2020-03
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6. MULTILEVEL NONLINEAR MIXED-EFFECTS MODEL AND MACHINE LEARNING FOR PREDICTING THE VOLUME OF Eucalyptus SPP. TREES
ABSTRACT Volumetric equations is one of the main tools for quantifying forest stand production, and is the basis for sustainable management of forest plantations. This study aimed to assess the quality of the volumetric estimation of Eucalyptus spp. trees using a mixed-effects model, artificial neural network (ANN) and support-vector machine (SVM). The datab
CERNE. Publicado em: 2020-03
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7. Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques
ABSTRACT The purpose of this study is to develop data mining models to predict restrained shrinkage crack widths of slag mortar cementitious composites. A database published by BILIR et al. [1] was used to develop these models. As a modelling tool R environment was used to apply these data mining (DM) techniques. Several algorithms were tested and analyzed u
Matéria (Rio J.). Publicado em: 25/11/2019
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8. Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network
Abstract: Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in sl
An. Acad. Bras. Ciênc.. Publicado em: 21/10/2019
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9. Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest
Resumo A modelagem do recrutamento em florestais tropicais é importante para estudos de sustentabilidade do manejo florestal, por dar subsídio adequado à recuperação do estoque de madeira. O objetivo do trabalho foi estimar o recrutamento após a colheita de madeira, empregando um modelo de rede neural artificial (RNA). A área de estudo está localizad
Ciênc. Florest.. Publicado em: 30/09/2019
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10. Proposal of automated computational method to support Virginia tobacco classification
RESUMO Este artigo propõe um método automático para classificação de folhas de tabaco curado. Tipicamente este processo é realizado de modo manual, possibilitando erros humanos. Aliado a isso, a existência de um procedimento comparativo automatizado, auxiliando na realização da classificação, poderá tornar tal processo mais rápido e transparente
Rev. bras. eng. agríc. ambient.. Publicado em: 09/09/2019
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11. Simulation and Prediction for a Satellite Temperature Sensors Based on Artificial Neural Network
ABSTRACT: Spacecrafts in space environment are exposed to several kinds of thermal sources such as radiation, albedo and emitted IR from the earth. The thermal control subsystem in spacecraft is used to keep all parts operating within allowable temperature ranges. A failure in one or many temperature sensors could lead to abnormal operation. Consequently, a
J. Aerosp. Technol. Manag.. Publicado em: 26/08/2019
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12. Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively
Abstract Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simu
Food Sci. Technol. Publicado em: 22/07/2019