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
Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help s...Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and constraints.It also compares these methods by the number of iterations and time required to display the result.This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods.The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding.Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool.The positive results of the user evaluation showed that the tool can be recommended to other users.展开更多
Vocabulary teaching is always a difficulty to English teachers while learners have found the task of accumulating vocabulary time-consuming,boring,and ineffective.This paper intends to explain vocabulary teaching and ...Vocabulary teaching is always a difficulty to English teachers while learners have found the task of accumulating vocabulary time-consuming,boring,and ineffective.This paper intends to explain vocabulary teaching and learning based on the wholeness principle of Gestalt Theory,which emphasizes the wholeness of experience and behaviors.It points out that without an organic whole,the learning process will be incomplete and ineffective.The process of learning is not the accumulation of piecemeal examination of the isolated elements.It is anticipated that the efficiency of classroom teaching and learning can be improved.展开更多
Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amo...Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.展开更多
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Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and constraints.It also compares these methods by the number of iterations and time required to display the result.This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods.The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding.Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool.The positive results of the user evaluation showed that the tool can be recommended to other users.
文摘Vocabulary teaching is always a difficulty to English teachers while learners have found the task of accumulating vocabulary time-consuming,boring,and ineffective.This paper intends to explain vocabulary teaching and learning based on the wholeness principle of Gestalt Theory,which emphasizes the wholeness of experience and behaviors.It points out that without an organic whole,the learning process will be incomplete and ineffective.The process of learning is not the accumulation of piecemeal examination of the isolated elements.It is anticipated that the efficiency of classroom teaching and learning can be improved.
基金supported by the National Natural Science Foundation of China (Grant Nos.62006219 and 62001266)Guangdong Innovative and Entrepre-neurial Research Team Program (grant No.2017ZT07C341)+2 种基金the Bureau of Industry and Information Technology of Shenzhen for the 2017 Graphene Manufacturing Innovation Center Project (No.201901171523)the China Postdoctoral Science Foundation (No.2020M680506)Guangdong Basic and Applied Basic Research Foundation (No.2020A1515110338).
文摘Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.
