On the Issue of Designing a System for Continuous Control of Road Materials Compaction for Pavers

2021 ◽  
Vol 12 (8) ◽  
pp. 413-419
Author(s):  
A. P. Prokopev ◽  
◽  
Zh. I. Nabizhanov ◽  
V. I. Ivanchura ◽  
R. T. Emelyanov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Computational Experiment ◽  
Asphalt Mixture ◽  
Measured Data ◽  
Continuous Control ◽  
Ann Model ◽  
Road Materials ◽  
Compaction Control ◽  
Input Variables

The results of the research on the creation of an automatic compaction control system (ACCS) for pavers in real time are considered. The research is based on the methods of artificial neural networks (ANN). In this paper, an ANN model is obtained, with the help of which it is possible to determine the compaction coefficient (CC) of an asphalt mixture. The input variables of the ACCS are the velocity of movement of the paver, the frequency of impacts of the tamper, the force in the pusher of the tamper, the type of mixture, the thickness of the layer. The results of a computational experiment on the calculation of Cc in real time are presented. The ANN is able to explain more than 98 % of the measured data.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Ship As a Wave Buoy: Using Simulated Data to Train Neural Networks for Real Time Estimation of Relative Wave Direction

2019 ◽  
Author(s):  
Bart Mak ◽  
Bülent Düz
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
General Model ◽  
Simulated Data ◽  
Time Estimation ◽  
Measured Data ◽  
Wave Direction ◽  
Sea State ◽  
Digital Twin ◽  
Extended Training

Abstract Being able to give real time on-board advice, without depending on extensive sets of measured data, is the ultimate goal of the digital twin concept. Ideally, the models used in a digital twin only rely on current in-service data, although they have been built using simulated and possibly some measured data. Working with just the 6-DOF motions of a ship, can the local sea state reliably be estimated using the digital twin concept? Does a general model exist to do so, without the need to measure or simulate the particular ship? In this paper, we discuss how simulations of an advancing ship, subjected to various sea states, can be used to estimate the relative wave direction from in-service motion measurements of the corresponding ship. Various types of neural networks are used and evaluated with simulated data and measured data. In order to study the generalization power of the neural networks, a range of ships has been simulated, with varying lengths, drafts and geometries. Neural networks have been trained on selections of the ships in this extended training set and evaluated on the remaining ships. Results show that the developed neural networks give a remarkable performance in simulation data. Furthermore, generalization over geometry is very good, opening the door to train a general model for estimating sea state characteristics. Using the same model for in-service measurements does not perform well enough yet and further research is required. The paper will include discussion on possible causes for this performance gap and some promising ideas for future work.

scholarly journals Estimation of critical force of the buckling composite structures using modelling methods

2019 ◽  
Vol 252 ◽  
pp. 03004 ◽  
Author(s):  
Karol Szklarek ◽  
Jakub Gajewski ◽  
David Valis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Finite Elements ◽  
Composite Structures ◽  
Fem Simulation ◽  
Critical Force ◽  
Stacking Sequence ◽  
Ann Model ◽  
Input Variables ◽  
Modelling Methods

The study reported in this paper employed Artificial Neural Networks (ANN) to predict the critical force of the buckling composite structures. The critical force depends upon various factors such as thickness, stacking sequence, etc. These factors have been identified in earlier studies by means of the Finite Elements Method (FEM). The critical force is affected by the above-mentioned factors. Various approaches have been applied in the course of the presented study. Apart from our FEM simulation, the ANN approach has been applied and the results were compared. The main contribution of these two approaches is the estimation of the critical force. The ANN model is trained to predict the critical force for different configurations of input variables.

scholarly journals Predicting fire resistance ratings of timber structures using artificial neural networks

2020 ◽  
Vol 14 (2) ◽  
pp. 28-39
Author(s):  
Pham Thanh Tung ◽  
Pham Thanh Hung
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Fire Resistance ◽  
Input Parameter ◽  
Training Algorithms ◽  
Ann Model ◽  
Wooden Floor ◽  
Artificial Neural ◽  
Input Variables

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter. Keywords: artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

scholarly journals Prediction of nitrate concentration in Danube River water by using artificial neural networks

2020 ◽  
Vol 20 (6) ◽  
pp. 2119-2132
Author(s):  
Lidija J. Stamenković ◽  
Sanja Mrazovac Kurilić ◽  
Vladanka Presburger Ulniković
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Water ◽  
Nitrate Concentration ◽  
Absolute Error ◽  
Quality Parameters ◽  
Danube River ◽  
Ann Model ◽  
Artificial Neural ◽  
Input Variables

Abstract This paper describes the development of a model based on artificial neural networks (ANN) which aims to predict the concentration of nitrates in river water. Another 26 water quality parameters were also monitored and used as input parameters. The models were trained and tested with data from ten monitoring stations on the Danube River, located in its course through Serbia, for the period from 2011 to 2016. Multilayer perceptron (MLP), standard three-layer network is used to develop models and two input variable selection techniques are used to reduce the number of input variables. The obtained results have shown the ability of ANN to predict the nitrate concentration in both developed models with a value of mean absolute error of 0.53 and 0.42 mg/L for the test data. Also, the application of IVS has contributed to reduce the number of input variables and to increase the performance of the model, especially in the case of variance inflation factor (VIF) analysis where the estimation of multicollinearity among variables and the elimination of excessive variables significantly influenced the prediction abilities of the ANN model, r – 0.91.

scholarly journals Modelling Using Machine Learning Models and Prediction of Monthly Global Irradiation

2021 ◽  
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Learning Models ◽  
Machine Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Machine Learning Models

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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