scholarly journals MODELING OF PAFO SYSTEM WITH NEURAL NETWORK IN SEAWATER DESALINATION FOR AGRICULTURAL APPLICATIONS

2020 ◽  
Vol 21 (supplement 1) ◽  
Author(s):  
Leila Javarani ◽  
Mohammad Malakootian ◽  
Amir Hessam Hassani ◽  
Amir Hossein Javid ◽  
Amir Hossein Javid ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Permeability ◽  
Water Flux ◽  
Real Data ◽  
Hydraulic Pressure ◽  
Seawater Desalination ◽  
Effective Parameters ◽  
Dissolved Matter ◽  
Agricultural Applications ◽  
And Performance

Brackish water, municipal and industrial wastewater is considered as a valuable source to increase agricultural production. PAFO system with the use of agricultural fertilizers as a new solution with the aim of creating hydraulic pressure less than 5 times to increase flux and water permeability compared to the system RO, FO in seawater desalination. The efficiency and performance of the system was determined by analyzing the parameters of water flux (jw), water permeability (A), permeability of dissolved matter (B), B/A ratio and finally modeling was done using MATLAB software with effective parameters in water flux based on chi model of neural network. According to the results of the model, appropriate performance of the model was determined based on the R coefficient and the distribution of the predicted points against the real data

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

Enhancing Water Permeability with Super-hydrophilic Metal-Organic Frameworks and Hydrophobic Straight Pores

2021 ◽  
Author(s):  
Mehdi Habibollahzadeh ◽  
Juran Noh ◽  
Liang Feng ◽  
Hong-Cai Zhou ◽  
Ahmed Abdel-Wahab ◽  
...  
Keyword(s):  
Osmotic Pressure ◽  
Water Permeability ◽  
Metal Organic Frameworks ◽  
Water Flux ◽  
Forward Osmosis ◽  
High Water ◽  
Metal Organic

High water flux and salt selectivity have been the most demanding goals for osmosis-based membranes. Osmotic pressure differences across membranes are particularly important in emerging forward osmosis and pressure retarded...

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

Effect of Modified NanoSiO2 Agents on the Morphologies and Performances of UHMWPE Microporous Membrane via Thermally Induced Phase Separation

2016 ◽  
Vol 848 ◽  
pp. 726-732 ◽  
Author(s):  
Rong Liu ◽  
Yan Wang ◽  
Jing Zhu ◽  
Zu Ming Hu ◽  
Jun Rong Yu
Keyword(s):  
Phase Separation ◽  
Water Flux ◽  
Microporous Membranes ◽  
Thermally Induced Phase Separation ◽  
X Ray Diffraction ◽  
Scanning Calorimetry ◽  
Thermally Induced ◽  
And Performance ◽  
Surface Modified ◽  
Ultra High Molecular Weight

The effects of Modified NanoSiO2 Agents on the morphology and performance of ultra-high-molecular weight polyethylene (UHMWPE) microporous membranes via thermally induced phase separation were investigated in this work. The NanoSiO2 was surface modified by silane coupling agent KH570 (KH570-NanoSiO2). Differential scanning calorimetry (DSC) and X-Ray Diffraction (XRD) were performed to obtain crystallization of UHMWPE/white oil/ KH570-NanoSiO2 doped system. The morphology and performance of the prepared UHMWPE microporous membranes were characterized with scanning electron microscopy (SEM) and microfiltration experiments. The results showed that the morphology of UHMWPE membrane could be disturbed by KH570-NanoSiO2. Porosity and the rejection of Bovine serum albumin (BSA) of the blend membrane increased with increasing concentration of Modified NanoSiO2, while the water flux slightly decreased.

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