scholarly journals Design of Computational Models for Hydroturbine Units Based on a Nonparametric Regression Approach with Adaptation by Evolutionary Algorithms

Computation ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 83
Author(s):  
Vladimir Viktorovich Bukhtoyarov ◽  
Vadim Sergeevich Tynchenko
Keyword(s):  
Regression Models ◽  
Computational Models ◽  
Kernel Smoothing ◽  
Quality Criterion ◽  
High Sensitivity ◽  
Multivariate Adaptive Regression Splines ◽  
Test Problems ◽  
Nonparametric Model ◽  
Modeling Error ◽  
Nonparametric Approach

This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya–Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modeling error by 20% and 28% for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.

scholarly journals Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

2021 ◽  
Author(s):  
Paulino José García-Nieto ◽  
Esperanza García-Gonzalo ◽  
José Pablo Paredes-Sánchez
Keyword(s):  
Critical Temperature ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Chemical Properties ◽  
Atomic Radius ◽  
Elastic Net ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Humpback Whales ◽  
Whale Optimization

AbstractThis study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

scholarly journals A prospective follow-up study of the relationship between high-sensitivity C-reactive protein and primary liver cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sarah Tan Siyin ◽  
Tong Liu ◽  
Wenqiang Li ◽  
Nan Yao ◽  
Guoshuai Xu ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Regression Models ◽  
Primary Liver Cancer ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Hs Crp ◽  
The Relationship ◽  
New Onset

Abstract Background Competing risk method has not been used in a large-scale prospective study to investigate whether increased levels of high-sensitivity C-reactive protein (hs-CRP) elevate the risk of primary liver cancer (PLC). Our study aims to prospectively investigate the relationship between hs-CRP and new-onset PLC. Methods and results Ninety-five thousand seven hundred fifty-nine participants without the diagnosis of PLC, and who had their demographic characteristics and biochemical parameters recorded, were analyzed from the Kailuan Cohort study. Cox proportional hazards regression models and competing risk regression models were used to evaluate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of PLC. During a median follow-up of 11.07 years, 357 incidental PLC cases were identified over a total of 1,035,039 person-years. The multivariable HRs (95%CI) for the association of hs-CRP of 1–3 mg/L group and hs-CRP>3 mg/L with PLC were 1.07(0.82 ~ 1.38), 1.51(1.15 ~ 1.98) in a Cox proportional hazard regression analysis adjusted for other potential confounders. In the cause-specific hazard model, the multivariable HRs (95%CI) for the association of hs-CRP of 1–3 mg/L group and hs-CRP>3 mg/L with PLC were 1.06(0.81 ~ 1.40), 1.50(1.14 ~ 1.99). Similar results were also observed in the sub-distribution hazard function model with corresponding multivariate HRs (95%CI) of 1.05(0.80 ~ 1.40), 1.49(1.13 ~ 1.98) in hs-CRP of 1–3 mg/L group and hs-CRP>3 mg/L group, respectively. Conclusions This prospective study found a significant association of higher levels of hs-CRP with new-onset PLC. The main clinical implications would be an increased awareness of hs-CRP and its correlation to the risk of PLC. This study should be a steppingstone to further research on chronic inflammation and PLC. Trial registration Registration number:ChiCTR–TNRC–11001489.

Prediction of Soluble Solids Content and Post-Storage Internal Quality of Bulida Apricots Using near Infrared Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Marena Manley ◽  
Elizabeth Joubert ◽  
Lindie Myburgh ◽  
Ester Lotz ◽  
Martin Kidd
Keyword(s):  
Cold Storage ◽  
Regression Models ◽  
Near Infrared ◽  
Storage Period ◽  
Multivariate Adaptive Regression Splines ◽  
Soluble Solids ◽  
Internal Quality ◽  
Soluble Solids Content ◽  
Classification Rate ◽  
Solids Content

The development of internal breakdown of South African Bulida apricots during cold storage, rendering the fruit unsuitable for canning, causes significant post-harvest losses. Regression models to predict internal post-storage quality using near infrared (NIR) spectroscopy and multivariate classification techniques were developed using NIR spectra of the intact fruit collected prior to storage and subjective quality evaluations performed after a cold storage period of four weeks. A correct classification rate of 69% was obtained using multivariate adaptive regression splines (MARS) compared to 50% obtained by soft independent modelling by class analogy (SIMCA). NIR regression models developed for soluble solids content (SSC) of intact fruit as well as for direct NIR measurements on the exposed fruit tissue gave similar results, thus confirming sufficient NIR light penetration into the intact fruit. The best prediction results were obtained when two spectral measurements per fruit (one on each half of the fruit), compared to single measurements, were used.

scholarly journals Technical Note: Nominal Stiffness Evaluation and Regression Analysis of GT-2 Rubber-Fiberglass Timing Belts

2018 ◽  
Author(s):  
Bozun Wang ◽  
Yefei Si ◽  
Charul Chadha ◽  
James T. Allison ◽  
Albert E. Patterson
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Linear Models ◽  
System Modeling ◽  
Technical Note ◽  
Effective Stiffness ◽  
Modeling Error ◽  
Mechatronic Systems ◽  
Stiffness Evaluation ◽  
Best Fit

GT-style rubber-fiberglass (RF) timing belts are designed to effectively transfer rotational motion from pulleys to linear motion in small machines and mechatronic systems. One of the characteristics of belts under this type of loading condition is that the length between load and pulleys changes during operation, thereby changing their effective stiffness. It has been shown that the effective stiffness of such a belt is a function of a "nominal stiffness" and the real-time belt section lengths. However, this nominal stiffness is not necessarily constant; it is common to assume linear proportional stiffness, but this often results in system modeling error. This technical note describes a brief study where the nominal stiffness of two lengths (400 mm and 760 mm ) of GT-2 RF timing belts was tested up to breaking point; regression analysis was performed on the results to best model the observed stiffness. The study was replicated three times, providing a total of six stiffness curves. It was found that cubic regression models (R^2 > 0.999) were the best fit, but that quadratic and linear models still provided acceptable representations of the whole dataset with R^2 values above 0.940.

Innovative Building Forms Determined by Orthotropic Properties of Folded Sheets Transformed Into Roof Shells

2020 ◽  
Vol 61 (2) ◽  
pp. 111-124
Author(s):  
Aleksandra Prokopska ◽  
Jacek Abramczyk
Keyword(s):  
Computational Models ◽  
High Sensitivity ◽  
Free Form ◽  
Shape Transformation ◽  
Steel Sheets ◽  
Shape Transformations ◽  
Thin Walled ◽  
Orthotropic Properties ◽  
Roof Shells ◽  
Qualitative And Quantitative Characteristics

Qualitative and quantitative characteristics of geometrical and mechanical changes of nominally plane steel sheets folded in one direction, caused by big elastic shape transformations were invented on the basis of the authors' tests, analyzes and computational models of thin-walled folded sheets transformed into shell shapes. Both geometrical and mechanical changes produce significant restrictions in using sheets for shell forms. The deliberate transformations and sheets' characteristics are required to obtain attractive and innovative forms of roof shells and their consistent structures as well as entire buildings. The search for effective solutions related to free forms of buildings and shape transformations of sheets especially in the fields of: shape transformation, effort and stabilization of their walls is necessary due to the high sensitivity of thin-walled open profiles to boundary conditions and loads. A method for shaping such free form buildings that effectively exploit specific orthotropic properties of the transformed sheeting is presented.

A Statistical Investigation of the Sensitivity of Ensemble-Based Kalman Filters to Covariance Filtering

2011 ◽  
Vol 139 (9) ◽  
pp. 3036-3051 ◽  
Author(s):  
Mikyoung Jun ◽  
Istvan Szunyogh ◽  
Marc G. Genton ◽  
Fuqing Zhang ◽  
Craig H. Bishop
Keyword(s):  
Covariance Matrix ◽  
Kernel Smoothing ◽  
Localization Length ◽  
Statistical Investigation ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Nonparametric Approach ◽  
Error Covariance ◽  
Sample Covariance

This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari–Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari–Cohn filter with any localization length. It is also shown that the Gaspari–Cohn filter tends to provide more accurate estimates of the covariance with shorter localization lengths. However, the analyses obtained by using longer localization lengths tend to be more accurate than those produced by using short localization lengths or the nonparametric approach. This seemingly paradoxical result is explained by showing that localization with longer localization lengths produces filtered estimates whose time mean is the most similar to the time mean of both the unfiltered estimate and the true covariance. This result suggests that a better metric of covariance filtering skill would be one that combined a measure of closeness to the sample covariance matrix for a very large ensemble with a measure of similarity between the climatological averages of the filtered and sample covariance.

Ridge Regression Based Development of Norris-Landzberg Acceleration Factors and Goldmann Constants for Leadfree Electronics

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Dinesh Arunachalam ◽  
Jeff Suhling
Keyword(s):  
Ridge Regression ◽  
Thermal Cycle ◽  
Regression Models ◽  
Coefficient Of Thermal Expansion ◽  
Model Development ◽  
High Sensitivity ◽  
Maximum Temperature ◽  
Temperature Cycle ◽  
Linear Regression Models ◽  
Part Selection

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on ridge regression models (RR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Ridge regression adds a small positive bias to the diagonal of the covariance matrix to prevent high sensitivity to variables that are correlated. The proposed procedure proves to be a better tool for prediction than multiple-linear regression models. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined.

Evaluation of standard error of forecast of runoff volume using linear regression models

2002 ◽  
Vol 29 (5) ◽  
pp. 635-640 ◽  
Author(s):  
Wuben Luo ◽  
Eric Weiss
Keyword(s):  
Linear Regression ◽  
Standard Error ◽  
Regression Models ◽  
Linear Regression Models ◽  
Modeling Error ◽  
Reservoir Operations ◽  
Microsoft Excel ◽  
Runoff Volume ◽  
Modeling Errors ◽  
Significant Underestimation

Optimizing reservoir operations requires forecasts of seasonal inflow and a good understanding of the associated uncertainties. When forecasting seasonal runoff volume to a reservoir using a linear regression model, hydrologic forecasters typically use the standard error of residuals as the standard error of forecast to give water managers a sense of uncertainties in the forecast. However, this practice accounts for only the random error and ignores the modeling error in the volume forecast, resulting in underestimation of the standard error of the forecast. The underestimation can become significant in extreme runoff years for which reservoir operations tend to be most critical. This paper presents the algorithm for calculating the standard error of forecast, which takes into consideration both random and modeling errors. A simple way of calculating the standard error of forecast using built-in functions in Microsoft Excel is described. An example is used to demonstrate the potentially significant underestimation of the true error of a forecast if modeling error is ignored.Key words: standard error of forecast, residuals, runoff volume forecast, regression analysis.

scholarly journals High sensitivity troponin T and I reflect left atrial function being assessed by cardiac magnetic resonance imaging

2017 ◽  
Vol 55 (2) ◽  
pp. 264-275 ◽  
Author(s):  
Michèle Natale ◽  
Michael Behnes ◽  
Seung-Hyun Kim ◽  
Julia Hoffmann ◽  
Nadine Reckord ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Magnetic Resonance Imaging ◽  
Left Atrial ◽  
Regression Models ◽  
High Sensitivity ◽  
Atrial Function ◽  
Left Atrial Function ◽  
Resonance Imaging

Background Left atrial function (LAF) plays an interactive role between pulmonary and systemic circulation. Cardiac biomarkers, such as amino-terminal pro-brain natriuretic peptide (NT-proBNP) and troponins, might reflect cardiac function. This study aims to evaluate the association between high sensitivity troponins (hsTn) and left atrial function in patients undergoing cardiac magnetic resonance imaging (cMRI). Methods Patients undergoing cardiac magnetic resonance imaging (cMRI) were enrolled prospectively. Patients with right ventricular dysfunction (<50%) were excluded. Blood samples for measurements of hsTn and NT-proBNP were collected at the time of cMRI. Results Eighty-four patients were included. Median LVEF was 59% (IQR 51–64%). HsTn correlated inversely with LAF within multivariable linear regression models (hsTnI: Beta −0.46; T −4.44; P = 0.0001; hsTnT: Beta −0.29; T −3.06; P = 0.003). High sensitivity troponins increased significantly according to decreasing stages of impaired LAF ( P = 0.0001). High sensitivity troponins discriminated patients with impaired LAF < 55% (hsTnT: AUC = 0.80; P = 0.0001; hsTnI: AUC = 0.74; P = 0.0001) and <45% (hsTnT: AUC = 0.75; P = 0.0001; hsTnI: AUC = 0.73; P = 0.001) and were still significantly associated in multivariable logistic regression models (LAF < 55%: hsTnT: OR = 21.78; P = 0.0001; hsTnI: OR = 5.96; P = 0.009; LAF < 45%: hsTnT: OR = 10.27; P = 0.0001; hsTnI: OR = 12.56; P = 0.001). Conclusions This study demonstrates that hsTn are able to reflect LAF being assessed by cardiac magnetic resonance imaging.

scholarly journals Inflammatory Markers Are Positively Associated with Serumtrans-Fatty Acids in an Adult American Population

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Mohsen Mazidi ◽  
Hong-kai Gao ◽  
Andre Pascal Kengne
Keyword(s):  
Fatty Acids ◽  
Linear Regression ◽  
Regression Models ◽  
Survey Design ◽  
High Sensitivity ◽  
Analysis Of Covariance ◽  
Linear Regression Models ◽  
Inflammatory Profile ◽  
Hs Crp ◽  
High Level

Background and Aim. The relationship between serumtrans-fatty acids (TFAs) and systemic inflammation markers is unclear. We investigated the association of serum TFAs with high sensitivity C-reactive protein (hs-CRP) and fibrinogen in adult Americans.Methods. The 1999 to 2000 National Health and Nutrition Examination Survey (NHANES) participants with measured data on hs-CRP and fibrinogen were included. TFAs were measured via capillary gas chromatography and mass spectrometry using negative chemical ionization. Analysis of covariance and multivariable-adjusted linear regression models were used to investigate the associations between these parameters, accounting for the survey design.Results. Of the 5446 eligible participants, 46.8% (n=2550) were men. The mean age was 47.1 years overall: 47.8 years in men and 46.5 years in women (p=0.085). After adjustment for age and sex, mean serum TFAs rose with the increasing quarters of hs-CRP and fibrinogen (bothp<0.001). In linear regression models adjusted for age, sex, race, education, marital status, body mass index, and smoking, serum TFAs were an independent predictor of plasma hs-CRP and fibrinogen levels.Conclusion. A high level of TFAs appears to be a contributor to an unfavourable inflammatory profile. Because serum TFAs concentrations are affected by dietary TFA intake, these data suggest a possible contribution of TFAs intake modulation in the prevention of inflammation-related chronic diseases.

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