With the deepening of educational reform,interdisciplinary thematic learning,as an emerging educational model,has become a focus of attention in the field of educational research.Based on the STEM(science,technology,e...With the deepening of educational reform,interdisciplinary thematic learning,as an emerging educational model,has become a focus of attention in the field of educational research.Based on the STEM(science,technology,engineering,and mathematics)education concept and CASES-T(Content,Activity,Situation,Evaluation,Strategy-Target)model,this study provides a theoretical basis for the teaching design and implementation of interdisciplinary thematic learning in middle school physical education.Through the analysis of specific interdisciplinary thematic learning cases,it aims to provide theoretical support and practical guidance for the reform of middle school physical education through the CASES-T model-based interdisciplinary thematic teaching design research in middle school physical education,in order to enhance students’learning effects,cultivate core literacy in physical education,and promote students’all-round development.展开更多
This paper intends to introduce briefly the thematic progression patterns in Systemic- Functional Grammar, then analyze its application in "The Great Learning" which is one of the classics of the Confucius a...This paper intends to introduce briefly the thematic progression patterns in Systemic- Functional Grammar, then analyze its application in "The Great Learning" which is one of the classics of the Confucius and his disciples. The analysis of the thematic progression patterns of "The Great Learning" is meaningful for both understanding and appreciating "The Great Learning".展开更多
Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that res...Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.展开更多
文摘With the deepening of educational reform,interdisciplinary thematic learning,as an emerging educational model,has become a focus of attention in the field of educational research.Based on the STEM(science,technology,engineering,and mathematics)education concept and CASES-T(Content,Activity,Situation,Evaluation,Strategy-Target)model,this study provides a theoretical basis for the teaching design and implementation of interdisciplinary thematic learning in middle school physical education.Through the analysis of specific interdisciplinary thematic learning cases,it aims to provide theoretical support and practical guidance for the reform of middle school physical education through the CASES-T model-based interdisciplinary thematic teaching design research in middle school physical education,in order to enhance students’learning effects,cultivate core literacy in physical education,and promote students’all-round development.
文摘This paper intends to introduce briefly the thematic progression patterns in Systemic- Functional Grammar, then analyze its application in "The Great Learning" which is one of the classics of the Confucius and his disciples. The analysis of the thematic progression patterns of "The Great Learning" is meaningful for both understanding and appreciating "The Great Learning".
基金National Natural Science Foundation of China(41471288).
文摘Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.
