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
Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with dam...Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.展开更多
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
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
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
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
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.
基金Project(2023YFB2390400)supported by the National Key R&D Programs for Young Scientists,ChinaProjects(U21A20159,52079133,52379112,52309123,41902288)supported by the National Natural Science Foundation of China+5 种基金Project(2024AFB041)supported by the Hubei Provincial Natural Science Foundation,ChinaProject(QTKS0034W23291)supported by the Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources,ChinaProject(2023SGG07)supported by the Visiting Researcher Fund Program of State Key Laboratory of Water Resources Engineering and Management,ChinaProject(2022KY56(ZDZX)-02)supported by the Key Research Program of FSDI,ChinaProject(SKS-2022103)supported by the Key Research Program of the Ministry of Water Resources,ChinaProject(202102AF080001)supported by the Yunnan Major Science and Technology Special Program,China。
文摘Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.
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.
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.
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.
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.
