Temat: neural modelling - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Influence of modelling phase transformations with the use of LSTM network on the accuracy of computations of residual stresses for the hardening process
Autorzy:: Wróbel, Joanna
Kulawik, Adam
Powiązania:: https://bibliotekanauki.pl/articles/27311451.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: hardening process
temperature
phase transformations in the solid state
effective stresses
numerical modelling
RNN
recurrent neural network
proces hartowania
temperatura
przemiany fazowe w stanie stałym
modelowanie numeryczne
rekurencyjna sieć neuronowa
naprężenie efektywne
Opis:: Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 4; art. no. e145681
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Comprehensive analysis of reclamation of spent lubricating oil using green solvent: RSM and ANN approach
Autorzy:: Sarkar, Sayantan
Datta, Deepshikha
Chowdhury, Somnath
Das, Bimal
Powiązania:: https://bibliotekanauki.pl/articles/2173421.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: modelling
optimization
extraction-flocculation
artificial neural network
genetic algorithm
modelowanie
optymalizacja
sztuczna sieć neuronowa
algorytm genetyczny
Opis:: Waste lubricating oil (WLO) is the most significant liquid hazardous waste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil.
Źródło:: Chemical and Process Engineering; 2022, 43, 2; 119--135
0208-6425
2300-1925
Pojawia się w:: Chemical and Process Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco
Autorzy:: El Azhari, Kaoutar
Abdallaoui, Badreddine
Dehbi, Ali
Abdalloui, Abdelaziz
Zineddine, Hamid
Powiązania:: https://bibliotekanauki.pl/articles/2174362.pdf
Data publikacji:: 2022
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: artificial neural network
ANN
learning algorithm
multi-layer perceptron
MLP
modelling
Rabat-Kenitra
relative humidity
Opis:: This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.
Źródło:: Journal of Water and Land Development; 2022, 54; 13--20
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Free running ship model tests of interaction between a moored ship and a passing ship
Autorzy:: Raszeja, Magdalena
Hejmlich, Andrzej
Nowicki, Jacek
Jaworski, Tomasz
Powiązania:: https://bibliotekanauki.pl/articles/24202583.pdf
Data publikacji:: 2022
Wydawca:: Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:: interaction forces
safe mooring
fuzzy model
neural network
numerical modelling
bypassing ship
Opis:: For many reasons, ship model interaction tests are performed in experimental towing tanks. This paper presents research on the hydrodynamic forces acting on a ship tied up at the solid berth, which is produced by other ships passing by using free-running ship models with much larger dimensions than those used in towing tanks. A passing ship model was controlled by a human operator – an experienced master. This enabled a study of the influence of the interaction impact on the course of the maneuver. The research was carried out at the Ship Handling Research and Training Centre in Iława. The ship model was moored alongside and equipped with multi-directional force sensors linking the ship model with a solid berth. Forces were measured as a function of the passing ship speed, side distance between both ships, ship sizes, and depth-to-draft ratio (H/T). Forces were measured in two planes: the longitudinal (surge) and the transversal (sway). A numerical database was processed and ordered according to the variables. The fuzzy model was created within a “Matlab” computing environment using a Sugeno-type self-learning neuron network model. The proposed Sugeno model was evaluated with other methods presented by Flory (2002), Seelig (2001), and PASS-MOOR by Wang (1975). The ultimate goal of this study was to simplify the method of predictive calculations for adjusting speed and distance when passing by the moored ship, which ensures compliance with safe port mooring requirements.
Źródło:: Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2022, 72 (144); 50--56
1733-8670
2392-0378
Pojawia się w:: Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Modelling and Analysis of the Synergistic Alloying Elements Effect on Hardenability of Steel
Autorzy:: Sitek, Wojciech
Trzaska, Jacek
Gemechu, W. F.
Powiązania:: https://bibliotekanauki.pl/articles/2203932.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: hardenability
artificial neural networks
multiple regression
steel alloy
modelling and simulation
hartowność
sztuczne sieci neuronowe
regresja wielokrotna
stal
modelowanie i symulacja
Opis:: The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models. Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods. Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve. The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions. It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application.
Źródło:: Archives of Foundry Engineering; 2022, 22, 4; 102--108
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Neural network approach to compressor modelling with surge margin consideration
Autorzy:: Loryś, Sergiusz Michał
Orkisz, Marek
Powiązania:: https://bibliotekanauki.pl/articles/2091364.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: modelling
compressor map
neural-network
Opis:: Artificial neural networks are gaining popularity thank to their fast and accurate response paired with low computing power requirements. They have been proven as a method for compressor performance prediction with satisfactory results. In this paper a new approach of artificial neural networks modelling is evaluated. The auxiliary parameter of ‘relative stability margin Z’ was introduced and used in learning process. This approach connects two methods of compressor modelling such as neural networks and auxiliary parameter utilization. Two models were created, one with utilization of the ‘relative stability margin Z’ as a direct indication of surge margin of any estimated condition, and other with standard compressor parameters. The results were compared by determination of fitting, interpolation and extrapolation capabilities of both approaches. The artificial neural networks used during the process was a two-layer feed-forward neural-network with Levenberg–Marquardt algorithm with Bayesian regularization. The experimental data was interpolated to increase the amount of learning data for the neural network. With the two models created, capabilities of this relatively simple type of neural-network to approximate compressor map was also assessed.
Źródło:: Archives of Thermodynamics; 2022, 43, 1; 89--108
1231-0956
2083-6023
Pojawia się w:: Archives of Thermodynamics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
Autorzy:: Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Borychenko, Olena
Kalinchyk, Vitalii
Powiązania:: https://bibliotekanauki.pl/articles/2174915.pdf
Data publikacji:: 2022
Wydawca:: Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:: classification
modelling
neural network
radial basis function network
RBF
multilayer perceptron
MLP
Opis:: This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
Źródło:: Rocznik Ochrona Środowiska; 2022, 24; 26--40
1506-218X
Pojawia się w:: Rocznik Ochrona Środowiska
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Prediction of Mechanical Properties of Woven Fabrics by ANN
Autorzy:: Elkateb, Sherien N.
Powiązania:: https://bibliotekanauki.pl/articles/2171977.pdf
Data publikacji:: 2022
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:: ANN
artificial neural network
mechanical properties
prediction performance
modelling
woven fabric
Opis:: This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.
Źródło:: Fibres & Textiles in Eastern Europe; 2022, 4 (151); 54--59
1230-3666
2300-7354
Pojawia się w:: Fibres & Textiles in Eastern Europe
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Soft modelling of the shaping of metal profiles in rapid tube hydroforming technology
Autorzy:: Sadłowska, Hanna
Kochański, Andrzej
Powiązania:: https://bibliotekanauki.pl/articles/29520063.pdf
Data publikacji:: 2022
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: rapid tube hydroforming
RTH
manufacturing
constitutive modelling
soft modelling
finite element method
FEM
artificial neural networks
ANN
hydroformowanie rur
produkcja
modelowanie konstytutywne
miękkie modelowanie
metoda elementów skończonych
MES
sztuczne sieci neuronowe
Opis:: The paper presents an approach to the impact of process parameters in innovative RTH (Rapid Tube Hydroforming) technology for shaping closed metal profiles in flexible and deformable dies. In order to implement the assumed deformation of the deformed profile, the RTH technology requires the monitoring and control of numerous technological parameters, including geometric, material, and technological variables. The paper proposes a two-stage research procedure considering hard modelling (constitutive) and soft modelling (data-driven). Due to the complexity of the technological process, it was required to develop a numerical finite element method FEM model focused on obtaining the adequate profile deformation measured by the ellipsoidality of the cylindrical profile. Based on the results of the numerical experiments, a preliminary soft mathematical model using ANN was developed. Analysing the soft model results, several statistical hypotheses were made and verified to investigate the significance of selected process parameters. Thanks to this, it was possible to select the most important process parameters, i.e., the properties of moulding sands used for RTH dies: the angle of internal friction and cohesion.
Źródło:: Computer Methods in Materials Science; 2022, 22, 4; 201-210
2720-4081
2720-3948
Pojawia się w:: Computer Methods in Materials Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: A neural-fuzzy approach for fault diagnosis of hybrid dynamical systems: demonstration on three-tank system
Autorzy:: Achbi, Mohammed Said
Kechida, Sihem
Mhamdi, Lotfi
Dhouibi, Hedi
Powiązania:: https://bibliotekanauki.pl/articles/1837950.pdf
Data publikacji:: 2021
Wydawca:: Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:: hybrid dynamic systems
modelling
residual generation
evaluation
monitoring
fault diagnosis
neural - fuzzy approach
Opis:: This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment.
Źródło:: Acta Mechanica et Automatica; 2021, 15, 1; 1-8
1898-4088
2300-5319
Pojawia się w:: Acta Mechanica et Automatica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Black box efficiency modelling of an electric drive unit utilizing methods of machine learning
Autorzy:: Bauer, Lukas
Stütz, Leon
Kley, Markus
Powiązania:: https://bibliotekanauki.pl/articles/1956031.pdf
Data publikacji:: 2021
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: electromobility
powertrain
electric drives
artificial neural network
efficiency modelling
elektromobilność
układ napędowy
napędy elektryczne
sztuczna sieć neuronowa
modelowanie wydajności
Opis:: The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.
Źródło:: Applied Computer Science; 2021, 17, 4; 5-19
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Forecasting the Energy Consumption of an Industrial Enterprise Based on the Neural Network Model
Autorzy:: Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Kalinchyk, Vitalii
Filyanin, Danylo
Powiązania:: https://bibliotekanauki.pl/articles/2069887.pdf
Data publikacji:: 2021
Wydawca:: Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:: electrical load
daily schedule
modelling
neural network
multilayer perceptron
MLP
Opis:: This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.
Źródło:: Rocznik Ochrona Środowiska; 2021, 23; 484--492
1506-218X
Pojawia się w:: Rocznik Ochrona Środowiska
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 13.

Tytuł:: Control of complex dynamic nonlinear loading process for electromagnetic mill
Autorzy:: Ogonowski, Szymon
Bismor, Dariusz
Ogonowski, Zbigniew
Powiązania:: https://bibliotekanauki.pl/articles/229680.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: predictive control
pole placement
nonlinear dynamics
neural modelling
electromagnetic mill
Opis:: Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gains cheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.
Źródło:: Archives of Control Sciences; 2020, 30, 3; 471-500
1230-2384
Pojawia się w:: Archives of Control Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 14.

Tytuł:: Data-driven discharge analysis: a case study for the Wernersbach catchment, Germany
Autorzy:: Popat, Eklavyya
Kuleshov, Alexey
Kronenberg, Rico
Bernhofer, Christian
Powiązania:: https://bibliotekanauki.pl/articles/108441.pdf
Data publikacji:: 2020
Wydawca:: Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:: artificial neural networks
data-driven modelling
event-based coefficient of rainfall-runoff
precipitation
multi-correlation analysis
soil moisture content
Opis:: This study focuses on precipitationdischarge data-driven models, with regression analysis between the weighted maximum rainfall and maximum discharge of flood events. It is also the first of its kind investigation for the Wernersbach catchment, which incorporates data-driven models in order to evaluate the suitability of the model in simulating the discharge from the catchment and provide good insights for future studies. The input parameters are hydrological and climate data collected from 2001 to 2009, including precipitation, rainfall-runoff and soil moisture. The statistical regression and artificial neural network models used are based on a data-driven multiple linear regression technique, and the same input parameters are applied for validation and calibration. The artificial neural network model has one hidden layer with a sigmoidal activation function and uses a linear activation function in the output layer. The artificial neural network is observed to model 0.7% and 0.5% of values, with and without extreme values respectively. With less than 1% error, the artificial neural network is observed to predict extreme events better compared to the conventional statistical regression model and is also better suited to the tasks of rainfall-runoff and flood forecasting. It is presumed that in the future this study’s conclusions would form the basis for more complex and detailed studies for the same catchment area.
Źródło:: Meteorology Hydrology and Water Management. Research and Operational Applications; 2020, 8, 1; 54-62
2299-3835
2353-5652
Pojawia się w:: Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 15.

Tytuł:: Development of cost estimation models based on ANN ensembles and the SVM method
Autorzy:: Juszczyk, Michał
Powiązania:: https://bibliotekanauki.pl/articles/396649.pdf
Data publikacji:: 2020
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: construction cost estimation
cost modelling
ensemble of neural networks
support vector machine
koszty budowy
modelowanie kosztów
zespół sieci neuronowych
Opis:: Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.
Źródło:: Civil and Environmental Engineering Reports; 2020, 30, 3; 48-67
2080-5187
2450-8594
Pojawia się w:: Civil and Environmental Engineering Reports
Dostawca treści:: Biblioteka Nauki

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