Academic literature on the topic 'Regression coefficient function'
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Journal articles on the topic "Regression coefficient function"
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Khammar, Amir Hamzeh, Mohsen Arefi, and Mohammad Ghasem Akbari. "Quantile Fuzzy Varying Coefficient Regression based on kernel function." Applied Soft Computing 107 (August 2021): 107313. http://dx.doi.org/10.1016/j.asoc.2021.107313.
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Li, Meng, and Hong-Wei Sun. "Asymptotic analysis of quantile regression learning based on coefficient dependent regularization." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 04 (July 2015): 1550018. http://dx.doi.org/10.1142/s0219691315500186.
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In this paper, we consider conditional quantile regression learning algorithms based on the pinball loss with data dependent hypothesis space and ℓ2-regularizer. Functions in this hypothesis space are linear combination of basis functions generated by a kernel function and sample data. The only conditions imposed on the kernel function are the continuity and boundedness which are pretty weak. Our main goal is to study the consistency of this regularized quantile regression learning. By concentration inequality with ℓ2-empirical covering numbers and operator decomposition techniques, satisfied error bounds and convergence rates are explicitly derived.
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Cao, MingXiang, and DaoJiang He. "Linearly admissible estimators on linear functions of regression coefficient under balanced loss function." Communications in Statistics - Theory and Methods 48, no. 11 (March 14, 2019): 2700–2706. http://dx.doi.org/10.1080/03610926.2018.1472788.
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Bae, M. S., and S. R. Ha. "Nonlinear regression approach to evaluate nutrient delivery coefficient." Water Science and Technology 53, no. 2 (January 1, 2006): 271–79. http://dx.doi.org/10.2166/wst.2006.061.
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Implementation of the Korean Total Maximum Daily Load Act calls for new tools to quantify nutrient losses from diffuse sources at a river basin district scale. In this study, it was elucidated that the nonlinear regression model (NRM) reduces the uncertainty of the boundary conditions of the water quality model. The NRM was proposed to analyse the delivery coefficients of surface waters and retention coefficients of pollutants. Delivery coefficient of pollution load was considered as a function of two variables: the watershed form ratio, Sf, which is a measurable geomorphologic variable and the retention coefficient, ϕ, which is an empirical constant representing the basin-wide retarding capacity of pollutant wash-off. This model was applied on the Geum River, one of the major basins in South Korea. The QUAL2E was used to simulate stream water quality using NRM. In this paper, we elucidate the possibility to use a nonlinear regression model for delivery and retention of nutrients in a drainage basin characterized as both data-rich and data-poor, and the magnitude of the nutrient loads and sources has been uncertain for a long time. Keywords Delivery coefficient; diffuse pollution; pollution load runoff; retention coefficient
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Wei, Laisheng. "EMPIRICAL BAYES ESTIMATION FOR ESTIMABLE FUNCTION OF REGRESSION COEFFICIENT IN A MULTIPLE LINEAR REGRESSION MODEL." Acta Mathematica Scientia 16 (1996): 22–33. http://dx.doi.org/10.1016/s0252-9602(17)30814-7.
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Guo, Luo, Zhihai Ma, and Lianjun Zhang. "Comparison of bandwidth selection in application of geographically weighted regression: a case study." Canadian Journal of Forest Research 38, no. 9 (September 2008): 2526–34. http://dx.doi.org/10.1139/x08-091.
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A forest plot with a clustered spatial pattern of tree locations was used to investigate the impacts of different kernel functions (fixed vs. adaptive) and different sizes of bandwidth on model fitting, model performance, and spatial characteristics of the geographically weighted regression (GWR) coefficient estimates and model residuals. Our results indicated that (i) the GWR models with smaller bandwidths fit the data better, yielded smaller model residuals across tree sizes, significantly reduced spatial autocorrelation and heterogeneity for model residuals, and generated better spatial patterns for model residuals; however, smaller bandwidth sizes produced a high level of coefficient variability; (ii) the GWR models based on the fixed spatial kernel function produced smoother spatial distributions for the model coefficients than those based on the adaptive kernel function; and (iii) the GWR cross-validation or Akaike’s information criterion (AIC) optimization process may not produce an “optimal” bandwidth for model fitting and performance. It was evident that the selection of spatial kernel function and bandwidth has a strong impact on the descriptive and predictive power of GWR models.
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Cao, Mingxiang, Xingzhong Xu, and Daojiang He. "Linearly admissible estimators of stochastic regression coefficient under balanced loss function." Statistics 48, no. 2 (February 6, 2013): 359–66. http://dx.doi.org/10.1080/02331888.2013.766794.
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Karunamuni, R. J., and L. Wei. "On empirical Bayes estimation of multivariate regression coefficient." International Journal of Mathematics and Mathematical Sciences 2006 (2006): 1–18. http://dx.doi.org/10.1155/ijmms/2006/51695.
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We investigate the empirical Bayes estimation problem of multivariate regression coefficients under squared error loss function. In particular, we consider the regression modelY=Xβ+ε, whereYis anm-vector of observations,Xis a knownm×kmatrix,βis an unknownk-vector, andεis anm-vector of unobservable random variables. The problem is squared error loss estimation ofβbased on some “previous” dataY1,…,Ynas well as the “current” data vectorYwhenβis distributed according to some unknown distributionG, whereYisatisfiesYi=Xβi+εi,i=1,…,n. We construct a new empirical Bayes estimator ofβwhenεi∼N(0,σ2Im),i=1,…,n. The performance of the proposed empirical Bayes estimator is measured using the mean squared error. The rates of convergence of the mean squared error are obtained.
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DU, JIANG, ZHONGZHAN ZHANG, and ZHIMENG SUN. "VARIABLE SELECTION FOR PARTIALLY LINEAR VARYING COEFFICIENT QUANTILE REGRESSION MODEL." International Journal of Biomathematics 06, no. 03 (May 2013): 1350015. http://dx.doi.org/10.1142/s1793524513500150.
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In this paper, we propose a variable selection procedure for partially linear varying coefficient model under quantile loss function with adaptive Lasso penalty. The functional coefficients are estimated by B-spline approximations. The proposed procedure simultaneously selects significant variables and estimates unknown parameters. The major advantage of the proposed procedures over the existing ones is easy to implement using existing software, and it requires no specification of the error distributions. Under the regularity conditions, we show that the proposed procedure can be as efficient as the Oracle estimator, and derive the optimal convergence rate of the functional coefficients. A simulation study and a real data application are undertaken to assess the finite sample performance of the proposed variable selection procedure.
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Hu, Guikai, Qingguo Li, and Ping Peng. "Minimax estimator of regression coefficient in normal distribution under balanced loss function." Linear Algebra and its Applications 436, no. 5 (March 2012): 1228–37. http://dx.doi.org/10.1016/j.laa.2011.08.013.
Full textDissertations / Theses on the topic "Regression coefficient function"
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Al-Shaikh, Enas. "Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464.
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Montoril, Michel Helcias. "Modelos de regressão com coeficientes funcionais para séries temporais." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-04042013-215702/.
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Nesta tese, consideramos o ajuste de modelos de regressão com coeficientes funcionais para séries temporais, por meio de splines, ondaletas clássicas e ondaletas deformadas. Consideramos os casos em que os erros do modelo são independentes e correlacionados. Através das três abordagens de estimação, obtemos taxas de convergência a zero para distâncias médias entre as funções do modelo e seus respectivos estimadores, propostos neste trabalho. No caso das abordagens de ondaletas (clássicas e deformadas), obtemos também resultados assintóticos em situações mais específicas, nas quais as funções do modelo pertencem a espaços de Sobolev e espaços de Besov. Além disso, estudos de simulação de Monte Carlo e aplicações a dados reais são apresentados. Por meio desses estudos numéricos, fazemos comparações entre as três abordagens de estimação propostas, e comparações entre outras abordagens já conhecidas na literatura, onde verificamos desempenhos satisfatórios, no sentido das abordagens propostas fornecerem resultados competitivos, quando comparados aos resultados oriundos de metodologias já utilizadas na literatura.In this thesis, we study about fitting functional-coefficient regression models for time series, by splines, wavelets and warped wavelets. We consider models with independent and correlated errors. Through the three estimation approaches, we obtain rates of convergence to zero for average distances between the functions of the model and their estimators proposed in this work. In the case of (warped) wavelets approach, we also obtain asymptotic results in more specific situations, in which the functions of the model belong to Sobolev and Besov spaces. Moreover, Monte Carlo simulation studies and applications to real data sets are presented. Through these numerical results, we make comparisons between the three estimation approaches proposed here and comparisons between other approaches known in the literature, where we verify interesting performances in the sense that the proposed approaches provide competitive results compared to the results from methodologies used in literature.
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Connell, R. J. "Unstable equilibrium : modelling waves and turbulence in water flow." Diss., Lincoln University, 2008. http://hdl.handle.net/10182/592.
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This thesis develops a one-dimensional version of a new data driven model of turbulence that uses the KL expansion to provide a spectral solution of the turbulent flow field based on analysis of Particle Image Velocimetry (PIV) turbulent data. The analysis derives a 2nd order random field over the whole flow domain that gives better turbulence properties in areas of non-uniform flow and where flow separates than the present models that are based on the Navier-Stokes Equations. These latter models need assumptions to decrease the number of calculations to enable them to run on present day computers or super-computers. These assumptions reduce the accuracy of these models. The improved flow field is gained at the expense of the model not being generic. Therefore the new data driven model can only be used for the flow situation of the data as the analysis shows that the kernel of the turbulent flow field of undular hydraulic jump could not be related to the surface waves, a key feature of the jump. The kernel developed has two parts, called the outer and inner parts. A comparison shows that the ratio of outer kernel to inner kernel primarily reflects the ratio of turbulent production to turbulent dissipation. The outer part, with a larger correlation length, reflects the larger structures of the flow that contain most of the turbulent energy production. The inner part reflects the smaller structures that contain most turbulent energy dissipation. The new data driven model can use a kernel with changing variance and/or regression coefficient over the domain, necessitating the use of both numerical and analytical methods. The model allows the use of a two-part regression coefficient kernel, the solution being the addition of the result from each part of the kernel. This research highlighted the need to assess the size of the structures calculated by the models based on the Navier-Stokes equations to validate these models. At present most studies use mean velocities and the turbulent fluctuations to validate a models performance. As the new data driven model gives better turbulence properties, it could be used in complicated flow situations, such as a rock groyne to give better assessment of the forces and pressures in the water flow resulting from turbulence fluctuations for the design of such structures. Further development to make the model usable includes; solving the numerical problem associated with the double kernel, reducing the number of modes required, obtaining a solution for the kernel of two-dimensional and three-dimensional flows, including the change in correlation length with time as presently the model gives instant realisations of the flow field and finally including third and fourth order statistics to improve the data driven model velocity field from having Gaussian distribution properties. As the third and fourth order statistics are Reynolds Number dependent this will enable the model to be applied to PIV data from physical scale models. In summary, this new data driven model is complementary to models based on the Navier-Stokes equations by providing better results in complicated design situations. Further research to develop the new model is viewed as an important step forward in the analysis of river control structures such as rock groynes that are prevalent on New Zealand Rivers protecting large cities.
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Xu, Fang. "A functional coefficient model view of the saving-investment relation /." 2008. http://www.gbv.de/dms/zbw/563578947.pdf.
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Ma, Guangyi. "Three Essays on Estimation and Testing of Nonparametric Models." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11768.
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In this dissertation, I focus on the development and application of nonparametric methods in econometrics. First, a constrained nonparametric regression method is developed to estimate a function and its derivatives subject to shape restrictions implied by economic theory. The constrained estimators can be viewed as a set of empirical likelihood-based reweighted local polynomial estimators. They are shown to be weakly consistent and have the same first order asymptotic distribution as the unconstrained estimators. When the shape restrictions are correctly specified, the constrained estimators can achieve a large degree of finite sample bias reduction and thus outperform the unconstrained estimators. The constrained nonparametric regression method is applied on the estimation of daily option pricing function and state-price density function.
Second, a modified Cumulative Sum of Squares (CUSQ) test is proposed to test structural changes in the unconditional volatility in a time-varying coefficient model. The proposed test is based on nonparametric residuals from local linear estimation of the time-varying coefficients. Asymptotic theory is provided to show that the new CUSQ test has standard null distribution and diverges at standard rate under the alternatives. Compared with a test based on least squares residuals, the new test enjoys correct size and good power properties. This is because, by estimating the model nonparametrically, one can circumvent the size distortion from potential structural changes in the mean. Empirical results from both simulation experiments and real data applications are presented to demonstrate the test's size and power properties.
Third, an empirical study of testing the Purchasing Power Parity (PPP) hypothesis is conducted in a functional-coefficient cointegration model, which is consistent with equilibrium models of exchange rate determination with the presence of trans- actions costs in international trade. Supporting evidence of PPP is found in the recent float exchange rate era. The cointegration relation of nominal exchange rate and price levels varies conditioning on the real exchange rate volatility. The cointegration coefficients are more stable and numerically near the value implied by PPP theory when the real exchange rate volatility is relatively lower.
Books on the topic "Regression coefficient function"
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Racine, Jean. A nonparametric variable kernel method for local adaptive smoothing of regression functions and associated response coefficients. Toronto, Ont: Dept. of Economics, York University, 1991.
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Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.
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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as linear/nonparametric functional regression, varying coefficient models, and linear/nonparametric functional processes (i.e. functional time series). Part II considers related benchmark methods/tools for functional data analysis, including curve registration methods for preprocessing functional data, functional principal component analysis, and resampling/bootstrap methods. Finally, Part III examines some of the fundamental mathematical aspects of the infinite-dimensional setting, with a focus on the stochastic background and operatorial statistics: vector-valued function integration, spectral and random measures linked to stationary processes, operator geometry, vector integration and stochastic integration in Banach spaces, and operatorial statistics linked to quantum statistics.
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Functional Relations, Random Coefficients, and Nonlinear Regression with Application to Kinetic Data. Springer, 2011.
Find full textBook chapters on the topic "Regression coefficient function"
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Abdelouahad, Abdelkaher Ait, Mohammed El Hassouni, Hocine Cherifi, and Driss Aboutajdine. "A New Image Distortion Measure Based on Natural Scene Statistics Modeling." In Geographic Information Systems, 616–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch037.
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In the field of Image Quality Assessment (IQA), this paper examines a Reduced Reference (RRIQA) measure based on the bi-dimensional empirical mode decomposition. The proposed measure belongs to Natural Scene Statistics (NSS) modeling approaches. First, the reference image is decomposed into Intrinsic Mode Functions (IMF); the authors then use the Generalized Gaussian Density (GGD) to model IMF coefficients distribution. At the receiver side, the same number of IMF is computed on the distorted image, and then the quality assessment is done by fitting error between the IMF coefficients histogram of the distorted image and the GGD estimate of IMF coefficients of the reference image, using the Kullback Leibler Divergence (KLD). In addition, the authors propose a new Support Vector Machine-based classification approach to evaluate the performances of the proposed measure instead of the logistic function-based regression. Experiments were conducted on the LIVE dataset.
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Arbaiy, Nureize, Junzo Watada, and Pei-Chun Lin. "Fuzzy Random Regression-Based Modeling in Uncertain Environment." In Sustaining Power Resources through Energy Optimization and Engineering, 127–46. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9755-3.ch006.
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The parameter value determination is important to avoid the developed mathematical model is troublesome and may yield inappropriate results. However, estimating the weights of the parameter or objective functions in the mathematical model is sometimes not easy in real situations, especially when the values are unavailable or difficult to decide. Additionally, various uncertainties include in the statistical data makes common mathematical analysis is not competent to deal with. Hence, this paper presents the Fuzzy Random Regression approach to determine the coefficient whereby statistical data used contain uncertainties namely, fuzziness and randomness. The proposed methods are able to provide coefficient information in the model setting and consideration of uncertainties in the evaluation process. The assessment of coefficient value is given by Weight Absolute Percentage Error of Fuzzy Decision. It clarifies the results between fuzzy decision and non-fuzzy decision that shows the distance of different between both approaches. Finally, a real-life application of production planning models is provided to illustrate the applicability of the proposed algorithms to a practical case study.
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Alamaniotis, Miltiadis, and Lefteri H. Tsoukalas. "Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures." In Critical Developments and Applications of Swarm Intelligence, 155–81. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5134-8.ch007.
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The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.
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Zhang, Yang, and Yue Wu. "Introducing Machine Learning Models to Response Surface Methodologies." In Response Surface Methodology in Engineering Science [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98191.
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Traditional response surface methodology (RSM) has utilized the ordinary least squared (OLS) technique to numerically estimate the coefficients for multiple influence factors to achieve the values of the responsive factor while considering the intersection and quadratic terms of the influencers if any. With the emergence and popularization of machine learning (ML), more competitive methods has been developed which can be adopted to complement or replace the tradition RSM method, i.e. the OLS with or without the polynomial terms. In this chapter, several commonly used regression models in the ML including the improved linear models (the least absolute shrinkage and selection operator model and the generalized linear model), the decision trees family (decision trees, random forests and gradient boosting trees), the model of the neural nets, (the multi-layer perceptrons) and the support vector machine will be introduced. Those ML models will provide a more flexible way to estimate the response surface function that is difficult to be represented by a polynomial as deployed in the traditional RSM. The advantage of the ML models in predicting precise response factor values is then demonstrated by implementation on an engineering case study. The case study has shown that the various choices of the ML models can reach a more satisfactory estimation for the responsive surface function in comparison to the RSM. The GDBT has exhibited to outperform the RSM with an accuracy improvement for 50% on unseen experimental data.
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Polyak, Ilya. "The GCM Validation." In Computational Statistics in Climatology. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099997.003.0009.
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In this chapter the observed and simulated (by the Hamburg GCM) Northern Hemisphere monthly surface air temperatures, averaged within different latitude bands, are statistically analyzed and compared. The objects used for the analysis are the two-dimensional spatial-temporal spectral and correlation characteristics, the multivariate autoregressive and linear regression model parameters, and the diffusion equation coefficients. A comparison shows that, generally, the shapes of the corresponding spectra and correlation functions are quite similar, but their numerical values and some features differ markedly, especially for the tropical regions. The spectra reveal a few randomly distributed maxima (along the frequency axis), the periods of which were not identical for both types of data. A comparative study of the estimates of the diffusion equation coefficients shows a significant distinction between the character of the meridional circulations of the observed and simulated systems. The approach developed gives approximate stochastic models and reasonable descriptions of the temperature processes and fields, thereby providing an opportunity for solving some of the vital problems of theoretical and practical aspects surrounding validation, diagnosis, and application of the GCM. The methodology and results presented make it clear that formalization of the statistical description of the surface air temperature fluctuations can be achieved by applying the standard techniques of multivariate modeling and multidimensional spectral and correlation analysis to the data which have been averaged spatially and temporally. The idea of the statistical approach to the problems of GCM variability validation is contained in the comparison (observed vs. modeled) of the probability distributions of the different atmospheric and ocean processes and fields. At first, such a statement sounds like a standard statistical approach, and its solution would be obvious and simple if the number of climate processes taking place jointly were not huge and if they did not present a tremendously complicated (in its interrelationships and feedbacks) deterministic-stochastic system. As is known, the Stochastic System Identification Theory (see Eikhoff, 1983) deals mostly with the methodology for identifying linear systems, The interdependences of climatic processes and fields are not linear, and the application of this theory can give only highly approximate results.
Conference papers on the topic "Regression coefficient function"
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Khammar, Amirhamzeh, Mohsen Arefi, and Mohammad Ghasem Akbari. "Robust fuzzy varying coefficient regression model based on Huber loss function." In 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS). IEEE, 2020. http://dx.doi.org/10.1109/cfis49607.2020.9238742.
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Sun, Yi Fei, Hao Bo Qiu, Liang Gao, Ke Lin, and Xue Zheng Chu. "Stochastic Response Surface Method Based on Weighted Regression and Its Application to Fatigue Reliability Analysis of Crankshaft." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-11095.
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Response surface method (RSM) is widely used in structural reliability analysis with implicit performance function (PF) which requires formidable computational effort. The ill conditioned coefficient matrix of normal equation in classical RSM prevents it from being used in high order conditions. The stochastic response surface method (SRSM), deriving from classical RSM, offers one alternative to solve this problem. Yet the regression method of conventional SRSM is based on normal least square method which ignores the different significance of each sample point through which the response surface function (RSF) is formed. To yield RSF close to the limit state which leads to better estimation of probability of failure, this paper introduces the weighted regression into SRSM and several examples with hypothetic explicit PF are given to test the performance of SRSM. In addition, we use this method in the fatigue reliability analysis of crankshaft with implicit PF. All these examples demonstrate the advantages of the proposed method.
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Quinones, Aisman, Rafael Goytisolo, Jorge Moya, and Roger Ocampo. "New Mathematical Model for the Form Factor of Involute Spur Gear’s Teeth." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80425.
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In this paper, a theoretical research is made on the influence of the friction force, the profile shift coefficient and the radial component of the normal force in the Form Factor applicable to the stress on spur gears’ teeth. A Gear’s FEM model was establish with the best approach to the real physical models, for the validation of the New Form Factor Model elaborated and then, the stress calculation in gear tooth root. Finally Using FEA and Multiple Lineal Regression, a new expression for the calculation of the stress concentration coefficient in the feet of the tooth, in function of the number of teeth and of the correction coefficient, was found.
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Quinones, Aisman, Rafael Goytisolo, Jorge Moya, and Roger Ocampo. "Infuence of the Friction Force, the Tooth Correction Coefficient and the Normal Force Radial Component in the Form Factor and the Stress in the Feet of Spur Gear’s Teeth." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80421.
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In this paper, a theoretical research is made on the influence of the friction force, the correction coefficient of the tooth and the radial component of the normal force in the Form Factor applicable to the stress on spur gears’ teeth. The Industrial Standards AGMA, ISO and DIN use the Lewis factor as the Form Factor but it doesn’t consider the above mentioned effects. The Standard GOST uses a Form Factor that considers the effect of the correction coefficient of the tooth and the radial component of the normal strength, but it doesn’t include the effect of the friction force. In this paper, a Mathematical Model is developed that incorporates all those effects. The obtained values of the form factors were represented graphically in function of the number of teeth, the correction coefficient and the friction coefficient. A graph is drawn for the driver gear and the driven gear, in which a remarkable influence of the simultaneous action of friction and correction coefficients is appreciated. In this new approach, it is found that the correction coefficients needed to optimize the resistance to the stress fracture of the teeth, in dependence of the values of the friction coefficient, should be greater that those used in the traditional approach. On the other hand, it has always been considered that gears with small number of teeth are the weakest with respect to stress fracture; however, in multiplying transmissions it is possible for driver gears with high number of teeth to be the weakest gear, given the favourable effect of the friction force on Form Factor in the driven gear and unfavourable in the driver gear. For the validation of the obtained results the Program of Finite Elements Analysis COSMOS Design Start 4.0 was used, obtaining very good results. Using FEA and Multiple Lineal Regression, a new expression for the calculation of the stress concentration coefficient in the feet of the tooth, in function of the number of teeth and of the correction coefficient, was found: kσMEF=1.497+0.126−0.003933Z
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Mullick, Subhash C., Suresh Kumar, and Basant K. Chourasia. "Wind Induced Heat Transfer Coefficient From Flat Horizontal Surfaces Exposed to Solar Radiation." In ASME 2007 Energy Sustainability Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/es2007-36163.
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Upward heat losses have strong effect on the performance of flat plate solar collectors under different operating conditions. Suitable equations for estimation of top heat loss coefficient have already been proposed [1,2]. The top heat loss coefficient is a function of wind induced convective heat transfer coefficient in a flat plate solar collector. It is, therefore, important to choose appropriate values of this convective heat transfer coefficient for correct estimation of the top heat loss coefficient. Researchers [3–6] have suggested different wind speed based correlations for estimation of the wind induced convective heat transfer coefficient. These correlations give different values of wind heat transfer coefficient thus resulting in variation in values of the top heat loss coefficient of a solar collector under same operating conditions. In present study, an attempt has been made to measure and study the wind induced convective heat transfer coefficient from exposed flat horizontal surfaces in real wind. For this purpose, three unglazed test plates of similar construction and different sizes were employed. Experiments were conducted on the three test plates over rooftop of a building in built environment. From experimental data of the test plate, of size 925mm × 865mm × 2mm, a correlation between wind heat transfer coefficient and wind speed has been obtained by linear regression. The obtained correlation has also been compared with work of other researchers [3–6]. Results obtained from experimental data of the three test plates provide some interesting information about wind induced convective heat transfer coefficient.
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Zhu, Changyun, and Guoliang Qin. "Design Technology of Centrifugal Fan Impeller Based on Response Surface Methodology." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30002.
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An optimization strategy called response surface methodology (RSM) is applied to a centrifugal fan impeller optimization design in this paper. RSM is used to generate an approximated model of objective function, for which a second-order polynomial function is chosen. The Design of experiment (DOE) technique coupled with CFD analysis is then ran to generate the database. The least-squares regression method (LS) is used to determine the coefficient of the RSM function. Finally, the Genetic Algorithms (GA) is applied to the objective function in order to obtain the optimal configuration. This paper also presents a solution to the problem of imprecise fitting of second-order RSM model by dividing the zone into several subzones which is proved to be effective in this paper. The optimization result shows that RSM is an effective and feasible optimization strategy for the centrifugal fan impeller design, and the complexity of the objective function and the overall optimization time could be significantly reduced.
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Alam, Shah Saud, and Christopher Depcik. "Adaptive Wiebe Function Parameters for a Port-Fuel Injected Hydrogen-Fueled Engine." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10031.
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Abstract Current unmanned aerial vehicle (UAV) propulsion technologies includes hydrogen fuel cells, battery systems, and internal combustion engines (ICE). However, relying on a single propulsion technology might result in a limited operational range. This can be mitigated by utilizing a hybrid configuration involving a battery pack and an ICE or a fuel cell for charging. Due to its significant weight advantage and high mass-specific energy content, hydrogen (H2) is an ideal fuel for both power plant options. However, use of H2 with an ICE requires precise operational control through combustion process simulation with the predictive approximation of the mass fraction burned profile. In this area, the relatively simple single-Wiebe function is widely deployed for a variety of different fuels, as well as combustion regimes. In general, the description of the single-Wiebe function includes the extent of complete combustion (a), magnitude of the maximum burn rate (m), and combustion duration (θd). However, the literature often provides values for these parameters without necessarily relating them to operational characteristics that can influence ICE power. As a result, it is critical to correlate the burn rate of the fuel to ICE operating parameters, such as the engine compression ratio, inlet pressure, mean piston speed, exhaust gas recirculation level, equivalence ratio, and spark timing. Therefore, in an attempt to physically define these parameters, this effort performs a sensitivity analysis using linear regression (least squares method) to assess the impact of engine operating conditions on the Wiebe function in comparison to experimental data for port-fuel injected hydrogen ICEs. The result is a model that can estimate the values of a, m, and θd in combination with a relatively high coefficient of determination (R2) when compared to the experimental mass fraction burned profiles. Finally, others can expand this methodology to any experimental data for engine and fuel-specific Wiebe parameter determination.
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Golestani, Maziar, and Mostafa Zeinoddini. "Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49814.
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Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of the model to fill data gaps was tested using different approaches. Concurrent wind and wave records (standard meteorological and spectral density data) from 4 stations in 2003, 2007, 2008 and 2009 were used both for the training the SVR system and its verification. The choice of these four locations facilitated the comparison of model performances in different geographical areas. The SVR model was then used to obtain predictions for the wave spectra and also time series of wave parameters (separately for each station) such as its Hs and Tp from spectra and wind records. New approach was used to predict wave spectra comparing to similar studies. Reasonably well correlation was found between the predicted and measured wave parameters. The SVR model was first trained and tested using various methods for selecting training data. Also different values for SVM parameters (e.g. tolerance of termination criterion, cost, and gamma in kernel function) were tested. The best possible results were obtained using a Unix shell script (in Linux) which automatically implements different values for different input parameters and finds the best regression by calculating statistical scores like correlation of coefficient, RMSE, bias and scatter index. Finally for a better understanding of the results, Quantile-Quantile plots were produced. The results show that SVR can be successfully used for prediction of Hs and wave spectrum out of a series of wind and spectral wave parameters inputs. Also it was noticed that SVR is an efficient tool to be used when data gaps are present in the data.
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Cai, Weihua, Mihir Sen, K. T. Yang, and Arturo Pacheco-Vega. "Genetic-Programming-Based Symbolic Regression for Heat Transfer Correlations of a Compact Heat Exchanger." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72293.
Full textAbstract:
We describe a symbolic regression methodology based on genetic programming to find correlations that can be used to estimate the performance of compact heat exchangers. Genetic programming is an evolutionary search technique in which functions represented as parse trees evolve as the search proceeds. An advantage of this approach is that functional forms of the correlation need not be assumed. The algorithm performs symbolic regression by seeking both the functional structure of the correlation and the coefficients therein that enable the closest fit to experimental data. This search is conducted within a functional domain constructed from sets of operators and terminals that are used to build tree-structures representing functions. A penalty function is used to prevent large correlations. The methodology is tested using first artificial data from a one-dimensional function and later a set of published heat exchanger experiments. Comparison with published results from the same data show that symbolic-regression correlations are as good or better. The effect of the penalty parameters on the “best function” is also analyzed.
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Pacheco-Vega, Arturo, Weihua Cai, Mihir Sen, and K. T. Yang. "Heat Transfer Correlations in an Air-Water Fin-Tube Compact Heat Exchanger by Symbolic Regression." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41977.
Full textAbstract:
In the present study we propose the application of evolutionary algorithms to find correlations that can predict the performance of a compact heat exchanger. Genetic programming (GP) is a search technique in which computer codes, representing functions as parse trees, evolve as the search proceeds. As a symbolic regression approach, GP looks for both the functional form and the coefficients that enable the closest fit to experimental data. Two different data sets are used to test the symbolic regression capability of genetic programming, the first being artificial data from a one-dimensional function, while the second are data generated by previously determined correlations from experimental measurements of a single-phase air-water heat exchanger. The results demonstrate that the correlations found by symbolic regression are able to predict well the data from which they were determined, and that the GP technique may be suitable for modeling the nonlinear behavior of heat exchangers. It is also shown that there is not a unique answer for the best-fit correlation from this procedure. The advantage of using genetic programming as symbolic regression is that no initial assumptions on the functional forms are needed, which is contrary to the traditional approach.