This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） w...This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.展开更多
The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This not...The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).展开更多
Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the rel...Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances.Magnetocaloric systems,i.e.refrigerators and heat pumps,are promising solutions due to their large theoretical Coefficient Of Performance(COP).However,there is still a long way to make such systems marketable.One barrier is the cost of the magnet and magnetocaloric materials,which can be overcome by decreasing the materials quantity,e.g.by optimizing the geometry with efficient dimensioning procedures.In this work,we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps:temperature span,heating power and COP.We used 4 different regressors:ordinary least squares,ridge,lasso and K-Nearest Neighbors(KNN).By using a dataset generated by numerical calculations,we have arrived at minimum average relative errors of the temperature span,heating power and COP of 23%,29%and 31%,respectively.While the lasso regressor is more appropriate when using small datasets,the ordinary least squares regressor shows the best performance when using more samples.The best order of polynomials range between 3,for the heating power,to 5,for the COP.The worse performance in predicting the three performance values occurs when using the KNN regressor.Furthermore,the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values.展开更多
Excessive structural forces generated inside tunnel linings could affect the safety and serviceability of tunnels,emphasizing the need to accurately predict the forces acting on tunnel linings during the preliminary d...Excessive structural forces generated inside tunnel linings could affect the safety and serviceability of tunnels,emphasizing the need to accurately predict the forces acting on tunnel linings during the preliminary design phase.In this study,an anisotropic soil model devel-oped by Norwegian Geotechnical Institute(NGI)based on the Active-Direct shear-Passive concept(NGI-ADP model)was adopted to conduct finite element(FE)analyses.A total of 682 cases were modeled to analyze the effects of five key parameters on twin-tunnel struc-tural forces;these parameters included twin-tunnel arrangements and subsurface soil properties:burial depth H,tunnel center-to-center distance D,soil strength s_(u)^(A),stiffness ratio G_(u)=s_(u)^(A),and degree of anisotropy ss_(u)^(P)=s_(u)^(A).The significant factors contributing to the bending moment and thrust force of the linings were the tunnel distance and overlying soil depth,respectively.The degree of anisotropy of the surrounding soil was found to be extremely important in simulating the twin-tunnel construction,and severe design errors could be made if the soil anisotropy is ignored.A cutting-edge application of machine learning in the construction of twin tunnels is presented;multivariate adaptive regression splines and decision tree regressor methods are developed to predict the maximum bending moment within the first tunnel’s linings based on the constructed FE cases.The developed prediction model can enable engineers to estimate the structural response of twin tunnels more accurately in order to meet the specific target reliability indices of projects.展开更多
This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to...This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.展开更多
Accurately assessing the State of Charge(SOC)is paramount for optimizing battery management systems,a cornerstone for ensuring peak battery performance and safety across diverse applications,encompassing vehicle power...Accurately assessing the State of Charge(SOC)is paramount for optimizing battery management systems,a cornerstone for ensuring peak battery performance and safety across diverse applications,encompassing vehicle powertrains and renewable energy storage systems.Confronted with the challenges of traditional SOC estimation methods,which often struggle with accuracy and cost-effectiveness,this research endeavors to elevate the precision of SOC estimation to a new level,thereby refining battery management strategies.Leveraging the power of integrated learning techniques,the study fuses Random Forest Regressor,Gradient Boosting Regressor,and Linear Regression into a comprehensive framework that substantially enhances the accuracy and overall performance of SOC predictions.By harnessing the publicly accessible National Aeronautics and Space Administration(NASA)Battery Cycle dataset,our analysis reveals that these integrated learning approaches significantly outperform traditional methods like Coulomb counting and electrochemical models,achieving remarkable improvements in SOC estimation accuracy,error reduction,and optimization of key metrics like R2 and Adjusted R2.This pioneering work propels the development of innovative battery management systems grounded in machine learning and deepens our comprehension of how this cutting-edge technology can revolutionize battery technology.展开更多
Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, vari...Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.展开更多
The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as lo...The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds.For sliding speed of 220 mm/s and sliding distance of 1000 m,the wear volume losses under loads of 10,20,30,40 and 50 N were calculated to be 15.0,19.0,24.3,33.9 and 37.4 mm3,respectively.Worn surfaces show that abrasion and oxidation were present at a load of 10 N,which changes into delamination at a load of 50 N.ANOVA results show that the contributions of load,sliding distance and sliding speed were 12.99%,83.04%and 3.97%,respectively.The artificial neural networks(ANN),support vector regressor(SVR)and random forest(RF)methods were applied for the prediction of wear volume loss of AZ91 alloy.The correlation coefficient(R2)values of SVR,RF and ANN for the test were 0.9245,0.9800 and 0.9845,respectively.Thus,the ANN model has promising results for the prediction of wear performance of AZ91 alloy.展开更多
A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent ...A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent advantage, this study focuses shift from obtaining crime prediction to on comparing model performance between these two types of models on crime prediction. In this study, we aimed to predict burglary occurrence in Los Angeles City, and compared a basic model just using prior year burglary occurrence with advanced models including linear regressor and random forest regressor. In addition, American Community Survey data was used to provide neighborhood level socio-economic features. After finishing data preprocessing steps that regularize the dataset, recursive feature elimination was utilized to determine the final features and the parameters of the two advanced models. Finally, to find out the best fit model, three metrics were used to evaluate model performance: R squared, adjusted R squared and mean squared error. The results indicate that linear regressor is the most suitable model among three models applied in the study with a slightly smaller mean squared error than that of basic model, whereas random forest model performed worse than the basic model. With a much more complex learning steps, advanced models did not show prominent advantages, and further research to extend the current study were discussed.展开更多
Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, ...Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, conditioning on D＊ = 0. For example, suppose Y is a person＇s wage, the unobserved D＊ indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in av- erage wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D＊ is obtained ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.展开更多
In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, ...In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.展开更多
Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggest...Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.展开更多
This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography datas...This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography dataset model for the crust and uppermost mantle beneath the study area. The focus of this paper’s opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the seismic hazard zones system ability to detect and associate seismic signals, or at estimating other seismic characteristics(crust acceleration and crust energy) while traditionally, methods cannot monitor the earthquakes system. This work has derived some physical equations for extraction of many variables as inputs for our developed machine learning model based on a reliable understanding of the tomography data to physical variables by preparing huge dataset from different physical conditions of crust. We have extracted the velocity values of the shear waves from the original NETCDF file, which contains the S velocity values for every one km of the depths of the crust for the study area from one km down to the uppermost mantle beneath the Middle East. For the first time, this study calculated new seismic hazard parameter called Peak Crust Acceleration(PCA) for seismic hazard analysis by considering the transmitted initial seismic energy through the Earthy wrote in python language ’s crust layers from hypocenter. All machine learning algorithms in this studusing anaconda platform the open-source Individual Edition(Distribution).展开更多
基金Supported by National Natural Science Foundation of China (No. 10761011,10671139,10901135)Natural Science Foundation of Yunnan Province(No. 2008CD081)Special Foundation for Middle and Young Excellent Teachers of Yunnan University
文摘This paper proposes some regularity conditions, which result in the existence, strong consistency and asymptotic normality of maximum quasi-likelihood estimator （MQLE） in quasi-likelihood nonlinear models （QLNM） with random regressors. The asymptotic results of generalized linear models （GLM） with random regressors are generalized to QLNM with random regressors.
文摘The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).
基金This work was supported by FCT-Portugal,project Network of Ex-treme Conditions Laboratories NECL-IFIMUP,NORTE-01-0145-FEDER-022096Project PTDC/EME-SIS/31575/2017-POCI-01-0145-FEDER-031575 is acknowledged.D.J.S.acknowledges his contract DL57/2016 reference SFRH-BPD-90571/2012.
文摘Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances.Magnetocaloric systems,i.e.refrigerators and heat pumps,are promising solutions due to their large theoretical Coefficient Of Performance(COP).However,there is still a long way to make such systems marketable.One barrier is the cost of the magnet and magnetocaloric materials,which can be overcome by decreasing the materials quantity,e.g.by optimizing the geometry with efficient dimensioning procedures.In this work,we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps:temperature span,heating power and COP.We used 4 different regressors:ordinary least squares,ridge,lasso and K-Nearest Neighbors(KNN).By using a dataset generated by numerical calculations,we have arrived at minimum average relative errors of the temperature span,heating power and COP of 23%,29%and 31%,respectively.While the lasso regressor is more appropriate when using small datasets,the ordinary least squares regressor shows the best performance when using more samples.The best order of polynomials range between 3,for the heating power,to 5,for the COP.The worse performance in predicting the three performance values occurs when using the KNN regressor.Furthermore,the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values.
基金supported by Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K201900102)Chongqing Construction Science and Technology Plan Project(2019-0045).
文摘Excessive structural forces generated inside tunnel linings could affect the safety and serviceability of tunnels,emphasizing the need to accurately predict the forces acting on tunnel linings during the preliminary design phase.In this study,an anisotropic soil model devel-oped by Norwegian Geotechnical Institute(NGI)based on the Active-Direct shear-Passive concept(NGI-ADP model)was adopted to conduct finite element(FE)analyses.A total of 682 cases were modeled to analyze the effects of five key parameters on twin-tunnel struc-tural forces;these parameters included twin-tunnel arrangements and subsurface soil properties:burial depth H,tunnel center-to-center distance D,soil strength s_(u)^(A),stiffness ratio G_(u)=s_(u)^(A),and degree of anisotropy ss_(u)^(P)=s_(u)^(A).The significant factors contributing to the bending moment and thrust force of the linings were the tunnel distance and overlying soil depth,respectively.The degree of anisotropy of the surrounding soil was found to be extremely important in simulating the twin-tunnel construction,and severe design errors could be made if the soil anisotropy is ignored.A cutting-edge application of machine learning in the construction of twin tunnels is presented;multivariate adaptive regression splines and decision tree regressor methods are developed to predict the maximum bending moment within the first tunnel’s linings based on the constructed FE cases.The developed prediction model can enable engineers to estimate the structural response of twin tunnels more accurately in order to meet the specific target reliability indices of projects.
基金supported by Naval Research Board,Defense Research Development Organization(DRDO),Government of India(No.DNRD/05/4003/NRB/160)
文摘This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.
文摘Accurately assessing the State of Charge(SOC)is paramount for optimizing battery management systems,a cornerstone for ensuring peak battery performance and safety across diverse applications,encompassing vehicle powertrains and renewable energy storage systems.Confronted with the challenges of traditional SOC estimation methods,which often struggle with accuracy and cost-effectiveness,this research endeavors to elevate the precision of SOC estimation to a new level,thereby refining battery management strategies.Leveraging the power of integrated learning techniques,the study fuses Random Forest Regressor,Gradient Boosting Regressor,and Linear Regression into a comprehensive framework that substantially enhances the accuracy and overall performance of SOC predictions.By harnessing the publicly accessible National Aeronautics and Space Administration(NASA)Battery Cycle dataset,our analysis reveals that these integrated learning approaches significantly outperform traditional methods like Coulomb counting and electrochemical models,achieving remarkable improvements in SOC estimation accuracy,error reduction,and optimization of key metrics like R2 and Adjusted R2.This pioneering work propels the development of innovative battery management systems grounded in machine learning and deepens our comprehension of how this cutting-edge technology can revolutionize battery technology.
基金support from Canadian Urban Transit Research and Innovation Consortium(CUTRIC)via Project Number 160028Natural Sciences and Engineering Research Council of Canada(NSERC)via a Discovery Grant。
文摘Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.
文摘The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds.For sliding speed of 220 mm/s and sliding distance of 1000 m,the wear volume losses under loads of 10,20,30,40 and 50 N were calculated to be 15.0,19.0,24.3,33.9 and 37.4 mm3,respectively.Worn surfaces show that abrasion and oxidation were present at a load of 10 N,which changes into delamination at a load of 50 N.ANOVA results show that the contributions of load,sliding distance and sliding speed were 12.99%,83.04%and 3.97%,respectively.The artificial neural networks(ANN),support vector regressor(SVR)and random forest(RF)methods were applied for the prediction of wear volume loss of AZ91 alloy.The correlation coefficient(R2)values of SVR,RF and ANN for the test were 0.9245,0.9800 and 0.9845,respectively.Thus,the ANN model has promising results for the prediction of wear performance of AZ91 alloy.
文摘A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent advantage, this study focuses shift from obtaining crime prediction to on comparing model performance between these two types of models on crime prediction. In this study, we aimed to predict burglary occurrence in Los Angeles City, and compared a basic model just using prior year burglary occurrence with advanced models including linear regressor and random forest regressor. In addition, American Community Survey data was used to provide neighborhood level socio-economic features. After finishing data preprocessing steps that regularize the dataset, recursive feature elimination was utilized to determine the final features and the parameters of the two advanced models. Finally, to find out the best fit model, three metrics were used to evaluate model performance: R squared, adjusted R squared and mean squared error. The results indicate that linear regressor is the most suitable model among three models applied in the study with a slightly smaller mean squared error than that of basic model, whereas random forest model performed worse than the basic model. With a much more complex learning steps, advanced models did not show prominent advantages, and further research to extend the current study were discussed.
文摘Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, conditioning on D＊ = 0. For example, suppose Y is a person＇s wage, the unobserved D＊ indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in av- erage wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D＊ is obtained ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.
文摘In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.
文摘Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.
文摘This paper for the first time improved a Robust Multi-Output machine learning regression model for seismic hazard zoning of Turkey, Iraq and Iran using constructed 3-D shear-wave velocity(Vs), seismic tomography dataset model for the crust and uppermost mantle beneath the study area. The focus of this paper’s opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the seismic hazard zones system ability to detect and associate seismic signals, or at estimating other seismic characteristics(crust acceleration and crust energy) while traditionally, methods cannot monitor the earthquakes system. This work has derived some physical equations for extraction of many variables as inputs for our developed machine learning model based on a reliable understanding of the tomography data to physical variables by preparing huge dataset from different physical conditions of crust. We have extracted the velocity values of the shear waves from the original NETCDF file, which contains the S velocity values for every one km of the depths of the crust for the study area from one km down to the uppermost mantle beneath the Middle East. For the first time, this study calculated new seismic hazard parameter called Peak Crust Acceleration(PCA) for seismic hazard analysis by considering the transmitted initial seismic energy through the Earthy wrote in python language ’s crust layers from hypocenter. All machine learning algorithms in this studusing anaconda platform the open-source Individual Edition(Distribution).
