试论深度学习在小学数学教学中的应用

深度学习是指在学习过程中,学习者把自己融入到自己学习内容的情景之中,根据自己已有的经验,调动自己的思维,主动的去理解、分析学习内容并采取各种学习策略来对学习内容中的信息进行深度加工,从而能有效的接受到新的学习内容。在进行探究新的学习任务时通过刨根问底、深入思考和主动探究等方式发挥批判性思维从而解决学习中存在的问题,实现了提高自己的学习能力获取学习信息的目的。本文将对在小学数学教学中如何引导学生进行深度学习进行探究。

三课:在单元整体教学中落实课程重构——以小学语文统编教材四年级上册第七单元为例

"三课"是单元课、学时课、整合课的简称[1],是在单元整体教学背景下提出的结构化课程重构策略。它的重构路径基于三个关键点,即单元教学目标的确定、单元教学规划的制定以及整体教学的实施。

Nonlinear inversion for magnetotelluric sounding based on deep belief network

To improve magnetotelluric(MT)nonlinear inversion accuracy and stability,this work introduces the deep belief network(DBN)algorithm.Firstly,a network frame is set up for training in different 2D MT models.The network inputs are the apparent resistivities of known models,and the outputs are the model parameters.The optimal network structure is achieved by determining the numbers of hidden layers and network nodes.Secondly,the learning process of the DBN is implemented to obtain the optimal solution of network connection weights for known geoelectric models.Finally,the trained DBN is verified through inversion tests,in which the network inputs are the apparent resistivities of unknown models,and the outputs are the corresponding model parameters.The experiment results show that the DBN can make full use of the global searching capability of the restricted Boltzmann machine(RBM)unsupervised learning and the local optimization of the back propagation(BP)neural network supervised learning.Comparing to the traditional neural network inversion,the calculation accuracy and stability of the DBN for MT data inversion are improved significantly.And the tests on synthetic data reveal that this method can be applied to MT data inversion and achieve good results compared with the least-square regularization inversion.

Robust multi-layer extreme learning machine using bias-variance tradeoff

As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ability.However,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured problems.To resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ability.Moreover,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training data.The generalization ability of the original ELM decreases under such circumstances.To address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex datasets.Simulation results show that the method has high generalization ability and strong robustness to noise.

An Intelligent Learning Algorithm for Improving BIM Object Classification and Recognition

Building information modeling(BIM)object classification takes a lot of time and energy.Misclassification or omission of any object may lead to the emergence of abnormal results,which have a great impact on the project workflow and results.Roundly understanding BIM object classification,by improving Swin Transformer classifier algorithm parameters,using the model primitives extracted from IFC format BIM model file,deep learning of 7 types of BIM object categories is taken.Through the performance and evaluation indicators obtained in training,the results improve the classification accuracy.

Intelligent Identification of Building Patches and Assessment of Roof Greening Suitability in High-density Urban Areas:A Case Study of Chengdu

With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.