Development of a machine vision system for the determination of some of the physical properties of very irregular small biomaterials

Kalantari, Davood; Jafari, Hassan; Kaveh, Mohammad; Szymanek, M.; Asghari, Ali; Marczuk, A.; Khalife, Esmail

doi:10.31545/intagr/145920

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Recently, inspired by the success of the neural networks, many machine-learningpowered methods have been proposed for irregular volume measurement, such as in [9,10]. The neural network includes two processes, forward propagation and back propagation.…”

Section: Related Workmentioning

confidence: 99%

Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

et al. 2022

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Irregular cavity volume measurement is a critical step in industrial production. This technology is used in a wide variety of applications. Traditional studies, such as waterflooding-based methods, have suffered from the following shortcomings, i.e., significant measurement error, low efficiency, complicated operation, and corrosion of devices. Recently, neural networks based on the air compression principle have been proposed to achieve irregular cavity volume measurement. However, the balance between data quality, network computation speed, convergence, and measurement accuracy is still underexplored. In this paper, we propose novel neural networks to achieve accurate measurement of irregular cavity volume. First, we propose a measurement method based on the air compression principle to analyze seven key parameters comprehensively. Moreover, we integrate the Hilbert–Schmidt independence criterion (HSIC) into fully connected neural networks (FCNNs) to build a trainable framework. This enables the proposed method to achieve power-efficient training. We evaluate the proposed neural network in the real world and compare it with typical procedures. The results show that the proposed method achieves the top performance for measurement accuracy and efficiency.

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Section: Related Workmentioning

confidence: 99%

Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

et al. 2022

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“…The pea seed coat tensile test results showed that an increase in moisture content was directly related to a decrease in the breaking force and obtained the same results for three pea varieties. Kalantari et al (2022) combined image processing techniques with 3D modelling software for the volume estimation of irregular plant material. The estimated plant volume was compared with traditional mathematical expressions and actual volume measurements from the fluid displacement method to verify the rationality of the technique.…”

Section: Introductionmentioning

confidence: 99%

Effects of mechanical differences in sugarcane on the quality of mechanical harvesting

Li¹,

Li²,

Lin³

et al. 2023

Int. Agrophys.

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The mechanization of the whole process of sugarcane harvesting is an integral part of reducing the cost of sugarcane production. In order to select sugarcane strains suitable for mechanized harvesting, in this experiment, a dynamic resistance strain gauge and other equipment determined the related mechanical property parameters such as density, elastic modulus, and Poisson's ratio of different sugarcane varieties. Ansys/ explicit dynamics were used to establish a finite element model of the disc cutting device and to simulate the forces exerted on the stalk during sugarcane harvesting and also the quality of the cut section. Then, after obtaining the regression equation analysis and the response surface through the cross-section mass, the field harvesting experiment verifies the simulation results. The results show that the density, elastic modulus, and Poisson's ratio significantly affect the cutting quality within the three types of mechanical parameters. Through response surface analysis, when the elastic modulus is 92 MPa, the density is 1145 kg m -3 , the Poisson's ratio is 0.404, the best cutting quality was obtained, and the section flatness is 57.09%. According to the cross-sectional regression equation, the calculation results are as follows: Liucheng 05-136 > Yuetang 94-128 > Guitang 42, which is the same as the actual harvest quality results under the two planting modes. This indicated that the difference in the mechanical properties of the sugarcane would significantly affect the quality of mechanical harvesting, which provides a reference direction for selecting sugarcane varieties suitable for mechanized harvesting.K e y w o r d s: elastic modulus, Poisson's ratio, mechanized harvesting, finite element

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Development of Machine Vision-Based Algorithm for Counting and Discriminating Filled and Unfilled Paddy Rice in Overlapping Mode

Hoseingholizadeh-Alashti,

Kalantari

2024

Rice Science

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Development of a machine vision system for the determination of some of the physical properties of very irregular small biomaterials

Cited by 3 publications

References 31 publications

Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

Effects of mechanical differences in sugarcane on the quality of mechanical harvesting

Development of Machine Vision-Based Algorithm for Counting and Discriminating Filled and Unfilled Paddy Rice in Overlapping Mode

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