The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o...The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.展开更多
Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough corr...Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca...In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.展开更多
多层Morphin算法扩展了对未知环境的预测范围,克服了传统Morphin算法搜索轨迹不灵活的缺点,但每个搜索节点生成的搜索弧数目固定,搜索和评估所花费的时间随着搜索层数的增多呈指数阶增加。针对该问题,提出了一种可变搜索弧Morphin算法(v...多层Morphin算法扩展了对未知环境的预测范围,克服了传统Morphin算法搜索轨迹不灵活的缺点,但每个搜索节点生成的搜索弧数目固定,搜索和评估所花费的时间随着搜索层数的增多呈指数阶增加。针对该问题,提出了一种可变搜索弧Morphin算法(variable search arc of Morphin,VSA-Morphin)。调整每层搜索节点生成的搜索弧数目,使之不再固定,而是随着层数增加而减少,从而缩短搜索和评估时间。利用MATLAB仿真测试表明,多层VSA-Morphin算法与多层Morphin算法所规划的路径基本一致,但运行时间却相对更少,从而验证了多层VSA-Morphin算法的有效性和正确性。展开更多
A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their...A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others.展开更多
Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcom...Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.展开更多
Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper ...Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.展开更多
This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time seri...This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.展开更多
Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectivenes...Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.展开更多
The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
基金supported by the Basic Research Special Plan of Yunnan Provincial Department of Science and Technology-General Project(Grant No.202101AT070094)。
文摘The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.
基金This project was supported by the National Natural Science Foundation of China (60672139, 60672140)the Excellent Ph.D. Paper Author Foundation of China (200237)the Natural Science Foundation of Shandong (2005ZX01).
文摘Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
基金funded by the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)
文摘In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.
基金Supported by the Key Research and Development Plan of Shandong Province(2018GGX106001)supported by the Natural Science Foundation of Shandong Province(ZR2017MEE066)Supported by the Science and Technology Plan of Shandong higher Education institutions(J16LB58)~~
文摘多层Morphin算法扩展了对未知环境的预测范围,克服了传统Morphin算法搜索轨迹不灵活的缺点,但每个搜索节点生成的搜索弧数目固定,搜索和评估所花费的时间随着搜索层数的增多呈指数阶增加。针对该问题,提出了一种可变搜索弧Morphin算法(variable search arc of Morphin,VSA-Morphin)。调整每层搜索节点生成的搜索弧数目,使之不再固定,而是随着层数增加而减少,从而缩短搜索和评估时间。利用MATLAB仿真测试表明,多层VSA-Morphin算法与多层Morphin算法所规划的路径基本一致,但运行时间却相对更少,从而验证了多层VSA-Morphin算法的有效性和正确性。
文摘A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others.
文摘Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.
基金Supported by the National Natural Science Founda-tion of China (49984001)
文摘Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.
文摘This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.
基金supported by the Technology Innovation Program of the Korea Evaluation Institute of Industrial Technology (KEIT)under the Ministry of Trade,Industry and Energy (MOTIE)of Republic of Korea (20012121)by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MSIT) (2022M3J7A106294)。
文摘Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
