Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongl...Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.展开更多
Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the abse...Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the absence of reliable,general uncertainty estimates.We present a unified,scalable uncertainty metric,U,built from a heterogeneous ensemble that reuses existing pretrained MLIPs.Across diverse chemistries and structures,U strongly tracks true prediction errors and robustly ranks configuration-level risk.Using U,we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels:for tungsten,we match full density-functional-theory(DFT)training using 4%of the DFT data;for MoNbTaW,a dataset distilled by U supports high-accuracy potential training.By filtering numerical label noise,the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data.This framework provides a practical reliability monitor and guides data selection and fine-tuning,enabling cost-efficient,accurate,and safer deployment of foundation models.展开更多
Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs label...Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.展开更多
Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer p...Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer patterns helps thefirm to develop efficient decisions and in turn,helps to optimize the enter-prise’s business and maximizes consumer satisfaction correspondingly.To con-duct an effective assessment about the customers,Naive Bayes(also called Simple Bayes),a machine learning model is utilized.However,the efficacious of the simple Bayes model is utterly relying on the consumer data used,and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied.However,in practice,the NB pre-mise is not true in consumer data,and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results.In this work,an ensem-ble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier.In ensemble variable selection,two different strategies are applied:one is based upon data perturbation(or homogeneous ensemble,same feature selector is applied to a different subsamples derived from the same learning set)and the other one is based upon function perturbation(or heterogeneous ensemble different feature selector is utilized to the same learning set).Further-more,the feature set captured from both ensemble strategies is applied to NB indi-vidually and the outcome obtained is computed.Finally,the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.展开更多
Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UC...Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.展开更多
基金The National Natural Science Foundation of China under contract No.61971455.
文摘Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.
基金sponsored by Nederlandse Organisatie voor WetenschappelijkOnderzoek (The Netherlands Organization for Scientific Research, NWO) domain Science for the use of supercomputer facilities. The authors also acknowledge the use of DelftBlue supercomputer, provided by Delft High Performance Computing Center (https://www.tudelft.nl/dhpc).
文摘Universal machine-learning interatomic potentials(uMLIPs)are emerging as foundation models for atomistic simulation,offering near-ab initio accuracy at far lower cost.Their safe,broad deployment is limited by the absence of reliable,general uncertainty estimates.We present a unified,scalable uncertainty metric,U,built from a heterogeneous ensemble that reuses existing pretrained MLIPs.Across diverse chemistries and structures,U strongly tracks true prediction errors and robustly ranks configuration-level risk.Using U,we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels:for tungsten,we match full density-functional-theory(DFT)training using 4%of the DFT data;for MoNbTaW,a dataset distilled by U supports high-accuracy potential training.By filtering numerical label noise,the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data.This framework provides a practical reliability monitor and guides data selection and fine-tuning,enabling cost-efficient,accurate,and safer deployment of foundation models.
基金financially supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.
文摘Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer patterns helps thefirm to develop efficient decisions and in turn,helps to optimize the enter-prise’s business and maximizes consumer satisfaction correspondingly.To con-duct an effective assessment about the customers,Naive Bayes(also called Simple Bayes),a machine learning model is utilized.However,the efficacious of the simple Bayes model is utterly relying on the consumer data used,and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied.However,in practice,the NB pre-mise is not true in consumer data,and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results.In this work,an ensem-ble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier.In ensemble variable selection,two different strategies are applied:one is based upon data perturbation(or homogeneous ensemble,same feature selector is applied to a different subsamples derived from the same learning set)and the other one is based upon function perturbation(or heterogeneous ensemble different feature selector is utilized to the same learning set).Further-more,the feature set captured from both ensemble strategies is applied to NB indi-vidually and the outcome obtained is computed.Finally,the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.
基金supported by the Basic Research Funds for the Central Universities (Grant No. 2652015116)the National Natural Science Foundation of China (Grant Nos. 51209187, 41530316 & 91125024)+1 种基金the National Key Research and Development Program of China (Grant No. 2016YFC0402805)the Beijing Higher Education Young Elite Teacher Project (Grant No. YETP0653)
文摘Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.
