Vocational education can effectively improve the vocational skills of employees,improve people’s traditional concept of vocational education,and focus on the training of vocational skills for students by using new ed...Vocational education can effectively improve the vocational skills of employees,improve people’s traditional concept of vocational education,and focus on the training of vocational skills for students by using new educational methods and concepts,so that they can master key vocational skills and develop key abilities.In this paper,three different learning models,Deep Knowledge Tracing(DKT),Dynamic Key-Value Memory Networks(DKVMN)and Double Deep Q-network(DDQN),are used to evaluate the indicators in the vocational education system.On the one hand,the influence of learning degree on the performance of the model is compared,on the other hand,the performance evaluation of three models under the same learning effect is compared,so as to obtain the best learning model applied to the field of skill training.In order to accurately evaluate the learning status of students,the loss function curves under three models are compared.Finally,the error rate of students in vocational skills education tends to be zero,and the learning process of intensive learning effectively improves students’mastery of skills and key abilities.展开更多
Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence...Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.展开更多
文摘Vocational education can effectively improve the vocational skills of employees,improve people’s traditional concept of vocational education,and focus on the training of vocational skills for students by using new educational methods and concepts,so that they can master key vocational skills and develop key abilities.In this paper,three different learning models,Deep Knowledge Tracing(DKT),Dynamic Key-Value Memory Networks(DKVMN)and Double Deep Q-network(DDQN),are used to evaluate the indicators in the vocational education system.On the one hand,the influence of learning degree on the performance of the model is compared,on the other hand,the performance evaluation of three models under the same learning effect is compared,so as to obtain the best learning model applied to the field of skill training.In order to accurately evaluate the learning status of students,the loss function curves under three models are compared.Finally,the error rate of students in vocational skills education tends to be zero,and the learning process of intensive learning effectively improves students’mastery of skills and key abilities.
文摘Eyewall replacement cycles(ERCs)greatly increase the destructive potential of tropical cyclones(TCs)by affecting the maximum wind speed,wind field size,and storm surge severity while simultaneously reducing confidence in TC forecasts,most prominently in intensity forecasting.Machine learning(ML)presents new opportunities to improve current forecasting and predictive capabilities,and its application will benefit forecasters and ultimately the public.The objective of this project was to create a proof-of-concept ML convolutional neural network(CNN)to predict ERCs using the 89 GHz microwave band for training and testing.The training set was comprised of North Atlantic basin(NATL)storms from 1999 to 2009.The testing set included NATL storms from 2019 to 2022.Twelve models were created,together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles(CE-PERCY),with each individual member achieving at least 80%in-training accuracy.Two versions were created:versions A and B.Using synthetic aperture radar,land-based radar,aircraft reconnaissance,Microwave-based Probability of ERC(M-PERC),National Hurricane Center reports,and microwave imagery,ERC analysis was conducted on the testing set.28 ERCs were identified throughout 14 hurricanes from 2019 to 2022.CE-PERCY performs well for a proof-of-concept,with versions A and B predicting 21 and 23 ERCs,respectively.This project successfully introduces a foundation for using ML CNNs in ERC prediction,demonstrates the viability of the technique,and proves that a large enough dataset of microwave imagery can be used in this specific application.
