Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection ...Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.展开更多
This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to bu...This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the student model.The diagnosis is based on the comparison of the behavinurs of the student and the ex- pert.The student model is consulted by the “explainer” and “debugging” procedures in order to re-order the sequence of the explanation.展开更多
It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be...It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be improved the learning interests and practical experience.展开更多
In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Facul...In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, from calendar year 2006 to 2010. Using regression analysis, descriptive model and explanatory model are derived. The descriptive model is linear with R2 = 0.8864. Using log-transformation, the explanatory model gives the nonlinear approximation with R2 = 0.8293. The model predicts that the number of students of Department of Mathematics, KMUTNB has a tendency to linearly increase with slope of 20 with 95% CI (6.8417, 33.1583). The application of the models in educational management is discussed.展开更多
促学评价,在我国高职学生的外语能力测评体系中尚属一个新的概念。过去十年中,国际教育测评领域正在经历从“对于学习的评价”(assessment of learning)到“为了学习的评价”(assessment for learning,即促学评价)的变革。文章探讨促学...促学评价,在我国高职学生的外语能力测评体系中尚属一个新的概念。过去十年中,国际教育测评领域正在经历从“对于学习的评价”(assessment of learning)到“为了学习的评价”(assessment for learning,即促学评价)的变革。文章探讨促学评价这一新型测评理念在测评学生英语写作能力上的应用,构建适用于我国高职院校学生的英语写作能力测评模式。在深受考试文化影响的我国,促学评价的引入可以有效降低高风险考试给学生学习带来的不利影响,促使测评领域的研究者重新思考测评对改进教学的重要作用,构建更加公平、有效的测评体系以更好地支撑教学,并为政策制定及资源配置决策提供必要信息。展开更多
基金This research work is supported by Sichuan Science and Technology Program(Grant No.2022YFS0586)the National Key R&D Program of China(Grant No.2019YFC1509301)the National Natural Science Foundation of China(Grant No.61976046).
文摘Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.
文摘This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the student model.The diagnosis is based on the comparison of the behavinurs of the student and the ex- pert.The student model is consulted by the “explainer” and “debugging” procedures in order to re-order the sequence of the explanation.
文摘It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be improved the learning interests and practical experience.
文摘In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, from calendar year 2006 to 2010. Using regression analysis, descriptive model and explanatory model are derived. The descriptive model is linear with R2 = 0.8864. Using log-transformation, the explanatory model gives the nonlinear approximation with R2 = 0.8293. The model predicts that the number of students of Department of Mathematics, KMUTNB has a tendency to linearly increase with slope of 20 with 95% CI (6.8417, 33.1583). The application of the models in educational management is discussed.
文摘促学评价,在我国高职学生的外语能力测评体系中尚属一个新的概念。过去十年中,国际教育测评领域正在经历从“对于学习的评价”(assessment of learning)到“为了学习的评价”(assessment for learning,即促学评价)的变革。文章探讨促学评价这一新型测评理念在测评学生英语写作能力上的应用,构建适用于我国高职院校学生的英语写作能力测评模式。在深受考试文化影响的我国,促学评价的引入可以有效降低高风险考试给学生学习带来的不利影响,促使测评领域的研究者重新思考测评对改进教学的重要作用,构建更加公平、有效的测评体系以更好地支撑教学,并为政策制定及资源配置决策提供必要信息。
