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
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
In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations...In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity.The proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic patterns.This approach not only improves the diversit.There are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning models.These are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble model.We implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline models.To train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor distributions.We used Monte Carlo simulations to extract these traits.Based on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.展开更多
Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays...Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.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
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.
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In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity.The proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic patterns.This approach not only improves the diversit.There are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning models.These are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble model.We implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline models.To train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor distributions.We used Monte Carlo simulations to extract these traits.Based on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.
文摘Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.
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.
