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
Effective teaching is every teacher's teaching goal.How to reach the aim is to reflect on the teaching.The following document describes the development on how to reflect the teaching.The main purpose of this analy...Effective teaching is every teacher's teaching goal.How to reach the aim is to reflect on the teaching.The following document describes the development on how to reflect the teaching.The main purpose of this analysis is to show how effective and important the reflection on the teaching and to show how useful the reflection which can make the teacher change the way of teaching,encouraging the students to adopt a deep approach in their learning.展开更多
At present, one of the important tasks of the ongoing college English teaching reform is to reform the traditional teaching mode,establishing the computer-based multimedia English teaching model. Based on the construc...At present, one of the important tasks of the ongoing college English teaching reform is to reform the traditional teaching mode,establishing the computer-based multimedia English teaching model. Based on the constructivist theory and the second language acquisi-tion theory, teachers who adopt the new teaching model should play multiple roles to maximize the teaching effect, being not only thecourse designer, organizer, but also the guide, helper and facilitator in the process of teaching students to construct knowledge and be en-gaged in classroom activities as well.展开更多
将Flipped Class Model引入到高校网球课教学,有助于激发学生对高校网球项目课学习的积极性,增强学生网球项目课学习的自主性,加强师生间的交流。运用SWOT态势分析法,将Flipped Class Model引入到高校网球教学,将会促进教师教学能力提...将Flipped Class Model引入到高校网球课教学,有助于激发学生对高校网球项目课学习的积极性,增强学生网球项目课学习的自主性,加强师生间的交流。运用SWOT态势分析法,将Flipped Class Model引入到高校网球教学,将会促进教师教学能力提高、教学过程中教师和学生角色转换、学生学习习惯改变等。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Effective teaching is every teacher's teaching goal.How to reach the aim is to reflect on the teaching.The following document describes the development on how to reflect the teaching.The main purpose of this analysis is to show how effective and important the reflection on the teaching and to show how useful the reflection which can make the teacher change the way of teaching,encouraging the students to adopt a deep approach in their learning.
文摘人体行为识别(Human Activity Recognition,HAR)是当前众多研究工作的基石,对于推动人机交互和智能数字化转型具有巨大潜力。由于目标域样本较难采集,现有方法在跨域识别方面表现不佳。为解决这一问题,提出一种新的WiFi使能跨域HAR方法,从WiFi信号中获取信道状态信息(Channel State Information,CSI)并转化为图像,在基于Wasserstein距离和梯度的生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)中引入双判别器,通过与源域样本和单目标域样本特征联合对抗,生成同时带有双域特征的虚拟样本。该方法还结合基于Mean Teacher的半监督学习设计识别分类(Recognition and Classification,RC)模块,通过对有标记样本与无标记样本分别构造损失函数,进行整体一致性损失的评估,实现对目标域样本的识别。实验结果证明了所提方法能够在减轻目标域样本采集压力的同时,实现较高的检测精度,在手势与动作的数据集上测试准确率分别达到92.71%和86.65%。
文摘At present, one of the important tasks of the ongoing college English teaching reform is to reform the traditional teaching mode,establishing the computer-based multimedia English teaching model. Based on the constructivist theory and the second language acquisi-tion theory, teachers who adopt the new teaching model should play multiple roles to maximize the teaching effect, being not only thecourse designer, organizer, but also the guide, helper and facilitator in the process of teaching students to construct knowledge and be en-gaged in classroom activities as well.
文摘将Flipped Class Model引入到高校网球课教学,有助于激发学生对高校网球项目课学习的积极性,增强学生网球项目课学习的自主性,加强师生间的交流。运用SWOT态势分析法,将Flipped Class Model引入到高校网球教学,将会促进教师教学能力提高、教学过程中教师和学生角色转换、学生学习习惯改变等。
