Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing...Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.展开更多
In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of energy.However,app...In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of energy.However,applying numerical reservoir simulation(NRS)to optimize production can induce high computational footprint.Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results.In this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir model.Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies.Upon blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production optimization.Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of 0.99.We also compared the optimization results produced by NRS and the proxies.The comparison recorded a good level of accuracy that was within 3%error.The proxies were also computationally 3 times faster than NRS.Hence,the proxies have served their practical purposes in this study.展开更多
文章基于深度学习方法,通过结合粒子群优化(Particle Swarm Optimization,PSO)和长短期记忆(Long Short Term Memory,LSTM)网络,提出了一种针对大数据的商品销售预测模型。文章首先分析了LSTM的结构,其次分析了PSO方法对LSTM的优化方式...文章基于深度学习方法,通过结合粒子群优化(Particle Swarm Optimization,PSO)和长短期记忆(Long Short Term Memory,LSTM)网络,提出了一种针对大数据的商品销售预测模型。文章首先分析了LSTM的结构,其次分析了PSO方法对LSTM的优化方式,提出了PSO-LSTM商品销量预测模型,最后使用Kaggle上的数据集进行训练和测试。将所提出的模型与标准LSTM模型进行比较,结果表明,所提方法的预测精度和稳定性均优于标准LSTM方法。展开更多
提出了一种基于粒子群优化(PSO)算法的长短期记忆网络(LSTM)方法,对质子交换膜燃料电池(PEMFC)的电堆电压进行了退化预测。首先,分析了PEMFC的退化机理。然后,应用LSTM建立了电压退化预测模型,并采用Dropout层来防止过拟合以提高模型的...提出了一种基于粒子群优化(PSO)算法的长短期记忆网络(LSTM)方法,对质子交换膜燃料电池(PEMFC)的电堆电压进行了退化预测。首先,分析了PEMFC的退化机理。然后,应用LSTM建立了电压退化预测模型,并采用Dropout层来防止过拟合以提高模型的泛化能力。此外,使用PSO算法优化LSTM方法中的初始学习率和Dropout概率以提升预测效果。最后,使用IEEE 2014 Data Challenge Data的燃料电池实际老化数据进行验证。结果表明,本文方法可以精确地预测燃料电池的退化,相比于传统的LSTM方法,预测精度提升了50%。展开更多
Compressional and shear sonic logs(DTC and DTS,respectively)are one of the effective means for determining petrophysical/geomechanical properties.However,the DTS log has limited availability mainly due to high acquisi...Compressional and shear sonic logs(DTC and DTS,respectively)are one of the effective means for determining petrophysical/geomechanical properties.However,the DTS log has limited availability mainly due to high acquisition costs.This study introduces a hybrid machine learning approach to generating synthetic DTS logs.Five wireline logs such as gamma ray(GR),density(RHOB),neutron porosity(NPHI),deep resistivity(Rt),and DTS logs are used as input data for three supervised-machine learning models including support vector machine for regression(SVR),deep neural network(DNN),and long short-term memory(LSTM).The hybrid machine learning model utilizes two additional techniques.First,as an unsupervised-learning approach,data clustering is integrated with general machine learning models for the purpose of improving model accuracy.All the machine learning models using the data-clustered approach show higher accuracies in predicting target(DTS)values,compared to non-clustered models.Second,particle swarm optimization(PSO)is combined with the models to determine optimal hyperparameters.The PSO algorithm proves time-effective,automated advantages as it gets feedback from previous computations so that is able to narrow down candidates for optimal hyperparameters.Compared to previous studies focusing on the performance comparison among machine learning algorithms,this study introduces an advanced approach to further improve the performance by integrating the unsupervised learning technique and PSO optimization with the general models.Based on this study result,we recommend the hybrid machine learning approach for improving the reliability and efficiency of synthetic log generation.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(U21A2019,61873058),Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Alexander von Humboldt Foundation of Germany.
文摘Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.
文摘In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of energy.However,applying numerical reservoir simulation(NRS)to optimize production can induce high computational footprint.Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results.In this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir model.Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies.Upon blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production optimization.Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of 0.99.We also compared the optimization results produced by NRS and the proxies.The comparison recorded a good level of accuracy that was within 3%error.The proxies were also computationally 3 times faster than NRS.Hence,the proxies have served their practical purposes in this study.
文摘文章基于深度学习方法,通过结合粒子群优化(Particle Swarm Optimization,PSO)和长短期记忆(Long Short Term Memory,LSTM)网络,提出了一种针对大数据的商品销售预测模型。文章首先分析了LSTM的结构,其次分析了PSO方法对LSTM的优化方式,提出了PSO-LSTM商品销量预测模型,最后使用Kaggle上的数据集进行训练和测试。将所提出的模型与标准LSTM模型进行比较,结果表明,所提方法的预测精度和稳定性均优于标准LSTM方法。
文摘提出了一种基于粒子群优化(PSO)算法的长短期记忆网络(LSTM)方法,对质子交换膜燃料电池(PEMFC)的电堆电压进行了退化预测。首先,分析了PEMFC的退化机理。然后,应用LSTM建立了电压退化预测模型,并采用Dropout层来防止过拟合以提高模型的泛化能力。此外,使用PSO算法优化LSTM方法中的初始学习率和Dropout概率以提升预测效果。最后,使用IEEE 2014 Data Challenge Data的燃料电池实际老化数据进行验证。结果表明,本文方法可以精确地预测燃料电池的退化,相比于传统的LSTM方法,预测精度提升了50%。
文摘Compressional and shear sonic logs(DTC and DTS,respectively)are one of the effective means for determining petrophysical/geomechanical properties.However,the DTS log has limited availability mainly due to high acquisition costs.This study introduces a hybrid machine learning approach to generating synthetic DTS logs.Five wireline logs such as gamma ray(GR),density(RHOB),neutron porosity(NPHI),deep resistivity(Rt),and DTS logs are used as input data for three supervised-machine learning models including support vector machine for regression(SVR),deep neural network(DNN),and long short-term memory(LSTM).The hybrid machine learning model utilizes two additional techniques.First,as an unsupervised-learning approach,data clustering is integrated with general machine learning models for the purpose of improving model accuracy.All the machine learning models using the data-clustered approach show higher accuracies in predicting target(DTS)values,compared to non-clustered models.Second,particle swarm optimization(PSO)is combined with the models to determine optimal hyperparameters.The PSO algorithm proves time-effective,automated advantages as it gets feedback from previous computations so that is able to narrow down candidates for optimal hyperparameters.Compared to previous studies focusing on the performance comparison among machine learning algorithms,this study introduces an advanced approach to further improve the performance by integrating the unsupervised learning technique and PSO optimization with the general models.Based on this study result,we recommend the hybrid machine learning approach for improving the reliability and efficiency of synthetic log generation.
