Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network

Abstract. Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

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Mitigating Construction Deficiencies: An Impact Analysis for Low-Cost Housing Developments Utilizing Artificial Neural Network

10.3233/faia210254 ◽

2021 ◽

Author(s):

Ramene U. Lim ◽

Dante L. Silva ◽

Kevin Lawrence M. De Jesus

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Low Cost ◽

Housing Development ◽

Development Projects ◽

Computational Tool ◽

Housing Developments ◽

Low Cost Housing ◽

Artificial Neural ◽

To Come

The aim of this study is to be able to come up with a supplemental project management policy guidelines and computational tool that will address the two major concerns in construction of low-cost housing, construction delays and workmanship defects. Through assessment of previous studies, factors causing delays and defects from the two major stakeholders involved in housing development projects were identified. With the use of the five-point Likert Scale in survey forms distributed to 60 professionals involved in housing development projects, factors were classified and identified according to its degree of impact on the overall construction efficiency. The statistics of these factors were organized and used to develop an Artificial Neural Network Model. The relative importance of the factors was measured using Garson’s Algorithm. The derived equations from the developed ANN Model were used in formulating the computational tool and supplemental policy guidelines that can now be used to evaluate the workmanship defects and delay ratings of different housing developments. The computational tool was tested by 10 experts with their current projects and was able to receive a 4.6 out of 5 rubric evaluation rating, showing the tool’s effectiveness in identifying and assessing the probability and impact of construction deficiencies on their projects.

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Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices

Applied Soft Computing ◽

10.1016/j.asoc.2017.12.043 ◽

2018 ◽

Vol 65 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Adriano da Silva Ferreira ◽

Carlos Henrique da Silva Santos ◽

Marcos Sergio Gonçalves ◽

Hugo Enrique Hernández Figueroa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Evolutionary Strategy ◽

Computational Tool ◽

Artificial Neural

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Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters

Foods ◽

10.3390/foods10123016 ◽

2021 ◽

Vol 10 (12) ◽

pp. 3016

Author(s):

Pedro Sousa Sampaio ◽

Ana Sofia Almeida ◽

Carla Moita Brites

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Method ◽

Width Ratio ◽

Physical Parameters ◽

Quality Prediction ◽

Rice Quality ◽

Length Width ◽

Artificial Neural ◽

Artificial Neural Network Ann

The main goal of this study was to test the ability of an artificial neural network (ANN) for rice quality prediction based on grain physical parameters and to conduct a comparison with multiple linear regression (MLR) using 66 samples in duplicate. The parameters used for rice quality prediction are related to biochemical composition (starch, amylose, ash, fat, and protein concentration) and pasting parameters (peak viscosity, trough, breakdown, final viscosity, and setback). These parameters were estimated based on grain appearance (length, width, length/width ratio, total whiteness, vitreous whiteness, and chalkiness), and milling yield (husked, milled, head) data. The MLR models were characterized by very low coefficient determination (R2 = 0.27–0.96) and a root-mean-square error (RMSE) (0.08–0.56). Meanwhile, the ANN models presented a range for R2 = 0.97–0.99, being characterized for R2 = 0.98 (training), R2 = 0.88 (validation), and R2 = 0.90 (testing). According to these results, the ANN algorithms could be used to obtain robust models to predict both biochemical and pasting profiles parameters in a fast and accurate form, which makes them suitable for application to simultaneous qualitative and quantitative analysis of rice quality. Moreover, the ANN prediction method represents a promising approach to estimate several targeted biochemical and viscosity parameters with a fast and clean approach that is interesting to industry and consumers, leading to better assessment of rice classification for authenticity purposes.

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Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2021.247648 ◽

2021 ◽

Vol 6 (9 (114)) ◽

pp. 64-74

Author(s):

Oleksandr Bezsonov ◽

Oleh Lebediev ◽

Valentyn Lebediev ◽

Yuriy Megel ◽

Dmytro Prochukhan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Live Weight ◽

Network Models ◽

Physical Parameters ◽

Neural Network Models ◽

Convolutional Network ◽

Body Dimensions ◽

Artificial Neural ◽

Animal Images

A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems.

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