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
针对基于知识蒸馏的工业图像异常检测中,预训练网络在迁移到工业图像领域时因领域差异和数据分布的不同而导致获取的特征存在偏差问题,提出了一种特征自适应师生模型。该模型利用特征自适应器来调整教师网络提取的预训练特征,实现跨域...针对基于知识蒸馏的工业图像异常检测中,预训练网络在迁移到工业图像领域时因领域差异和数据分布的不同而导致获取的特征存在偏差问题,提出了一种特征自适应师生模型。该模型利用特征自适应器来调整教师网络提取的预训练特征,实现跨域特征转换和减少领域偏差。为了避免特征自适应器过度调整预训练特征,导致与原始预训练特征差异过大而降低泛化性能,提出监督损失来约束调整后的特征。此外,进一步为了提高模型对异常特征的表征和判别能力,设计了一个新的学生网络和提出了一种对抗损失来拉远教师和学生网络中异常特征之间的差异,拉近两者正常特征之间的差异。在多个工业数据集MVTec AD、BTAD、VisA和MVTec 3D AD上验证了该模型的有效性。展开更多
当前特殊音乐教育面临的师资配置不足、课程结构单一、教学方法固化等困境,制约了音乐促进残障学生社会融入的功能。AI技术的发展为特殊音乐教育提供了新的动能,重构了特殊音乐教育的生态和作用路径。文章基于生态学整体视角,整合技术...当前特殊音乐教育面临的师资配置不足、课程结构单一、教学方法固化等困境,制约了音乐促进残障学生社会融入的功能。AI技术的发展为特殊音乐教育提供了新的动能,重构了特殊音乐教育的生态和作用路径。文章基于生态学整体视角,整合技术、教育与社会三个维度,构建了技术-教育-社会三维度的AI赋能特殊音乐教育模型(AI-Empowered Music Teaching Model,简称AIMT模型),旨在应对残障学生社会融入这一特殊教育核心难题。AIMT模型通过AI技术、教育过程重构与社会政策导向的协同作用,有助于实现残障学生的个性化发展,实质性提升其情感、认知和社会交往能力,促进其社会融入。AIMT模型为特殊音乐教育提供了可行的教学实践范式,拓展了AI技术在特殊音乐教育中的应用路径,也为残障学生的社会融入提供了系统性支持。展开更多
目的对医学生胜任力评价研究进行范围综述,旨在为建立科学、实用可行的医学生胜任力评价体系提供参考依据。方法计算机检索CNKI、SinoMed、WanFang Data、PubMed、Web of Science和Embase数据库,检索时限为建库至2025年6月。基于Arksey...目的对医学生胜任力评价研究进行范围综述,旨在为建立科学、实用可行的医学生胜任力评价体系提供参考依据。方法计算机检索CNKI、SinoMed、WanFang Data、PubMed、Web of Science和Embase数据库,检索时限为建库至2025年6月。基于Arksey和O’Malley的范围界定审查方法框架进行范围综述。结果共纳入研究31篇,涵盖13篇理论研究、6篇实证研究、12篇混合研究。纳入研究报告了医学教育领域主流的医学生胜任力评价方法,包括客观结构化临床考试、360°评估、小型临床评估练习、多项选择题测试、操作技能直接观察5种。胜任力模型构建研究多以米勒金字塔、布鲁姆教育目标分类学、现行的医师胜任力共识等为核心理论框架,主要采用的建模方法包括文献分析法、行为事件访谈法、德尔菲法、问卷调查法等混合研究方法,国内胜任力模型以“知识、技能、素养”为要素指标核心,问卷条目形式主要以5级李克特评分(8项,57.1%)为主。研究的信度验证较完善,但超半数研究(7项,53.8%)未报告效度指标。结论当前胜任力研究领域基础理论发展成熟且类型丰富,医学生胜任力模型构建方法呈现多样化特征。现有的医学生胜任力评价方法存在一定的局限性,医学生胜任力模型要素指标尚不统一,国内外构建的医学生胜任力模型在维度领域划分及条目数量方面存在较大差异。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.
文摘针对基于知识蒸馏的工业图像异常检测中,预训练网络在迁移到工业图像领域时因领域差异和数据分布的不同而导致获取的特征存在偏差问题,提出了一种特征自适应师生模型。该模型利用特征自适应器来调整教师网络提取的预训练特征,实现跨域特征转换和减少领域偏差。为了避免特征自适应器过度调整预训练特征,导致与原始预训练特征差异过大而降低泛化性能,提出监督损失来约束调整后的特征。此外,进一步为了提高模型对异常特征的表征和判别能力,设计了一个新的学生网络和提出了一种对抗损失来拉远教师和学生网络中异常特征之间的差异,拉近两者正常特征之间的差异。在多个工业数据集MVTec AD、BTAD、VisA和MVTec 3D AD上验证了该模型的有效性。
文摘当前特殊音乐教育面临的师资配置不足、课程结构单一、教学方法固化等困境,制约了音乐促进残障学生社会融入的功能。AI技术的发展为特殊音乐教育提供了新的动能,重构了特殊音乐教育的生态和作用路径。文章基于生态学整体视角,整合技术、教育与社会三个维度,构建了技术-教育-社会三维度的AI赋能特殊音乐教育模型(AI-Empowered Music Teaching Model,简称AIMT模型),旨在应对残障学生社会融入这一特殊教育核心难题。AIMT模型通过AI技术、教育过程重构与社会政策导向的协同作用,有助于实现残障学生的个性化发展,实质性提升其情感、认知和社会交往能力,促进其社会融入。AIMT模型为特殊音乐教育提供了可行的教学实践范式,拓展了AI技术在特殊音乐教育中的应用路径,也为残障学生的社会融入提供了系统性支持。
文摘目的对医学生胜任力评价研究进行范围综述,旨在为建立科学、实用可行的医学生胜任力评价体系提供参考依据。方法计算机检索CNKI、SinoMed、WanFang Data、PubMed、Web of Science和Embase数据库,检索时限为建库至2025年6月。基于Arksey和O’Malley的范围界定审查方法框架进行范围综述。结果共纳入研究31篇,涵盖13篇理论研究、6篇实证研究、12篇混合研究。纳入研究报告了医学教育领域主流的医学生胜任力评价方法,包括客观结构化临床考试、360°评估、小型临床评估练习、多项选择题测试、操作技能直接观察5种。胜任力模型构建研究多以米勒金字塔、布鲁姆教育目标分类学、现行的医师胜任力共识等为核心理论框架,主要采用的建模方法包括文献分析法、行为事件访谈法、德尔菲法、问卷调查法等混合研究方法,国内胜任力模型以“知识、技能、素养”为要素指标核心,问卷条目形式主要以5级李克特评分(8项,57.1%)为主。研究的信度验证较完善,但超半数研究(7项,53.8%)未报告效度指标。结论当前胜任力研究领域基础理论发展成熟且类型丰富,医学生胜任力模型构建方法呈现多样化特征。现有的医学生胜任力评价方法存在一定的局限性,医学生胜任力模型要素指标尚不统一,国内外构建的医学生胜任力模型在维度领域划分及条目数量方面存在较大差异。
