Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats

2014 ◽  
Vol 30 (04) ◽  
pp. 153-174
Author(s):  
Dejan V. Radojcic ◽  
Michael G. Morabito ◽  
Aleksandar P. Simic ◽  
Antonio B. Zgradic
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Prediction Method ◽  
Standard Series ◽  
Mathematical Representations ◽  
Resistance Prediction ◽  
Artificial Neural ◽  
Data Tables ◽  
Compare And Contrast

Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1770
Author(s):  
Javier González-Enrique ◽  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Lipika Deka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term Memory ◽  
Prediction Method ◽  
Monitoring Network ◽  
Exogenous Variables ◽  
Prediction Horizon ◽  
Artificial Neural ◽  
Input Variables ◽  
Analytical Tools

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.

scholarly journals A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

2021 ◽  
Vol 118 (3) ◽  
pp. 507-516
Author(s):  
Vin Cent Tai ◽  
Yong Chai Tan ◽  
Nor Faiza Abd Rahman ◽  
Chee Ming Chia ◽  
Mirzhakyp Zhakiya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbines ◽  
Prediction Method ◽  
Horizontal Axis ◽  
Power Curve ◽  
Horizontal Axis Wind Turbines ◽  
Artificial Neural

scholarly journals Artificial neural networks and regression analysis for volume estimation in native species

2021 ◽  
Vol 25 (10) ◽  
pp. 664-669
Author(s):  
Lucas M. Amorim ◽  
Elton da S. Leite ◽  
Deoclides R. de Souza ◽  
Liniker F. da Silva ◽  
Carlos R. de Mello ◽  
...  
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Native Species ◽  
Middle Layer ◽  
Volume Measurement ◽  
Volume Estimation ◽  
Individual Volume ◽  
Artificial Neural ◽  
The Individual

ABSTRACT Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.

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