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Wyszukujesz frazę "model regresji" wg kryterium: Temat


Wyświetlanie 1-6 z 6
Tytuł:
Creep Behaviors at 275 °C for Aluminum-Matrix Nano-composite under Different Stress Levels
Autorzy:
Azadi, M.
Behmanesh, A.
Aroo, H.
Powiązania:
https://bibliotekanauki.pl/articles/2079834.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
aluminum alloy
nanocomposite
nanoparticles
regression model
creep
stress
stop aluminium
nanokompozyt
nanocząstki
model regresji
pełzanie
naprężenia
Opis:
Aluminum alloys, due to appropriate strength to weight ratio, are widely used in various industries, including automotive engines. This type of structures, due to high-temperature operations, are affected by the creep phenomenon; thus, the limited lifetime is expected for them. Therefore, in designing these types of parts, it is necessary to have sufficient information about the creep behavior and the material strength. One way to improve the properties is to add nanoparticles and fabricate a metal-based nano-composite. In the present research, failure mechanisms and creep properties of piston aluminum alloys were experimentally studied. In experiments, working conditions of combustion engine pistons were simulated. The material was composed of the aluminum matrix, which was reinforced by silicon oxide nanoparticles. The stir-casting method was used to produce the nano-composite by aluminum alloys and 1 wt.% of nanoparticles. The extraordinary model included the relationships between the stress and the temperature on the strain rate and the creep lifetime, as well as various theories such as the regression model. For this purpose, the creep test was performed on the standard sample at different stress levels and a specific temperature of 275 o. By plotting strain-time and strain rate-time curves, it was found that the creep lifetime decreased by increasing stress levels from 75 MPa to 125 MPa. Moreover, by comparing the creep test results of nanoparticle-reinforced alloys and nanoparticle-free alloys, 40% fall was observed in the reinforced material lifetime under 75 MPa. An increase in the strain rate was also seen under the mentioned stress. It is noteworthy that under 125 MPa, the creep lifetime and the strain rate of the reinforced alloy increased and decreased, respectively, compared to the piston alloy. Finally, by analyzing output data by the Minitab software, the sensitivity of the results to input parameters was investigated.
Źródło:
Archives of Foundry Engineering; 2021, 21, 3; 81-89
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of projection pursuit regression model for blasting vibration velocity peak prediction
Autorzy:
Shi, Jianjun
An, Huaming
Wei, Xin
Powiązania:
https://bibliotekanauki.pl/articles/1852582.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wibracje wybuchowe
przewidywanie
prędkość drgań
model regresji
pościg projekcji
płytki tunel
algorytm genetyczny
blasting vibration
vibration velocity
prediction
projection pursuit
regression model
shallow tunnel
genetic algorithm
Opis:
Based on Projection Pursuit Regression Theory (PPRT), a projection pursuit regression model has been established for forecasting the peak value of blasting vibration velocity. The model is then used to predict the peak value of blasting vibration velocity in a tunnel excavation blasting in Beijing. In order to train and test the model, 15 sets of measured samples from the tunnel project are used as the input data. It is found that predicting results by projection pursuit regression model on the basis of the input data is much more reasonable than that predicted by the traditional Sodaovsk algorithm and modified Sodaovsk formula. The results show that the average predicting error of the projection pursuit regression model is 6.36%, which is closer to the measured values. Thus, the projection pursuit prediction model is a practical and reasonable tool for forecasting the peak value of blasting vibration velocity.
Źródło:
Archives of Civil Engineering; 2021, 67, 2; 653-673
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The identification method of the coal mill motor power model with the use of machine learning techniques
Autorzy:
Łabęda-Grudziak, Zofia Magdalena
Lipiński, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/2090698.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coal mill motor power
nonlinear model identification
machine learning
additive regression models
process monitoring
moc silnika młyna węglowego
identyfikacja modelu nieliniowa
nauczanie maszynowe
model regresji addytywny
monitorowanie procesu
Opis:
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; e135842, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The identification method of the coal mill motor power model with the use of machine learning techniques
Autorzy:
Łabęda-Grudziak, Zofia Magdalena
Lipiński, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/2086819.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coal mill motor power
nonlinear model identification
machine learning
additive regression models
process monitoring
moc silnika młyna węglowego
nieliniowa identyfikacja modelu
nauczanie maszynowe
model regresji addytywny
monitorowanie procesu
Opis:
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as well as these aimed at verifying the quality of the evaluated model. In order to meet the tasks of machine learning, additive regression model was applied. Identification of the additive model parameters was performed on the basis of iterative backfitting algorithm combined with nonparametric estimation techniques. The proposed models have predictive nature and are aimed at simulation of the motor power signal of a coal mill during its regular operation, startup and shutdown. A comparative analysis has been performed of the models structured differently in terms of identification quality and sensitivity to the existence of an exemplary disturbance in the form of overhangs in the coal bunker. Tests carried out on the basis of real measuring data registered in the Polish power unit with a capacity of 200 MW confirm the effectiveness of the method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; art. no. e135842, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping
Autorzy:
Drzewiecki, W.
Powiązania:
https://bibliotekanauki.pl/articles/145416.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
modele regresji
nieprzepuszczalność
subpiksel
impervious area
sub-pixel classification
machine learning
model ensembles
Landsat
Opis:
We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
Źródło:
Geodesy and Cartography; 2017, 66, 2; 171-209
2080-6736
2300-2581
Pojawia się w:
Geodesy and Cartography
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a mathematical model of the generalized diagnostic indicator on the basis of full factorial experiment
Autorzy:
Sychenko, V. G.
Mironov, D. V.
Powiązania:
https://bibliotekanauki.pl/articles/223935.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electricity
traction substation
maintenance
diagnostics
full factorial experiment
mathematical model
regression equation
Elektryczność
podstacja trakcyjna
konserwacja
diagnostyka
eksperyment czynnikowy
model matematyczny
równanie regresji
Opis:
Purpose. The aim of this work is to develop a mathematical model of the generalized diagnostic indicator of the technical state of traction substations electrical equipment. Methodology. The main tenets of the experiment planning theory, methods of structural-functional and multi-factor analysis, methods of mathematical and numerical modeling have been used to solve the set tasks. Results. To obtain the mathematical model of the generalized diagnostic indicator, a full factorial experiment for DC circuit breaker have been conducted. The plan of the experiment and factors affecting the change of the unit technical condition have been selected. The regression equation in variables coded values and the polynomial mathematical model of the generalized diagnostic indicator of the circuit breaker technical condition have been obtained. On the basis of regression equation analysis the character of influence of circuit breaker diagnostic indicators values on generalized diagnostic indicator changes has been defined. As a result of repeated performances of the full factorial experiment the mathematical models for other types of traction substations power equipment have been obtained. Originality. An improved theoretical approach to the construction of generalized diagnostic indicators mathematical models for main types of traction substations electric equipment with using the methods of experiments planning theory has been suggested. Practical value. The obtained polynomial mathematical models of the generalized diagnostic indicator D can be used for constructing the automated system of monitoring and forecasting of the traction substations equipment technical condition, which allows improving the performance of processing the diagnostic information and ensuring the accuracy of the diagnosis. Analysing and forecasting the electrical equipment technical condition with the using of mathematical models of generalized diagnostic indicator changes process allows constructing the optimal strategy of maintenance and repair based on the actual technical condition of the electrical equipment. This will reduce material and financial costs of maintenance and repair work as well as the equipment downtime caused by planned inspections and repair improving reliability and uptime of electrical equipment.
Źródło:
Archives of Transport; 2017, 43, 3; 125-133
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-6 z 6

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