Teaching Design of Integrated Course of Reading and Writing Based on In-depth Learning in Senior High School — Take Unit 3 Computers as an Example

In-depth learning is a learning method, which can transfer knowledge and solve problems in various complex and real situations through learners' critical understanding and thinking towards learning content. Guided by the subject core competence, the integrated course of English reading and writing should combine autonomous and cooperative learning to promote the perceptual understanding, and then design multi angle writing training such as text structure imitation, plot rewriting and ending continuation, so as to promote the application and transfer of language knowledge to promote students' in-depth learning.

Exchange Rate Transaction of International Trade Goods Based on Fuzzy Granulation and Deep Learning

The change of international trade goods exchange rate transaction has an impact on economic operations and economic stability. Therefore, an international trade goods exchange rate transaction based on fuzzy granulation and in-depth learning is proposed. Based on fuzzy information granulation and BP neural network, this paper analyzes the interest rate evaluation theory. For the future expectation of currency exchange rate, portfolio equilibrium determines the proportional relationship of each component in the portfolio and analyzes the impact of asset price and exchange rate change according to this relationship. Then, it points out the risk evaluation index system, calculates the risk degree of exchange rate transaction of international trade goods, and then evaluates the risk of exchange rate transaction of international trade goods. It completes the research on exchange rate transactions of international trade goods based on fuzzy granulation and in-depth learning. The experimental results show that excessive exchange rate fluctuation will bring the same proportion fluctuation to the asset price in the financial market, and the coordination between exchange rates and the coordination of exchange rate and asset price can promote the steady growth of national economy.

OptiDepthNet : A Real-time Unsupervised Monocular Depth Estimation Network

With the development of depth learning, the accuracy and effect of the algorithm applied to monocular depth estimation have been greatly improved, but the existing algorithms need a lot of computing resources. At present, how to apply the existing algorithms to UAV and its small robot is an urgent need.Based on full convolution neural network and Kitti dataset, this paper uses deep separable convolution to optimize the network architecture, reduce training parameters and improve computing speed. Experimental results show that our method is very effective and has a certain reference value in the development direction of monocular depth estimation algorithm.

Research on Image Recognition System Based on Computer Deep Learning Neural Network

The selection and extraction of image recognition by artificial means needs more complicated work, which is not conducive to the recognition and extraction of important features. Deep learning and neural network represent the iterative expansion of computer intelligent tech, and bring significant results to image recognition. Based on this, this paper first gives the concept and model of neural network, then studies the utilization of deep learning neural network in image recognition, and finally analyses the picture recognition system on account of in-depth learning neural network.

Research on 3D Reconstruction Technology of Power Line Based on Image Semantic Segmentation

Because it was difficult to distinguish the characteristics of the power lines by the traditional methods of extracting the power lines, which led to the current situation of incomplete reconstruction and a large number of noise in the process of rebuilding the power lines only by the inclined photographing. In this paper, the power line information in the image is segmented pixel by pixel by introducing in-depth learning semantics segmenting neural network. The three-dimensional coordinates of the power line are calculated by the principle of multi-view three-dimensional reconstruction. Finally, the power line is fitted by the catenary equation to complete the incomplete power line reconstruction. The results show that the fitted power line model has high accuracy and meets the requirements of power related applications. Based on the traditional three-dimensional reconstruction, a new idea for power line reconstruction is proposed.

Power Quality Disturbance Identification and Optimization Based on Machine Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

Research and Implementation of the Text Matching Algorithm in the Field of Housing Law and Policy Based on Deep Learning

Machine learning enables machines to learn rules from a large amount of data input from the outside world through algorithms, so as to identify and judge. It is the main task of the government to further emphasize the importance of improving the housing security mechanism, expand the proportion of affordable housing, increase financial investment, improve the construction quality of affordable housing, and ensure fair distribution. It can be seen that the legal system of housing security is essentially a system to solve the social problems brought by housing marketization, and it is an important part of the whole national housing system. More and more attention has been paid to solving the housing difficulties of low- and middle-income people and establishing a housing security legal system suitable for China’s national conditions and development stage. Aiming at the deep learning problem, a text matching algorithm suitable for the field of housing law and policy is proposed. Classifier based on matching algorithm is a promising classification technology. The research on the legal system of housing security is in the exploratory stage, involving various theoretical and practical research studies. Compare the improved depth learning algorithm with the general algorithm, so as to clearly understand the advantages and disadvantages of the improved depth learning algorithm and depth learning algorithm. This paper introduces the practical application of the deep learning model and fast learning algorithm in detail. Creatively put forward to transform it into an independent public law basis or into an independent savings system.