The objective of the study is to examine the effects of Problem based English writing instruction on Thai Upper secondary school students' critical thinking abilities and argumentative writing skill.In this study,...The objective of the study is to examine the effects of Problem based English writing instruction on Thai Upper secondary school students' critical thinking abilities and argumentative writing skill.In this study,the researcher constructed 18weeks' training in a prestigious secondary school M6 level.The instruments used in this research include lesson plans,writing assignments,instructional material and topics.Critical thinking pre- test and post test were used to measure students' critical thinking development.Scoring rubric was designed to measure students' argumentative writing development.The data were analyzed using mean,t-test,correlation through SPSS system.The results of the analyses reveal that 1) students who learn through Problem based English Writing Instruction gained significantly higher average scores on the critical thinking post test than the critical thinking pre-test at the significance level of 0.05 and the mean difference is 0.5 which is referred to large effect;2) students who learn through Problem based English Writing Instruction gained significantly higher average score on their fifth argumentative writing assignment than the average score of their first argumentative writing assignment at the significance level of 0.05,with the mean of the effect size at 0.84 which referred to large effect.Problem-based English writing instruction was proved to be an effective way in improving students' Critical thinking and argumentative writing.展开更多
To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studie...To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.展开更多
The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in...The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.展开更多
Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thic...Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.展开更多
Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate a...Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.展开更多
受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信...受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信息,为预报降雨或设计降雨的时空展布提供了可能。设计了一套降雨时空展布方法,包括数据收集、标准化处理、降雨场次划分、时空模式提取、标准网格插值、时空展布等环节,并提出每个环节的处理步骤和关键问题。以北京市为例,对该技术方法进行了验证,选择了12 h、24 h、72 h 3个历时的场次降雨,提取的时空模式和展布结果表达出了场次降雨的时空动态性,并与历史的降雨过程表现出很好的匹配性。表明该方法可用于城市地区降雨典型模式提取,以及降雨过程的时空展布。该方法利用历史降雨数据提取降雨展布模板,输入场次降雨(或预报降雨)的总雨量,得到该场次降雨的时空分布过程,展布结果可为洪水预报提供更为准确的降雨输入条件。展开更多
根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and exci...根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and excitation)注意力模块和残差模块与原始的卷积模块串联,提高特征提取能力和网络学习能力。同时减少生成网络中转换器的残差块层数,降低过拟合。实验结果表明,基于SE注意力CycleGAN网络方法自动生成的蓝印花布新纹样主观性上更贴合原始风格,与原图更加接近,有助于蓝印花布的数字化传承和创新。展开更多
文摘The objective of the study is to examine the effects of Problem based English writing instruction on Thai Upper secondary school students' critical thinking abilities and argumentative writing skill.In this study,the researcher constructed 18weeks' training in a prestigious secondary school M6 level.The instruments used in this research include lesson plans,writing assignments,instructional material and topics.Critical thinking pre- test and post test were used to measure students' critical thinking development.Scoring rubric was designed to measure students' argumentative writing development.The data were analyzed using mean,t-test,correlation through SPSS system.The results of the analyses reveal that 1) students who learn through Problem based English Writing Instruction gained significantly higher average scores on the critical thinking post test than the critical thinking pre-test at the significance level of 0.05 and the mean difference is 0.5 which is referred to large effect;2) students who learn through Problem based English Writing Instruction gained significantly higher average score on their fifth argumentative writing assignment than the average score of their first argumentative writing assignment at the significance level of 0.05,with the mean of the effect size at 0.84 which referred to large effect.Problem-based English writing instruction was proved to be an effective way in improving students' Critical thinking and argumentative writing.
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSNthe Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002111 Project under Grant B08028.
文摘To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.
基金supported by the 2021 Annual Scientific Research Funding Project of Liaoning Pro-vincial Department of Education(Nos.LJKZ0535,LJKZ0526)the Natural Science Foundation of Liaoning Province(No.2021-MS-300)。
文摘The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.
基金Supported by the National Natural Science Foundation of China(42272110)CNPC-China University of Petroleum(Beijing)Strategic Cooperation Project(ZLZX2020-02).
文摘Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.
基金financially supported by the Ministry of Science and Technology of China (2017YFA0204503 and 2018YFA0703200)the National Natural Science Foundation of China (52121002,U21A6002 and 22003046)+1 种基金the Tianjin Natural Science Foundation (20JCJQJC00300)“A Multi-Scale and High-Efficiency Computing Platform for Advanced Functional Materials”program,funded by Haihe Laboratory in Tianjin (22HHXCJC00007)。
文摘Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.
文摘受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信息,为预报降雨或设计降雨的时空展布提供了可能。设计了一套降雨时空展布方法,包括数据收集、标准化处理、降雨场次划分、时空模式提取、标准网格插值、时空展布等环节,并提出每个环节的处理步骤和关键问题。以北京市为例,对该技术方法进行了验证,选择了12 h、24 h、72 h 3个历时的场次降雨,提取的时空模式和展布结果表达出了场次降雨的时空动态性,并与历史的降雨过程表现出很好的匹配性。表明该方法可用于城市地区降雨典型模式提取,以及降雨过程的时空展布。该方法利用历史降雨数据提取降雨展布模板,输入场次降雨(或预报降雨)的总雨量,得到该场次降雨的时空分布过程,展布结果可为洪水预报提供更为准确的降雨输入条件。
文摘根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and excitation)注意力模块和残差模块与原始的卷积模块串联,提高特征提取能力和网络学习能力。同时减少生成网络中转换器的残差块层数,降低过拟合。实验结果表明,基于SE注意力CycleGAN网络方法自动生成的蓝印花布新纹样主观性上更贴合原始风格,与原图更加接近,有助于蓝印花布的数字化传承和创新。
