Special Issue on AI-Driven Internet Technologies for Sustainable and Smart Cities 2022

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Encrypted Storage Method of Oral English Teaching Resources based on Cloud Platform

Abstract: With the development of the times, the secure storage of educational resources has become one of the key security problems faced by colleges and universities. On the one hand, the cost of traditional resource storage is too expensive, on the other hand, its encryption and access efficiency are low. To solve this problem, this research takes the cloud platform serves as the main carrier for the encrypted storage of school teaching resources. On this basis, the convolutional neural network is encrypted and optimized, and the argmax algorithm is improved to improve the access efficiency of encrypted data. Finally, the effectiveness and superiority of the design method are compared and analyzed through the method of performance detection. The results show that the maximum consumption time of encryption and decryption of the encrypted storage model is no more than 20000ms, which is significantly less than that of the traditional model. The running time of the argmax output encryption module is 1.76ms and the running loss is 0.26 MB, which is less than that of the traditional model. It can be seen that the encrypted storage model has stronger encryption performance and access performance, and has a better application effect in the encrypted storage of oral English teaching resources with a large amount of access data and frequent updates.

Author 1: Tongsheng Si

Keywords: Cloud platform; oral language; encryption; resource storage

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Denoising Method of Interior Design Image based on Median Filtering Algorithm

Abstract: Interior design image generation process is prone to the interference of many factors, resulting in the interior design image denoising effect decreases, denoising time increases, so the interior design image denoising method based on median filtering algorithm is proposed. The architecture of interior design image collection is set up, including video signal conversion module, compression coding module, programmable logic chip module and power module. The interior design image collection is realized by using sensors to collect interior design related video information and converting video signals. Based on the results of image acquisition, the median filtering algorithm based on rough set theory is used to realize the denoising of interior design images. Experimental results show that the denoising effect of the proposed method is better, the average signal-to-noise ratio of interior design images is 54.6dB, and the denoising time is always lower than 0.3s, which can be widely used in practice.

Author 1: Tao Li

Keywords: Median filtering algorithm; interior design; image denoising; image acquisition architecture; the rough set

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Application of CAD Aided Intelligent Technology in Landscape Design

Abstract: The current landscape design methods ignore the depth rendering of scene elements, resulting in low spatial utilization of landscape plant diversity index and landscape spatial pattern. Therefore, this study explores the application of CAD in landscape design. AutoCAD aided intelligent technology is adopted to display the scene in multiple directions and from all angles with terrain design, planning design and planting design as the main contents. Using 3D graphics engine to render landscape elements. On this basis, the spatial coordination planning model of plant landscape is established. The color attribute of landscape space staggered pattern is added to 3D visual reconstruction model by image library function, and the CAD intelligent technology is applied in landscape design. The results shows, the method scored higher in graphic refresh rate, visual brightness and visual contrast, a higher plant diversity coefficient in multiple iterations, and a higher spatial utilization ratio of the landscape pattern than the other two design methods, and the spatial utilization ratio of the landscape pattern of the proposed method is higher than that of the reference method.

Author 1: Juan Du

Keywords: Landscape design; CAD intelligent technology; engine rendering; image library function; 3D visual reconstruction；digital design; landscape architecture

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Filtering and Enhancement Method of Ancient Architectural Decoration Image based on Neural Network

Abstract: Due to poor ambient light or uneven lighting, the old decoration image acquisition methods are easy to cause the image blur. To solve this problem, this paper proposes a neural network-based filtering enhancement method for ancient architectural decoration images, which preserves image details by enhancing contrast, smoothing noise reduction and edge sharpening. Based on the convolutional neural network which is composed of encoder, decoder and layer hop connection, the residual network and hole convolution are introduced, and the hole U-Net neural network is constructed to fuse the pixel feature blocks of different levels. This method enhanced the image contrast according to the gray level and frequency histogram, and aiming at the gray value of the pixel to be processed in the image. And the middle value of the gray value of the neighborhood pixel is used to filter the noise of the ancient building decoration image. The paper also analyzes the joint strength of beams and columns in ancient buildings, and calculates the elastic constants of beams and columns and the stress at the joint of them, considering the image texture characteristics of the wood in ancient buildings with the mortise and tenon connection of beams and columns. Experimental results show that the proposed method has good noise suppression performance, can effectively obtain image detail features, and significantly improve the subjective visual effect of ancient architectural decoration images.

Author 1: Yanan Wang

Keywords: Neural network; decorative images of ancient buildings; filter enhancement method; encoder; decoder; pixel gray value

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A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

Abstract: There is noise interference in low-illumination images, which makes it difficult to extract weak signals. For this reason, this paper proposes a low-illumination image weak signal enhancement algorithm based on neural network. Multi-scale normalization is performed on low-light images, and multi-scale Retinex is used to enhance weak signals in low-light images. On this basis, the GAN artificial neural network is used to detect the weak signal of the weak signal in the image, the normalization of the weak signal of the low-illumination image is completed based on the residual network, the self-encoding parameters of the depth residual are generated, and the weak signal enhancement result of the low-illumination image is output. The experimental results show that the method in this paper has better enhancement effect on low-illumination images and better image denoising effect. When the scale value is large, the low-contrast area of the low-illumination image has a better enhancement effect. The saturated area of the low-light image has a better enhancement effect.

Author 1: Dawei Yin
Author 2: Jianwei Li

Keywords: Artificial neural network; GAN neural network; low-light image; weak signal enhancement

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Digital Intelligent Management Platform for High-Rise Building Construction Based on BIM Technology

Abstract: In this study, the digital intelligent management platform of high-rise building construction based on BIM technology is used for real-time monitoring and management of construction progress and quality. In the data acquisition and processing layer, construction site data is obtained through RFID technology. After processing such as cleaning and integration, it is input to the BIM model layer to dynamically generate various real-time BIM models, and these real-time BIM model information is input to the application layer to query, monitor and correct the construction progress and quality. The results are presented by the display layer. The actual application results show that the real-time BIM models generated by the platform have clear details and can realize the query, monitoring and correction functions for the construction progress of high-rise buildings, and effectively correct the construction progress according to the construction progress monitoring query results to achieve the unification with the planned progress. It can effectively realize the visual measurement of the size of each component in construction and monitor the construction quality in real time.

Author 1: Rui Deng
Author 2: Chun’e Li

Keywords: BIM technology; high-rise building construction; digitization; intelligent management; BIM model; RFID technology

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Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning

Abstract: The intelligentization of building interior decoration construction is of great significance to the construction of smart city, and robot automation has brought an opportunity for this. Robot self-decoration is the development trend in the future. One of the key issues involved, is the self-planning of mobile path. In this regard, the research adopts the proximal policy optimization algorithms (PPO) to improve the self-planning path ability of the decoration robot. For the information of lidar and robot status, the Full Connect Neural Network (FCNN) is used to process it. In addition, the reward function and the corresponding Credit Assignment Problem (CAP) model are designed, to accelerate the learning process of path planning. Aiming at the dynamic uncertainty in the actual environment, the adaptive loss function is used to build an auxiliary model to predict the environmental change. The simulation results show that the research and design strategy significantly improves the learning efficiency and path planning success rate of the decoration robot, and shows good adaptability to the dynamic environment, which has important reference significance for the practical application of the decoration robot.

Author 1: Li Zhang
Author 2: Aimin Qin

Keywords: Interior decoration; path planning; deep reinforcement learning; reward function; credit allocation

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Research on Face Recognition Technology of Subway Automatic Ticketing System based on Neural Network and Deep Learning

Abstract: Face recognition technology is the core technology of the subway ticketing system, which is related to the efficiency of people's ticket purchase. In order to improve people's experience of taking public transport, it is necessary to improve the performance of face recognition technology. In this study, the Back Propagation (BP) algorithm is used to optimize the parameters of the SoftMax classifier of the convolutional neural network, and the branch structure is added to the structure of the SphereFace-36 convolutional neural network to extract the local features of the face. Based on the improved neural network, the face recognition system of the subway automatic ticketing system is established. The results show that the area under the ROC curve is the highest for validation and identification of the optimization model; The recognition accuracy of the optimized model in different data sets is 1.0%, 0.7%, 1.1%, 0.9% and 0.6% higher than that of SphereFace-36 respectively, and its specificity is higher than that of SphereFace-36, with the maximum difference of 9%; The average accuracy of global feature extraction and recognition of the optimized network model is 83.01%. In the simulation experiment, the optimized model can accurately recognize facial features, which has high practical value and can be applied to the automatic ticketing system.

Author 1: Shuang Wu
Author 2: Xin Lin
Author 3: Tong Yao

Keywords: Automatic ticketing system; BP; CNN; deep learning; face recognition; SphereFac； SoftMax classifier

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Research on Improved Xgboost Algorithm for Big Data Analysis of e-Commerce Customer Churn

Abstract: With the increasing cost of acquiring new users for e-commerce enterprises, it has become an important task for e-commerce enterprises to actively carry out customer churn management. Therefore, based on the distributed gradient enhancement library algorithm (XGBoost), this research proposes a big data analysis study on e-commerce customer churn. First, it conducts an evaluation analysis on e-commerce customer segmentation and combines the random forest algorithm (RF) to build an RF XGBoost prediction model based on customer churn. Finally, it verifies the performance of the prediction model. The results show that the area under receiver operating characteristic curve (AUC) value, prediction accuracy, recall rate, and F1 value of the RF-XGBoost model are significantly better than those of the RF, XGBoost, and ID3 decision trees to build an e-commerce customer churn prediction model; The average output error of RF-XGBoost model is 0.42, and the average output error is relatively good, indicating that the model proposed in this study has a smaller error and higher accuracy. It can make a general assessment of the customer churn of e-commerce enterprises, and then provide data support for the customer maintenance work of e-commerce enterprises. It is helpful to analyze the relevant factors affecting customer churn, to Equationte targeted customer service programs, thus improving the economic benefits of e-commerce enterprises.

Author 1: Li Li

Keywords: E-commerce; customer churn; random Forest; XGBoost; big data

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