Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation...Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN).展开更多
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest...Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest(RF)and extreme gradient boosting(XGBoost).As an illustration,the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County,Chongqing,China.For comparison,the predictive performance of RF,XGBoost,support vector machine(SVM),and logistic regression(LR)is systematically investigated based on the well-established confusion matrix,which contains the known indices of recall rate,precision,and accuracy.Furthermore,the feature importance of the 12 influencing variables is also explored.Results show that the accuracy of the XGBoost and RF for both the training and testing data is superior to that of SVM and LR,revealing the superiority of the ensemble learning models(i.e.XGBoost and RF)in the slope stability prediction of Yunyang County.Among the 12 influencing factors,the profile shape is the most important one.The proposed ensemble learning-based method offers a promising way to rationally capture the slope status.It can be extended to the prediction of slope stability of other landslide-prone areas of interest.展开更多
This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one ...This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one thousand finite element cases were numerically analyzed,followed by extensive parametric studies.Surrogate models were developed via ensemble learning methods(ELMs),including the e Xtreme Gradient Boosting(XGBoost),and Random Forest Regression(RFR)to predict the maximum lateral wall deformation(δhmax).Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression(DTR),Multilayer Perceptron Regression(MLPR),and Multivariate Adaptive Regression Splines(MARS).This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast,alternative way.展开更多
The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this...The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.展开更多
The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local eng...The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.展开更多
针对模型VDSR(very deep super resolution)收敛速度慢,训练前需要对原始图像进行预处理,以及网络中存在的冗余性等问题,提出了一种基于深度跳跃级联的单幅图像超分辨率重建(DCSR)算法。DCSR算法省去了图像预处理,直接在低分辨率图像上...针对模型VDSR(very deep super resolution)收敛速度慢,训练前需要对原始图像进行预处理,以及网络中存在的冗余性等问题,提出了一种基于深度跳跃级联的单幅图像超分辨率重建(DCSR)算法。DCSR算法省去了图像预处理,直接在低分辨率图像上提取浅层特征,并使用亚像素卷积对图像进行放大;通过使用跳跃级联块可以充分利用每个卷积层提取到图像特征,实现特征重用,减少网络的冗余性。网络的跳跃级联块可以直接从输出到每一层建立短连接,加快网络的收敛速度,缓解梯度消失问题。实验结果表明,在几种公开数据集上,所提算法的峰值信噪比、结构相似度值均高于现有的几种算法,充分证明了所提算法的出色性能。展开更多
In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each...In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.展开更多
This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient a...This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.展开更多
文摘Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN).
基金supports from National Natural Science Foundation of China(Grant No.52008058)National Key R&D Program of China(Grant No.2019YFC1509605)High-end Foreign Expert Introduction program(Grant No.G20200022005).
文摘Slope stability prediction plays a significant role in landslide disaster prevention and mitigation.This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest(RF)and extreme gradient boosting(XGBoost).As an illustration,the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County,Chongqing,China.For comparison,the predictive performance of RF,XGBoost,support vector machine(SVM),and logistic regression(LR)is systematically investigated based on the well-established confusion matrix,which contains the known indices of recall rate,precision,and accuracy.Furthermore,the feature importance of the 12 influencing variables is also explored.Results show that the accuracy of the XGBoost and RF for both the training and testing data is superior to that of SVM and LR,revealing the superiority of the ensemble learning models(i.e.XGBoost and RF)in the slope stability prediction of Yunyang County.Among the 12 influencing factors,the profile shape is the most important one.The proposed ensemble learning-based method offers a promising way to rationally capture the slope status.It can be extended to the prediction of slope stability of other landslide-prone areas of interest.
基金supported by the High-end Foreign Expert Introduction program(No.G20190022002)Chongqing Construction Science and Technology Plan Project(2019-0045)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900102)The financial support is gratefully acknowledged。
文摘This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one thousand finite element cases were numerically analyzed,followed by extensive parametric studies.Surrogate models were developed via ensemble learning methods(ELMs),including the e Xtreme Gradient Boosting(XGBoost),and Random Forest Regression(RFR)to predict the maximum lateral wall deformation(δhmax).Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression(DTR),Multilayer Perceptron Regression(MLPR),and Multivariate Adaptive Regression Splines(MARS).This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast,alternative way.
基金the National Natural Science Foundation of China(Nos.51608380 and 51538009)the Key Innovation Team Program of the Innovation Talents Promotion Plan by Ministry of Science and Technology of China(No.2016RA4059)the Specific Consultant Research Project of Shanghai Tunnel Engineering Company Ltd.(No.STEC/KJB/XMGL/0130),China。
文摘The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.
基金supported by National Research Foundation(NRF)of Singapore,under its Virtual Singapore program(Grant No.NRF2019VSG-GMS-001)by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office under the Land and Livability National Innovation Challenge(L2 NIC)Research Program(Grant No.L2NICCFP2-2015-1)。
文摘The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation.Although the conventional site investigation methods(i.e.borehole drilling) could provide local engineering geological information,the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved.With the development of computer science,machine learning(ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically.However,few studies have been reported on the adoption of ML models for the prediction of the rockhead position.In this paper,we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information.The framework of the natural gradient boosting(NGBoost) algorithm combined with the extreme gradient boosting(XGBoost)is used as the basic learner.The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree(GBRT),the light gradient boosting machine(LightGBM),the multivariate linear regression(MLR),the artificial neural network(ANN),and the support vector machine(SVM).The results demonstrate that the XGBoost algorithm,the core algorithm of the probabilistic NXGBoost model,outperformed the other conventional ML models with a coefficient of determination(R2)of 0.89 and a root mean squared error(RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data.The probabilistic N-XGBoost model not only achieved a higher prediction accuracy,but also provided a predictive estimation of the uncertainty.Thus,the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.
文摘针对模型VDSR(very deep super resolution)收敛速度慢,训练前需要对原始图像进行预处理,以及网络中存在的冗余性等问题,提出了一种基于深度跳跃级联的单幅图像超分辨率重建(DCSR)算法。DCSR算法省去了图像预处理,直接在低分辨率图像上提取浅层特征,并使用亚像素卷积对图像进行放大;通过使用跳跃级联块可以充分利用每个卷积层提取到图像特征,实现特征重用,减少网络的冗余性。网络的跳跃级联块可以直接从输出到每一层建立短连接,加快网络的收敛速度,缓解梯度消失问题。实验结果表明,在几种公开数据集上,所提算法的峰值信噪比、结构相似度值均高于现有的几种算法,充分证明了所提算法的出色性能。
基金This work was supported in part by the US Department of Energy(DOE),Office of Electricity and Office of Energy Efficiency and Renewable Energy under contract DE-AC05-00OR22725in part by CURENT,an Engineering Research Center funded by US National Science Foundation(NSF)and DOE under NSF award EEC-1041877in part by NSF award ECCS-1809458.
文摘In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.
基金supported by Key innovation team program of innovation talents promotion plan by MOST of China(No.2016RA4059)Natural Science Foundation Committee Program of China(No.51778474)Science and Technology Project of Yunnan Provincial Transportation Department(No.25 of 2018)。
文摘This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.
